Outcomes Research in Review

Study Overview

Objective. To examine the efficacy and safety of weight-loss surgery in adolescents.

Design. Prospective observational study.

Setting and participants. Adolescents (aged 13–19 years) with severe obesity undergoing bariatric surgery at 5 U.S. hospitals and medical centers from March 2007 through February 2012. Participants were enrolled in the Teen-Longitudinal Assessment of Bariatric Surgery (Teen-LABS) study, a longitudinal prospective study that investigated the risks and benefits of adolescent bariatric surgery.

Main outcome measures. Data was collected on weight, comorbidities, cardiometabolic risk factors, nutritional status, and weight-related quality of life at research visits scheduled at 6 months, 1 year, 2 years, and 3 years post bariatric surgery. Researchers measured height and weight and blood pressure directly and calculated BMI. They assessed for comorbidities and cardiometabolic risk factors through urine and serum laboratory tests of lipids, glomerular filtration rate, albumin, glycated hemoglobin, fasting glucose level, and insulin. They assessed nutritional status with laboratory values for serum albumin, folate, vitamin B₁₂, 25-hydroxyvitamin D, parathyroid hormone, ferritin, transferrin, vitamin A, and vitamin B₁ erythrocyte transketolase. Researchers conducted interviews with the participants to collect information about subsequent medical or surgical procedures or, if participants missed a research visit, they obtained information through chart reviews. Finally, weight-related quality of life was assessed with the Impact of Weight on Quality of Life-Kids instrument, a validated self-report measure with 27 items divided into 4 subscales: physical comfort, body esteem, social life, and family relations.

Main results. Analysis was conducted on results for 228 of 242 participants who received Roux-en-Y gastric bypass (n = 161) and sleeve gastrectomy (n = 67). Results for 14 participants who received adjustable gastric banding were not included due to the small size of that group. Mean weight loss was 41 kg while mean height increased by only 0.51 cm. The mean percentage of weight loss was 27% overall and was similar in both groups, 28% in participants who underwent gastric bypass and 26% in those who underwent sleeve gastrectomy. At the 3-year visit, there were statistically significant improvements in comorbidities: 74% of the 96 participants with elevated blood pressure, 66% of the 171 participants with dyslipidemia, and 86% of the 36 participants with abnormal kidney function at baseline had values within the normal range. None of 3 participants with type 1 diabetes at baseline had resolution. However, 29 participants had type 2 diabetes (median glycolated hemoglobin 6.3% at baseline) and 19 of 20 of them for whom data were available at 3 years were in remission, with a median glycolated hemoglobin of 5.3%. There was an increase in the number of participants with micronutrient deficiencies at the 3-year mark: the percentage of participants with low ferritin levels increased from 5% at baseline to 57%, those with low vitamin B₁₂ increased from < 1% to 8%, and those with low vitamin A increased from 6% to 16%. During the 3-year follow-up period, 30 participants underwent 44 intrabdominal procedures related to the bariatric procedure and 29 participants underwent 48 endoscopic procedures, including stricture dilatation (n = 11). Total scores on the Impact of Weight on Quality of Life-Kids instrument improved from a mean of 63 at baseline to 83 at 3 years.

Conclusion. Overall there were significant improvements in weight, comorbidities, cardiometabolic health, and weight-related quality of life. However, there were also risks, including increased micronutrient deficiencies and the need for subsequent invasive abdominal procedures.

Commentary

Pediatric obesity is one of the most significant health problems facing children and adolescents. According to the most recent estimates, 34.5% of all adolescents aged 12 to 19 years are overweight or obese [1]. Pediatric obesity has serious short- and long-term psychosocial and physical implications. Obese adolescents suffer from social marginalization, poor self-concept, and lower health-related quality of life [2,3]. They are at greater risk for metabolic syndrome, diabetes, obstructive sleep apnea, and conditions associated with coronary artery disease such as hyperlipidemia and hypertension [4,5]. Additionally, obesity in adolescence is strongly associated with early mortality and years of life lost [6].

Despite extensive research and public health campaigns, rates of adolescent obesity have not decreased since 2003 [1]. Diet and behavioral approaches have had limited success and are rarely sustained over time. Bariatric surgery is an approach that has been used safely and effectively in severely obese adults and is increasingly being used for adolescents as well [7]. The results of this study are encouraging in that they suggest that bariatric surgery is effective in adolescents, leading to significant and sustained weight loss over 3 years and improved cardiometabolic health and weight-related quality of life.

The procedures are not without risks as demonstrated by the findings of micronutrient deficiencies and the need for follow-up intraabdominal and endoscopic procedures. The number of follow-up procedures and the fact that they continued into the third year is concerning. More details about this finding, such as characteristics of participants who required them, would be helpful. Further research to determine risk factors associated with complications that require subsequent invasive procedures is important for developing criteria for selection of candidates for bariatric surgery. Additionally, there was no information on impact of the follow-up procedures on participants or the conditions that precipitated them. In addition, there was no information on physical sequelae that can cause ongoing distress for patients, eg, chronic abdominal cramping and pain. The authors measured weight-related quality of life but measuring overall quality of life post-procedure would have captured the impact of post-procedure dietary restrictions and any medical problems. Such data could be helpful in decision-making about the use of bariatric procedures in this population versus noninvasive approaches to management.

As the authors note, treating severe obesity in adolescence rather than waiting until adulthood may have significant implications for improved health in adulthood, particularly in preventing or reversing cardiovascular damage related to obesity-related cardiometabolic risk factors. However, what is not known yet is whether the positive outcomes, beginning with weight loss, are sustained through adulthood. This 3-year longitudinal study was the first to examine factors over an extended time period, however, considering the average life expectancy of an adolescent, it provides only a relatively short-term outlook. A longitudinal study that follows a cohort of adolescents from the time of the bariatric procedure into middle age or beyond is needed. Such a study would also provide needed information about the long-term consequences of repeated intraabdominal procedures and the persistence or resolution of micronutrient deficiencies and their effects on health.

The strengths of this study are its prospective longitudinal design and its high rate of cohort completion (99% of participants remained actively involved, completing 88% of follow-up visits). As the authors note, the lack of a control group of adolescents treated with diet and behavioral approaches prevents any definitive statement about the benefits and risks compared to nonsurgical approaches. However, previous research indicates that weight loss is not as great nor sustained when nonsurgical approaches are used.

Applications for Clinical Practice

The use of bariatric surgery in adolescents is a promising approach to a major health problem that has proven resistant to concerted medical and public health efforts and the use of nonsurgical treatments. Ongoing longitudinal research is needed but the positive outcomes seen here—sustained significant weight loss, improvement in cardiometabolic risk factors and comorbidities, and improved weight-related quality of life—indicate that bariatric surgery is an effective treatment for adolescent obesity when diet and behavioral approaches have failed. However, the occurrence of post-procedure complications also highlights the need for caution. Clinicians must carefully weigh the risk-benefit ratio for each individual, taking into consideration the long-term implications of severe obesity, any potential for significant weight loss with diet and behavioral changes, and the positive outcomes of bariatric surgery demonstrated here.

 —Karen Roush, PhD, RN

Study Overview

Objective. To examine the efficacy and safety of weight-loss surgery in adolescents.

Design. Prospective observational study.

Setting and participants. Adolescents (aged 13–19 years) with severe obesity undergoing bariatric surgery at 5 U.S. hospitals and medical centers from March 2007 through February 2012. Participants were enrolled in the Teen-Longitudinal Assessment of Bariatric Surgery (Teen-LABS) study, a longitudinal prospective study that investigated the risks and benefits of adolescent bariatric surgery.

Main outcome measures. Data was collected on weight, comorbidities, cardiometabolic risk factors, nutritional status, and weight-related quality of life at research visits scheduled at 6 months, 1 year, 2 years, and 3 years post bariatric surgery. Researchers measured height and weight and blood pressure directly and calculated BMI. They assessed for comorbidities and cardiometabolic risk factors through urine and serum laboratory tests of lipids, glomerular filtration rate, albumin, glycated hemoglobin, fasting glucose level, and insulin. They assessed nutritional status with laboratory values for serum albumin, folate, vitamin B₁₂, 25-hydroxyvitamin D, parathyroid hormone, ferritin, transferrin, vitamin A, and vitamin B₁ erythrocyte transketolase. Researchers conducted interviews with the participants to collect information about subsequent medical or surgical procedures or, if participants missed a research visit, they obtained information through chart reviews. Finally, weight-related quality of life was assessed with the Impact of Weight on Quality of Life-Kids instrument, a validated self-report measure with 27 items divided into 4 subscales: physical comfort, body esteem, social life, and family relations.

Main results. Analysis was conducted on results for 228 of 242 participants who received Roux-en-Y gastric bypass (n = 161) and sleeve gastrectomy (n = 67). Results for 14 participants who received adjustable gastric banding were not included due to the small size of that group. Mean weight loss was 41 kg while mean height increased by only 0.51 cm. The mean percentage of weight loss was 27% overall and was similar in both groups, 28% in participants who underwent gastric bypass and 26% in those who underwent sleeve gastrectomy. At the 3-year visit, there were statistically significant improvements in comorbidities: 74% of the 96 participants with elevated blood pressure, 66% of the 171 participants with dyslipidemia, and 86% of the 36 participants with abnormal kidney function at baseline had values within the normal range. None of 3 participants with type 1 diabetes at baseline had resolution. However, 29 participants had type 2 diabetes (median glycolated hemoglobin 6.3% at baseline) and 19 of 20 of them for whom data were available at 3 years were in remission, with a median glycolated hemoglobin of 5.3%. There was an increase in the number of participants with micronutrient deficiencies at the 3-year mark: the percentage of participants with low ferritin levels increased from 5% at baseline to 57%, those with low vitamin B₁₂ increased from < 1% to 8%, and those with low vitamin A increased from 6% to 16%. During the 3-year follow-up period, 30 participants underwent 44 intrabdominal procedures related to the bariatric procedure and 29 participants underwent 48 endoscopic procedures, including stricture dilatation (n = 11). Total scores on the Impact of Weight on Quality of Life-Kids instrument improved from a mean of 63 at baseline to 83 at 3 years.

Conclusion. Overall there were significant improvements in weight, comorbidities, cardiometabolic health, and weight-related quality of life. However, there were also risks, including increased micronutrient deficiencies and the need for subsequent invasive abdominal procedures.

Commentary

Pediatric obesity is one of the most significant health problems facing children and adolescents. According to the most recent estimates, 34.5% of all adolescents aged 12 to 19 years are overweight or obese [1]. Pediatric obesity has serious short- and long-term psychosocial and physical implications. Obese adolescents suffer from social marginalization, poor self-concept, and lower health-related quality of life [2,3]. They are at greater risk for metabolic syndrome, diabetes, obstructive sleep apnea, and conditions associated with coronary artery disease such as hyperlipidemia and hypertension [4,5]. Additionally, obesity in adolescence is strongly associated with early mortality and years of life lost [6].

Despite extensive research and public health campaigns, rates of adolescent obesity have not decreased since 2003 [1]. Diet and behavioral approaches have had limited success and are rarely sustained over time. Bariatric surgery is an approach that has been used safely and effectively in severely obese adults and is increasingly being used for adolescents as well [7]. The results of this study are encouraging in that they suggest that bariatric surgery is effective in adolescents, leading to significant and sustained weight loss over 3 years and improved cardiometabolic health and weight-related quality of life.

The procedures are not without risks as demonstrated by the findings of micronutrient deficiencies and the need for follow-up intraabdominal and endoscopic procedures. The number of follow-up procedures and the fact that they continued into the third year is concerning. More details about this finding, such as characteristics of participants who required them, would be helpful. Further research to determine risk factors associated with complications that require subsequent invasive procedures is important for developing criteria for selection of candidates for bariatric surgery. Additionally, there was no information on impact of the follow-up procedures on participants or the conditions that precipitated them. In addition, there was no information on physical sequelae that can cause ongoing distress for patients, eg, chronic abdominal cramping and pain. The authors measured weight-related quality of life but measuring overall quality of life post-procedure would have captured the impact of post-procedure dietary restrictions and any medical problems. Such data could be helpful in decision-making about the use of bariatric procedures in this population versus noninvasive approaches to management.

As the authors note, treating severe obesity in adolescence rather than waiting until adulthood may have significant implications for improved health in adulthood, particularly in preventing or reversing cardiovascular damage related to obesity-related cardiometabolic risk factors. However, what is not known yet is whether the positive outcomes, beginning with weight loss, are sustained through adulthood. This 3-year longitudinal study was the first to examine factors over an extended time period, however, considering the average life expectancy of an adolescent, it provides only a relatively short-term outlook. A longitudinal study that follows a cohort of adolescents from the time of the bariatric procedure into middle age or beyond is needed. Such a study would also provide needed information about the long-term consequences of repeated intraabdominal procedures and the persistence or resolution of micronutrient deficiencies and their effects on health.

The strengths of this study are its prospective longitudinal design and its high rate of cohort completion (99% of participants remained actively involved, completing 88% of follow-up visits). As the authors note, the lack of a control group of adolescents treated with diet and behavioral approaches prevents any definitive statement about the benefits and risks compared to nonsurgical approaches. However, previous research indicates that weight loss is not as great nor sustained when nonsurgical approaches are used.

Applications for Clinical Practice

The use of bariatric surgery in adolescents is a promising approach to a major health problem that has proven resistant to concerted medical and public health efforts and the use of nonsurgical treatments. Ongoing longitudinal research is needed but the positive outcomes seen here—sustained significant weight loss, improvement in cardiometabolic risk factors and comorbidities, and improved weight-related quality of life—indicate that bariatric surgery is an effective treatment for adolescent obesity when diet and behavioral approaches have failed. However, the occurrence of post-procedure complications also highlights the need for caution. Clinicians must carefully weigh the risk-benefit ratio for each individual, taking into consideration the long-term implications of severe obesity, any potential for significant weight loss with diet and behavioral changes, and the positive outcomes of bariatric surgery demonstrated here.

 —Karen Roush, PhD, RN

Study Overview

Objective. To examine the efficacy and safety of weight-loss surgery in adolescents.

Design. Prospective observational study.

Setting and participants. Adolescents (aged 13–19 years) with severe obesity undergoing bariatric surgery at 5 U.S. hospitals and medical centers from March 2007 through February 2012. Participants were enrolled in the Teen-Longitudinal Assessment of Bariatric Surgery (Teen-LABS) study, a longitudinal prospective study that investigated the risks and benefits of adolescent bariatric surgery.

Main outcome measures. Data was collected on weight, comorbidities, cardiometabolic risk factors, nutritional status, and weight-related quality of life at research visits scheduled at 6 months, 1 year, 2 years, and 3 years post bariatric surgery. Researchers measured height and weight and blood pressure directly and calculated BMI. They assessed for comorbidities and cardiometabolic risk factors through urine and serum laboratory tests of lipids, glomerular filtration rate, albumin, glycated hemoglobin, fasting glucose level, and insulin. They assessed nutritional status with laboratory values for serum albumin, folate, vitamin B₁₂, 25-hydroxyvitamin D, parathyroid hormone, ferritin, transferrin, vitamin A, and vitamin B₁ erythrocyte transketolase. Researchers conducted interviews with the participants to collect information about subsequent medical or surgical procedures or, if participants missed a research visit, they obtained information through chart reviews. Finally, weight-related quality of life was assessed with the Impact of Weight on Quality of Life-Kids instrument, a validated self-report measure with 27 items divided into 4 subscales: physical comfort, body esteem, social life, and family relations.

Main results. Analysis was conducted on results for 228 of 242 participants who received Roux-en-Y gastric bypass (n = 161) and sleeve gastrectomy (n = 67). Results for 14 participants who received adjustable gastric banding were not included due to the small size of that group. Mean weight loss was 41 kg while mean height increased by only 0.51 cm. The mean percentage of weight loss was 27% overall and was similar in both groups, 28% in participants who underwent gastric bypass and 26% in those who underwent sleeve gastrectomy. At the 3-year visit, there were statistically significant improvements in comorbidities: 74% of the 96 participants with elevated blood pressure, 66% of the 171 participants with dyslipidemia, and 86% of the 36 participants with abnormal kidney function at baseline had values within the normal range. None of 3 participants with type 1 diabetes at baseline had resolution. However, 29 participants had type 2 diabetes (median glycolated hemoglobin 6.3% at baseline) and 19 of 20 of them for whom data were available at 3 years were in remission, with a median glycolated hemoglobin of 5.3%. There was an increase in the number of participants with micronutrient deficiencies at the 3-year mark: the percentage of participants with low ferritin levels increased from 5% at baseline to 57%, those with low vitamin B₁₂ increased from < 1% to 8%, and those with low vitamin A increased from 6% to 16%. During the 3-year follow-up period, 30 participants underwent 44 intrabdominal procedures related to the bariatric procedure and 29 participants underwent 48 endoscopic procedures, including stricture dilatation (n = 11). Total scores on the Impact of Weight on Quality of Life-Kids instrument improved from a mean of 63 at baseline to 83 at 3 years.

Conclusion. Overall there were significant improvements in weight, comorbidities, cardiometabolic health, and weight-related quality of life. However, there were also risks, including increased micronutrient deficiencies and the need for subsequent invasive abdominal procedures.

Commentary

Pediatric obesity is one of the most significant health problems facing children and adolescents. According to the most recent estimates, 34.5% of all adolescents aged 12 to 19 years are overweight or obese [1]. Pediatric obesity has serious short- and long-term psychosocial and physical implications. Obese adolescents suffer from social marginalization, poor self-concept, and lower health-related quality of life [2,3]. They are at greater risk for metabolic syndrome, diabetes, obstructive sleep apnea, and conditions associated with coronary artery disease such as hyperlipidemia and hypertension [4,5]. Additionally, obesity in adolescence is strongly associated with early mortality and years of life lost [6].

Despite extensive research and public health campaigns, rates of adolescent obesity have not decreased since 2003 [1]. Diet and behavioral approaches have had limited success and are rarely sustained over time. Bariatric surgery is an approach that has been used safely and effectively in severely obese adults and is increasingly being used for adolescents as well [7]. The results of this study are encouraging in that they suggest that bariatric surgery is effective in adolescents, leading to significant and sustained weight loss over 3 years and improved cardiometabolic health and weight-related quality of life.

The procedures are not without risks as demonstrated by the findings of micronutrient deficiencies and the need for follow-up intraabdominal and endoscopic procedures. The number of follow-up procedures and the fact that they continued into the third year is concerning. More details about this finding, such as characteristics of participants who required them, would be helpful. Further research to determine risk factors associated with complications that require subsequent invasive procedures is important for developing criteria for selection of candidates for bariatric surgery. Additionally, there was no information on impact of the follow-up procedures on participants or the conditions that precipitated them. In addition, there was no information on physical sequelae that can cause ongoing distress for patients, eg, chronic abdominal cramping and pain. The authors measured weight-related quality of life but measuring overall quality of life post-procedure would have captured the impact of post-procedure dietary restrictions and any medical problems. Such data could be helpful in decision-making about the use of bariatric procedures in this population versus noninvasive approaches to management.

As the authors note, treating severe obesity in adolescence rather than waiting until adulthood may have significant implications for improved health in adulthood, particularly in preventing or reversing cardiovascular damage related to obesity-related cardiometabolic risk factors. However, what is not known yet is whether the positive outcomes, beginning with weight loss, are sustained through adulthood. This 3-year longitudinal study was the first to examine factors over an extended time period, however, considering the average life expectancy of an adolescent, it provides only a relatively short-term outlook. A longitudinal study that follows a cohort of adolescents from the time of the bariatric procedure into middle age or beyond is needed. Such a study would also provide needed information about the long-term consequences of repeated intraabdominal procedures and the persistence or resolution of micronutrient deficiencies and their effects on health.

The strengths of this study are its prospective longitudinal design and its high rate of cohort completion (99% of participants remained actively involved, completing 88% of follow-up visits). As the authors note, the lack of a control group of adolescents treated with diet and behavioral approaches prevents any definitive statement about the benefits and risks compared to nonsurgical approaches. However, previous research indicates that weight loss is not as great nor sustained when nonsurgical approaches are used.

Applications for Clinical Practice

The use of bariatric surgery in adolescents is a promising approach to a major health problem that has proven resistant to concerted medical and public health efforts and the use of nonsurgical treatments. Ongoing longitudinal research is needed but the positive outcomes seen here—sustained significant weight loss, improvement in cardiometabolic risk factors and comorbidities, and improved weight-related quality of life—indicate that bariatric surgery is an effective treatment for adolescent obesity when diet and behavioral approaches have failed. However, the occurrence of post-procedure complications also highlights the need for caution. Clinicians must carefully weigh the risk-benefit ratio for each individual, taking into consideration the long-term implications of severe obesity, any potential for significant weight loss with diet and behavioral changes, and the positive outcomes of bariatric surgery demonstrated here.

 —Karen Roush, PhD, RN

Study Overview

Objective. To examine the association of individual fruit and vegetable intake with the risk of developing hypertension.

Design. Meta-analysis.

Setting and participants. Subjects were derived from the Nurses’ Health Study (n = 121,700 women, aged 30–55 years in 1976), the Nurses’ Health Study II (n = 116,430 women, aged 25–42 years in 1989), and the Health Professionals Follow-up Study (n = 51,529 men, aged 40–75 years in 1986). Participants returned a questionnaire every 2 years reporting a diagnosis of hypertension by a health care provider. Participants also answered qualitative–quantitative food frequency questionnaires (FFQs) every 4 years, reporting an intake of > 130 foods and beverages. Participants who reported a diagnosis of hypertension at the baseline questionnaire were excluded from the analysis.

Main outcome measures. Self-reported incident hypertension.

Results. Compared to participants whose consumption of fruits and vegetables was ≤ 4 servings/week, those whose intake was ≥ 4 servings/day had multivariable pooled hazard ratios for incident hypertension of 0.92 (95% confidence interval [CI], 0.87–0.97) for total whole fruit intake and 0.95 (CI, 0.86–1.04) for total vegetable intake. Similarly, compared to participants who did not increase their fruit or vegetable consumption, the pooled hazard ratios for those whose intake increased by ≥ 7 servings/week were 0.94 (0.90–0.97) for total whole fruit intake and 0.98 (0.94–1.01) for total vegetable intake. When individual fruit and vegetable consumption was analyzed, consumption levels of ≥ 4 servings/week (as opposed to < 1 serving/month) of broccoli, carrots, tofu or soybeans, raisins, and apples were associated with lower hypertension risk. String beans, brussel sprouts, and cantaloupe were associated with increased risk of hypertension.

Conclusion. The study findings suggested that greater long-term intake and increased consumption of whole fruits may reduce the risk of developing hypertension.

Commentary

Hypertension is a major risk factor for cardiovascular disease and a growing public health concern. Effective public health interventions that will lead to population-wide reductions in blood pressure are needed. The adoption of a healthy diet and low sodium intake is recommended by the American Heart Association in order to prevent hypertension in adults [1]. However, specific information about the benefits of long-term intake and individual foods is limited.

This study aimed to examine the association of individual fruit and vegetable intake with the risk of developing hypertension in 3 large prospective cohort studies in the United States. It was found that greater long-term intake and increased consumption of whole fruits may reduce risk of developing hypertension. Participants with higher fruit and vegetable intakes were more physically active, older, had higher daily caloric intakes, and were less likely to be smokers.

This study was novel in that it examined individual fruit and vegetable consumption. All 3 studies provided a large sample, which increased precision and power in the statistical analysis. Researchers were focused on establishing an association between the risk of hypertension and fruit and vegetable consumption; therefore, hazard ratios were presented and Cox regression and multivariate analysis were used, which are appropriate statistical methods for this type of study.

Some limitations should be mentioned. Blood pressure was not directly measured. Food intake was measured using a dietary questionnaire and may not have accurately represented actual intake. Also, participants were mostly non-Hispanic white men and women and other population groups were not well represented.

Applications for Clinical Practice

Reducing the risk for hypertension by increasing fruit consumption needs to be examined in other population groups and studies. In the meantime, clinicians can continue to recommend an eating plan that is rich in fruits, vegetables, and low-fat dairy products and reduced in saturated fat, total fat, and cholesterol.

—Paloma Cesar de Sales, BS, RN, MS

Study Overview

Objective. To examine the association of individual fruit and vegetable intake with the risk of developing hypertension.

Design. Meta-analysis.

Setting and participants. Subjects were derived from the Nurses’ Health Study (n = 121,700 women, aged 30–55 years in 1976), the Nurses’ Health Study II (n = 116,430 women, aged 25–42 years in 1989), and the Health Professionals Follow-up Study (n = 51,529 men, aged 40–75 years in 1986). Participants returned a questionnaire every 2 years reporting a diagnosis of hypertension by a health care provider. Participants also answered qualitative–quantitative food frequency questionnaires (FFQs) every 4 years, reporting an intake of > 130 foods and beverages. Participants who reported a diagnosis of hypertension at the baseline questionnaire were excluded from the analysis.

Main outcome measures. Self-reported incident hypertension.

Results. Compared to participants whose consumption of fruits and vegetables was ≤ 4 servings/week, those whose intake was ≥ 4 servings/day had multivariable pooled hazard ratios for incident hypertension of 0.92 (95% confidence interval [CI], 0.87–0.97) for total whole fruit intake and 0.95 (CI, 0.86–1.04) for total vegetable intake. Similarly, compared to participants who did not increase their fruit or vegetable consumption, the pooled hazard ratios for those whose intake increased by ≥ 7 servings/week were 0.94 (0.90–0.97) for total whole fruit intake and 0.98 (0.94–1.01) for total vegetable intake. When individual fruit and vegetable consumption was analyzed, consumption levels of ≥ 4 servings/week (as opposed to < 1 serving/month) of broccoli, carrots, tofu or soybeans, raisins, and apples were associated with lower hypertension risk. String beans, brussel sprouts, and cantaloupe were associated with increased risk of hypertension.

Conclusion. The study findings suggested that greater long-term intake and increased consumption of whole fruits may reduce the risk of developing hypertension.

Commentary

Hypertension is a major risk factor for cardiovascular disease and a growing public health concern. Effective public health interventions that will lead to population-wide reductions in blood pressure are needed. The adoption of a healthy diet and low sodium intake is recommended by the American Heart Association in order to prevent hypertension in adults [1]. However, specific information about the benefits of long-term intake and individual foods is limited.

This study aimed to examine the association of individual fruit and vegetable intake with the risk of developing hypertension in 3 large prospective cohort studies in the United States. It was found that greater long-term intake and increased consumption of whole fruits may reduce risk of developing hypertension. Participants with higher fruit and vegetable intakes were more physically active, older, had higher daily caloric intakes, and were less likely to be smokers.

This study was novel in that it examined individual fruit and vegetable consumption. All 3 studies provided a large sample, which increased precision and power in the statistical analysis. Researchers were focused on establishing an association between the risk of hypertension and fruit and vegetable consumption; therefore, hazard ratios were presented and Cox regression and multivariate analysis were used, which are appropriate statistical methods for this type of study.

Some limitations should be mentioned. Blood pressure was not directly measured. Food intake was measured using a dietary questionnaire and may not have accurately represented actual intake. Also, participants were mostly non-Hispanic white men and women and other population groups were not well represented.

Applications for Clinical Practice

Reducing the risk for hypertension by increasing fruit consumption needs to be examined in other population groups and studies. In the meantime, clinicians can continue to recommend an eating plan that is rich in fruits, vegetables, and low-fat dairy products and reduced in saturated fat, total fat, and cholesterol.

—Paloma Cesar de Sales, BS, RN, MS

Study Overview

Objective. To examine the association of individual fruit and vegetable intake with the risk of developing hypertension.

Design. Meta-analysis.

Setting and participants. Subjects were derived from the Nurses’ Health Study (n = 121,700 women, aged 30–55 years in 1976), the Nurses’ Health Study II (n = 116,430 women, aged 25–42 years in 1989), and the Health Professionals Follow-up Study (n = 51,529 men, aged 40–75 years in 1986). Participants returned a questionnaire every 2 years reporting a diagnosis of hypertension by a health care provider. Participants also answered qualitative–quantitative food frequency questionnaires (FFQs) every 4 years, reporting an intake of > 130 foods and beverages. Participants who reported a diagnosis of hypertension at the baseline questionnaire were excluded from the analysis.

Main outcome measures. Self-reported incident hypertension.

Results. Compared to participants whose consumption of fruits and vegetables was ≤ 4 servings/week, those whose intake was ≥ 4 servings/day had multivariable pooled hazard ratios for incident hypertension of 0.92 (95% confidence interval [CI], 0.87–0.97) for total whole fruit intake and 0.95 (CI, 0.86–1.04) for total vegetable intake. Similarly, compared to participants who did not increase their fruit or vegetable consumption, the pooled hazard ratios for those whose intake increased by ≥ 7 servings/week were 0.94 (0.90–0.97) for total whole fruit intake and 0.98 (0.94–1.01) for total vegetable intake. When individual fruit and vegetable consumption was analyzed, consumption levels of ≥ 4 servings/week (as opposed to < 1 serving/month) of broccoli, carrots, tofu or soybeans, raisins, and apples were associated with lower hypertension risk. String beans, brussel sprouts, and cantaloupe were associated with increased risk of hypertension.

Conclusion. The study findings suggested that greater long-term intake and increased consumption of whole fruits may reduce the risk of developing hypertension.

Commentary

Hypertension is a major risk factor for cardiovascular disease and a growing public health concern. Effective public health interventions that will lead to population-wide reductions in blood pressure are needed. The adoption of a healthy diet and low sodium intake is recommended by the American Heart Association in order to prevent hypertension in adults [1]. However, specific information about the benefits of long-term intake and individual foods is limited.

This study aimed to examine the association of individual fruit and vegetable intake with the risk of developing hypertension in 3 large prospective cohort studies in the United States. It was found that greater long-term intake and increased consumption of whole fruits may reduce risk of developing hypertension. Participants with higher fruit and vegetable intakes were more physically active, older, had higher daily caloric intakes, and were less likely to be smokers.

This study was novel in that it examined individual fruit and vegetable consumption. All 3 studies provided a large sample, which increased precision and power in the statistical analysis. Researchers were focused on establishing an association between the risk of hypertension and fruit and vegetable consumption; therefore, hazard ratios were presented and Cox regression and multivariate analysis were used, which are appropriate statistical methods for this type of study.

Some limitations should be mentioned. Blood pressure was not directly measured. Food intake was measured using a dietary questionnaire and may not have accurately represented actual intake. Also, participants were mostly non-Hispanic white men and women and other population groups were not well represented.

Applications for Clinical Practice

Reducing the risk for hypertension by increasing fruit consumption needs to be examined in other population groups and studies. In the meantime, clinicians can continue to recommend an eating plan that is rich in fruits, vegetables, and low-fat dairy products and reduced in saturated fat, total fat, and cholesterol.

—Paloma Cesar de Sales, BS, RN, MS

Study Overview

Objective. To compare weight regain after rapid versus slower loss of an equivalent amount of weight.

Study design. Randomized clinical trial.

Setting and participants. This study took place in a single medical center in the Netherlands. Investigators recruited 61 adults (no age range provided) with body mass index (BMI) between 28–35 kg/m² and at a stable weight (no change of > 3 kg for the past 2 months) to participate in a weight loss study. Individuals with type 2 diabetes, dyslipidemia, uncontrolled hypertension, or liver, heart or kidney disease were excluded, as were those who were currently pregnant or reported consuming more than moderate amounts of alcohol.

Once consented, participants were randomized into one of 2 study arms. The rapid weight loss arm was prescribed a very-low-calorie diet (VLCD) with just 500 kcal/day (43% protein/43% carb/14% fat) for 5 weeks, after which they transitioned to a 4-week “weight stable” period, and then a 9-month follow-up period (overall follow-up time of ~11 months; 10 months after weight loss). In contrast, the slower weight loss arm was prescribed a low-calorie diet (LCD) with 1250 kcal/day (29% protein/48% carb/23% fat) for 12 weeks, after which they also transitioned to a 4-week weight stable period and 9 months of follow-up (overall follow-up time of ~13 months; 10 months after weight loss). VLCD (rapid weight loss) participants received 3 meal replacement shakes per day (totaling 500 kcal) during the weight loss period and were also told they could consume unlimited amounts of low-calorie vegetables. The LCD (slower weight loss) participants received 1 meal replacement shake per day during their 12 weeks of weight loss and were responsible for providing the remainder of their own meals and snacks according to guidelines from a study dietitian. Following active weight loss, both groups then shifted to higher-calorie, food-based diets during a “weight stable” 4-week period and were responsible during this time for providing all of their own food. The researchers do not specify the details of the diet composition for this weight stable period. Exposure to the registered dietitian was the same in both groups, with 5 consultations during weight loss (weekly for VLCD, presumably more spaced out for LCD) and 4 during weight stable period. No further diet advice or meal replacement support was given during the 9-month follow-up period, but participants came in for monthly weigh-ins.

Main outcome measure. The primary outcome measure was change in weight (ie, amount of weight regained) during the 9-month follow-up period, compared between groups using an independent samples t test. Additional biometric measures included change in waist circumference and changes in body composition. For the latter, the researchers used a “Bod Pod” to conduct air-displacement plethysmography and determine what percentage of an individual’s weight was fat mass (FM) versus lean mass/water (FFM [fat-free mass]). They then compared the amount of FFM lost between groups, again using the independent samples t test.

The researchers also collected information on self-reported physical activity (questionnaire) and self-reported history of weight cycling (number of times a participant had previously lost and regained at least 5 kg) prior to this study. These were not outcomes per-se, but were collected so that they could be examined as correlates of the biometric outcomes above, using Pearson and Spearman’s correlation coefficients.

Results. The LCD (n = 29) and VLCD (n = 28) groups were similar at baseline with no significant differences reported. Of the 61 individuals initially enrolled, 57 (93%) completed the study. Summary statistics are reported only for these 57 individuals. No imputation or other methods for handling missing data were used. There were slightly more women than men in the study (53% women); the average (SD) age was 51.8 (1.9) years in the LCD group and 50.7 (1.5) years in the VLCD group. Mean starting BMI was 31 kg/m² (31.3 [0.5] in LCD, 31.0 [0.4] in VLCD) and both groups had just under 40% body fat at baseline (39.9% [1.8] in LCD, 39.7% [1.5] in VLCD).

After 12 weeks of weight loss for LCD, or 5 weeks of weight loss for VLCD, both groups lost a similar amount of total weight (8.2 [0.5] kg in LCD vs. 9.0 [0.4] kg in VLCD), then had no significant changes in weight during the subsequent 4-week “weight stable” period. However, during the weight stable period VLCD patients had an average 0.8 (0.6) cm increase in waist circumference (a rebounding after a decrease of 7.7 cm during weight loss), while LCD patients on average had a continued decrease of 1.0 (0.5 cm) in waist circumference (P = 0.003).

There was no significant difference between groups for the primary outcome of weight regain during 9-months of follow-up (4.2 [0.6] kg regained for LCD, 4.5 [0.7] for VLCD; P = 0.73). The only significant correlates of weight regain were amount of FFM lost (more lean mass lost predicted more weight regain), and amount of physical activity reported during follow-up (more activity predicted less regain). Participant sex, age, starting BMI, history of weight cycling, and amount of weight lost did not correlate with rate of re-gain.

One area where there was a significant between-group difference, both after initial weight loss and persisting after the weight stable period, was in the amount of FFM lost (a rough approximation of lost lean mass, eg, muscle mass). VLCD participants had more FFM loss (1.6 [0.2] kg) than LCD participants (0.6 [0.2] kg) (P < 0.01) after active weight loss, and continued to have significantly more FFM loss (0.8 [0.2] kg vs. 0.2 [0.2] kg) after the 4-week weight stable period.

There were no between-group differences at the end of weight loss or at the end of follow-up for hip or waist circumference or for blood pressure.

Conclusion. The authors conclude that rate of weight loss does not affect one’s risk of weight regain after a diet, after a similar amount of weight has been lost.

Commentary

The failure of most diets to produce durable weight loss is a frustration for patients, clinicians, and researchers. In general, regardless of the composition of a diet, the majority of patients will regain some or all of their lost weight within several years after completing the diet. The reasons for weight regain are complex, and include reversion to old eating or physical activity behaviors but also a strong physiologic drive by the body to reverse weight loss that it perceives as a threat to health [1].

One area in diet research that has recently generated some controversy is whether or not rate of initial weight loss might impact a patient’s ability to maintain that weight loss, with the conventional wisdom (and national guidelines, in some cases), suggesting that slower weight loss is preferable to rapid weight loss for this reason [2]. A handful of studies have challenged this notion, however, and suggested that rapid weight loss does not necessarily lead to greater weight regain [3,4]. Previous such studies, however, have not generally been designed to compare regain after equal amounts of weight loss, which may make their results more difficult to interpret.

The present study contributes another piece of evidence to the argument that rapid initial weight loss may not increase a patient’s risk of regain. This small randomized trial is timely and has several features that make it a unique contribution. First, the design of the study allowed for both groups, despite losing weight at very different rates, to reach the same amount of total weight loss before being followed forward in time. This made weight regain much easier to compare between groups during follow-up. Second, the study included measurement of changing body composition—ie, what kind of weight was being lost (fat vs. fat-free mass)—rather than just the total amount of weight. This allowed the researchers to present data for an outcome that is mechanistically related to metabolic rate (and therefore weight regain), and one that might have implications for longer-term health after rapid versus more moderate-pace weight loss.

Several aspects of the study design, however, may limit the impact of the findings. For example, in both arms, while a certain type of diet was “prescribed,” there is no comment about assessment of participant fidelity to the prescribed diet, and there is potential for very different levels of adherence between groups, especially in active weight loss, when basically all meals were provided to the VLCD arm, but LCD subjects were responsible for about 90% of their own meals. This could have led to larger discrepancies between prescribed and actual diet in the LCD arm relative to VLCD. Granted, the rate of weight loss was the exposure of interest, and that clearly varied between groups as expected, implying at least moderate fidelity to prescribed caloric content of each diet, but how much protein vs. fat vs. carb was actually consumed by each group is not clear. Additionally, while 9 months of post weight-loss follow-up is certainly a good start in terms of follow-up duration, it may not have been sufficient to observe differences that would later emerge between the groups for weight regain. Other long-term weight loss maintenance studies have followed patients for several years or longer after initial weight loss [5].

Using data from all participants, the researchers reported that the amount of FFM an individual lost was a predictor of weight regain during follow-up. This finding is in keeping with the idea that more lean mass loss leads to lower metabolic rate and predisposes to weight regain (hence the conventional wisdom to avoid rapid weight loss with low-protein diets). In keeping with this theme, VLCD patients, whose protein intakes and activity levels were lower, did lose more FFM (ie, lean mass) than LCD patients. It was therefore surprising that in between-group analyses there was no statistical difference in weight regain. On some level, this raises concerns about the robustness of the overall finding. Perhaps with a larger sample, more precise measures of FFM lost (eg, with DEXA scanning instead of the “bod pod” or longer follow-up, this difference in lost lean mass between groups actually would have predicted greater weight regain for VLCD patients. The researchers attribute some of the FFM loss after the caloric restriction phase to decreased water and glycogen stores, rather than muscle mass, and speculate that this is why no impact on weight regain was seen between groups.

From a generalizability standpoint, there are important safety concerns with the use of VLCDs, aside from subsequent risk of weight regain, that are not addressed with this study. Many patients simply cannot tolerate a 500 kcal per day diet, including those with more severe obesity (who have higher daily energy requirements) or those with complicated chronic medical conditions who might be at higher risk of complications from such low energy intake. Accordingly, these kinds of patients were not included in this study, so it is not clear whether results might generalize to them.

Applications for Clinical Practice

Despite the conventional wisdom that slower weight loss may be more sustainable over time, several recent trials have suggested otherwise. Nonetheless, rapid weight loss produced with the use of VLCDs is not appropriate for every patient and must be carefully overseen by a weight management professional. Furthermore, rapid weight loss may place patients at increased risk of preferentially losing lean mass, which does correlate with risk of weight regain and could set them up for other health problems in the long-term. More studies are needed in this area before a definitive judgment can be made regarding the long term risks and benefits of rapid versus moderate-pace weight loss.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To compare weight regain after rapid versus slower loss of an equivalent amount of weight.

Study design. Randomized clinical trial.

Setting and participants. This study took place in a single medical center in the Netherlands. Investigators recruited 61 adults (no age range provided) with body mass index (BMI) between 28–35 kg/m² and at a stable weight (no change of > 3 kg for the past 2 months) to participate in a weight loss study. Individuals with type 2 diabetes, dyslipidemia, uncontrolled hypertension, or liver, heart or kidney disease were excluded, as were those who were currently pregnant or reported consuming more than moderate amounts of alcohol.

Once consented, participants were randomized into one of 2 study arms. The rapid weight loss arm was prescribed a very-low-calorie diet (VLCD) with just 500 kcal/day (43% protein/43% carb/14% fat) for 5 weeks, after which they transitioned to a 4-week “weight stable” period, and then a 9-month follow-up period (overall follow-up time of ~11 months; 10 months after weight loss). In contrast, the slower weight loss arm was prescribed a low-calorie diet (LCD) with 1250 kcal/day (29% protein/48% carb/23% fat) for 12 weeks, after which they also transitioned to a 4-week weight stable period and 9 months of follow-up (overall follow-up time of ~13 months; 10 months after weight loss). VLCD (rapid weight loss) participants received 3 meal replacement shakes per day (totaling 500 kcal) during the weight loss period and were also told they could consume unlimited amounts of low-calorie vegetables. The LCD (slower weight loss) participants received 1 meal replacement shake per day during their 12 weeks of weight loss and were responsible for providing the remainder of their own meals and snacks according to guidelines from a study dietitian. Following active weight loss, both groups then shifted to higher-calorie, food-based diets during a “weight stable” 4-week period and were responsible during this time for providing all of their own food. The researchers do not specify the details of the diet composition for this weight stable period. Exposure to the registered dietitian was the same in both groups, with 5 consultations during weight loss (weekly for VLCD, presumably more spaced out for LCD) and 4 during weight stable period. No further diet advice or meal replacement support was given during the 9-month follow-up period, but participants came in for monthly weigh-ins.

Main outcome measure. The primary outcome measure was change in weight (ie, amount of weight regained) during the 9-month follow-up period, compared between groups using an independent samples t test. Additional biometric measures included change in waist circumference and changes in body composition. For the latter, the researchers used a “Bod Pod” to conduct air-displacement plethysmography and determine what percentage of an individual’s weight was fat mass (FM) versus lean mass/water (FFM [fat-free mass]). They then compared the amount of FFM lost between groups, again using the independent samples t test.

The researchers also collected information on self-reported physical activity (questionnaire) and self-reported history of weight cycling (number of times a participant had previously lost and regained at least 5 kg) prior to this study. These were not outcomes per-se, but were collected so that they could be examined as correlates of the biometric outcomes above, using Pearson and Spearman’s correlation coefficients.

Results. The LCD (n = 29) and VLCD (n = 28) groups were similar at baseline with no significant differences reported. Of the 61 individuals initially enrolled, 57 (93%) completed the study. Summary statistics are reported only for these 57 individuals. No imputation or other methods for handling missing data were used. There were slightly more women than men in the study (53% women); the average (SD) age was 51.8 (1.9) years in the LCD group and 50.7 (1.5) years in the VLCD group. Mean starting BMI was 31 kg/m² (31.3 [0.5] in LCD, 31.0 [0.4] in VLCD) and both groups had just under 40% body fat at baseline (39.9% [1.8] in LCD, 39.7% [1.5] in VLCD).

After 12 weeks of weight loss for LCD, or 5 weeks of weight loss for VLCD, both groups lost a similar amount of total weight (8.2 [0.5] kg in LCD vs. 9.0 [0.4] kg in VLCD), then had no significant changes in weight during the subsequent 4-week “weight stable” period. However, during the weight stable period VLCD patients had an average 0.8 (0.6) cm increase in waist circumference (a rebounding after a decrease of 7.7 cm during weight loss), while LCD patients on average had a continued decrease of 1.0 (0.5 cm) in waist circumference (P = 0.003).

There was no significant difference between groups for the primary outcome of weight regain during 9-months of follow-up (4.2 [0.6] kg regained for LCD, 4.5 [0.7] for VLCD; P = 0.73). The only significant correlates of weight regain were amount of FFM lost (more lean mass lost predicted more weight regain), and amount of physical activity reported during follow-up (more activity predicted less regain). Participant sex, age, starting BMI, history of weight cycling, and amount of weight lost did not correlate with rate of re-gain.

One area where there was a significant between-group difference, both after initial weight loss and persisting after the weight stable period, was in the amount of FFM lost (a rough approximation of lost lean mass, eg, muscle mass). VLCD participants had more FFM loss (1.6 [0.2] kg) than LCD participants (0.6 [0.2] kg) (P < 0.01) after active weight loss, and continued to have significantly more FFM loss (0.8 [0.2] kg vs. 0.2 [0.2] kg) after the 4-week weight stable period.

There were no between-group differences at the end of weight loss or at the end of follow-up for hip or waist circumference or for blood pressure.

Conclusion. The authors conclude that rate of weight loss does not affect one’s risk of weight regain after a diet, after a similar amount of weight has been lost.

Commentary

The failure of most diets to produce durable weight loss is a frustration for patients, clinicians, and researchers. In general, regardless of the composition of a diet, the majority of patients will regain some or all of their lost weight within several years after completing the diet. The reasons for weight regain are complex, and include reversion to old eating or physical activity behaviors but also a strong physiologic drive by the body to reverse weight loss that it perceives as a threat to health [1].

One area in diet research that has recently generated some controversy is whether or not rate of initial weight loss might impact a patient’s ability to maintain that weight loss, with the conventional wisdom (and national guidelines, in some cases), suggesting that slower weight loss is preferable to rapid weight loss for this reason [2]. A handful of studies have challenged this notion, however, and suggested that rapid weight loss does not necessarily lead to greater weight regain [3,4]. Previous such studies, however, have not generally been designed to compare regain after equal amounts of weight loss, which may make their results more difficult to interpret.

The present study contributes another piece of evidence to the argument that rapid initial weight loss may not increase a patient’s risk of regain. This small randomized trial is timely and has several features that make it a unique contribution. First, the design of the study allowed for both groups, despite losing weight at very different rates, to reach the same amount of total weight loss before being followed forward in time. This made weight regain much easier to compare between groups during follow-up. Second, the study included measurement of changing body composition—ie, what kind of weight was being lost (fat vs. fat-free mass)—rather than just the total amount of weight. This allowed the researchers to present data for an outcome that is mechanistically related to metabolic rate (and therefore weight regain), and one that might have implications for longer-term health after rapid versus more moderate-pace weight loss.

Several aspects of the study design, however, may limit the impact of the findings. For example, in both arms, while a certain type of diet was “prescribed,” there is no comment about assessment of participant fidelity to the prescribed diet, and there is potential for very different levels of adherence between groups, especially in active weight loss, when basically all meals were provided to the VLCD arm, but LCD subjects were responsible for about 90% of their own meals. This could have led to larger discrepancies between prescribed and actual diet in the LCD arm relative to VLCD. Granted, the rate of weight loss was the exposure of interest, and that clearly varied between groups as expected, implying at least moderate fidelity to prescribed caloric content of each diet, but how much protein vs. fat vs. carb was actually consumed by each group is not clear. Additionally, while 9 months of post weight-loss follow-up is certainly a good start in terms of follow-up duration, it may not have been sufficient to observe differences that would later emerge between the groups for weight regain. Other long-term weight loss maintenance studies have followed patients for several years or longer after initial weight loss [5].

Using data from all participants, the researchers reported that the amount of FFM an individual lost was a predictor of weight regain during follow-up. This finding is in keeping with the idea that more lean mass loss leads to lower metabolic rate and predisposes to weight regain (hence the conventional wisdom to avoid rapid weight loss with low-protein diets). In keeping with this theme, VLCD patients, whose protein intakes and activity levels were lower, did lose more FFM (ie, lean mass) than LCD patients. It was therefore surprising that in between-group analyses there was no statistical difference in weight regain. On some level, this raises concerns about the robustness of the overall finding. Perhaps with a larger sample, more precise measures of FFM lost (eg, with DEXA scanning instead of the “bod pod” or longer follow-up, this difference in lost lean mass between groups actually would have predicted greater weight regain for VLCD patients. The researchers attribute some of the FFM loss after the caloric restriction phase to decreased water and glycogen stores, rather than muscle mass, and speculate that this is why no impact on weight regain was seen between groups.

From a generalizability standpoint, there are important safety concerns with the use of VLCDs, aside from subsequent risk of weight regain, that are not addressed with this study. Many patients simply cannot tolerate a 500 kcal per day diet, including those with more severe obesity (who have higher daily energy requirements) or those with complicated chronic medical conditions who might be at higher risk of complications from such low energy intake. Accordingly, these kinds of patients were not included in this study, so it is not clear whether results might generalize to them.

Applications for Clinical Practice

Despite the conventional wisdom that slower weight loss may be more sustainable over time, several recent trials have suggested otherwise. Nonetheless, rapid weight loss produced with the use of VLCDs is not appropriate for every patient and must be carefully overseen by a weight management professional. Furthermore, rapid weight loss may place patients at increased risk of preferentially losing lean mass, which does correlate with risk of weight regain and could set them up for other health problems in the long-term. More studies are needed in this area before a definitive judgment can be made regarding the long term risks and benefits of rapid versus moderate-pace weight loss.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To compare weight regain after rapid versus slower loss of an equivalent amount of weight.

Study design. Randomized clinical trial.

Setting and participants. This study took place in a single medical center in the Netherlands. Investigators recruited 61 adults (no age range provided) with body mass index (BMI) between 28–35 kg/m² and at a stable weight (no change of > 3 kg for the past 2 months) to participate in a weight loss study. Individuals with type 2 diabetes, dyslipidemia, uncontrolled hypertension, or liver, heart or kidney disease were excluded, as were those who were currently pregnant or reported consuming more than moderate amounts of alcohol.

Once consented, participants were randomized into one of 2 study arms. The rapid weight loss arm was prescribed a very-low-calorie diet (VLCD) with just 500 kcal/day (43% protein/43% carb/14% fat) for 5 weeks, after which they transitioned to a 4-week “weight stable” period, and then a 9-month follow-up period (overall follow-up time of ~11 months; 10 months after weight loss). In contrast, the slower weight loss arm was prescribed a low-calorie diet (LCD) with 1250 kcal/day (29% protein/48% carb/23% fat) for 12 weeks, after which they also transitioned to a 4-week weight stable period and 9 months of follow-up (overall follow-up time of ~13 months; 10 months after weight loss). VLCD (rapid weight loss) participants received 3 meal replacement shakes per day (totaling 500 kcal) during the weight loss period and were also told they could consume unlimited amounts of low-calorie vegetables. The LCD (slower weight loss) participants received 1 meal replacement shake per day during their 12 weeks of weight loss and were responsible for providing the remainder of their own meals and snacks according to guidelines from a study dietitian. Following active weight loss, both groups then shifted to higher-calorie, food-based diets during a “weight stable” 4-week period and were responsible during this time for providing all of their own food. The researchers do not specify the details of the diet composition for this weight stable period. Exposure to the registered dietitian was the same in both groups, with 5 consultations during weight loss (weekly for VLCD, presumably more spaced out for LCD) and 4 during weight stable period. No further diet advice or meal replacement support was given during the 9-month follow-up period, but participants came in for monthly weigh-ins.

Main outcome measure. The primary outcome measure was change in weight (ie, amount of weight regained) during the 9-month follow-up period, compared between groups using an independent samples t test. Additional biometric measures included change in waist circumference and changes in body composition. For the latter, the researchers used a “Bod Pod” to conduct air-displacement plethysmography and determine what percentage of an individual’s weight was fat mass (FM) versus lean mass/water (FFM [fat-free mass]). They then compared the amount of FFM lost between groups, again using the independent samples t test.

The researchers also collected information on self-reported physical activity (questionnaire) and self-reported history of weight cycling (number of times a participant had previously lost and regained at least 5 kg) prior to this study. These were not outcomes per-se, but were collected so that they could be examined as correlates of the biometric outcomes above, using Pearson and Spearman’s correlation coefficients.

Results. The LCD (n = 29) and VLCD (n = 28) groups were similar at baseline with no significant differences reported. Of the 61 individuals initially enrolled, 57 (93%) completed the study. Summary statistics are reported only for these 57 individuals. No imputation or other methods for handling missing data were used. There were slightly more women than men in the study (53% women); the average (SD) age was 51.8 (1.9) years in the LCD group and 50.7 (1.5) years in the VLCD group. Mean starting BMI was 31 kg/m² (31.3 [0.5] in LCD, 31.0 [0.4] in VLCD) and both groups had just under 40% body fat at baseline (39.9% [1.8] in LCD, 39.7% [1.5] in VLCD).

After 12 weeks of weight loss for LCD, or 5 weeks of weight loss for VLCD, both groups lost a similar amount of total weight (8.2 [0.5] kg in LCD vs. 9.0 [0.4] kg in VLCD), then had no significant changes in weight during the subsequent 4-week “weight stable” period. However, during the weight stable period VLCD patients had an average 0.8 (0.6) cm increase in waist circumference (a rebounding after a decrease of 7.7 cm during weight loss), while LCD patients on average had a continued decrease of 1.0 (0.5 cm) in waist circumference (P = 0.003).

There was no significant difference between groups for the primary outcome of weight regain during 9-months of follow-up (4.2 [0.6] kg regained for LCD, 4.5 [0.7] for VLCD; P = 0.73). The only significant correlates of weight regain were amount of FFM lost (more lean mass lost predicted more weight regain), and amount of physical activity reported during follow-up (more activity predicted less regain). Participant sex, age, starting BMI, history of weight cycling, and amount of weight lost did not correlate with rate of re-gain.

One area where there was a significant between-group difference, both after initial weight loss and persisting after the weight stable period, was in the amount of FFM lost (a rough approximation of lost lean mass, eg, muscle mass). VLCD participants had more FFM loss (1.6 [0.2] kg) than LCD participants (0.6 [0.2] kg) (P < 0.01) after active weight loss, and continued to have significantly more FFM loss (0.8 [0.2] kg vs. 0.2 [0.2] kg) after the 4-week weight stable period.

There were no between-group differences at the end of weight loss or at the end of follow-up for hip or waist circumference or for blood pressure.

Conclusion. The authors conclude that rate of weight loss does not affect one’s risk of weight regain after a diet, after a similar amount of weight has been lost.

Commentary

The failure of most diets to produce durable weight loss is a frustration for patients, clinicians, and researchers. In general, regardless of the composition of a diet, the majority of patients will regain some or all of their lost weight within several years after completing the diet. The reasons for weight regain are complex, and include reversion to old eating or physical activity behaviors but also a strong physiologic drive by the body to reverse weight loss that it perceives as a threat to health [1].

One area in diet research that has recently generated some controversy is whether or not rate of initial weight loss might impact a patient’s ability to maintain that weight loss, with the conventional wisdom (and national guidelines, in some cases), suggesting that slower weight loss is preferable to rapid weight loss for this reason [2]. A handful of studies have challenged this notion, however, and suggested that rapid weight loss does not necessarily lead to greater weight regain [3,4]. Previous such studies, however, have not generally been designed to compare regain after equal amounts of weight loss, which may make their results more difficult to interpret.

The present study contributes another piece of evidence to the argument that rapid initial weight loss may not increase a patient’s risk of regain. This small randomized trial is timely and has several features that make it a unique contribution. First, the design of the study allowed for both groups, despite losing weight at very different rates, to reach the same amount of total weight loss before being followed forward in time. This made weight regain much easier to compare between groups during follow-up. Second, the study included measurement of changing body composition—ie, what kind of weight was being lost (fat vs. fat-free mass)—rather than just the total amount of weight. This allowed the researchers to present data for an outcome that is mechanistically related to metabolic rate (and therefore weight regain), and one that might have implications for longer-term health after rapid versus more moderate-pace weight loss.

Several aspects of the study design, however, may limit the impact of the findings. For example, in both arms, while a certain type of diet was “prescribed,” there is no comment about assessment of participant fidelity to the prescribed diet, and there is potential for very different levels of adherence between groups, especially in active weight loss, when basically all meals were provided to the VLCD arm, but LCD subjects were responsible for about 90% of their own meals. This could have led to larger discrepancies between prescribed and actual diet in the LCD arm relative to VLCD. Granted, the rate of weight loss was the exposure of interest, and that clearly varied between groups as expected, implying at least moderate fidelity to prescribed caloric content of each diet, but how much protein vs. fat vs. carb was actually consumed by each group is not clear. Additionally, while 9 months of post weight-loss follow-up is certainly a good start in terms of follow-up duration, it may not have been sufficient to observe differences that would later emerge between the groups for weight regain. Other long-term weight loss maintenance studies have followed patients for several years or longer after initial weight loss [5].

Using data from all participants, the researchers reported that the amount of FFM an individual lost was a predictor of weight regain during follow-up. This finding is in keeping with the idea that more lean mass loss leads to lower metabolic rate and predisposes to weight regain (hence the conventional wisdom to avoid rapid weight loss with low-protein diets). In keeping with this theme, VLCD patients, whose protein intakes and activity levels were lower, did lose more FFM (ie, lean mass) than LCD patients. It was therefore surprising that in between-group analyses there was no statistical difference in weight regain. On some level, this raises concerns about the robustness of the overall finding. Perhaps with a larger sample, more precise measures of FFM lost (eg, with DEXA scanning instead of the “bod pod” or longer follow-up, this difference in lost lean mass between groups actually would have predicted greater weight regain for VLCD patients. The researchers attribute some of the FFM loss after the caloric restriction phase to decreased water and glycogen stores, rather than muscle mass, and speculate that this is why no impact on weight regain was seen between groups.

From a generalizability standpoint, there are important safety concerns with the use of VLCDs, aside from subsequent risk of weight regain, that are not addressed with this study. Many patients simply cannot tolerate a 500 kcal per day diet, including those with more severe obesity (who have higher daily energy requirements) or those with complicated chronic medical conditions who might be at higher risk of complications from such low energy intake. Accordingly, these kinds of patients were not included in this study, so it is not clear whether results might generalize to them.

Applications for Clinical Practice

Despite the conventional wisdom that slower weight loss may be more sustainable over time, several recent trials have suggested otherwise. Nonetheless, rapid weight loss produced with the use of VLCDs is not appropriate for every patient and must be carefully overseen by a weight management professional. Furthermore, rapid weight loss may place patients at increased risk of preferentially losing lean mass, which does correlate with risk of weight regain and could set them up for other health problems in the long-term. More studies are needed in this area before a definitive judgment can be made regarding the long term risks and benefits of rapid versus moderate-pace weight loss.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To determine the efficacy and safety of delayed antibiotic prescribing strategies in acute uncomplicated respiratory infections.

Design. Randomized, multicenter, open-label clinical trial.

Setting and participants. The setting was 23 primary care centers in Spain. The study recruited patients who were 18 years of age or older with an acute uncomplicated respiratory infection (acute pharyngitis, rhinosinusitis, acute bronchitis, exacerbations of chronic bronchitis or mild to moderate chronic obstructive pulmonary disease). Patients with these infections were included by the physicians as long as they were unsure of whether to use antibiotics or not. The study protocol has been published elsewhere [1].

Intervention. Patients were randomized to 1 of 4 potential prescription strategies: (1) a delayed patient-led prescription strategy where patients were given an antibiotic prescription at first consultation but instructed to fill the prescription only if they felt substantially worse or saw no improvement in symptoms in the first few days after initial consultation; (2) a delayed prescription collection strategy requiring patients to collect their prescription from the primary care center reception desk 3 days after the first consultation; (3) an immediate prescription strategy; or (4) no antibiotic strategy. The patient-led and delayed collection strategies were considered delayed prescription strategies.

Main outcome measures. Duration of symptoms and severity of symptoms. Patients filled out a daily questionnaire for a maximum of 30 days, which listed common symptoms such as fever, discomfort or general pain, cough, difficulty sleeping, and changes in everyday life, and specific symptoms according to condition. Patients assessed severity of their symptoms using 6-point Likert scale, with scores of 1-2 considered mild, 3-4 moderate, and 5-6 severe. Secondary outcomes included antibiotic use, patient satisfaction, patients’ beliefs in the effectiveness of antibiotics, and absenteeism (absence from work or doing their daily activities).

Main results. A total of 405 patients were recruited, 398 of whom were included in the analysis. 136 patients (34.2%) were men. The mean (SD) age was 45 (17) years and 265 patients (72%) had at least a secondary education level. The most common infection was pharyngitis (n = 184; 46.2%), followed by acute bronchitis (n = 128; 32.2%). The mean severity of symptoms ranged from 1.8 to 3.5 points on the Likert scale, and mean (SD) duration of symptoms described on first visit was 6 (6) days. The mean (SD) general health status on first visit was 54 (20) based on a scale with 0 indicating worst health status and 100 indicating best health status. 314 patients (80.1%) were nonsmokers, and 372 patients (93.5%) did not have a respiratory comorbidity. The presence of symptoms on first visit was similar among the 4 groups.

The duration of the common symptoms of fever, discomfort or general pain, and cough was shorter in the immediate prescription group versus the no prescription group (P < 0.05 for all). In the immediate prescription group, the duration of patient symptoms after first visit was significantly different from that of the prescription collection and patient-led prescription groups only for discomfort or general pain. The mean (SD) duration of severe symptoms was 3.6 (3.3) days for the immediate prescription group, 4.0 (4.2) days for the prescription collection group, 5.1 (6.3) days for the patient-led prescription group, and 4.7 (3.6) days for the no prescription group. The median (interquartile range [IQR]) of severe symptoms was 3 (1–4) days for the prescription collection group and 3 (2–6) days for the patient-led prescription group. The median (IQR) of the maximum severity for any symptom was 5 (3–5) for the immediate prescription group and the prescription collection group; 5 (4–5) for the patient-led prescription group; and 5 (4–6) for the no prescription group. Patients randomized to the no prescription strategy or to either of the delayed strategies used fewer antibiotics and less frequently believed in antibiotic effectiveness. Among patients in the immediate prescription group, 91.1% used antibiotics; in the delayed patient-led, delayed collection, and no prescription groups, the  rates of antibiotic use were 32.6%, 23.0%, and 12.1%, respectively. There were very few adverse events across groups, although the no prescription group had 3 adverse events compared with 0-1 in the other groups. Satisfaction was similar across groups.

Conclusion. Delayed strategies were associated with slightly greater but clinically similar symptom burden and duration and also with substantially reduced antibiotic use when compared with an immediate strategy.

Commentary

Acute respiratory infections are a common reasons for physician visits. These infections tend to be self-limiting and overuse of antibiotics for these infections is widespread. Approximately 60% of patients with a sore throat and ~70% of patients with acute uncomplicated bronchitis receive antibiotic prescriptions despite the literature suggesting no or limited benefit [2,3].Antibiotic resistance is a growing problem and the main cause of this problem is misuse of antibiotics.

Often physicians feel pressured into prescribing anti-biotics due to patient expectation and patient satisfaction metrics. In the face of the critical need to reduce overuse, delayed antibiotic prescribing strategies offers a compromise between immediate and no prescription [4]. Delayed prescribing strategies have been evaluated previously [5–8], with findings suggesting they do reduce antibiotic use. This study strengthens the evidence base supporting the delayed strategy.

This study has a few limitations. The sample size was small, and symptom data was obtained via patient self-report. In addition, the randomization procedure was not described. However, the investigators were able to achieve good patient retention, with very few patients lost to follow-up. The investigators used an intention to treat analysis; thus, the estimate of treatment effect size can be considered conservative.

In terms of baseline characteristics of the study participants, there was a lower overall education level, fewer smokers, and less respiratory comorbidity (defined as only cardiovascular comorbidity [P = 0.12] and diabetes [P = 0.19]) in the patient-led group. Otherwise, groups were very well-matched. Most patients in the study had pharyngitis and bronchitis, limiting the inferences for patients with rhinosinusitis or exacerbation of mild-to-moderate COPD.

Applications for Clinical Practice

Delayed antibiotic prescribing for acute uncomplicated respiratory infections appears to be an acceptable strategy for reducing the overuse of antibiotics. As patients may lack knowledge of this prescribing strategy [9], clinicians may need to spend time explaining the concept. Using the term “back-up antibiotics” instead of “delayed prescription” [10] may help to increase patients’ understanding and acceptance.

—Ajay Dharod, MD

Study Overview

Objective. To determine the efficacy and safety of delayed antibiotic prescribing strategies in acute uncomplicated respiratory infections.

Design. Randomized, multicenter, open-label clinical trial.

Setting and participants. The setting was 23 primary care centers in Spain. The study recruited patients who were 18 years of age or older with an acute uncomplicated respiratory infection (acute pharyngitis, rhinosinusitis, acute bronchitis, exacerbations of chronic bronchitis or mild to moderate chronic obstructive pulmonary disease). Patients with these infections were included by the physicians as long as they were unsure of whether to use antibiotics or not. The study protocol has been published elsewhere [1].

Intervention. Patients were randomized to 1 of 4 potential prescription strategies: (1) a delayed patient-led prescription strategy where patients were given an antibiotic prescription at first consultation but instructed to fill the prescription only if they felt substantially worse or saw no improvement in symptoms in the first few days after initial consultation; (2) a delayed prescription collection strategy requiring patients to collect their prescription from the primary care center reception desk 3 days after the first consultation; (3) an immediate prescription strategy; or (4) no antibiotic strategy. The patient-led and delayed collection strategies were considered delayed prescription strategies.

Main outcome measures. Duration of symptoms and severity of symptoms. Patients filled out a daily questionnaire for a maximum of 30 days, which listed common symptoms such as fever, discomfort or general pain, cough, difficulty sleeping, and changes in everyday life, and specific symptoms according to condition. Patients assessed severity of their symptoms using 6-point Likert scale, with scores of 1-2 considered mild, 3-4 moderate, and 5-6 severe. Secondary outcomes included antibiotic use, patient satisfaction, patients’ beliefs in the effectiveness of antibiotics, and absenteeism (absence from work or doing their daily activities).

Main results. A total of 405 patients were recruited, 398 of whom were included in the analysis. 136 patients (34.2%) were men. The mean (SD) age was 45 (17) years and 265 patients (72%) had at least a secondary education level. The most common infection was pharyngitis (n = 184; 46.2%), followed by acute bronchitis (n = 128; 32.2%). The mean severity of symptoms ranged from 1.8 to 3.5 points on the Likert scale, and mean (SD) duration of symptoms described on first visit was 6 (6) days. The mean (SD) general health status on first visit was 54 (20) based on a scale with 0 indicating worst health status and 100 indicating best health status. 314 patients (80.1%) were nonsmokers, and 372 patients (93.5%) did not have a respiratory comorbidity. The presence of symptoms on first visit was similar among the 4 groups.

The duration of the common symptoms of fever, discomfort or general pain, and cough was shorter in the immediate prescription group versus the no prescription group (P < 0.05 for all). In the immediate prescription group, the duration of patient symptoms after first visit was significantly different from that of the prescription collection and patient-led prescription groups only for discomfort or general pain. The mean (SD) duration of severe symptoms was 3.6 (3.3) days for the immediate prescription group, 4.0 (4.2) days for the prescription collection group, 5.1 (6.3) days for the patient-led prescription group, and 4.7 (3.6) days for the no prescription group. The median (interquartile range [IQR]) of severe symptoms was 3 (1–4) days for the prescription collection group and 3 (2–6) days for the patient-led prescription group. The median (IQR) of the maximum severity for any symptom was 5 (3–5) for the immediate prescription group and the prescription collection group; 5 (4–5) for the patient-led prescription group; and 5 (4–6) for the no prescription group. Patients randomized to the no prescription strategy or to either of the delayed strategies used fewer antibiotics and less frequently believed in antibiotic effectiveness. Among patients in the immediate prescription group, 91.1% used antibiotics; in the delayed patient-led, delayed collection, and no prescription groups, the  rates of antibiotic use were 32.6%, 23.0%, and 12.1%, respectively. There were very few adverse events across groups, although the no prescription group had 3 adverse events compared with 0-1 in the other groups. Satisfaction was similar across groups.

Conclusion. Delayed strategies were associated with slightly greater but clinically similar symptom burden and duration and also with substantially reduced antibiotic use when compared with an immediate strategy.

Commentary

Acute respiratory infections are a common reasons for physician visits. These infections tend to be self-limiting and overuse of antibiotics for these infections is widespread. Approximately 60% of patients with a sore throat and ~70% of patients with acute uncomplicated bronchitis receive antibiotic prescriptions despite the literature suggesting no or limited benefit [2,3].Antibiotic resistance is a growing problem and the main cause of this problem is misuse of antibiotics.

Often physicians feel pressured into prescribing anti-biotics due to patient expectation and patient satisfaction metrics. In the face of the critical need to reduce overuse, delayed antibiotic prescribing strategies offers a compromise between immediate and no prescription [4]. Delayed prescribing strategies have been evaluated previously [5–8], with findings suggesting they do reduce antibiotic use. This study strengthens the evidence base supporting the delayed strategy.

This study has a few limitations. The sample size was small, and symptom data was obtained via patient self-report. In addition, the randomization procedure was not described. However, the investigators were able to achieve good patient retention, with very few patients lost to follow-up. The investigators used an intention to treat analysis; thus, the estimate of treatment effect size can be considered conservative.

In terms of baseline characteristics of the study participants, there was a lower overall education level, fewer smokers, and less respiratory comorbidity (defined as only cardiovascular comorbidity [P = 0.12] and diabetes [P = 0.19]) in the patient-led group. Otherwise, groups were very well-matched. Most patients in the study had pharyngitis and bronchitis, limiting the inferences for patients with rhinosinusitis or exacerbation of mild-to-moderate COPD.

Applications for Clinical Practice

Delayed antibiotic prescribing for acute uncomplicated respiratory infections appears to be an acceptable strategy for reducing the overuse of antibiotics. As patients may lack knowledge of this prescribing strategy [9], clinicians may need to spend time explaining the concept. Using the term “back-up antibiotics” instead of “delayed prescription” [10] may help to increase patients’ understanding and acceptance.

—Ajay Dharod, MD

Study Overview

Objective. To determine the efficacy and safety of delayed antibiotic prescribing strategies in acute uncomplicated respiratory infections.

Design. Randomized, multicenter, open-label clinical trial.

Setting and participants. The setting was 23 primary care centers in Spain. The study recruited patients who were 18 years of age or older with an acute uncomplicated respiratory infection (acute pharyngitis, rhinosinusitis, acute bronchitis, exacerbations of chronic bronchitis or mild to moderate chronic obstructive pulmonary disease). Patients with these infections were included by the physicians as long as they were unsure of whether to use antibiotics or not. The study protocol has been published elsewhere [1].

Intervention. Patients were randomized to 1 of 4 potential prescription strategies: (1) a delayed patient-led prescription strategy where patients were given an antibiotic prescription at first consultation but instructed to fill the prescription only if they felt substantially worse or saw no improvement in symptoms in the first few days after initial consultation; (2) a delayed prescription collection strategy requiring patients to collect their prescription from the primary care center reception desk 3 days after the first consultation; (3) an immediate prescription strategy; or (4) no antibiotic strategy. The patient-led and delayed collection strategies were considered delayed prescription strategies.

Main outcome measures. Duration of symptoms and severity of symptoms. Patients filled out a daily questionnaire for a maximum of 30 days, which listed common symptoms such as fever, discomfort or general pain, cough, difficulty sleeping, and changes in everyday life, and specific symptoms according to condition. Patients assessed severity of their symptoms using 6-point Likert scale, with scores of 1-2 considered mild, 3-4 moderate, and 5-6 severe. Secondary outcomes included antibiotic use, patient satisfaction, patients’ beliefs in the effectiveness of antibiotics, and absenteeism (absence from work or doing their daily activities).

Main results. A total of 405 patients were recruited, 398 of whom were included in the analysis. 136 patients (34.2%) were men. The mean (SD) age was 45 (17) years and 265 patients (72%) had at least a secondary education level. The most common infection was pharyngitis (n = 184; 46.2%), followed by acute bronchitis (n = 128; 32.2%). The mean severity of symptoms ranged from 1.8 to 3.5 points on the Likert scale, and mean (SD) duration of symptoms described on first visit was 6 (6) days. The mean (SD) general health status on first visit was 54 (20) based on a scale with 0 indicating worst health status and 100 indicating best health status. 314 patients (80.1%) were nonsmokers, and 372 patients (93.5%) did not have a respiratory comorbidity. The presence of symptoms on first visit was similar among the 4 groups.

The duration of the common symptoms of fever, discomfort or general pain, and cough was shorter in the immediate prescription group versus the no prescription group (P < 0.05 for all). In the immediate prescription group, the duration of patient symptoms after first visit was significantly different from that of the prescription collection and patient-led prescription groups only for discomfort or general pain. The mean (SD) duration of severe symptoms was 3.6 (3.3) days for the immediate prescription group, 4.0 (4.2) days for the prescription collection group, 5.1 (6.3) days for the patient-led prescription group, and 4.7 (3.6) days for the no prescription group. The median (interquartile range [IQR]) of severe symptoms was 3 (1–4) days for the prescription collection group and 3 (2–6) days for the patient-led prescription group. The median (IQR) of the maximum severity for any symptom was 5 (3–5) for the immediate prescription group and the prescription collection group; 5 (4–5) for the patient-led prescription group; and 5 (4–6) for the no prescription group. Patients randomized to the no prescription strategy or to either of the delayed strategies used fewer antibiotics and less frequently believed in antibiotic effectiveness. Among patients in the immediate prescription group, 91.1% used antibiotics; in the delayed patient-led, delayed collection, and no prescription groups, the  rates of antibiotic use were 32.6%, 23.0%, and 12.1%, respectively. There were very few adverse events across groups, although the no prescription group had 3 adverse events compared with 0-1 in the other groups. Satisfaction was similar across groups.

Conclusion. Delayed strategies were associated with slightly greater but clinically similar symptom burden and duration and also with substantially reduced antibiotic use when compared with an immediate strategy.

Commentary

Acute respiratory infections are a common reasons for physician visits. These infections tend to be self-limiting and overuse of antibiotics for these infections is widespread. Approximately 60% of patients with a sore throat and ~70% of patients with acute uncomplicated bronchitis receive antibiotic prescriptions despite the literature suggesting no or limited benefit [2,3].Antibiotic resistance is a growing problem and the main cause of this problem is misuse of antibiotics.

Often physicians feel pressured into prescribing anti-biotics due to patient expectation and patient satisfaction metrics. In the face of the critical need to reduce overuse, delayed antibiotic prescribing strategies offers a compromise between immediate and no prescription [4]. Delayed prescribing strategies have been evaluated previously [5–8], with findings suggesting they do reduce antibiotic use. This study strengthens the evidence base supporting the delayed strategy.

This study has a few limitations. The sample size was small, and symptom data was obtained via patient self-report. In addition, the randomization procedure was not described. However, the investigators were able to achieve good patient retention, with very few patients lost to follow-up. The investigators used an intention to treat analysis; thus, the estimate of treatment effect size can be considered conservative.

In terms of baseline characteristics of the study participants, there was a lower overall education level, fewer smokers, and less respiratory comorbidity (defined as only cardiovascular comorbidity [P = 0.12] and diabetes [P = 0.19]) in the patient-led group. Otherwise, groups were very well-matched. Most patients in the study had pharyngitis and bronchitis, limiting the inferences for patients with rhinosinusitis or exacerbation of mild-to-moderate COPD.

Applications for Clinical Practice

Delayed antibiotic prescribing for acute uncomplicated respiratory infections appears to be an acceptable strategy for reducing the overuse of antibiotics. As patients may lack knowledge of this prescribing strategy [9], clinicians may need to spend time explaining the concept. Using the term “back-up antibiotics” instead of “delayed prescription” [10] may help to increase patients’ understanding and acceptance.

—Ajay Dharod, MD

Study Overview

Objective. To determine the effectiveness of high-dose vitamin D versus low-dose vitamin D in reducing the risk of functional decline in older adults.

Design. Double-blind randomized controlled trial.

Setting and participants. This single-center study was conducted at the University of Zurich. Home-dwelling adults aged 70 and over were recruited through newspaper advertisement in Zurich from December 2009 to May 2010. Inclusion criteria included maintenance of mobility with or without a walking aid, having the ability to use public transportation to attend clinic visits, and scoring at least 27 on the Mini-Mental State Examination. Exclusion criteria include supplemental vitamin D use exceeding 800 IU per day and unwillingness to discontinue additional calcium and vitamin D supplementation, current cancer, malabsorption syndrome, heavy alcohol consumption, uncontrolled hypocalcemia, severe visual or hearing impairment, use of medications affecting calcium metabolism, diseases causing hypercalcemia, planned travel to sunny locations for longer than 2 months per year, maximum calcium supplement dose of 250 mg/day, use of medications affecting serum 25-hydroxyvitamin D (25[OH]D) level, body mass index ≥ 40, diseases predisposing to falls, hypercalcemia, kidney disease with creatinine clearance < 15, or kidney stone within 10 years prior to enrollment.

Intervention. Participants were randomized to receive either monthly supplementation of 24,000 IU of vitamin D3 per month (low-dose group), 60,000 IU of vitamin D3 once per month (high-dose group), or 24,000 IU of vitamin D3 plus 300 µg of calcifediol once per month. It was hypothesized that higher monthly doses of vitamin D or in combination with calcifediol, which is a liver metabolite approximately 2 to 3 times more potent than vitamin D3, will increase levels of 25(OH)D and reduce the risk of functional decline.

Main outcome measures. Lower extremity function using the Short Physical Performance Battery and 25(OH)D levels at 6 and 12 months. Study nurses called participants monthly to assess falls, adverse events, and adherence to study medications.

Main results. A total of 200 participants were enrolled. Average age was 78 years (SD = 5) and 67% were female; all had a history of falls in the previous year and average baseline 25(OH)D levels ranged from 18.4 to 20.9 ng/mL in the three groups. Adherence to the study medication exceeded 94% throughout the study trial in all treatment groups.

At 6 and 12 months, 25(OH)D levels increased by an average of 12.7 and 11.7 ng/mL in the low-dose group, an average of 18.3 and 19.2 ng/mL in the high-dose group, and an average of 27.6 and 25.8 ng/mL in the calcifediol-added group. The mean changes in physical performance score indicating lower extremity function did not differ significantly among treatment groups (P = 0.26), but for one measure—the 5 successive chair stands—the 2 high-dose groups had less improvement when compared with the low-dose group. At 12 months, 66.9% of the high-dose group and 66.1% in the group with calcifediol fell during the study period, which was more than the low-dose group (47.9%, P = 0.048). The mean number of falls was also higher among the high-dose and calcifediol groups when compared with the low-dose group.

Conclusion. Higher doses of vitamin D were not better than lower doses of vitamin D in improving lower extremity function and were associated with higher risk of falls.

Commentary

Vitamin D deficiency is common among older adults and is associated with sarcopenia, functional decline, falls, and fractures [1,2]. Prior meta-analysis has supported that supplementation with vitamin D may lead to improved outcomes in fracture prevention [3]. However, the US Preventive Services Task Force, using more recent evidence reviews and an updated meta-analysis [4], found evidence lacking regarding the benefit of supplementation with vitamin D in community-dwelling postmenopausal women at doses > 400 IU, found no benefit in this group for doses ≤ 400, and found evidence lacking for supplementation in men or premenopausal women at any dose [5]. At the same time, the USPSTF also recommends exercise or physical therapy and vitamin D supplementation (800 IU daily) to prevent falls in community-dwelling adults ≥ 65 years at increased risk for falls [6]. This is consistent with the Institute of Medicine’s recommendation of 800 IU per day for older adults [7].

The current study attempted to elucidate the potential impact of high-dose vitamin D supplementation, hypothesizing that higher doses will achieve improvement in vitamin D levels and better outcomes in terms of lower extremity function and falls. However, the investigators found that rather than lowering risk of falls, higher-dose vitamin D was associated with elevated risk of falls without the benefit of improving lower extremity function. This is not the first study that has demonstrated that higher doses of vitamin D supplementation may be associated with harm. A prior randomized controlled trial utilizing a different dosing strategy of annual high- dose vitamin D supplementation also found that higher doses were associated with increased risks of falls [8]. Nonetheless, it helps support the notion that in vitamin D supplementation, more is not necessarily better.

The study is not without its drawbacks. The sample size was relatively small and the trial may have been underpowered to detect whether there may be certain patients for whom high-dose vitamin D supplementation may have a role. Also, the study was based in Zurich, which has a relatively uniform population, and study results may not be generalizable to populations in other countries.

Applications for Clinical Practice

The study lends support to the current recommendation of the Institute of Medicine—800 IU a day—for fall prevention, which is equivalent in dose to the 24,000 IU per month utilized in the trial. One of the questions not answered by the study is whether high-dose supplementation for adults who have severe deficiency in vitamin D is beneficial or harmful when compared with lower-dose supplementation. In clinical practice, clinicians often check an initial level of vitamin D and aim for a target level with supplementation. Among those patients with extremely low baseline levels, a lower-dose regimen of 800 IU a day may not yield a normalized level of vitamin D. Further studies are needed to elucidate whether there may be a role for higher-dose supplementation in these individuals. Nonetheless, it is clear that the current evidence does not support the routine use of high-dose vitamin D supplementation; it does not lead to better lower extremity function and may cause harm.

 —William Hung, MD, MPH

Study Overview

Objective. To determine the effectiveness of high-dose vitamin D versus low-dose vitamin D in reducing the risk of functional decline in older adults.

Design. Double-blind randomized controlled trial.

Setting and participants. This single-center study was conducted at the University of Zurich. Home-dwelling adults aged 70 and over were recruited through newspaper advertisement in Zurich from December 2009 to May 2010. Inclusion criteria included maintenance of mobility with or without a walking aid, having the ability to use public transportation to attend clinic visits, and scoring at least 27 on the Mini-Mental State Examination. Exclusion criteria include supplemental vitamin D use exceeding 800 IU per day and unwillingness to discontinue additional calcium and vitamin D supplementation, current cancer, malabsorption syndrome, heavy alcohol consumption, uncontrolled hypocalcemia, severe visual or hearing impairment, use of medications affecting calcium metabolism, diseases causing hypercalcemia, planned travel to sunny locations for longer than 2 months per year, maximum calcium supplement dose of 250 mg/day, use of medications affecting serum 25-hydroxyvitamin D (25[OH]D) level, body mass index ≥ 40, diseases predisposing to falls, hypercalcemia, kidney disease with creatinine clearance < 15, or kidney stone within 10 years prior to enrollment.

Intervention. Participants were randomized to receive either monthly supplementation of 24,000 IU of vitamin D3 per month (low-dose group), 60,000 IU of vitamin D3 once per month (high-dose group), or 24,000 IU of vitamin D3 plus 300 µg of calcifediol once per month. It was hypothesized that higher monthly doses of vitamin D or in combination with calcifediol, which is a liver metabolite approximately 2 to 3 times more potent than vitamin D3, will increase levels of 25(OH)D and reduce the risk of functional decline.

Main outcome measures. Lower extremity function using the Short Physical Performance Battery and 25(OH)D levels at 6 and 12 months. Study nurses called participants monthly to assess falls, adverse events, and adherence to study medications.

Main results. A total of 200 participants were enrolled. Average age was 78 years (SD = 5) and 67% were female; all had a history of falls in the previous year and average baseline 25(OH)D levels ranged from 18.4 to 20.9 ng/mL in the three groups. Adherence to the study medication exceeded 94% throughout the study trial in all treatment groups.

At 6 and 12 months, 25(OH)D levels increased by an average of 12.7 and 11.7 ng/mL in the low-dose group, an average of 18.3 and 19.2 ng/mL in the high-dose group, and an average of 27.6 and 25.8 ng/mL in the calcifediol-added group. The mean changes in physical performance score indicating lower extremity function did not differ significantly among treatment groups (P = 0.26), but for one measure—the 5 successive chair stands—the 2 high-dose groups had less improvement when compared with the low-dose group. At 12 months, 66.9% of the high-dose group and 66.1% in the group with calcifediol fell during the study period, which was more than the low-dose group (47.9%, P = 0.048). The mean number of falls was also higher among the high-dose and calcifediol groups when compared with the low-dose group.

Conclusion. Higher doses of vitamin D were not better than lower doses of vitamin D in improving lower extremity function and were associated with higher risk of falls.

Commentary

Vitamin D deficiency is common among older adults and is associated with sarcopenia, functional decline, falls, and fractures [1,2]. Prior meta-analysis has supported that supplementation with vitamin D may lead to improved outcomes in fracture prevention [3]. However, the US Preventive Services Task Force, using more recent evidence reviews and an updated meta-analysis [4], found evidence lacking regarding the benefit of supplementation with vitamin D in community-dwelling postmenopausal women at doses > 400 IU, found no benefit in this group for doses ≤ 400, and found evidence lacking for supplementation in men or premenopausal women at any dose [5]. At the same time, the USPSTF also recommends exercise or physical therapy and vitamin D supplementation (800 IU daily) to prevent falls in community-dwelling adults ≥ 65 years at increased risk for falls [6]. This is consistent with the Institute of Medicine’s recommendation of 800 IU per day for older adults [7].

The current study attempted to elucidate the potential impact of high-dose vitamin D supplementation, hypothesizing that higher doses will achieve improvement in vitamin D levels and better outcomes in terms of lower extremity function and falls. However, the investigators found that rather than lowering risk of falls, higher-dose vitamin D was associated with elevated risk of falls without the benefit of improving lower extremity function. This is not the first study that has demonstrated that higher doses of vitamin D supplementation may be associated with harm. A prior randomized controlled trial utilizing a different dosing strategy of annual high- dose vitamin D supplementation also found that higher doses were associated with increased risks of falls [8]. Nonetheless, it helps support the notion that in vitamin D supplementation, more is not necessarily better.

The study is not without its drawbacks. The sample size was relatively small and the trial may have been underpowered to detect whether there may be certain patients for whom high-dose vitamin D supplementation may have a role. Also, the study was based in Zurich, which has a relatively uniform population, and study results may not be generalizable to populations in other countries.

Applications for Clinical Practice

The study lends support to the current recommendation of the Institute of Medicine—800 IU a day—for fall prevention, which is equivalent in dose to the 24,000 IU per month utilized in the trial. One of the questions not answered by the study is whether high-dose supplementation for adults who have severe deficiency in vitamin D is beneficial or harmful when compared with lower-dose supplementation. In clinical practice, clinicians often check an initial level of vitamin D and aim for a target level with supplementation. Among those patients with extremely low baseline levels, a lower-dose regimen of 800 IU a day may not yield a normalized level of vitamin D. Further studies are needed to elucidate whether there may be a role for higher-dose supplementation in these individuals. Nonetheless, it is clear that the current evidence does not support the routine use of high-dose vitamin D supplementation; it does not lead to better lower extremity function and may cause harm.

 —William Hung, MD, MPH

Study Overview

Objective. To determine the effectiveness of high-dose vitamin D versus low-dose vitamin D in reducing the risk of functional decline in older adults.

Design. Double-blind randomized controlled trial.

Setting and participants. This single-center study was conducted at the University of Zurich. Home-dwelling adults aged 70 and over were recruited through newspaper advertisement in Zurich from December 2009 to May 2010. Inclusion criteria included maintenance of mobility with or without a walking aid, having the ability to use public transportation to attend clinic visits, and scoring at least 27 on the Mini-Mental State Examination. Exclusion criteria include supplemental vitamin D use exceeding 800 IU per day and unwillingness to discontinue additional calcium and vitamin D supplementation, current cancer, malabsorption syndrome, heavy alcohol consumption, uncontrolled hypocalcemia, severe visual or hearing impairment, use of medications affecting calcium metabolism, diseases causing hypercalcemia, planned travel to sunny locations for longer than 2 months per year, maximum calcium supplement dose of 250 mg/day, use of medications affecting serum 25-hydroxyvitamin D (25[OH]D) level, body mass index ≥ 40, diseases predisposing to falls, hypercalcemia, kidney disease with creatinine clearance < 15, or kidney stone within 10 years prior to enrollment.

Intervention. Participants were randomized to receive either monthly supplementation of 24,000 IU of vitamin D3 per month (low-dose group), 60,000 IU of vitamin D3 once per month (high-dose group), or 24,000 IU of vitamin D3 plus 300 µg of calcifediol once per month. It was hypothesized that higher monthly doses of vitamin D or in combination with calcifediol, which is a liver metabolite approximately 2 to 3 times more potent than vitamin D3, will increase levels of 25(OH)D and reduce the risk of functional decline.

Main outcome measures. Lower extremity function using the Short Physical Performance Battery and 25(OH)D levels at 6 and 12 months. Study nurses called participants monthly to assess falls, adverse events, and adherence to study medications.

Main results. A total of 200 participants were enrolled. Average age was 78 years (SD = 5) and 67% were female; all had a history of falls in the previous year and average baseline 25(OH)D levels ranged from 18.4 to 20.9 ng/mL in the three groups. Adherence to the study medication exceeded 94% throughout the study trial in all treatment groups.

At 6 and 12 months, 25(OH)D levels increased by an average of 12.7 and 11.7 ng/mL in the low-dose group, an average of 18.3 and 19.2 ng/mL in the high-dose group, and an average of 27.6 and 25.8 ng/mL in the calcifediol-added group. The mean changes in physical performance score indicating lower extremity function did not differ significantly among treatment groups (P = 0.26), but for one measure—the 5 successive chair stands—the 2 high-dose groups had less improvement when compared with the low-dose group. At 12 months, 66.9% of the high-dose group and 66.1% in the group with calcifediol fell during the study period, which was more than the low-dose group (47.9%, P = 0.048). The mean number of falls was also higher among the high-dose and calcifediol groups when compared with the low-dose group.

Conclusion. Higher doses of vitamin D were not better than lower doses of vitamin D in improving lower extremity function and were associated with higher risk of falls.

Commentary

Vitamin D deficiency is common among older adults and is associated with sarcopenia, functional decline, falls, and fractures [1,2]. Prior meta-analysis has supported that supplementation with vitamin D may lead to improved outcomes in fracture prevention [3]. However, the US Preventive Services Task Force, using more recent evidence reviews and an updated meta-analysis [4], found evidence lacking regarding the benefit of supplementation with vitamin D in community-dwelling postmenopausal women at doses > 400 IU, found no benefit in this group for doses ≤ 400, and found evidence lacking for supplementation in men or premenopausal women at any dose [5]. At the same time, the USPSTF also recommends exercise or physical therapy and vitamin D supplementation (800 IU daily) to prevent falls in community-dwelling adults ≥ 65 years at increased risk for falls [6]. This is consistent with the Institute of Medicine’s recommendation of 800 IU per day for older adults [7].

The current study attempted to elucidate the potential impact of high-dose vitamin D supplementation, hypothesizing that higher doses will achieve improvement in vitamin D levels and better outcomes in terms of lower extremity function and falls. However, the investigators found that rather than lowering risk of falls, higher-dose vitamin D was associated with elevated risk of falls without the benefit of improving lower extremity function. This is not the first study that has demonstrated that higher doses of vitamin D supplementation may be associated with harm. A prior randomized controlled trial utilizing a different dosing strategy of annual high- dose vitamin D supplementation also found that higher doses were associated with increased risks of falls [8]. Nonetheless, it helps support the notion that in vitamin D supplementation, more is not necessarily better.

The study is not without its drawbacks. The sample size was relatively small and the trial may have been underpowered to detect whether there may be certain patients for whom high-dose vitamin D supplementation may have a role. Also, the study was based in Zurich, which has a relatively uniform population, and study results may not be generalizable to populations in other countries.

Applications for Clinical Practice

The study lends support to the current recommendation of the Institute of Medicine—800 IU a day—for fall prevention, which is equivalent in dose to the 24,000 IU per month utilized in the trial. One of the questions not answered by the study is whether high-dose supplementation for adults who have severe deficiency in vitamin D is beneficial or harmful when compared with lower-dose supplementation. In clinical practice, clinicians often check an initial level of vitamin D and aim for a target level with supplementation. Among those patients with extremely low baseline levels, a lower-dose regimen of 800 IU a day may not yield a normalized level of vitamin D. Further studies are needed to elucidate whether there may be a role for higher-dose supplementation in these individuals. Nonetheless, it is clear that the current evidence does not support the routine use of high-dose vitamin D supplementation; it does not lead to better lower extremity function and may cause harm.

 —William Hung, MD, MPH

Study Overview

Objective. To assess how patients look for patient-obtained medication information (POMI) to prepare for a clinical appointment, whether they share those findings with their provider, and how health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI.

Design. Cross-sectional survey-based study.

Setting and participants. The study took place over 1 week at 2 academic medical centers located in Las Vegas, Nevada, and Washington, DC. At a central waiting area at each facility, patients aged 18 and older waiting for their clinical appointment were invited to complete a survey, either on a computer tablet or with paper and pencil, before and after their appointment.

Measures and analysis. The pre-survey included demographic measures (age, gender, education, and ethnicity), the reason for the visit (routine care, sick visit, follow-up after survey, and follow-up after emergency room visit), and an item to assess self-report of perceived general health (from poor to excellent). Health literacy was assessed by a self-report measure that included subscales for the 3 dimensions of health literacy: functional, communicative, and critical health literacy [1]; together, these capture the ability of patients to retain health knowledge, gather and communicate health concepts, and apply health information. Patient activation was scored using the Patient Activation Measure (13 Likert-style items, total scale range 0–100); patient activation combines a patient’s self-reported knowledge, skill, and confidence for self-management of general health or a chronic condition [2]. Information seeking was measured by time spent (did not look for information, 1 hour, 2 hours, 3 hours, or more than 3 hours), and information channels used to look for POMI (eg, magazines/newspapers, internet website or search engine) were presented dichotomously (yes/no).

The post-survey first asked whether the participant shared information with their provider (yes/no). If the participant said yes, 4 items assessed their perception of the provider’s response, including amount of time spent discussing POMI, how seriously the provider considered the information, and overall reaction (scored as a mean, each item measured from 1–5, with 5 indicating the most positive reactions). For hypothesis testing, logistic regression models were used to test the effects of the independent variables. To explore the relationship between health literacy/patient activation and physician response, correlations were calculated.

Main results. Over 400 patients were asked to participate, and of these a total of 243 (60.75%) patients were eligible, consented, and completed surveys. Participants were predominantly white (57.6%), female (63%), had some college education or higher (80.2%), and had a clinical appointment for routine care (69.3%). The mean age was 47.04 years (SD, 15.78), the mean health status was 3.20 (SD, 0.94), and the mean Patient Activation Measure was 72.43 (SD, 16.00).

More than half of participants (58.26%) who responded to the item about information seeking indicated seeking POMI prior to their clinical appointment. Of these, the majority (88.7%) reported using the internet, particularly WebMD, as an information channel. Significant predictors of information seeking included age (P = 0.01, OR = 0.973), communicative health literacy (P = 0.01, or = 1.975), and critical health literacy (P = 0.05, OR = 1.518). Lower age, higher communicative health literacy, and higher critical health literacy increased the likelihood of the patient seeking POMI prior to the clinical appointment. Other assessed predictors were not significant, including gender, functional health literacy, patient activation, reason for visit, and reported health status.

58.2% of the 141 information-seeking patients talked to their health care provider about the information they found. However, no predictor variables included in a logistic regression analysis were significant, including age, gender, reason for visit, reported health status, functional health literacy, communicative health literacy, critical health literacy, and patient activation. For the research question (how do health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI), the mean score on the 4-item measure was 4.08 (SD, 0.90), indicating a generally positive response; most reported the physician response was good or higher. Patient activation correlated positively with perceived physician response (r = 0.245, P = 0.03).

Conclusion. The lack of data to predict who will introduce POMI at the medical visit is disconcerting. Providers might consider directly asking or passively surveying what outside information sources the patient has engaged with, regardless of whether patient introduces the information or does not introduce it.

Commentary

Patient engagement plays an important role in health care [3]. Activated patients often have skills and confidence to engage in their health care and with their provider, which often contributes to better health outcomes and care experiences [2,4] as well as lower health care costs [5]. Health information is needed to make informed decisions, manage health, and practice healthy behaviors [6], and patients are increasingly taking an active role in seeking out medical or health information outside of the clinical encounter in order to make shared health decisions with their provider [7]. Indeed, one of the Healthy People 2020 goals is to “Use health communication strategies and health information technology  to improve population health outcomes and health care quality, and to achieve health equity” [8].

However, seeking POMI requires health literacy skills and supportive relationships, particularly when navigating the many channels and complexities of publicly available health information [8]. This is especially true on the internet, where there is often varying accuracy and clarity of information presented. According to 2011 data from the Pew Research Center [9], 74% of adults in the United States use the internet, and of those adults 80% have looked online for health information; 34% have read another person’s commentary or experience about health or medical issues on an online news group, website, or blog; 25% have watched an online video about health or medical issues; and 24% have consulted online reviews of particular drugs or medical treatments.

A general strength of this study was the cross-sectional design, which allowed for surveying patients around attitudes, motivations, and behaviors immediately before and after their clinical encounter. According to the authors, this study design was aimed to extend knowledge around information seeking and provider discussions that have occurred distally and relied on patient long-term recall. Additionally, this study surveyed a variety of patients (not limited to either primary or specialist appointments) at 2 different academic medical centers, and gave patients a choice to either take the survey on a computer tablet or traditional paper and pencil. Further, the authors assessed the reliability of scales used and included a number of predictor variables in the logistic regression models for hypothesis testing.

The authors acknowledged several limitations, including the use of convenience sampling and self-reported data with volunteer participants, which can result in self-selection bias and social desirability bias. As study participants were self-selecting, low health literacy patients may have been more likely to not volunteer to take the survey, which might explain the relatively high mean scores on the health literacy measures. Further, participants were mostly white, female, college-educated, health literate, and scheduled for a routine visit, which limits the generalizability of the study findings and the ability to identify significant predictors.

Regarding the study design, pre-/post-tests are usually used to measure the change in a situation, phenomenon, problem, or attitude. However, as the authors did not aim to measure any change during the clinical encounter itself, the use of only a post-test may have been more appropriate. The use of a pre-/post-test design may have increased the likelihood of patients both recalling POMI before the encounter and then sharing POMI with their provider. Also, in the post-survey, the authors only asked follow-up questions of patients that shared POMI with their provider. An open-response question could have been included to explore further why some patients chose not to introduce POMI during the clinical encounter. Lastly, the authors may have been able to reach more patients with lower health literacy if surveys were administered at public hospitals as opposed to academic medical centers. While some providers may perceive that patients in academic medical centers are more complex or may have limited access to care [10], patients at public hospitals and safety net hospitals tend to be of lower income and have limited or no insurance [11,12].

Applications For Clinical Practice

There are documented communication-enhancing techniques and strategies that providers and other health professionals use, particularly among patients with low health literacy [13]. Based on this study, the authors conclude that providers may try another strategy of directly asking or passively surveying any POMI, regardless of whether the patient initiates this conversation. Other research has acknowledged that recognition of health literacy status allows for the use of appropriate communication tools [14]. However, providers need to recognize barriers to health information seeking, particularly among minorities and underserved populations [15], as well as the potential for embarrassment that patients might experience as a result of revealing misunderstandings of health information or general reading difficulties [16]. This study highlights the need for further research to identify predictors of health information seeking and especially health information sharing by patients during the clinical encounter.

—Katrina F. Mateo, MPH

Study Overview

Objective. To assess how patients look for patient-obtained medication information (POMI) to prepare for a clinical appointment, whether they share those findings with their provider, and how health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI.

Design. Cross-sectional survey-based study.

Setting and participants. The study took place over 1 week at 2 academic medical centers located in Las Vegas, Nevada, and Washington, DC. At a central waiting area at each facility, patients aged 18 and older waiting for their clinical appointment were invited to complete a survey, either on a computer tablet or with paper and pencil, before and after their appointment.

Measures and analysis. The pre-survey included demographic measures (age, gender, education, and ethnicity), the reason for the visit (routine care, sick visit, follow-up after survey, and follow-up after emergency room visit), and an item to assess self-report of perceived general health (from poor to excellent). Health literacy was assessed by a self-report measure that included subscales for the 3 dimensions of health literacy: functional, communicative, and critical health literacy [1]; together, these capture the ability of patients to retain health knowledge, gather and communicate health concepts, and apply health information. Patient activation was scored using the Patient Activation Measure (13 Likert-style items, total scale range 0–100); patient activation combines a patient’s self-reported knowledge, skill, and confidence for self-management of general health or a chronic condition [2]. Information seeking was measured by time spent (did not look for information, 1 hour, 2 hours, 3 hours, or more than 3 hours), and information channels used to look for POMI (eg, magazines/newspapers, internet website or search engine) were presented dichotomously (yes/no).

The post-survey first asked whether the participant shared information with their provider (yes/no). If the participant said yes, 4 items assessed their perception of the provider’s response, including amount of time spent discussing POMI, how seriously the provider considered the information, and overall reaction (scored as a mean, each item measured from 1–5, with 5 indicating the most positive reactions). For hypothesis testing, logistic regression models were used to test the effects of the independent variables. To explore the relationship between health literacy/patient activation and physician response, correlations were calculated.

Main results. Over 400 patients were asked to participate, and of these a total of 243 (60.75%) patients were eligible, consented, and completed surveys. Participants were predominantly white (57.6%), female (63%), had some college education or higher (80.2%), and had a clinical appointment for routine care (69.3%). The mean age was 47.04 years (SD, 15.78), the mean health status was 3.20 (SD, 0.94), and the mean Patient Activation Measure was 72.43 (SD, 16.00).

More than half of participants (58.26%) who responded to the item about information seeking indicated seeking POMI prior to their clinical appointment. Of these, the majority (88.7%) reported using the internet, particularly WebMD, as an information channel. Significant predictors of information seeking included age (P = 0.01, OR = 0.973), communicative health literacy (P = 0.01, or = 1.975), and critical health literacy (P = 0.05, OR = 1.518). Lower age, higher communicative health literacy, and higher critical health literacy increased the likelihood of the patient seeking POMI prior to the clinical appointment. Other assessed predictors were not significant, including gender, functional health literacy, patient activation, reason for visit, and reported health status.

58.2% of the 141 information-seeking patients talked to their health care provider about the information they found. However, no predictor variables included in a logistic regression analysis were significant, including age, gender, reason for visit, reported health status, functional health literacy, communicative health literacy, critical health literacy, and patient activation. For the research question (how do health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI), the mean score on the 4-item measure was 4.08 (SD, 0.90), indicating a generally positive response; most reported the physician response was good or higher. Patient activation correlated positively with perceived physician response (r = 0.245, P = 0.03).

Conclusion. The lack of data to predict who will introduce POMI at the medical visit is disconcerting. Providers might consider directly asking or passively surveying what outside information sources the patient has engaged with, regardless of whether patient introduces the information or does not introduce it.

Commentary

Patient engagement plays an important role in health care [3]. Activated patients often have skills and confidence to engage in their health care and with their provider, which often contributes to better health outcomes and care experiences [2,4] as well as lower health care costs [5]. Health information is needed to make informed decisions, manage health, and practice healthy behaviors [6], and patients are increasingly taking an active role in seeking out medical or health information outside of the clinical encounter in order to make shared health decisions with their provider [7]. Indeed, one of the Healthy People 2020 goals is to “Use health communication strategies and health information technology  to improve population health outcomes and health care quality, and to achieve health equity” [8].

However, seeking POMI requires health literacy skills and supportive relationships, particularly when navigating the many channels and complexities of publicly available health information [8]. This is especially true on the internet, where there is often varying accuracy and clarity of information presented. According to 2011 data from the Pew Research Center [9], 74% of adults in the United States use the internet, and of those adults 80% have looked online for health information; 34% have read another person’s commentary or experience about health or medical issues on an online news group, website, or blog; 25% have watched an online video about health or medical issues; and 24% have consulted online reviews of particular drugs or medical treatments.

A general strength of this study was the cross-sectional design, which allowed for surveying patients around attitudes, motivations, and behaviors immediately before and after their clinical encounter. According to the authors, this study design was aimed to extend knowledge around information seeking and provider discussions that have occurred distally and relied on patient long-term recall. Additionally, this study surveyed a variety of patients (not limited to either primary or specialist appointments) at 2 different academic medical centers, and gave patients a choice to either take the survey on a computer tablet or traditional paper and pencil. Further, the authors assessed the reliability of scales used and included a number of predictor variables in the logistic regression models for hypothesis testing.

The authors acknowledged several limitations, including the use of convenience sampling and self-reported data with volunteer participants, which can result in self-selection bias and social desirability bias. As study participants were self-selecting, low health literacy patients may have been more likely to not volunteer to take the survey, which might explain the relatively high mean scores on the health literacy measures. Further, participants were mostly white, female, college-educated, health literate, and scheduled for a routine visit, which limits the generalizability of the study findings and the ability to identify significant predictors.

Regarding the study design, pre-/post-tests are usually used to measure the change in a situation, phenomenon, problem, or attitude. However, as the authors did not aim to measure any change during the clinical encounter itself, the use of only a post-test may have been more appropriate. The use of a pre-/post-test design may have increased the likelihood of patients both recalling POMI before the encounter and then sharing POMI with their provider. Also, in the post-survey, the authors only asked follow-up questions of patients that shared POMI with their provider. An open-response question could have been included to explore further why some patients chose not to introduce POMI during the clinical encounter. Lastly, the authors may have been able to reach more patients with lower health literacy if surveys were administered at public hospitals as opposed to academic medical centers. While some providers may perceive that patients in academic medical centers are more complex or may have limited access to care [10], patients at public hospitals and safety net hospitals tend to be of lower income and have limited or no insurance [11,12].

Applications For Clinical Practice

There are documented communication-enhancing techniques and strategies that providers and other health professionals use, particularly among patients with low health literacy [13]. Based on this study, the authors conclude that providers may try another strategy of directly asking or passively surveying any POMI, regardless of whether the patient initiates this conversation. Other research has acknowledged that recognition of health literacy status allows for the use of appropriate communication tools [14]. However, providers need to recognize barriers to health information seeking, particularly among minorities and underserved populations [15], as well as the potential for embarrassment that patients might experience as a result of revealing misunderstandings of health information or general reading difficulties [16]. This study highlights the need for further research to identify predictors of health information seeking and especially health information sharing by patients during the clinical encounter.

—Katrina F. Mateo, MPH

Study Overview

Objective. To assess how patients look for patient-obtained medication information (POMI) to prepare for a clinical appointment, whether they share those findings with their provider, and how health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI.

Design. Cross-sectional survey-based study.

Setting and participants. The study took place over 1 week at 2 academic medical centers located in Las Vegas, Nevada, and Washington, DC. At a central waiting area at each facility, patients aged 18 and older waiting for their clinical appointment were invited to complete a survey, either on a computer tablet or with paper and pencil, before and after their appointment.

Measures and analysis. The pre-survey included demographic measures (age, gender, education, and ethnicity), the reason for the visit (routine care, sick visit, follow-up after survey, and follow-up after emergency room visit), and an item to assess self-report of perceived general health (from poor to excellent). Health literacy was assessed by a self-report measure that included subscales for the 3 dimensions of health literacy: functional, communicative, and critical health literacy [1]; together, these capture the ability of patients to retain health knowledge, gather and communicate health concepts, and apply health information. Patient activation was scored using the Patient Activation Measure (13 Likert-style items, total scale range 0–100); patient activation combines a patient’s self-reported knowledge, skill, and confidence for self-management of general health or a chronic condition [2]. Information seeking was measured by time spent (did not look for information, 1 hour, 2 hours, 3 hours, or more than 3 hours), and information channels used to look for POMI (eg, magazines/newspapers, internet website or search engine) were presented dichotomously (yes/no).

The post-survey first asked whether the participant shared information with their provider (yes/no). If the participant said yes, 4 items assessed their perception of the provider’s response, including amount of time spent discussing POMI, how seriously the provider considered the information, and overall reaction (scored as a mean, each item measured from 1–5, with 5 indicating the most positive reactions). For hypothesis testing, logistic regression models were used to test the effects of the independent variables. To explore the relationship between health literacy/patient activation and physician response, correlations were calculated.

Main results. Over 400 patients were asked to participate, and of these a total of 243 (60.75%) patients were eligible, consented, and completed surveys. Participants were predominantly white (57.6%), female (63%), had some college education or higher (80.2%), and had a clinical appointment for routine care (69.3%). The mean age was 47.04 years (SD, 15.78), the mean health status was 3.20 (SD, 0.94), and the mean Patient Activation Measure was 72.43 (SD, 16.00).

More than half of participants (58.26%) who responded to the item about information seeking indicated seeking POMI prior to their clinical appointment. Of these, the majority (88.7%) reported using the internet, particularly WebMD, as an information channel. Significant predictors of information seeking included age (P = 0.01, OR = 0.973), communicative health literacy (P = 0.01, or = 1.975), and critical health literacy (P = 0.05, OR = 1.518). Lower age, higher communicative health literacy, and higher critical health literacy increased the likelihood of the patient seeking POMI prior to the clinical appointment. Other assessed predictors were not significant, including gender, functional health literacy, patient activation, reason for visit, and reported health status.

58.2% of the 141 information-seeking patients talked to their health care provider about the information they found. However, no predictor variables included in a logistic regression analysis were significant, including age, gender, reason for visit, reported health status, functional health literacy, communicative health literacy, critical health literacy, and patient activation. For the research question (how do health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI), the mean score on the 4-item measure was 4.08 (SD, 0.90), indicating a generally positive response; most reported the physician response was good or higher. Patient activation correlated positively with perceived physician response (r = 0.245, P = 0.03).

Conclusion. The lack of data to predict who will introduce POMI at the medical visit is disconcerting. Providers might consider directly asking or passively surveying what outside information sources the patient has engaged with, regardless of whether patient introduces the information or does not introduce it.

Commentary

Patient engagement plays an important role in health care [3]. Activated patients often have skills and confidence to engage in their health care and with their provider, which often contributes to better health outcomes and care experiences [2,4] as well as lower health care costs [5]. Health information is needed to make informed decisions, manage health, and practice healthy behaviors [6], and patients are increasingly taking an active role in seeking out medical or health information outside of the clinical encounter in order to make shared health decisions with their provider [7]. Indeed, one of the Healthy People 2020 goals is to “Use health communication strategies and health information technology  to improve population health outcomes and health care quality, and to achieve health equity” [8].

However, seeking POMI requires health literacy skills and supportive relationships, particularly when navigating the many channels and complexities of publicly available health information [8]. This is especially true on the internet, where there is often varying accuracy and clarity of information presented. According to 2011 data from the Pew Research Center [9], 74% of adults in the United States use the internet, and of those adults 80% have looked online for health information; 34% have read another person’s commentary or experience about health or medical issues on an online news group, website, or blog; 25% have watched an online video about health or medical issues; and 24% have consulted online reviews of particular drugs or medical treatments.

A general strength of this study was the cross-sectional design, which allowed for surveying patients around attitudes, motivations, and behaviors immediately before and after their clinical encounter. According to the authors, this study design was aimed to extend knowledge around information seeking and provider discussions that have occurred distally and relied on patient long-term recall. Additionally, this study surveyed a variety of patients (not limited to either primary or specialist appointments) at 2 different academic medical centers, and gave patients a choice to either take the survey on a computer tablet or traditional paper and pencil. Further, the authors assessed the reliability of scales used and included a number of predictor variables in the logistic regression models for hypothesis testing.

The authors acknowledged several limitations, including the use of convenience sampling and self-reported data with volunteer participants, which can result in self-selection bias and social desirability bias. As study participants were self-selecting, low health literacy patients may have been more likely to not volunteer to take the survey, which might explain the relatively high mean scores on the health literacy measures. Further, participants were mostly white, female, college-educated, health literate, and scheduled for a routine visit, which limits the generalizability of the study findings and the ability to identify significant predictors.

Regarding the study design, pre-/post-tests are usually used to measure the change in a situation, phenomenon, problem, or attitude. However, as the authors did not aim to measure any change during the clinical encounter itself, the use of only a post-test may have been more appropriate. The use of a pre-/post-test design may have increased the likelihood of patients both recalling POMI before the encounter and then sharing POMI with their provider. Also, in the post-survey, the authors only asked follow-up questions of patients that shared POMI with their provider. An open-response question could have been included to explore further why some patients chose not to introduce POMI during the clinical encounter. Lastly, the authors may have been able to reach more patients with lower health literacy if surveys were administered at public hospitals as opposed to academic medical centers. While some providers may perceive that patients in academic medical centers are more complex or may have limited access to care [10], patients at public hospitals and safety net hospitals tend to be of lower income and have limited or no insurance [11,12].

Applications For Clinical Practice

There are documented communication-enhancing techniques and strategies that providers and other health professionals use, particularly among patients with low health literacy [13]. Based on this study, the authors conclude that providers may try another strategy of directly asking or passively surveying any POMI, regardless of whether the patient initiates this conversation. Other research has acknowledged that recognition of health literacy status allows for the use of appropriate communication tools [14]. However, providers need to recognize barriers to health information seeking, particularly among minorities and underserved populations [15], as well as the potential for embarrassment that patients might experience as a result of revealing misunderstandings of health information or general reading difficulties [16]. This study highlights the need for further research to identify predictors of health information seeking and especially health information sharing by patients during the clinical encounter.

—Katrina F. Mateo, MPH

Study Overview

Objective. To examine the cross-sectional relationships between standing time, obesity, and metabolic syndrome.

Design. Cross-sectional study.

Setting and participants. Participants were patients aged 20–79 years old attending Cooper Clinic in Dallas for a preventive medicine visit who enrolled in the Cooper Center Longitudinal Study, an ongoing prospective investigation established in 1970 to explore the effects of physical activity on morbidity and mortality [1]. Included in the analysis were those enrolled starting in 2010, when questions pertaining to standing patterns began to be included in the medical history. Patients who did not have complete information or who had a history of myocardial infarction, stroke, or cancer were excluded.

Measures. Obesity was directly measured using body mass index (≥ 30), waist circumference (men: ≥ 102 cm; women: ≥ 88 cm), and body fat percentage (men: ≥ 25%; women ≥ 30%) and was adjusted for history of diabetes and hypertension. Metabolic syndrome, a clustering of risk factors that increase the risk for heart disease, stroke, and diabetes, was assessed. Participants’ standing patterns were ascertained from responses to survey questions derived from the Canada Fitness Survey Questionnaire (“For those activities that you do most days of the week, such as work, school, and housework, how much time do you spend standing: Almost all of the time, ¾ of the time, ½ of the time, ¼ of the time, almost none of the time?”). Leisure-time physical activity was determined based on responses to survey questions, and answers were used to categorize participants as either meeting or not meeting the Physical Activity Guidelines for Americans.

Results. The study sample consisted of 7075 participants, who were primarily white and college educated. Over two-thirds were men and the mean age was 50.0 ± 10.1 years. Multivariable analysis showed that in men, increased standing was significantly associated with a lower likelihood of elevated body fat percentage. Specifically, standing a quarter of the time was linked to a 32% reduced likelihood of obesity (body fat percentage), standing half the time was associated with a 59% reduced likelihood of obesity, but standing more than three-quarters of the time was not associated with a lower risk of obesity. In women, standing a quarter, half, and three-quarters of the time was associated with 35%, 47%, and 57% respective reductions in the likelihood of abdominal obesity (waist circumference). No relationship between standing and metabolic syndrome was found among women or men.

The study also examined whether physical activity in conjunction with standing provided additional reduction risk for obesity. The study showed that 150 minutes of moderate activity and/or 75 minutes of vigorous activity per week added to standing time was associated with significant reduction in the probability of obesity and metabolic syndrome in both women and men.

Conclusion. Standing a quarter of the time per day or more is associated with reduced odds of obesity. The inverse relationship of standing to obesity and metabolic syndrome is more robust when combined with health-promoting leisure-time physical activity.

Commentary

Obesity is considered one of the main risk factors for cardiovascular diseases worldwide. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer, some of the leading causes of preventable death. The effects of obesity among Americans add more than $147 billion in medical costs to the U.S. economy annually [2].

Obesity is a national epidemic, with more than 78.9 million obese adults in the United States [2]. Studies have shown that Americans are currently less active as compared to past decades [3]. This decline in physical activity combined with other factors, such as the ubiquity of low-cost high-energy foods and beverages, has likely contributed to the high rate of obesity.

This cross-sectional study aimed to assess the relationship between standing time, obesity, and metabolic syndrome alongside and independent of leisure-time physical activity. The researchers found that standing for at least one quarter of the day is linked to lower odds of obesity, which was directly assessed through 3 measures: BMI, body fat percentage, and waist circumference. The apparent benefit of standing is an important finding in light of obesity being such an important public health concern.

The large sample size is a strength of this study in terms of statistical power; however, there are important limitations that must be acknowledged. First, given the cross-sectional design, no causal inferences can be made. Moreover, while obesity and metabolic syndrome were objectively measured, standing and physical activity were based on self-report, which may lead to over- or underestimation of these behaviors. In addition, due to the survey measure used in the study, it is unclear whether study participants were standing still or standing and moving. More information in this regard would be helpful. Longitudinal research is encouraged in order to provide better evidence of these relationships and their effects.

In addition, cultural aspects were not assessed in this study. Racial and ethnic differences may influence the relationship between the variables of physical activity and obesity reduction.

Applications for Clinical Practice

Obesity is a complex but preventable health problem commonly associated with sedentary lifestyle. Physical activity is recommended as a component of weight management for prevention of weight gain and for weight loss [4]. Whether standing more often will aid in reducing obesity cannot be determined from this study.

—Paloma Cesar de Sales, BS, RN, MS

Study Overview

Objective. To examine the cross-sectional relationships between standing time, obesity, and metabolic syndrome.

Design. Cross-sectional study.

Setting and participants. Participants were patients aged 20–79 years old attending Cooper Clinic in Dallas for a preventive medicine visit who enrolled in the Cooper Center Longitudinal Study, an ongoing prospective investigation established in 1970 to explore the effects of physical activity on morbidity and mortality [1]. Included in the analysis were those enrolled starting in 2010, when questions pertaining to standing patterns began to be included in the medical history. Patients who did not have complete information or who had a history of myocardial infarction, stroke, or cancer were excluded.

Measures. Obesity was directly measured using body mass index (≥ 30), waist circumference (men: ≥ 102 cm; women: ≥ 88 cm), and body fat percentage (men: ≥ 25%; women ≥ 30%) and was adjusted for history of diabetes and hypertension. Metabolic syndrome, a clustering of risk factors that increase the risk for heart disease, stroke, and diabetes, was assessed. Participants’ standing patterns were ascertained from responses to survey questions derived from the Canada Fitness Survey Questionnaire (“For those activities that you do most days of the week, such as work, school, and housework, how much time do you spend standing: Almost all of the time, ¾ of the time, ½ of the time, ¼ of the time, almost none of the time?”). Leisure-time physical activity was determined based on responses to survey questions, and answers were used to categorize participants as either meeting or not meeting the Physical Activity Guidelines for Americans.

Results. The study sample consisted of 7075 participants, who were primarily white and college educated. Over two-thirds were men and the mean age was 50.0 ± 10.1 years. Multivariable analysis showed that in men, increased standing was significantly associated with a lower likelihood of elevated body fat percentage. Specifically, standing a quarter of the time was linked to a 32% reduced likelihood of obesity (body fat percentage), standing half the time was associated with a 59% reduced likelihood of obesity, but standing more than three-quarters of the time was not associated with a lower risk of obesity. In women, standing a quarter, half, and three-quarters of the time was associated with 35%, 47%, and 57% respective reductions in the likelihood of abdominal obesity (waist circumference). No relationship between standing and metabolic syndrome was found among women or men.

The study also examined whether physical activity in conjunction with standing provided additional reduction risk for obesity. The study showed that 150 minutes of moderate activity and/or 75 minutes of vigorous activity per week added to standing time was associated with significant reduction in the probability of obesity and metabolic syndrome in both women and men.

Conclusion. Standing a quarter of the time per day or more is associated with reduced odds of obesity. The inverse relationship of standing to obesity and metabolic syndrome is more robust when combined with health-promoting leisure-time physical activity.

Commentary

Obesity is considered one of the main risk factors for cardiovascular diseases worldwide. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer, some of the leading causes of preventable death. The effects of obesity among Americans add more than $147 billion in medical costs to the U.S. economy annually [2].

Obesity is a national epidemic, with more than 78.9 million obese adults in the United States [2]. Studies have shown that Americans are currently less active as compared to past decades [3]. This decline in physical activity combined with other factors, such as the ubiquity of low-cost high-energy foods and beverages, has likely contributed to the high rate of obesity.

This cross-sectional study aimed to assess the relationship between standing time, obesity, and metabolic syndrome alongside and independent of leisure-time physical activity. The researchers found that standing for at least one quarter of the day is linked to lower odds of obesity, which was directly assessed through 3 measures: BMI, body fat percentage, and waist circumference. The apparent benefit of standing is an important finding in light of obesity being such an important public health concern.

The large sample size is a strength of this study in terms of statistical power; however, there are important limitations that must be acknowledged. First, given the cross-sectional design, no causal inferences can be made. Moreover, while obesity and metabolic syndrome were objectively measured, standing and physical activity were based on self-report, which may lead to over- or underestimation of these behaviors. In addition, due to the survey measure used in the study, it is unclear whether study participants were standing still or standing and moving. More information in this regard would be helpful. Longitudinal research is encouraged in order to provide better evidence of these relationships and their effects.

In addition, cultural aspects were not assessed in this study. Racial and ethnic differences may influence the relationship between the variables of physical activity and obesity reduction.

Applications for Clinical Practice

Obesity is a complex but preventable health problem commonly associated with sedentary lifestyle. Physical activity is recommended as a component of weight management for prevention of weight gain and for weight loss [4]. Whether standing more often will aid in reducing obesity cannot be determined from this study.

—Paloma Cesar de Sales, BS, RN, MS

Study Overview

Objective. To examine the cross-sectional relationships between standing time, obesity, and metabolic syndrome.

Design. Cross-sectional study.

Setting and participants. Participants were patients aged 20–79 years old attending Cooper Clinic in Dallas for a preventive medicine visit who enrolled in the Cooper Center Longitudinal Study, an ongoing prospective investigation established in 1970 to explore the effects of physical activity on morbidity and mortality [1]. Included in the analysis were those enrolled starting in 2010, when questions pertaining to standing patterns began to be included in the medical history. Patients who did not have complete information or who had a history of myocardial infarction, stroke, or cancer were excluded.

Measures. Obesity was directly measured using body mass index (≥ 30), waist circumference (men: ≥ 102 cm; women: ≥ 88 cm), and body fat percentage (men: ≥ 25%; women ≥ 30%) and was adjusted for history of diabetes and hypertension. Metabolic syndrome, a clustering of risk factors that increase the risk for heart disease, stroke, and diabetes, was assessed. Participants’ standing patterns were ascertained from responses to survey questions derived from the Canada Fitness Survey Questionnaire (“For those activities that you do most days of the week, such as work, school, and housework, how much time do you spend standing: Almost all of the time, ¾ of the time, ½ of the time, ¼ of the time, almost none of the time?”). Leisure-time physical activity was determined based on responses to survey questions, and answers were used to categorize participants as either meeting or not meeting the Physical Activity Guidelines for Americans.

Results. The study sample consisted of 7075 participants, who were primarily white and college educated. Over two-thirds were men and the mean age was 50.0 ± 10.1 years. Multivariable analysis showed that in men, increased standing was significantly associated with a lower likelihood of elevated body fat percentage. Specifically, standing a quarter of the time was linked to a 32% reduced likelihood of obesity (body fat percentage), standing half the time was associated with a 59% reduced likelihood of obesity, but standing more than three-quarters of the time was not associated with a lower risk of obesity. In women, standing a quarter, half, and three-quarters of the time was associated with 35%, 47%, and 57% respective reductions in the likelihood of abdominal obesity (waist circumference). No relationship between standing and metabolic syndrome was found among women or men.

The study also examined whether physical activity in conjunction with standing provided additional reduction risk for obesity. The study showed that 150 minutes of moderate activity and/or 75 minutes of vigorous activity per week added to standing time was associated with significant reduction in the probability of obesity and metabolic syndrome in both women and men.

Conclusion. Standing a quarter of the time per day or more is associated with reduced odds of obesity. The inverse relationship of standing to obesity and metabolic syndrome is more robust when combined with health-promoting leisure-time physical activity.

Commentary

Obesity is considered one of the main risk factors for cardiovascular diseases worldwide. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer, some of the leading causes of preventable death. The effects of obesity among Americans add more than $147 billion in medical costs to the U.S. economy annually [2].

Obesity is a national epidemic, with more than 78.9 million obese adults in the United States [2]. Studies have shown that Americans are currently less active as compared to past decades [3]. This decline in physical activity combined with other factors, such as the ubiquity of low-cost high-energy foods and beverages, has likely contributed to the high rate of obesity.

This cross-sectional study aimed to assess the relationship between standing time, obesity, and metabolic syndrome alongside and independent of leisure-time physical activity. The researchers found that standing for at least one quarter of the day is linked to lower odds of obesity, which was directly assessed through 3 measures: BMI, body fat percentage, and waist circumference. The apparent benefit of standing is an important finding in light of obesity being such an important public health concern.

The large sample size is a strength of this study in terms of statistical power; however, there are important limitations that must be acknowledged. First, given the cross-sectional design, no causal inferences can be made. Moreover, while obesity and metabolic syndrome were objectively measured, standing and physical activity were based on self-report, which may lead to over- or underestimation of these behaviors. In addition, due to the survey measure used in the study, it is unclear whether study participants were standing still or standing and moving. More information in this regard would be helpful. Longitudinal research is encouraged in order to provide better evidence of these relationships and their effects.

In addition, cultural aspects were not assessed in this study. Racial and ethnic differences may influence the relationship between the variables of physical activity and obesity reduction.

Applications for Clinical Practice

Obesity is a complex but preventable health problem commonly associated with sedentary lifestyle. Physical activity is recommended as a component of weight management for prevention of weight gain and for weight loss [4]. Whether standing more often will aid in reducing obesity cannot be determined from this study.

—Paloma Cesar de Sales, BS, RN, MS

Study Overview

Objective. To determine whether in-person visits for primary care patients resulted in improved weight loss maintenance relative to monthly mailings, with both groups receiving access to portion-controlled meals.

Design. Randomized clinical trial.

Setting and participants. This study took place within 2 university-affiliated primary care clinics in Colorado. For the first phase of the study, investigators enrolled 104 obese adult patients (18–79 years; BMI 30–49.9 kg/m²) who had been diagnosed with at least one of the following: type 2 diabetes, sleep apnea, hypertension, or hyperlipidemia. Patients who had independently lost weight prior to trial entry (> 5% in 6 months), were on weight-gain–promoting medications such as steroids, or had previously undergone bariatric surgery were excluded. The trial started with a 6-month run-in phase where active weight loss was promoted using a high-intensity behavioral intervention based on the Diabetes Prevention Program as well as access to subsidized portion-controlled foods (Nutrisystem). At the end of the 6-month run-in, the remaining participants (n = 84, 79.3%) were then randomized, stratified by gender and whether or not they achieved 5% weight loss, into the 2 main study arms.

Intervention. The experimental study arm (n = 41, “intensified maintenance”) relied on monthly in-person visits and monthly phone calls to prevent weight regain (thus, these participants had twice monthly contact during maintenance). Both visit types in this arm were conducted by a graduate-level research assistant and included some structured educational content as well as problem-solving around diet and lifestyle issues. In contrast, the control arm (n = 43, “standard maintenance”) relied just on monthly mailings (or emails) of educational and support materials to promote weight loss maintenance. Participants in both groups had the opportunity to purchase subsidized portion-controlled foods/meals from Nutrisystem in order to facilitate continued adherence to the caloric restriction required for weight loss maintenance.

Main outcome measures. The primary outcome for this trial was change in weight, measured in kgs, during the 12-month maintenance period. Other biometric outcomes included changes in blood pressure, serum glucose, lipid levels, and the inflammatory marker hs-CRP. Patient-reported outcomes included changes in medication use. The investigators used intention-to-treat analysis, with mixed linear models adjusted for age and gender. No imputation techniques for missing data are reported, although complete follow-up data was obtained on 94% of patients.

Results. Participants in the standard and intensified weight maintenance arms of the trial were similar with respect to measured baseline characteristics. The average age of participants was 56 years, and three-quarters (75%) were female. The majority in both groups were white (77% in standard arm; 88% in intense), and over half had either a college or advanced degree (58.1% in standard arm, 51.2% in intense). Approximately one- third had diabetes (32.6% in standard arm, 34.1% in intense) and over half had hypertension (67.4% in standard arm, 63.4% in intense). Of the 84 participants who were randomized in the weight maintenance phase of the study, 79 completed the 12-month follow up (94%; no difference in attrition between groups).

After 12 months of maintenance, participants in the intensified maintenance arm regained just 1.6 (± 1.3) kg of lost weight, while those in the standard arm regained 5.0 (± 0.8) kg, a statistically significant difference (P = 0.01). The investigators also examined the subgroup of participants who, after the 6-month run-in, had lost at least 5% of their initial body weight. For these individuals, almost three-quarters in the intensified maintenance arm (71.9%) maintained that > 5% loss by 18 months, compared to 51.7% in the standard group. This difference between groups was not statistically significant. There was a significant difference between groups for change in hs-CRP over the 12-month maintenance period, with the intensive group’s hs-CRP ending up an average of 1.46 mg/L lower than that of the standard group (P = 0.03). Although there was a similar trend favoring the intensive intervention for other biometric measures (change in waist circumference, glucose, blood pressure, and lipids were all more favorable in this arm), the between-group differences for these measures did not reach statistical significance. No significant differences between groups were observed with respect to changes in medication use over the 12-month maintenance intervention.

Conclusion. After 5 months of active weight loss, twice-monthly contact (using one in-person and one phone visit) plus portion-controlled foods during a 12-month weight maintenance phase resulted in significantly less weight regain than monthly mail or email-based counseling plus portion-controlled foods.

Commentary

Behavioral weight loss interventions, which typically require high-intensity in-person counseling over several months to a year, may be difficult to accomplish in the average primary care practice [1]. On the other hand, it may be the case that primary care practices are well-suited to assist patients who have already lost weight, as they enter weight-loss maintenance. While numerous studies have shown that patients who adhere to calorie-restricted diets (almost regardless of diet composition) are able to achieve clinically significant weight loss, less is known about effective methods of preventing weight regain. Several large trials have suggested that, as is the case with behavioral weight loss interventions, maintenance interventions are also more successful if they include regular contact, at least some of which is face-to-face [2,3]. These visits, along with other practices such as self-weighing and food diaries, may help patients maintain the energy balance necessary to stay at their new, lower body weight. There remains a gap, however, in terms of knowing whether the maintenance interventions from large randomized trials can be translated into the sometimes messy real world of clinical practice, where clinicians and patients are typically overburdened and busy.

The current study by Tsai et al does address some aspects of this important question. By recruiting “real-world” chronically ill patients from a primary care practice to participate in the trial, the results of this study may be more likely to generalize to the patient populations seen by practicing clinicians than the typically healthier, younger, community-recruited volunteers in large trials. Additionally, although the interventions in this study were not delivered by the primary care practice per se, they were low enough in intensity that they could theoretically be translated into most clinical practice settings, assuming reimbursement is not an issue. Monthly in-person visits certainly could be done by a physician (as under current CMS reimbursement guidelines), but would not have to be (the visits in this study were done by a graduate student with no formal training in behavioral interventions), and telephone visits could easily be done by clinical support staff. Even with this low level of visit intensity, patients had significantly less weight regain than those who were receiving monthly email or postal mail support (which, realistically, would still require some work on the part of primary care practices). Furthermore, there were suggestions of numerous parallel cardiometabolic benefits that might have been statistically significant with a larger sample size. This study benefited from several strengths in addition to its highly practical point of view. It was a randomized trial with a strong control group and long follow-up duration (18 months total). It used a run-in period for weight loss so that all who entered maintenance were doing so based on exposure to the same weight loss intervention. Happily, though, the investigators did not require successful weight loss (> 5%) for entry into the maintenance phase, which likely further contributed to the generalizability of their results. Another area where the run-in likely helped was with retention of subjects—94% of those randomized for maintenance contributed complete data at the end of the 12-month study period.

As acknowledged by the authors, this study also has some important limitations. As with most weight loss/diet interventions, the participants in this study were mostly female, and mostly non-Hispanic white, and thus a substantially less diverse population than is represented by patients with obesity in the US. Furthermore, although some aspects of the patient population did promote generalizability (recruitment from primary care, chronic illness burden), these patients were fairly highly educated, which may have impacted their adherence and results.

The use of subsidized portion-controlled meals in this study, while evidence-based, may have clouded the results somewhat. Perhaps the effect of both interventions would have been less pronounced had patients not been provided with subsidies to access these foods. In their discussion, the investigators acknowledge that the study lacked a comparison group with no access to portion-controlled foods and that, in a post-hoc analysis, greater use of these foods corresponded with better weight loss and weight loss maintenance among all participants.

Finally, although it was beyond the scope of this study, this trial does not provide any information about how weight loss medications in either the weight loss or maintenance phases might impact these types of interventions. Now that the FDA has approved a number of such medications for long-term use, it would be very helpful to have more information about how medications might be integrated into these types of strategies, for interested patients, as physicians could clearly play an integral role in the pharmacologic management of weight, alongside effective behavioral interventions.

Applications for Clinical Practice

Low-to-moderate intensity in-person and telephone-based visits during weight maintenance may help to protect against weight regain, and could realistically be an option for many primary care practices and their patients. However, aside from Medicare patients, for whom monthly primary care–based weight maintenance visits are now covered, physicians would need to understand how to code and bill such visits appropriately in order to avoid having patients face unexpected charges.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To determine whether in-person visits for primary care patients resulted in improved weight loss maintenance relative to monthly mailings, with both groups receiving access to portion-controlled meals.

Design. Randomized clinical trial.

Setting and participants. This study took place within 2 university-affiliated primary care clinics in Colorado. For the first phase of the study, investigators enrolled 104 obese adult patients (18–79 years; BMI 30–49.9 kg/m²) who had been diagnosed with at least one of the following: type 2 diabetes, sleep apnea, hypertension, or hyperlipidemia. Patients who had independently lost weight prior to trial entry (> 5% in 6 months), were on weight-gain–promoting medications such as steroids, or had previously undergone bariatric surgery were excluded. The trial started with a 6-month run-in phase where active weight loss was promoted using a high-intensity behavioral intervention based on the Diabetes Prevention Program as well as access to subsidized portion-controlled foods (Nutrisystem). At the end of the 6-month run-in, the remaining participants (n = 84, 79.3%) were then randomized, stratified by gender and whether or not they achieved 5% weight loss, into the 2 main study arms.

Intervention. The experimental study arm (n = 41, “intensified maintenance”) relied on monthly in-person visits and monthly phone calls to prevent weight regain (thus, these participants had twice monthly contact during maintenance). Both visit types in this arm were conducted by a graduate-level research assistant and included some structured educational content as well as problem-solving around diet and lifestyle issues. In contrast, the control arm (n = 43, “standard maintenance”) relied just on monthly mailings (or emails) of educational and support materials to promote weight loss maintenance. Participants in both groups had the opportunity to purchase subsidized portion-controlled foods/meals from Nutrisystem in order to facilitate continued adherence to the caloric restriction required for weight loss maintenance.

Main outcome measures. The primary outcome for this trial was change in weight, measured in kgs, during the 12-month maintenance period. Other biometric outcomes included changes in blood pressure, serum glucose, lipid levels, and the inflammatory marker hs-CRP. Patient-reported outcomes included changes in medication use. The investigators used intention-to-treat analysis, with mixed linear models adjusted for age and gender. No imputation techniques for missing data are reported, although complete follow-up data was obtained on 94% of patients.

Results. Participants in the standard and intensified weight maintenance arms of the trial were similar with respect to measured baseline characteristics. The average age of participants was 56 years, and three-quarters (75%) were female. The majority in both groups were white (77% in standard arm; 88% in intense), and over half had either a college or advanced degree (58.1% in standard arm, 51.2% in intense). Approximately one- third had diabetes (32.6% in standard arm, 34.1% in intense) and over half had hypertension (67.4% in standard arm, 63.4% in intense). Of the 84 participants who were randomized in the weight maintenance phase of the study, 79 completed the 12-month follow up (94%; no difference in attrition between groups).

After 12 months of maintenance, participants in the intensified maintenance arm regained just 1.6 (± 1.3) kg of lost weight, while those in the standard arm regained 5.0 (± 0.8) kg, a statistically significant difference (P = 0.01). The investigators also examined the subgroup of participants who, after the 6-month run-in, had lost at least 5% of their initial body weight. For these individuals, almost three-quarters in the intensified maintenance arm (71.9%) maintained that > 5% loss by 18 months, compared to 51.7% in the standard group. This difference between groups was not statistically significant. There was a significant difference between groups for change in hs-CRP over the 12-month maintenance period, with the intensive group’s hs-CRP ending up an average of 1.46 mg/L lower than that of the standard group (P = 0.03). Although there was a similar trend favoring the intensive intervention for other biometric measures (change in waist circumference, glucose, blood pressure, and lipids were all more favorable in this arm), the between-group differences for these measures did not reach statistical significance. No significant differences between groups were observed with respect to changes in medication use over the 12-month maintenance intervention.

Conclusion. After 5 months of active weight loss, twice-monthly contact (using one in-person and one phone visit) plus portion-controlled foods during a 12-month weight maintenance phase resulted in significantly less weight regain than monthly mail or email-based counseling plus portion-controlled foods.

Commentary

Behavioral weight loss interventions, which typically require high-intensity in-person counseling over several months to a year, may be difficult to accomplish in the average primary care practice [1]. On the other hand, it may be the case that primary care practices are well-suited to assist patients who have already lost weight, as they enter weight-loss maintenance. While numerous studies have shown that patients who adhere to calorie-restricted diets (almost regardless of diet composition) are able to achieve clinically significant weight loss, less is known about effective methods of preventing weight regain. Several large trials have suggested that, as is the case with behavioral weight loss interventions, maintenance interventions are also more successful if they include regular contact, at least some of which is face-to-face [2,3]. These visits, along with other practices such as self-weighing and food diaries, may help patients maintain the energy balance necessary to stay at their new, lower body weight. There remains a gap, however, in terms of knowing whether the maintenance interventions from large randomized trials can be translated into the sometimes messy real world of clinical practice, where clinicians and patients are typically overburdened and busy.

The current study by Tsai et al does address some aspects of this important question. By recruiting “real-world” chronically ill patients from a primary care practice to participate in the trial, the results of this study may be more likely to generalize to the patient populations seen by practicing clinicians than the typically healthier, younger, community-recruited volunteers in large trials. Additionally, although the interventions in this study were not delivered by the primary care practice per se, they were low enough in intensity that they could theoretically be translated into most clinical practice settings, assuming reimbursement is not an issue. Monthly in-person visits certainly could be done by a physician (as under current CMS reimbursement guidelines), but would not have to be (the visits in this study were done by a graduate student with no formal training in behavioral interventions), and telephone visits could easily be done by clinical support staff. Even with this low level of visit intensity, patients had significantly less weight regain than those who were receiving monthly email or postal mail support (which, realistically, would still require some work on the part of primary care practices). Furthermore, there were suggestions of numerous parallel cardiometabolic benefits that might have been statistically significant with a larger sample size. This study benefited from several strengths in addition to its highly practical point of view. It was a randomized trial with a strong control group and long follow-up duration (18 months total). It used a run-in period for weight loss so that all who entered maintenance were doing so based on exposure to the same weight loss intervention. Happily, though, the investigators did not require successful weight loss (> 5%) for entry into the maintenance phase, which likely further contributed to the generalizability of their results. Another area where the run-in likely helped was with retention of subjects—94% of those randomized for maintenance contributed complete data at the end of the 12-month study period.

As acknowledged by the authors, this study also has some important limitations. As with most weight loss/diet interventions, the participants in this study were mostly female, and mostly non-Hispanic white, and thus a substantially less diverse population than is represented by patients with obesity in the US. Furthermore, although some aspects of the patient population did promote generalizability (recruitment from primary care, chronic illness burden), these patients were fairly highly educated, which may have impacted their adherence and results.

The use of subsidized portion-controlled meals in this study, while evidence-based, may have clouded the results somewhat. Perhaps the effect of both interventions would have been less pronounced had patients not been provided with subsidies to access these foods. In their discussion, the investigators acknowledge that the study lacked a comparison group with no access to portion-controlled foods and that, in a post-hoc analysis, greater use of these foods corresponded with better weight loss and weight loss maintenance among all participants.

Finally, although it was beyond the scope of this study, this trial does not provide any information about how weight loss medications in either the weight loss or maintenance phases might impact these types of interventions. Now that the FDA has approved a number of such medications for long-term use, it would be very helpful to have more information about how medications might be integrated into these types of strategies, for interested patients, as physicians could clearly play an integral role in the pharmacologic management of weight, alongside effective behavioral interventions.

Applications for Clinical Practice

Low-to-moderate intensity in-person and telephone-based visits during weight maintenance may help to protect against weight regain, and could realistically be an option for many primary care practices and their patients. However, aside from Medicare patients, for whom monthly primary care–based weight maintenance visits are now covered, physicians would need to understand how to code and bill such visits appropriately in order to avoid having patients face unexpected charges.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To determine whether in-person visits for primary care patients resulted in improved weight loss maintenance relative to monthly mailings, with both groups receiving access to portion-controlled meals.

Design. Randomized clinical trial.

Setting and participants. This study took place within 2 university-affiliated primary care clinics in Colorado. For the first phase of the study, investigators enrolled 104 obese adult patients (18–79 years; BMI 30–49.9 kg/m²) who had been diagnosed with at least one of the following: type 2 diabetes, sleep apnea, hypertension, or hyperlipidemia. Patients who had independently lost weight prior to trial entry (> 5% in 6 months), were on weight-gain–promoting medications such as steroids, or had previously undergone bariatric surgery were excluded. The trial started with a 6-month run-in phase where active weight loss was promoted using a high-intensity behavioral intervention based on the Diabetes Prevention Program as well as access to subsidized portion-controlled foods (Nutrisystem). At the end of the 6-month run-in, the remaining participants (n = 84, 79.3%) were then randomized, stratified by gender and whether or not they achieved 5% weight loss, into the 2 main study arms.

Intervention. The experimental study arm (n = 41, “intensified maintenance”) relied on monthly in-person visits and monthly phone calls to prevent weight regain (thus, these participants had twice monthly contact during maintenance). Both visit types in this arm were conducted by a graduate-level research assistant and included some structured educational content as well as problem-solving around diet and lifestyle issues. In contrast, the control arm (n = 43, “standard maintenance”) relied just on monthly mailings (or emails) of educational and support materials to promote weight loss maintenance. Participants in both groups had the opportunity to purchase subsidized portion-controlled foods/meals from Nutrisystem in order to facilitate continued adherence to the caloric restriction required for weight loss maintenance.

Main outcome measures. The primary outcome for this trial was change in weight, measured in kgs, during the 12-month maintenance period. Other biometric outcomes included changes in blood pressure, serum glucose, lipid levels, and the inflammatory marker hs-CRP. Patient-reported outcomes included changes in medication use. The investigators used intention-to-treat analysis, with mixed linear models adjusted for age and gender. No imputation techniques for missing data are reported, although complete follow-up data was obtained on 94% of patients.

Results. Participants in the standard and intensified weight maintenance arms of the trial were similar with respect to measured baseline characteristics. The average age of participants was 56 years, and three-quarters (75%) were female. The majority in both groups were white (77% in standard arm; 88% in intense), and over half had either a college or advanced degree (58.1% in standard arm, 51.2% in intense). Approximately one- third had diabetes (32.6% in standard arm, 34.1% in intense) and over half had hypertension (67.4% in standard arm, 63.4% in intense). Of the 84 participants who were randomized in the weight maintenance phase of the study, 79 completed the 12-month follow up (94%; no difference in attrition between groups).

After 12 months of maintenance, participants in the intensified maintenance arm regained just 1.6 (± 1.3) kg of lost weight, while those in the standard arm regained 5.0 (± 0.8) kg, a statistically significant difference (P = 0.01). The investigators also examined the subgroup of participants who, after the 6-month run-in, had lost at least 5% of their initial body weight. For these individuals, almost three-quarters in the intensified maintenance arm (71.9%) maintained that > 5% loss by 18 months, compared to 51.7% in the standard group. This difference between groups was not statistically significant. There was a significant difference between groups for change in hs-CRP over the 12-month maintenance period, with the intensive group’s hs-CRP ending up an average of 1.46 mg/L lower than that of the standard group (P = 0.03). Although there was a similar trend favoring the intensive intervention for other biometric measures (change in waist circumference, glucose, blood pressure, and lipids were all more favorable in this arm), the between-group differences for these measures did not reach statistical significance. No significant differences between groups were observed with respect to changes in medication use over the 12-month maintenance intervention.

Conclusion. After 5 months of active weight loss, twice-monthly contact (using one in-person and one phone visit) plus portion-controlled foods during a 12-month weight maintenance phase resulted in significantly less weight regain than monthly mail or email-based counseling plus portion-controlled foods.

Commentary

Behavioral weight loss interventions, which typically require high-intensity in-person counseling over several months to a year, may be difficult to accomplish in the average primary care practice [1]. On the other hand, it may be the case that primary care practices are well-suited to assist patients who have already lost weight, as they enter weight-loss maintenance. While numerous studies have shown that patients who adhere to calorie-restricted diets (almost regardless of diet composition) are able to achieve clinically significant weight loss, less is known about effective methods of preventing weight regain. Several large trials have suggested that, as is the case with behavioral weight loss interventions, maintenance interventions are also more successful if they include regular contact, at least some of which is face-to-face [2,3]. These visits, along with other practices such as self-weighing and food diaries, may help patients maintain the energy balance necessary to stay at their new, lower body weight. There remains a gap, however, in terms of knowing whether the maintenance interventions from large randomized trials can be translated into the sometimes messy real world of clinical practice, where clinicians and patients are typically overburdened and busy.

The current study by Tsai et al does address some aspects of this important question. By recruiting “real-world” chronically ill patients from a primary care practice to participate in the trial, the results of this study may be more likely to generalize to the patient populations seen by practicing clinicians than the typically healthier, younger, community-recruited volunteers in large trials. Additionally, although the interventions in this study were not delivered by the primary care practice per se, they were low enough in intensity that they could theoretically be translated into most clinical practice settings, assuming reimbursement is not an issue. Monthly in-person visits certainly could be done by a physician (as under current CMS reimbursement guidelines), but would not have to be (the visits in this study were done by a graduate student with no formal training in behavioral interventions), and telephone visits could easily be done by clinical support staff. Even with this low level of visit intensity, patients had significantly less weight regain than those who were receiving monthly email or postal mail support (which, realistically, would still require some work on the part of primary care practices). Furthermore, there were suggestions of numerous parallel cardiometabolic benefits that might have been statistically significant with a larger sample size. This study benefited from several strengths in addition to its highly practical point of view. It was a randomized trial with a strong control group and long follow-up duration (18 months total). It used a run-in period for weight loss so that all who entered maintenance were doing so based on exposure to the same weight loss intervention. Happily, though, the investigators did not require successful weight loss (> 5%) for entry into the maintenance phase, which likely further contributed to the generalizability of their results. Another area where the run-in likely helped was with retention of subjects—94% of those randomized for maintenance contributed complete data at the end of the 12-month study period.

As acknowledged by the authors, this study also has some important limitations. As with most weight loss/diet interventions, the participants in this study were mostly female, and mostly non-Hispanic white, and thus a substantially less diverse population than is represented by patients with obesity in the US. Furthermore, although some aspects of the patient population did promote generalizability (recruitment from primary care, chronic illness burden), these patients were fairly highly educated, which may have impacted their adherence and results.

The use of subsidized portion-controlled meals in this study, while evidence-based, may have clouded the results somewhat. Perhaps the effect of both interventions would have been less pronounced had patients not been provided with subsidies to access these foods. In their discussion, the investigators acknowledge that the study lacked a comparison group with no access to portion-controlled foods and that, in a post-hoc analysis, greater use of these foods corresponded with better weight loss and weight loss maintenance among all participants.

Finally, although it was beyond the scope of this study, this trial does not provide any information about how weight loss medications in either the weight loss or maintenance phases might impact these types of interventions. Now that the FDA has approved a number of such medications for long-term use, it would be very helpful to have more information about how medications might be integrated into these types of strategies, for interested patients, as physicians could clearly play an integral role in the pharmacologic management of weight, alongside effective behavioral interventions.

Applications for Clinical Practice

Low-to-moderate intensity in-person and telephone-based visits during weight maintenance may help to protect against weight regain, and could realistically be an option for many primary care practices and their patients. However, aside from Medicare patients, for whom monthly primary care–based weight maintenance visits are now covered, physicians would need to understand how to code and bill such visits appropriately in order to avoid having patients face unexpected charges.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To evaluate the effectiveness and impact of using MyFitnessPal, a free, popular smartphone application (“app”), for weight loss.

Study design. 2-arm randomized controlled trial.

Setting and participants. Participants were recruited from 2 primary care clinics in the University of California, Los Angeles heath system. The inclusion criteria for the study were ≥ 18 years of age, body mass index (BMI) ≥ 25 kg/m², an interest in losing weight, and ownership of a smartphone. The exclusion criteria included pregnancy, hemodialysis, life expectancy less than 6 months, lack of interest in weight loss, and current use of a smartphone app for weight loss. Out of 633 individuals assessed, 212 were eligible for the study. Participants were block randomized by BMI 25–30 kg/m² and BMI > 30 kg/m² to either usual primary care (n = 107) or usual primary care plus the app (n = 105).

Intervention. MyFitnessPal (MFP) was selected for this study based on previous focus groups with overweight primary care patients. MFP is a calorie-counting app that incorporates evidence-based and theory-based approaches to weight loss. Users can enter their current weight, goal weight, and goal rate of weight loss, which allows the app to generate the user’s daily, individualized calorie goal. MFP users also input daily weight, food intake, and physical activity, which produce certain outputs, including calorie counts, weight trends, and nutritional summaries based on food consumed.

Participants in the intervention arm received help from research assistants in downloading MFP onto their smartphones and received a phone call 1 week after enrollment to assist with any technical issues with the app. Those in the control group were told to choose any preferred activity for weight loss. Both groups received usual care from their primary care provider, with an additional two follow-up visits at 3 and 6 months. At the 3-month follow-up visit, all participants received a nutrition educational handout from www.myplate.gov.

Main outcomes measures. The main outcome measure was weight change at 6 months. Blood pressure, weight, systolic blood pressure (SPB), and 3 self-reported behavioral mediators of weight loss (exercise, diet, and self-efficacy in weight loss) were measured and collected for all participants at baseline and at 3 and 6 months. This study also gathered data from the MyFitnessPal company to measure frequency of app usage. At the 6-month follow-up visit, research assistants asked participants in the intervention arm about their experience using MFP, while those in the control group were asked if they had used MFP in the past 6 months to assess contamination. The authors used a linear mixed effects model (PROC MIXED) in SAS data processing software to investigate the differences in weight change, SBP change, and change in behavioral survey items between the 2 groups while controlling for clinic site. In addition, they performed 2 sensitivity analyses to evaluate the impact of possible informative dropout (income, education, diet experience, treatment group, and baseline value), and the effect of excluding one control group outlier.

Results. The majority of participants were female (73%) with a mean age of 43.4 years (SD = 14.3). The mean BMI was 33.4 kg/m² (SD = 7.09), and 48% of the participants identified themselves as non-Hispanic white. At the 3-month visit, 26% and 21% of the participants from the intervention and control arms were lost to follow-up. Additionally, at 6 months, 32% and 19% of intervention and control group participants were lost to follow-up.

There was no significant difference in weight change between the two groups at 3 months (control, + 0.54 lb; intervention, –0.06 lb; P = 0.53) or at 6 months (control, +0.6 lb; intervention, –0.07 lb; P = 0.63); between group difference at 3 months was –0.6 lb (95% confidence interval [CI], –2.5 to 1.3 lb; P = 0.53) and at 6 months was –0.67 lb (CI, –3.3 to 2.1 lb; P = 0.63). The sensitivity analysis based on possible missing data also suggested the same outcome with between group difference at 6 months at 0.08 lb (CI, –3.04 to 3.20 lb; P = 0.96). The difference in systolic blood pressure was not significant between the groups.

Participants in the intervention arm used a personal calorie goal more often than those in the control group, with a mean between group difference at 3 months of 1.9 days per week (CI, 1.0 to 2.8; P < 0.001) and a mean between group difference at 6 months of 2.0 days per week (CI, 1.1 to 2.9; P < 0.001). The results also showed that the use of calorie goal feature was significant at 3 months (P < 0.001) and at 6 months (P < 0.001). At the 3-month visit, the authors found that individuals in the intervention group reported decreased self-efficacy in achieving their weight loss goals when compared to their counterparts (–0.85 on a 10-point scale; CI, –1.6 to –0.1; P = 0.026), but self-efficacy was insignificant at the 6-month follow-up. Additionally, the results suggested no difference in self-reported behaviors regarding diet, exercise, and self-efficacy in weight loss between the groups.

The mean number of logins was 61 during the course of the study, and the median total logins was 19. Interestingly, the data showed that there was a rapid decline of logins after enrollment for most participants in the intervention arm. There were 94 users who logged in to the app during the first month and 34 who logged in during the last month of the study. Out of 107 participants from the control group, 14 used MFP during the trial.

Despite a sharp decline in usage, MFP users in the intervention group were satisfied with the app: 79% were somewhat to completely satisfied, 92% would recommend it to a friend, and 80% planned to continue using MFP after the study. The study indicated that there were several aspects that the users liked about MFP including ease of use (100%), feedback on progress (88%), and 48% indicated that it was fun to use. Fewer participants appreciated features such as the reminder feature (42%) and social networking feature (13%). A common theme the authors found in MFP users was increased awareness of food choices, and more caution about food choices. Of those that stopped using MFP, some comments regarding MFP included that it was tedious (84%) and not easy to use (24%).

Conclusion. While most participants were satisfied with MFP, encouraging use of the app did not lead to more weight loss in primary care patients compared to usual care. There was decreased engagement with MFP over time.

Commentary

Despite efforts by the federal government to address the obesity epidemic in the United States, there is still a high prevalence of obesity among children and adults. Approximately 17% of youth and 35% of adults in the United States have a BMI in the obese range (> 30 kg/m²) [1]. In addition to a high association between obesity and chronic diseases such as type 2 diabetes mellitus, hypertension, and hypercholesterolemia[2], the obesity epidemic carries a staggering financial burden. The annual cost of obesity in the U.S. was estimated to be $147 billion in 2008, and the medical costs for each person with obesity was $1429 higher than those with normal weight [3,4]. Therefore, finding cost-effective and easily accessible methods to manage obesity is imperative. The Pew Research Center found that 64% of adults in the U.S. own a smartphone, and 62% of those have used their phone to look up health information[5]. Thus, smartphone applications that deliver weight management information and strategies may be a cost-effective and feasible means to reach a large population.

This study assessed the impact of MyFitnessPal, a free and widely popular mobile application, as an approach to reduce weight among patients in a primary care setting. The authors compared weight change between patients who received usual care from primary care providers (PCPs) and those who used MFP in addition to their usual care. They found no significant difference in weight loss between the two groups at 3 and 6 months during the trial. Despite this negative finding, this study makes important contributions to the e-health literature and highlights important considerations for similar studies.

While the exact reasons for lack of effect are unclear, the lack of effectiveness of such a brief intervention is not surprising. It is important to note that the PCPs did not assist or reinforce the patients to use the MFP app in this study. A systematic review of technology-assisted interventions in primary care suggests that technologies such as smartphone apps could be used as a successful tools for weight loss in the primary care setting, but that there needs to be guidance and feedback from a health care team[6]. Further, the intervention may not have been intense enough to achieve clinically significant weight loss. The U.S. Preventive Service Task Force recommends intensive interventions with at least 12 to 26 visits over 12 months to address obesity[7]. Thus, future studies of this app should include more intensive counseling from the healthcare team to determine if this improves adherence to lifestyle changes.

Strengths of this study include the use of a randomized controlled trial design, double-blinding of both participants and PCPs, and analyses for outlier and missing data. These design considerations increase the validity of the trial outcome. In addition, the authors rigorously assessed the relationship between the exposure and the outcome. The group set a high goal of enrollment to account for potential high rate of attrition (up to 50%). Since there was considerable loss to follow-up at both 3 and 6 months, the authors performed sensitivity analyses to determine if this biased the outcomes. The team assessed crossover contamination at the end of the study by asking participants in the control group if they used MFP during the trial. The authors also conducted a linear regression to examine if baseline self-efficacy was a significant predictor of weight change, controlling for the interaction between self-efficacy and group assignment. Additionally, sensitivity analyses were performed to see if the result from one outlier in the control group who achieved significant weight loss biased the findings.

As one of the first studies to investigate the use of a mobile app for weight loss in the primary care setting, the pragmatic design was impressive. The study showed that introduction to MFP can be done in 5 minutes and that the intervention was highly acceptable to patients. Since research assistants provided minimal guidance to enrolled participants, this study design provided an opportunity to observe the app’s efficacy in the real-world setting for the general public.

Although the authors efficiently designed a valid and pragmatic study, there were several aspects of the study that could be improved. As previously stated by the authors, this study did not explicitly measure the readiness for change, hence it was challenging to accurately and systematically determine the motivation of those in the intervention arm. Additionally, despite the diverse income and race of participants, the majority of the subjects were college-educated. In 2014, only 34% of the U.S. population completed a bachelor’s degree or high, and only 8% completed a master’s or higher degree [8]. This may limit the generalizability of the study, as education level could influence app acceptance and usage behavior.

Applications for Clinical Practice

With a large number of people using smartphones, there are increasing opportunities to use this delivery system to provide weight management information and support strategies for weight loss and lifestyle behavior changes. Previous studies showed that smartphone technology is a promising tool to facilitate weight management[9, 10], but the best practices for implementation of smart phones in the primary setting for weight management are still unknown. Based on this study and previous research done on technology intervention on health, providing the MFP app alone without additional counseling or intervention will not lead to clinically significant weight loss in the majority of patients. Future studies should determine if adding the MFP app is more efficacious when part of a more intensive behavioral intervention. Further studies should also determine whether PCP support in assessing readiness to use health apps and other behavior change technologies increases adherence and weight loss.

—Pich Seekaew, BS, and Melanie Jay MD, MS

Study Overview

Objective. To evaluate the effectiveness and impact of using MyFitnessPal, a free, popular smartphone application (“app”), for weight loss.

Study design. 2-arm randomized controlled trial.

Setting and participants. Participants were recruited from 2 primary care clinics in the University of California, Los Angeles heath system. The inclusion criteria for the study were ≥ 18 years of age, body mass index (BMI) ≥ 25 kg/m², an interest in losing weight, and ownership of a smartphone. The exclusion criteria included pregnancy, hemodialysis, life expectancy less than 6 months, lack of interest in weight loss, and current use of a smartphone app for weight loss. Out of 633 individuals assessed, 212 were eligible for the study. Participants were block randomized by BMI 25–30 kg/m² and BMI > 30 kg/m² to either usual primary care (n = 107) or usual primary care plus the app (n = 105).

Intervention. MyFitnessPal (MFP) was selected for this study based on previous focus groups with overweight primary care patients. MFP is a calorie-counting app that incorporates evidence-based and theory-based approaches to weight loss. Users can enter their current weight, goal weight, and goal rate of weight loss, which allows the app to generate the user’s daily, individualized calorie goal. MFP users also input daily weight, food intake, and physical activity, which produce certain outputs, including calorie counts, weight trends, and nutritional summaries based on food consumed.

Participants in the intervention arm received help from research assistants in downloading MFP onto their smartphones and received a phone call 1 week after enrollment to assist with any technical issues with the app. Those in the control group were told to choose any preferred activity for weight loss. Both groups received usual care from their primary care provider, with an additional two follow-up visits at 3 and 6 months. At the 3-month follow-up visit, all participants received a nutrition educational handout from www.myplate.gov.

Main outcomes measures. The main outcome measure was weight change at 6 months. Blood pressure, weight, systolic blood pressure (SPB), and 3 self-reported behavioral mediators of weight loss (exercise, diet, and self-efficacy in weight loss) were measured and collected for all participants at baseline and at 3 and 6 months. This study also gathered data from the MyFitnessPal company to measure frequency of app usage. At the 6-month follow-up visit, research assistants asked participants in the intervention arm about their experience using MFP, while those in the control group were asked if they had used MFP in the past 6 months to assess contamination. The authors used a linear mixed effects model (PROC MIXED) in SAS data processing software to investigate the differences in weight change, SBP change, and change in behavioral survey items between the 2 groups while controlling for clinic site. In addition, they performed 2 sensitivity analyses to evaluate the impact of possible informative dropout (income, education, diet experience, treatment group, and baseline value), and the effect of excluding one control group outlier.

Results. The majority of participants were female (73%) with a mean age of 43.4 years (SD = 14.3). The mean BMI was 33.4 kg/m² (SD = 7.09), and 48% of the participants identified themselves as non-Hispanic white. At the 3-month visit, 26% and 21% of the participants from the intervention and control arms were lost to follow-up. Additionally, at 6 months, 32% and 19% of intervention and control group participants were lost to follow-up.

There was no significant difference in weight change between the two groups at 3 months (control, + 0.54 lb; intervention, –0.06 lb; P = 0.53) or at 6 months (control, +0.6 lb; intervention, –0.07 lb; P = 0.63); between group difference at 3 months was –0.6 lb (95% confidence interval [CI], –2.5 to 1.3 lb; P = 0.53) and at 6 months was –0.67 lb (CI, –3.3 to 2.1 lb; P = 0.63). The sensitivity analysis based on possible missing data also suggested the same outcome with between group difference at 6 months at 0.08 lb (CI, –3.04 to 3.20 lb; P = 0.96). The difference in systolic blood pressure was not significant between the groups.

Participants in the intervention arm used a personal calorie goal more often than those in the control group, with a mean between group difference at 3 months of 1.9 days per week (CI, 1.0 to 2.8; P < 0.001) and a mean between group difference at 6 months of 2.0 days per week (CI, 1.1 to 2.9; P < 0.001). The results also showed that the use of calorie goal feature was significant at 3 months (P < 0.001) and at 6 months (P < 0.001). At the 3-month visit, the authors found that individuals in the intervention group reported decreased self-efficacy in achieving their weight loss goals when compared to their counterparts (–0.85 on a 10-point scale; CI, –1.6 to –0.1; P = 0.026), but self-efficacy was insignificant at the 6-month follow-up. Additionally, the results suggested no difference in self-reported behaviors regarding diet, exercise, and self-efficacy in weight loss between the groups.

The mean number of logins was 61 during the course of the study, and the median total logins was 19. Interestingly, the data showed that there was a rapid decline of logins after enrollment for most participants in the intervention arm. There were 94 users who logged in to the app during the first month and 34 who logged in during the last month of the study. Out of 107 participants from the control group, 14 used MFP during the trial.

Despite a sharp decline in usage, MFP users in the intervention group were satisfied with the app: 79% were somewhat to completely satisfied, 92% would recommend it to a friend, and 80% planned to continue using MFP after the study. The study indicated that there were several aspects that the users liked about MFP including ease of use (100%), feedback on progress (88%), and 48% indicated that it was fun to use. Fewer participants appreciated features such as the reminder feature (42%) and social networking feature (13%). A common theme the authors found in MFP users was increased awareness of food choices, and more caution about food choices. Of those that stopped using MFP, some comments regarding MFP included that it was tedious (84%) and not easy to use (24%).

Conclusion. While most participants were satisfied with MFP, encouraging use of the app did not lead to more weight loss in primary care patients compared to usual care. There was decreased engagement with MFP over time.

Commentary

Despite efforts by the federal government to address the obesity epidemic in the United States, there is still a high prevalence of obesity among children and adults. Approximately 17% of youth and 35% of adults in the United States have a BMI in the obese range (> 30 kg/m²) [1]. In addition to a high association between obesity and chronic diseases such as type 2 diabetes mellitus, hypertension, and hypercholesterolemia[2], the obesity epidemic carries a staggering financial burden. The annual cost of obesity in the U.S. was estimated to be $147 billion in 2008, and the medical costs for each person with obesity was $1429 higher than those with normal weight [3,4]. Therefore, finding cost-effective and easily accessible methods to manage obesity is imperative. The Pew Research Center found that 64% of adults in the U.S. own a smartphone, and 62% of those have used their phone to look up health information[5]. Thus, smartphone applications that deliver weight management information and strategies may be a cost-effective and feasible means to reach a large population.

This study assessed the impact of MyFitnessPal, a free and widely popular mobile application, as an approach to reduce weight among patients in a primary care setting. The authors compared weight change between patients who received usual care from primary care providers (PCPs) and those who used MFP in addition to their usual care. They found no significant difference in weight loss between the two groups at 3 and 6 months during the trial. Despite this negative finding, this study makes important contributions to the e-health literature and highlights important considerations for similar studies.

While the exact reasons for lack of effect are unclear, the lack of effectiveness of such a brief intervention is not surprising. It is important to note that the PCPs did not assist or reinforce the patients to use the MFP app in this study. A systematic review of technology-assisted interventions in primary care suggests that technologies such as smartphone apps could be used as a successful tools for weight loss in the primary care setting, but that there needs to be guidance and feedback from a health care team[6]. Further, the intervention may not have been intense enough to achieve clinically significant weight loss. The U.S. Preventive Service Task Force recommends intensive interventions with at least 12 to 26 visits over 12 months to address obesity[7]. Thus, future studies of this app should include more intensive counseling from the healthcare team to determine if this improves adherence to lifestyle changes.

Strengths of this study include the use of a randomized controlled trial design, double-blinding of both participants and PCPs, and analyses for outlier and missing data. These design considerations increase the validity of the trial outcome. In addition, the authors rigorously assessed the relationship between the exposure and the outcome. The group set a high goal of enrollment to account for potential high rate of attrition (up to 50%). Since there was considerable loss to follow-up at both 3 and 6 months, the authors performed sensitivity analyses to determine if this biased the outcomes. The team assessed crossover contamination at the end of the study by asking participants in the control group if they used MFP during the trial. The authors also conducted a linear regression to examine if baseline self-efficacy was a significant predictor of weight change, controlling for the interaction between self-efficacy and group assignment. Additionally, sensitivity analyses were performed to see if the result from one outlier in the control group who achieved significant weight loss biased the findings.

As one of the first studies to investigate the use of a mobile app for weight loss in the primary care setting, the pragmatic design was impressive. The study showed that introduction to MFP can be done in 5 minutes and that the intervention was highly acceptable to patients. Since research assistants provided minimal guidance to enrolled participants, this study design provided an opportunity to observe the app’s efficacy in the real-world setting for the general public.

Although the authors efficiently designed a valid and pragmatic study, there were several aspects of the study that could be improved. As previously stated by the authors, this study did not explicitly measure the readiness for change, hence it was challenging to accurately and systematically determine the motivation of those in the intervention arm. Additionally, despite the diverse income and race of participants, the majority of the subjects were college-educated. In 2014, only 34% of the U.S. population completed a bachelor’s degree or high, and only 8% completed a master’s or higher degree [8]. This may limit the generalizability of the study, as education level could influence app acceptance and usage behavior.

Applications for Clinical Practice

With a large number of people using smartphones, there are increasing opportunities to use this delivery system to provide weight management information and support strategies for weight loss and lifestyle behavior changes. Previous studies showed that smartphone technology is a promising tool to facilitate weight management[9, 10], but the best practices for implementation of smart phones in the primary setting for weight management are still unknown. Based on this study and previous research done on technology intervention on health, providing the MFP app alone without additional counseling or intervention will not lead to clinically significant weight loss in the majority of patients. Future studies should determine if adding the MFP app is more efficacious when part of a more intensive behavioral intervention. Further studies should also determine whether PCP support in assessing readiness to use health apps and other behavior change technologies increases adherence and weight loss.

—Pich Seekaew, BS, and Melanie Jay MD, MS

Study Overview

Objective. To evaluate the effectiveness and impact of using MyFitnessPal, a free, popular smartphone application (“app”), for weight loss.

Study design. 2-arm randomized controlled trial.

Setting and participants. Participants were recruited from 2 primary care clinics in the University of California, Los Angeles heath system. The inclusion criteria for the study were ≥ 18 years of age, body mass index (BMI) ≥ 25 kg/m², an interest in losing weight, and ownership of a smartphone. The exclusion criteria included pregnancy, hemodialysis, life expectancy less than 6 months, lack of interest in weight loss, and current use of a smartphone app for weight loss. Out of 633 individuals assessed, 212 were eligible for the study. Participants were block randomized by BMI 25–30 kg/m² and BMI > 30 kg/m² to either usual primary care (n = 107) or usual primary care plus the app (n = 105).

Intervention. MyFitnessPal (MFP) was selected for this study based on previous focus groups with overweight primary care patients. MFP is a calorie-counting app that incorporates evidence-based and theory-based approaches to weight loss. Users can enter their current weight, goal weight, and goal rate of weight loss, which allows the app to generate the user’s daily, individualized calorie goal. MFP users also input daily weight, food intake, and physical activity, which produce certain outputs, including calorie counts, weight trends, and nutritional summaries based on food consumed.

Participants in the intervention arm received help from research assistants in downloading MFP onto their smartphones and received a phone call 1 week after enrollment to assist with any technical issues with the app. Those in the control group were told to choose any preferred activity for weight loss. Both groups received usual care from their primary care provider, with an additional two follow-up visits at 3 and 6 months. At the 3-month follow-up visit, all participants received a nutrition educational handout from www.myplate.gov.

Main outcomes measures. The main outcome measure was weight change at 6 months. Blood pressure, weight, systolic blood pressure (SPB), and 3 self-reported behavioral mediators of weight loss (exercise, diet, and self-efficacy in weight loss) were measured and collected for all participants at baseline and at 3 and 6 months. This study also gathered data from the MyFitnessPal company to measure frequency of app usage. At the 6-month follow-up visit, research assistants asked participants in the intervention arm about their experience using MFP, while those in the control group were asked if they had used MFP in the past 6 months to assess contamination. The authors used a linear mixed effects model (PROC MIXED) in SAS data processing software to investigate the differences in weight change, SBP change, and change in behavioral survey items between the 2 groups while controlling for clinic site. In addition, they performed 2 sensitivity analyses to evaluate the impact of possible informative dropout (income, education, diet experience, treatment group, and baseline value), and the effect of excluding one control group outlier.

Results. The majority of participants were female (73%) with a mean age of 43.4 years (SD = 14.3). The mean BMI was 33.4 kg/m² (SD = 7.09), and 48% of the participants identified themselves as non-Hispanic white. At the 3-month visit, 26% and 21% of the participants from the intervention and control arms were lost to follow-up. Additionally, at 6 months, 32% and 19% of intervention and control group participants were lost to follow-up.

There was no significant difference in weight change between the two groups at 3 months (control, + 0.54 lb; intervention, –0.06 lb; P = 0.53) or at 6 months (control, +0.6 lb; intervention, –0.07 lb; P = 0.63); between group difference at 3 months was –0.6 lb (95% confidence interval [CI], –2.5 to 1.3 lb; P = 0.53) and at 6 months was –0.67 lb (CI, –3.3 to 2.1 lb; P = 0.63). The sensitivity analysis based on possible missing data also suggested the same outcome with between group difference at 6 months at 0.08 lb (CI, –3.04 to 3.20 lb; P = 0.96). The difference in systolic blood pressure was not significant between the groups.

Participants in the intervention arm used a personal calorie goal more often than those in the control group, with a mean between group difference at 3 months of 1.9 days per week (CI, 1.0 to 2.8; P < 0.001) and a mean between group difference at 6 months of 2.0 days per week (CI, 1.1 to 2.9; P < 0.001). The results also showed that the use of calorie goal feature was significant at 3 months (P < 0.001) and at 6 months (P < 0.001). At the 3-month visit, the authors found that individuals in the intervention group reported decreased self-efficacy in achieving their weight loss goals when compared to their counterparts (–0.85 on a 10-point scale; CI, –1.6 to –0.1; P = 0.026), but self-efficacy was insignificant at the 6-month follow-up. Additionally, the results suggested no difference in self-reported behaviors regarding diet, exercise, and self-efficacy in weight loss between the groups.

The mean number of logins was 61 during the course of the study, and the median total logins was 19. Interestingly, the data showed that there was a rapid decline of logins after enrollment for most participants in the intervention arm. There were 94 users who logged in to the app during the first month and 34 who logged in during the last month of the study. Out of 107 participants from the control group, 14 used MFP during the trial.

Despite a sharp decline in usage, MFP users in the intervention group were satisfied with the app: 79% were somewhat to completely satisfied, 92% would recommend it to a friend, and 80% planned to continue using MFP after the study. The study indicated that there were several aspects that the users liked about MFP including ease of use (100%), feedback on progress (88%), and 48% indicated that it was fun to use. Fewer participants appreciated features such as the reminder feature (42%) and social networking feature (13%). A common theme the authors found in MFP users was increased awareness of food choices, and more caution about food choices. Of those that stopped using MFP, some comments regarding MFP included that it was tedious (84%) and not easy to use (24%).

Conclusion. While most participants were satisfied with MFP, encouraging use of the app did not lead to more weight loss in primary care patients compared to usual care. There was decreased engagement with MFP over time.

Commentary

Despite efforts by the federal government to address the obesity epidemic in the United States, there is still a high prevalence of obesity among children and adults. Approximately 17% of youth and 35% of adults in the United States have a BMI in the obese range (> 30 kg/m²) [1]. In addition to a high association between obesity and chronic diseases such as type 2 diabetes mellitus, hypertension, and hypercholesterolemia[2], the obesity epidemic carries a staggering financial burden. The annual cost of obesity in the U.S. was estimated to be $147 billion in 2008, and the medical costs for each person with obesity was $1429 higher than those with normal weight [3,4]. Therefore, finding cost-effective and easily accessible methods to manage obesity is imperative. The Pew Research Center found that 64% of adults in the U.S. own a smartphone, and 62% of those have used their phone to look up health information[5]. Thus, smartphone applications that deliver weight management information and strategies may be a cost-effective and feasible means to reach a large population.

This study assessed the impact of MyFitnessPal, a free and widely popular mobile application, as an approach to reduce weight among patients in a primary care setting. The authors compared weight change between patients who received usual care from primary care providers (PCPs) and those who used MFP in addition to their usual care. They found no significant difference in weight loss between the two groups at 3 and 6 months during the trial. Despite this negative finding, this study makes important contributions to the e-health literature and highlights important considerations for similar studies.

While the exact reasons for lack of effect are unclear, the lack of effectiveness of such a brief intervention is not surprising. It is important to note that the PCPs did not assist or reinforce the patients to use the MFP app in this study. A systematic review of technology-assisted interventions in primary care suggests that technologies such as smartphone apps could be used as a successful tools for weight loss in the primary care setting, but that there needs to be guidance and feedback from a health care team[6]. Further, the intervention may not have been intense enough to achieve clinically significant weight loss. The U.S. Preventive Service Task Force recommends intensive interventions with at least 12 to 26 visits over 12 months to address obesity[7]. Thus, future studies of this app should include more intensive counseling from the healthcare team to determine if this improves adherence to lifestyle changes.

Strengths of this study include the use of a randomized controlled trial design, double-blinding of both participants and PCPs, and analyses for outlier and missing data. These design considerations increase the validity of the trial outcome. In addition, the authors rigorously assessed the relationship between the exposure and the outcome. The group set a high goal of enrollment to account for potential high rate of attrition (up to 50%). Since there was considerable loss to follow-up at both 3 and 6 months, the authors performed sensitivity analyses to determine if this biased the outcomes. The team assessed crossover contamination at the end of the study by asking participants in the control group if they used MFP during the trial. The authors also conducted a linear regression to examine if baseline self-efficacy was a significant predictor of weight change, controlling for the interaction between self-efficacy and group assignment. Additionally, sensitivity analyses were performed to see if the result from one outlier in the control group who achieved significant weight loss biased the findings.

As one of the first studies to investigate the use of a mobile app for weight loss in the primary care setting, the pragmatic design was impressive. The study showed that introduction to MFP can be done in 5 minutes and that the intervention was highly acceptable to patients. Since research assistants provided minimal guidance to enrolled participants, this study design provided an opportunity to observe the app’s efficacy in the real-world setting for the general public.

Although the authors efficiently designed a valid and pragmatic study, there were several aspects of the study that could be improved. As previously stated by the authors, this study did not explicitly measure the readiness for change, hence it was challenging to accurately and systematically determine the motivation of those in the intervention arm. Additionally, despite the diverse income and race of participants, the majority of the subjects were college-educated. In 2014, only 34% of the U.S. population completed a bachelor’s degree or high, and only 8% completed a master’s or higher degree [8]. This may limit the generalizability of the study, as education level could influence app acceptance and usage behavior.

Applications for Clinical Practice

With a large number of people using smartphones, there are increasing opportunities to use this delivery system to provide weight management information and support strategies for weight loss and lifestyle behavior changes. Previous studies showed that smartphone technology is a promising tool to facilitate weight management[9, 10], but the best practices for implementation of smart phones in the primary setting for weight management are still unknown. Based on this study and previous research done on technology intervention on health, providing the MFP app alone without additional counseling or intervention will not lead to clinically significant weight loss in the majority of patients. Future studies should determine if adding the MFP app is more efficacious when part of a more intensive behavioral intervention. Further studies should also determine whether PCP support in assessing readiness to use health apps and other behavior change technologies increases adherence and weight loss.

—Pich Seekaew, BS, and Melanie Jay MD, MS

Study Overview

Objective. To determine if CHA₂DS₂-VASc score, a score commonly used to assess risk of cerebrovascular events among adults with atrial fibrillation, predicts ischemic stroke, thromboembolism, and death in a cohort of patients with heart failure with and without atrial fibrillation.

Design. Prospective cohort study.

Setting and participants. Patients in Denmark aged 50 years or older discharged with a primary diagnosis of incident heart failure between 1 Jan 2000 and 31 December 2012. Patients with atrial fibrillation were identified by a hospital diagnosis of atrial fibrillation or atrial flutter from 1994 onwards. The study excluded patients treated with vitamin K antagonist within 6 months prior to heart failure diagnosis and patients with a diagnosis of cancer or chronic obstructive pulmonary disease. The study utilized 3 national Danish registries: the National Patient Registry (which records all hospital admissions and diagnoses using ICD-10), the National Prescription Registry (prescription data), and the Civil Registry System (demographics and vital statistics). The registries were linked and have been well validated.

Main outcome measure. The primary outcome measure was defined as a hospital diagnosis of ischemic stroke or thromboembolic events, transient ischemic attack, systemic embolism, pulmonary embolism or myocardial infarction within 1 year after heart failure diagnosis. A secondary outcome measure was all-cause death at 1 year.

Analysis. Patients were risk stratified using the CHA₂DS₂-VASc score. Patients were given 1 point for congestive heart failure, hypertension, age 65 to 74 years, diabetes mellitus, vascular disease, and female sex and 2 points for age 75 years or older and previous thromboembolic events. The authors conducted a time-to-event analysis to examine the relationship between CHA₂DS₂-VASc score and the risk of ischemic stroke, thromboembolic event, and death separately among those with atrial fibrillation and without. Patients were censored if they began anticoagulation therapy during follow-up. The properties of CHA₂DS₂-VASc score in predicting the risk of outcomes were quantified using C statistics. Multiple sensitivity analyses were conducted to account for patients who had a diagnosis of atrial fibrillation shortly after diagnosis of heart failure, to include patients with chronic obstructive pulmonary disease, and split sample analysis by date of heart failure diagnosis was conducted.

Main results. A total of 42,987 patients with incident heart failure during 2000–2012 were included in the cohort, with 21.9% of these having atrial fibrillation at baseline. The median follow-up period was 1.8 years. For patients with heart failure with or without a diagnosis of atrial fibrillation, the 1-year absolute risk for all outcomes were high and increased with increasing CHA₂DS₂-VASc score. For ischemic stroke and death, absolute risks were higher among patient with heart failure and atrial fibrillation when compared with patients without atrial fibrillation. At high CHA₂DS₂-VASc score, the risk of thromboembolism was higher among patients without atrial fibrillation when compared with those with atrial fibrillation. CHA₂DS₂-VASc score predicted the end point of ischemic stroke at 1 and 5 years modestly with C statistics 0.67 and 0.69 among those without atrial fibrillation and 0.64 and 0.71 among those with atrial fibrillation. The negative predictive value for all events at 1 year was around 90% when using a cutoff score of 1 for patients without atrial fibrillation, but only around 75% at 5 years.

Conclusions. Although the CHA₂DS₂-VASc score was developed to predict ischemic stroke among patients with atrial fibrillation, it also has modest predictive accuracy when applied to patients with heart failure without atrial fibrillation. Among patients with heart failure with a high CHA₂DS₂-VASc score, the risks of all adverse outcomes were high regardless of whether concomitant atrial fibrillation was present, and the risk of thromboembolism was higher among those without atrial fibrillation than those with concomitant atrial fibrillation. Because of the modest predictive accuracy, the clinical utility of CHA₂DS₂-VASc among patients with heart failure needs to be further determined.

Commentary

Clinical prediction rules are increasingly relied upon in clinical setting to drive medical decision making, allowing clinicians to weigh risks and benefits of interventions in a concrete, evidence-based manner [1]. The CHA₂DS₂-VASc score, endorsed in guidelines for assessing risk of stroke among patients with atrial fibrillation, is widely used in clinical practice [2,3] in helping make decisions about treatment, such as use of anticoagulation. The use of the clinical prediction rule for patients with heart failure but without atrial fibrillation is a novel application of the widely used rule. The rationale is that the CHA₂DS₂-VASc score includes within it a cluster of stroke risk factors that increases risk of stroke whether atrial fibrillation is present or not and thus perhaps capture stroke risk beyond whether a patient has atrial fibrillation [4]. The authors selected a patient group with high rate of mortality—those with incident heart failure—to evaluate the hypothesis that the CHA₂DS₂-VASc score could predict stroke outcomes in heart failure patients without atrial fibrillation in a manner similar to that in atrial fibrillation populations, and that at high CHA₂DS₂-VASc scores, the risk for stroke would be comparable among heart failure patients.

What the authors found is that the scoring algorithm was able to predict stroke occurrence modestly whether or not atrial fibrillation was present, and that stroke risk was high among those at the highest scores regardless of whether patients had atrial fibrillation. These findings underscore the potential use of the scoring algorithm beyond the population with atrial fibrillation, and also highlighted the need for further research in the highest risk group of heart failure patients without atrial fibrillation to determine whether anticoagulation may reduce stroke risk in this population. Minor study limitations included the use of an administrative dataset, in which diagnosis information may be incomplete or erroneous, and the potential limited generalizability of the study, given differences in the makeup of the Danish study population compared with other populations.

Applications for Clinical Practice

The study explores the use of clinical predication rule beyond the condition for which it is developed and found that particularly in high-risk groups, risk scores still predicted adverse events, albeit modestly. For clinicians, it highlights both the utility of the risk scores and the current gap in knowledge about stroke prevention in the highest-risk group of patients without atrial fibrillation. Further studies are needed to determine if anticoagulation therapy applies to this high-risk group for stroke prevention.

 —William W. Hung, MD, MPH

Study Overview

Objective. To determine if CHA₂DS₂-VASc score, a score commonly used to assess risk of cerebrovascular events among adults with atrial fibrillation, predicts ischemic stroke, thromboembolism, and death in a cohort of patients with heart failure with and without atrial fibrillation.

Design. Prospective cohort study.

Setting and participants. Patients in Denmark aged 50 years or older discharged with a primary diagnosis of incident heart failure between 1 Jan 2000 and 31 December 2012. Patients with atrial fibrillation were identified by a hospital diagnosis of atrial fibrillation or atrial flutter from 1994 onwards. The study excluded patients treated with vitamin K antagonist within 6 months prior to heart failure diagnosis and patients with a diagnosis of cancer or chronic obstructive pulmonary disease. The study utilized 3 national Danish registries: the National Patient Registry (which records all hospital admissions and diagnoses using ICD-10), the National Prescription Registry (prescription data), and the Civil Registry System (demographics and vital statistics). The registries were linked and have been well validated.

Main outcome measure. The primary outcome measure was defined as a hospital diagnosis of ischemic stroke or thromboembolic events, transient ischemic attack, systemic embolism, pulmonary embolism or myocardial infarction within 1 year after heart failure diagnosis. A secondary outcome measure was all-cause death at 1 year.

Analysis. Patients were risk stratified using the CHA₂DS₂-VASc score. Patients were given 1 point for congestive heart failure, hypertension, age 65 to 74 years, diabetes mellitus, vascular disease, and female sex and 2 points for age 75 years or older and previous thromboembolic events. The authors conducted a time-to-event analysis to examine the relationship between CHA₂DS₂-VASc score and the risk of ischemic stroke, thromboembolic event, and death separately among those with atrial fibrillation and without. Patients were censored if they began anticoagulation therapy during follow-up. The properties of CHA₂DS₂-VASc score in predicting the risk of outcomes were quantified using C statistics. Multiple sensitivity analyses were conducted to account for patients who had a diagnosis of atrial fibrillation shortly after diagnosis of heart failure, to include patients with chronic obstructive pulmonary disease, and split sample analysis by date of heart failure diagnosis was conducted.

Main results. A total of 42,987 patients with incident heart failure during 2000–2012 were included in the cohort, with 21.9% of these having atrial fibrillation at baseline. The median follow-up period was 1.8 years. For patients with heart failure with or without a diagnosis of atrial fibrillation, the 1-year absolute risk for all outcomes were high and increased with increasing CHA₂DS₂-VASc score. For ischemic stroke and death, absolute risks were higher among patient with heart failure and atrial fibrillation when compared with patients without atrial fibrillation. At high CHA₂DS₂-VASc score, the risk of thromboembolism was higher among patients without atrial fibrillation when compared with those with atrial fibrillation. CHA₂DS₂-VASc score predicted the end point of ischemic stroke at 1 and 5 years modestly with C statistics 0.67 and 0.69 among those without atrial fibrillation and 0.64 and 0.71 among those with atrial fibrillation. The negative predictive value for all events at 1 year was around 90% when using a cutoff score of 1 for patients without atrial fibrillation, but only around 75% at 5 years.

Conclusions. Although the CHA₂DS₂-VASc score was developed to predict ischemic stroke among patients with atrial fibrillation, it also has modest predictive accuracy when applied to patients with heart failure without atrial fibrillation. Among patients with heart failure with a high CHA₂DS₂-VASc score, the risks of all adverse outcomes were high regardless of whether concomitant atrial fibrillation was present, and the risk of thromboembolism was higher among those without atrial fibrillation than those with concomitant atrial fibrillation. Because of the modest predictive accuracy, the clinical utility of CHA₂DS₂-VASc among patients with heart failure needs to be further determined.

Commentary

Clinical prediction rules are increasingly relied upon in clinical setting to drive medical decision making, allowing clinicians to weigh risks and benefits of interventions in a concrete, evidence-based manner [1]. The CHA₂DS₂-VASc score, endorsed in guidelines for assessing risk of stroke among patients with atrial fibrillation, is widely used in clinical practice [2,3] in helping make decisions about treatment, such as use of anticoagulation. The use of the clinical prediction rule for patients with heart failure but without atrial fibrillation is a novel application of the widely used rule. The rationale is that the CHA₂DS₂-VASc score includes within it a cluster of stroke risk factors that increases risk of stroke whether atrial fibrillation is present or not and thus perhaps capture stroke risk beyond whether a patient has atrial fibrillation [4]. The authors selected a patient group with high rate of mortality—those with incident heart failure—to evaluate the hypothesis that the CHA₂DS₂-VASc score could predict stroke outcomes in heart failure patients without atrial fibrillation in a manner similar to that in atrial fibrillation populations, and that at high CHA₂DS₂-VASc scores, the risk for stroke would be comparable among heart failure patients.

What the authors found is that the scoring algorithm was able to predict stroke occurrence modestly whether or not atrial fibrillation was present, and that stroke risk was high among those at the highest scores regardless of whether patients had atrial fibrillation. These findings underscore the potential use of the scoring algorithm beyond the population with atrial fibrillation, and also highlighted the need for further research in the highest risk group of heart failure patients without atrial fibrillation to determine whether anticoagulation may reduce stroke risk in this population. Minor study limitations included the use of an administrative dataset, in which diagnosis information may be incomplete or erroneous, and the potential limited generalizability of the study, given differences in the makeup of the Danish study population compared with other populations.

Applications for Clinical Practice

The study explores the use of clinical predication rule beyond the condition for which it is developed and found that particularly in high-risk groups, risk scores still predicted adverse events, albeit modestly. For clinicians, it highlights both the utility of the risk scores and the current gap in knowledge about stroke prevention in the highest-risk group of patients without atrial fibrillation. Further studies are needed to determine if anticoagulation therapy applies to this high-risk group for stroke prevention.

 —William W. Hung, MD, MPH

Study Overview

Objective. To determine if CHA₂DS₂-VASc score, a score commonly used to assess risk of cerebrovascular events among adults with atrial fibrillation, predicts ischemic stroke, thromboembolism, and death in a cohort of patients with heart failure with and without atrial fibrillation.

Design. Prospective cohort study.

Setting and participants. Patients in Denmark aged 50 years or older discharged with a primary diagnosis of incident heart failure between 1 Jan 2000 and 31 December 2012. Patients with atrial fibrillation were identified by a hospital diagnosis of atrial fibrillation or atrial flutter from 1994 onwards. The study excluded patients treated with vitamin K antagonist within 6 months prior to heart failure diagnosis and patients with a diagnosis of cancer or chronic obstructive pulmonary disease. The study utilized 3 national Danish registries: the National Patient Registry (which records all hospital admissions and diagnoses using ICD-10), the National Prescription Registry (prescription data), and the Civil Registry System (demographics and vital statistics). The registries were linked and have been well validated.

Main outcome measure. The primary outcome measure was defined as a hospital diagnosis of ischemic stroke or thromboembolic events, transient ischemic attack, systemic embolism, pulmonary embolism or myocardial infarction within 1 year after heart failure diagnosis. A secondary outcome measure was all-cause death at 1 year.

Analysis. Patients were risk stratified using the CHA₂DS₂-VASc score. Patients were given 1 point for congestive heart failure, hypertension, age 65 to 74 years, diabetes mellitus, vascular disease, and female sex and 2 points for age 75 years or older and previous thromboembolic events. The authors conducted a time-to-event analysis to examine the relationship between CHA₂DS₂-VASc score and the risk of ischemic stroke, thromboembolic event, and death separately among those with atrial fibrillation and without. Patients were censored if they began anticoagulation therapy during follow-up. The properties of CHA₂DS₂-VASc score in predicting the risk of outcomes were quantified using C statistics. Multiple sensitivity analyses were conducted to account for patients who had a diagnosis of atrial fibrillation shortly after diagnosis of heart failure, to include patients with chronic obstructive pulmonary disease, and split sample analysis by date of heart failure diagnosis was conducted.

Main results. A total of 42,987 patients with incident heart failure during 2000–2012 were included in the cohort, with 21.9% of these having atrial fibrillation at baseline. The median follow-up period was 1.8 years. For patients with heart failure with or without a diagnosis of atrial fibrillation, the 1-year absolute risk for all outcomes were high and increased with increasing CHA₂DS₂-VASc score. For ischemic stroke and death, absolute risks were higher among patient with heart failure and atrial fibrillation when compared with patients without atrial fibrillation. At high CHA₂DS₂-VASc score, the risk of thromboembolism was higher among patients without atrial fibrillation when compared with those with atrial fibrillation. CHA₂DS₂-VASc score predicted the end point of ischemic stroke at 1 and 5 years modestly with C statistics 0.67 and 0.69 among those without atrial fibrillation and 0.64 and 0.71 among those with atrial fibrillation. The negative predictive value for all events at 1 year was around 90% when using a cutoff score of 1 for patients without atrial fibrillation, but only around 75% at 5 years.

Conclusions. Although the CHA₂DS₂-VASc score was developed to predict ischemic stroke among patients with atrial fibrillation, it also has modest predictive accuracy when applied to patients with heart failure without atrial fibrillation. Among patients with heart failure with a high CHA₂DS₂-VASc score, the risks of all adverse outcomes were high regardless of whether concomitant atrial fibrillation was present, and the risk of thromboembolism was higher among those without atrial fibrillation than those with concomitant atrial fibrillation. Because of the modest predictive accuracy, the clinical utility of CHA₂DS₂-VASc among patients with heart failure needs to be further determined.

Commentary

Clinical prediction rules are increasingly relied upon in clinical setting to drive medical decision making, allowing clinicians to weigh risks and benefits of interventions in a concrete, evidence-based manner [1]. The CHA₂DS₂-VASc score, endorsed in guidelines for assessing risk of stroke among patients with atrial fibrillation, is widely used in clinical practice [2,3] in helping make decisions about treatment, such as use of anticoagulation. The use of the clinical prediction rule for patients with heart failure but without atrial fibrillation is a novel application of the widely used rule. The rationale is that the CHA₂DS₂-VASc score includes within it a cluster of stroke risk factors that increases risk of stroke whether atrial fibrillation is present or not and thus perhaps capture stroke risk beyond whether a patient has atrial fibrillation [4]. The authors selected a patient group with high rate of mortality—those with incident heart failure—to evaluate the hypothesis that the CHA₂DS₂-VASc score could predict stroke outcomes in heart failure patients without atrial fibrillation in a manner similar to that in atrial fibrillation populations, and that at high CHA₂DS₂-VASc scores, the risk for stroke would be comparable among heart failure patients.

What the authors found is that the scoring algorithm was able to predict stroke occurrence modestly whether or not atrial fibrillation was present, and that stroke risk was high among those at the highest scores regardless of whether patients had atrial fibrillation. These findings underscore the potential use of the scoring algorithm beyond the population with atrial fibrillation, and also highlighted the need for further research in the highest risk group of heart failure patients without atrial fibrillation to determine whether anticoagulation may reduce stroke risk in this population. Minor study limitations included the use of an administrative dataset, in which diagnosis information may be incomplete or erroneous, and the potential limited generalizability of the study, given differences in the makeup of the Danish study population compared with other populations.

Applications for Clinical Practice

The study explores the use of clinical predication rule beyond the condition for which it is developed and found that particularly in high-risk groups, risk scores still predicted adverse events, albeit modestly. For clinicians, it highlights both the utility of the risk scores and the current gap in knowledge about stroke prevention in the highest-risk group of patients without atrial fibrillation. Further studies are needed to determine if anticoagulation therapy applies to this high-risk group for stroke prevention.

 —William W. Hung, MD, MPH