Federal Practitioner

A burst sprinkler pipe and broken steam system caused significant infrastructure failures and wreaked havoc on patient care at Walter Reed National Military Medical Center in January. 

An email sent to Walter Reed staff from the medical center’s director, Navy Capt. Melissa C. Austin, said 60,000 gallons of water, or enough “to fill a 25x50 foot swimming pool” flooded throughout the facility on Jan. 20 before it was contained, damaging 50 rooms and 6 elevators. 

Frozen pipes burst due to extreme cold, and the issues were exacerbated by aging infrastructure and “deferred maintenance due to underfunding,” the Defense Health Agency (DHA), which oversees Walter Reed, said in a public statement. 

The damage was severe enough to impact patient care. The facility had to evacuate the neonatal intensive care unit as well as several clinics. The steam system outages also meant operating rooms had fewer clean surgical tools available and had to send them to regional hospitals for sterilization, staffers told The Washington Post. Health care workers could not “flash sterilize” equipment in emergencies, further risking patient safety.

Rick McNamara, a spokesperson for the Defense Health Network National Capital Region, confirmed other hospitals are “sharing the burden” to sterilize equipment. McNamara said it could take 6 weeks to complete the immediate repairs, which will cost between $1 million and $2 million.

Patient appointments were delayed, and nonemergency procedures were canceled or delayed. Overall, 212 patients were “deferred or rescheduled,” and 56 other patients were sent to other hospitals to receive care.

Defense Secretary Pete Hegseth said on Jan. 31 the problem was “real and unacceptable” in response to a video circulating on social media that showed flooding.

Acknowledging that the water damage “temporarily impacted health care operations,” the Defense Department says DHA and Walter Reed staff were “working diligently around the clock” to find and implement solutions while minimizing disruptions to patient care: “High waters and loss of steam pressure impacted the capacity of services delivered, but the ability to deliver the hospital’s core capabilities of safe, quality care was never compromised,” the agency said.

In response to the flooding, the hospital moved quickly to provide the required urgent care: “We are utilizing all the hospitals and clinics in the National Capital Region Network from Malcom Grow at Joint Base Andrews to Kimbrough Ambulatory Care Center at Fort Meade to the Alexander T. Augusta Military Medical Center at Fort Belvoir,” Capt. Austin said. 

DHA is also funding emergency work orders and contract modifications required to return Walter Reed to full operational capability. It is prioritizing resources for repairs and is collaborating with the Naval Installations Command and Naval Support Activity Bethesda to implement necessary repairs.

“This acute issue is being managed aggressively to ensure patient care continues to be delivered safely,” DHA said

A burst sprinkler pipe and broken steam system caused significant infrastructure failures and wreaked havoc on patient care at Walter Reed National Military Medical Center in January. 

An email sent to Walter Reed staff from the medical center’s director, Navy Capt. Melissa C. Austin, said 60,000 gallons of water, or enough “to fill a 25x50 foot swimming pool” flooded throughout the facility on Jan. 20 before it was contained, damaging 50 rooms and 6 elevators. 

Frozen pipes burst due to extreme cold, and the issues were exacerbated by aging infrastructure and “deferred maintenance due to underfunding,” the Defense Health Agency (DHA), which oversees Walter Reed, said in a public statement. 

The damage was severe enough to impact patient care. The facility had to evacuate the neonatal intensive care unit as well as several clinics. The steam system outages also meant operating rooms had fewer clean surgical tools available and had to send them to regional hospitals for sterilization, staffers told The Washington Post. Health care workers could not “flash sterilize” equipment in emergencies, further risking patient safety.

Rick McNamara, a spokesperson for the Defense Health Network National Capital Region, confirmed other hospitals are “sharing the burden” to sterilize equipment. McNamara said it could take 6 weeks to complete the immediate repairs, which will cost between $1 million and $2 million.

Patient appointments were delayed, and nonemergency procedures were canceled or delayed. Overall, 212 patients were “deferred or rescheduled,” and 56 other patients were sent to other hospitals to receive care.

Defense Secretary Pete Hegseth said on Jan. 31 the problem was “real and unacceptable” in response to a video circulating on social media that showed flooding.

Acknowledging that the water damage “temporarily impacted health care operations,” the Defense Department says DHA and Walter Reed staff were “working diligently around the clock” to find and implement solutions while minimizing disruptions to patient care: “High waters and loss of steam pressure impacted the capacity of services delivered, but the ability to deliver the hospital’s core capabilities of safe, quality care was never compromised,” the agency said.

In response to the flooding, the hospital moved quickly to provide the required urgent care: “We are utilizing all the hospitals and clinics in the National Capital Region Network from Malcom Grow at Joint Base Andrews to Kimbrough Ambulatory Care Center at Fort Meade to the Alexander T. Augusta Military Medical Center at Fort Belvoir,” Capt. Austin said. 

DHA is also funding emergency work orders and contract modifications required to return Walter Reed to full operational capability. It is prioritizing resources for repairs and is collaborating with the Naval Installations Command and Naval Support Activity Bethesda to implement necessary repairs.

“This acute issue is being managed aggressively to ensure patient care continues to be delivered safely,” DHA said

A burst sprinkler pipe and broken steam system caused significant infrastructure failures and wreaked havoc on patient care at Walter Reed National Military Medical Center in January. 

An email sent to Walter Reed staff from the medical center’s director, Navy Capt. Melissa C. Austin, said 60,000 gallons of water, or enough “to fill a 25x50 foot swimming pool” flooded throughout the facility on Jan. 20 before it was contained, damaging 50 rooms and 6 elevators. 

Frozen pipes burst due to extreme cold, and the issues were exacerbated by aging infrastructure and “deferred maintenance due to underfunding,” the Defense Health Agency (DHA), which oversees Walter Reed, said in a public statement. 

The damage was severe enough to impact patient care. The facility had to evacuate the neonatal intensive care unit as well as several clinics. The steam system outages also meant operating rooms had fewer clean surgical tools available and had to send them to regional hospitals for sterilization, staffers told The Washington Post. Health care workers could not “flash sterilize” equipment in emergencies, further risking patient safety.

Rick McNamara, a spokesperson for the Defense Health Network National Capital Region, confirmed other hospitals are “sharing the burden” to sterilize equipment. McNamara said it could take 6 weeks to complete the immediate repairs, which will cost between $1 million and $2 million.

Patient appointments were delayed, and nonemergency procedures were canceled or delayed. Overall, 212 patients were “deferred or rescheduled,” and 56 other patients were sent to other hospitals to receive care.

Defense Secretary Pete Hegseth said on Jan. 31 the problem was “real and unacceptable” in response to a video circulating on social media that showed flooding.

Acknowledging that the water damage “temporarily impacted health care operations,” the Defense Department says DHA and Walter Reed staff were “working diligently around the clock” to find and implement solutions while minimizing disruptions to patient care: “High waters and loss of steam pressure impacted the capacity of services delivered, but the ability to deliver the hospital’s core capabilities of safe, quality care was never compromised,” the agency said.

In response to the flooding, the hospital moved quickly to provide the required urgent care: “We are utilizing all the hospitals and clinics in the National Capital Region Network from Malcom Grow at Joint Base Andrews to Kimbrough Ambulatory Care Center at Fort Meade to the Alexander T. Augusta Military Medical Center at Fort Belvoir,” Capt. Austin said. 

DHA is also funding emergency work orders and contract modifications required to return Walter Reed to full operational capability. It is prioritizing resources for repairs and is collaborating with the Naval Installations Command and Naval Support Activity Bethesda to implement necessary repairs.

“This acute issue is being managed aggressively to ensure patient care continues to be delivered safely,” DHA said

The risk of detecting colorectal cancer (CRC) increases by up to 13-fold in the presence of prior fecal hemoglobin (f-Hb) concentrations in fecal immunochemical tests (FIT), especially negative ones, according to a large international dose-response meta-analysis.

Although the association with neoplasia decreased as f-Hb levels rose, the findings support the development of risk-stratified screening strategies based on these concentrations, according to researchers led by Danica M.N. van den Berg, MSc, a PhD candidate and econometrics researcher in the department of public health at Erasmus University Medical Center in Rotterdam, the Netherlands.

Higher f-Hb concentrations in prior negative screening tests are strongly associated with an increased risk of detecting colorectal neoplasia in subsequent screenings, van den Berg said in an interview. “Gastroenterologists and other clinicians should consider the value of f-Hb concentrations in refining screening protocols and personalizing patient care to detect colorectal neoplasia earlier and more accurately.”

Published in Gastroenterology, the study was prompted by prior research showing individuals with f-Hb concentrations just below the positivity cutoff had an elevated CRC risk vs those with low or no f-Hb. “However, global variations in FIT positivity cutoffs and f-Hb category definitions complicated cross-study comparisons,” van den Berg said. 

Given the lack of an established dose-response relationship, the study aimed to clarify how f-Hb levels in previous screenings correlate with colorectal neoplasia detection. “Understanding this relationship is crucial for developing risk-stratified colorectal cancer screening strategies based on prior FIT results, which could improve the harm-benefit balance of screening,” she said.

According to van den Berg, f-Hb concentrations could help determine optimal CRC screening intervals by identifying higher-risk individuals who could benefit from more frequent testing, while those with lower concentrations could be screened less frequently.

 

Study Details

The systematic review and meta-analysis are the first to focus on the dose-response relationship between f-Hb levels in prior FIT screenings and colorectal neoplasia detection, van den Berg said. It included 13 ethnically diverse studies published during 2011-2023 with 4,493,223 individuals from Spain, France, the Netherlands, Taiwan, Denmark, Scotland, Ireland, Korea, Italy, and Norway. Most studies were cohort-based, and one was a randomized controlled trial.

All studies demonstrated a positive association between f-Hb in previous screenings and colorectal neoplasia detection. Almost all reported the f-Hb concentration measured in the prior screening round, while one study combined the f-Hb concentration of two previous screening rounds by using the cumulative f-Hb value. There was, however, wide variability in the stool positivity cut-offs in the included studies, ranging from 10 μg f-Hb/g to 80 μg f-Hb/g.

With an overall effect size of 0.69 (95% CI, 0.59-0.79), pooled analysis revealed that in the next screening round, individuals with f-Hb concentrations in stool of 5, 10, 20, and 40 μg/g had a threefold, fivefold, eightfold, and 13-fold higher risk for colorectal neoplasia, respectively, vs individuals showing 0 μg/g. Although there was significant study heterogeneity (I² = 97.5%, P < .001), sensitivity analyses confirmed the consistency of findings. Interestingly, subgroup analyses indicated that f-Hb concentrations from a previous negative test were especially predictive of advanced neoplasia in subsequent screenings.

“This is a strategy worth pursuing and evaluating in the United States,” said gastroenterologist Theodore R. Levin, MD, a research scientist at Kaiser Permanente Division of Research in Northern California, commenting on the study but not involved in it. “However, there is no currently available FIT brand in the US that reports f-Hb concentration. All FITs in the United States report as a qualitative positive-negative result.”

The Dutch investigation aligns with prior studies demonstrating a positive association between f-Hb concentrations in previous screenings and the detection of colorectal neoplasia. “Our working hypothesis was that risk increases in a decreasing manner as f-Hb concentrations rise, and the findings supported this hypothesis,” van den Berg said.

Other research has projected f-Hb level risk stratification to be effective and perhaps cost-effective in reducing delayed diagnosis of CRC.

 

Feasibility of Implementation

In large national screening programs in Europe, Asia, and Australia, as well as those of Kaiser Permanente and the Veterans Health Administration in the United States, information on f-Hb concentrations is already available.

“Therefore, incorporating an Hb-based approach should be relatively easy and affordable,” van den Berg said, and may help to optimize resource use while maintaining high detection rates. “However, the more critical question is whether such an approach would be acceptable to the target population.” To that end, randomized controlled trials in Italy and the Netherlands are offering tailored invitation intervals based on prior f-Hb concentrations and may provide insight into the real-world application of risk-stratified screening.

Among the many variables to be considered in the context of population-wide screening are cost-effectiveness, acceptability, and practicality, as well as invitation intervals, positivity cut-off levels, and start and stop ages for screening. “A key focus will be understanding the acceptability of risk-stratified colorectal cancer screening based on f-Hb among the target population and addressing any information needs they may have, as these are critical factors for successful implementation,” said van den Berg. Her group is currently studying the most effective and cost-effective risk-based strategy for CRC screening based on f-Hb levels.

The authors cautioned that since individuals with undetectable f-Hb levels make up the majority of those with negative FIT results, care must be taken that reducing screening frequency for this low-risk group does not lead to unfavorable outcomes at the population level.

This study was funded by the Dutch Organization for Scientific Research, which had no role in study design, data collection, analysis, interpretation, or writing. The authors declared no competing interests. Levin disclosed no competing interests relevant to his comments.

A version of this article first appeared on Medscape.com. 

The risk of detecting colorectal cancer (CRC) increases by up to 13-fold in the presence of prior fecal hemoglobin (f-Hb) concentrations in fecal immunochemical tests (FIT), especially negative ones, according to a large international dose-response meta-analysis.

Although the association with neoplasia decreased as f-Hb levels rose, the findings support the development of risk-stratified screening strategies based on these concentrations, according to researchers led by Danica M.N. van den Berg, MSc, a PhD candidate and econometrics researcher in the department of public health at Erasmus University Medical Center in Rotterdam, the Netherlands.

Higher f-Hb concentrations in prior negative screening tests are strongly associated with an increased risk of detecting colorectal neoplasia in subsequent screenings, van den Berg said in an interview. “Gastroenterologists and other clinicians should consider the value of f-Hb concentrations in refining screening protocols and personalizing patient care to detect colorectal neoplasia earlier and more accurately.”

Published in Gastroenterology, the study was prompted by prior research showing individuals with f-Hb concentrations just below the positivity cutoff had an elevated CRC risk vs those with low or no f-Hb. “However, global variations in FIT positivity cutoffs and f-Hb category definitions complicated cross-study comparisons,” van den Berg said. 

Given the lack of an established dose-response relationship, the study aimed to clarify how f-Hb levels in previous screenings correlate with colorectal neoplasia detection. “Understanding this relationship is crucial for developing risk-stratified colorectal cancer screening strategies based on prior FIT results, which could improve the harm-benefit balance of screening,” she said.

According to van den Berg, f-Hb concentrations could help determine optimal CRC screening intervals by identifying higher-risk individuals who could benefit from more frequent testing, while those with lower concentrations could be screened less frequently.

 

Study Details

The systematic review and meta-analysis are the first to focus on the dose-response relationship between f-Hb levels in prior FIT screenings and colorectal neoplasia detection, van den Berg said. It included 13 ethnically diverse studies published during 2011-2023 with 4,493,223 individuals from Spain, France, the Netherlands, Taiwan, Denmark, Scotland, Ireland, Korea, Italy, and Norway. Most studies were cohort-based, and one was a randomized controlled trial.

All studies demonstrated a positive association between f-Hb in previous screenings and colorectal neoplasia detection. Almost all reported the f-Hb concentration measured in the prior screening round, while one study combined the f-Hb concentration of two previous screening rounds by using the cumulative f-Hb value. There was, however, wide variability in the stool positivity cut-offs in the included studies, ranging from 10 μg f-Hb/g to 80 μg f-Hb/g.

With an overall effect size of 0.69 (95% CI, 0.59-0.79), pooled analysis revealed that in the next screening round, individuals with f-Hb concentrations in stool of 5, 10, 20, and 40 μg/g had a threefold, fivefold, eightfold, and 13-fold higher risk for colorectal neoplasia, respectively, vs individuals showing 0 μg/g. Although there was significant study heterogeneity (I² = 97.5%, P < .001), sensitivity analyses confirmed the consistency of findings. Interestingly, subgroup analyses indicated that f-Hb concentrations from a previous negative test were especially predictive of advanced neoplasia in subsequent screenings.

“This is a strategy worth pursuing and evaluating in the United States,” said gastroenterologist Theodore R. Levin, MD, a research scientist at Kaiser Permanente Division of Research in Northern California, commenting on the study but not involved in it. “However, there is no currently available FIT brand in the US that reports f-Hb concentration. All FITs in the United States report as a qualitative positive-negative result.”

The Dutch investigation aligns with prior studies demonstrating a positive association between f-Hb concentrations in previous screenings and the detection of colorectal neoplasia. “Our working hypothesis was that risk increases in a decreasing manner as f-Hb concentrations rise, and the findings supported this hypothesis,” van den Berg said.

Other research has projected f-Hb level risk stratification to be effective and perhaps cost-effective in reducing delayed diagnosis of CRC.

 

Feasibility of Implementation

In large national screening programs in Europe, Asia, and Australia, as well as those of Kaiser Permanente and the Veterans Health Administration in the United States, information on f-Hb concentrations is already available.

“Therefore, incorporating an Hb-based approach should be relatively easy and affordable,” van den Berg said, and may help to optimize resource use while maintaining high detection rates. “However, the more critical question is whether such an approach would be acceptable to the target population.” To that end, randomized controlled trials in Italy and the Netherlands are offering tailored invitation intervals based on prior f-Hb concentrations and may provide insight into the real-world application of risk-stratified screening.

Among the many variables to be considered in the context of population-wide screening are cost-effectiveness, acceptability, and practicality, as well as invitation intervals, positivity cut-off levels, and start and stop ages for screening. “A key focus will be understanding the acceptability of risk-stratified colorectal cancer screening based on f-Hb among the target population and addressing any information needs they may have, as these are critical factors for successful implementation,” said van den Berg. Her group is currently studying the most effective and cost-effective risk-based strategy for CRC screening based on f-Hb levels.

The authors cautioned that since individuals with undetectable f-Hb levels make up the majority of those with negative FIT results, care must be taken that reducing screening frequency for this low-risk group does not lead to unfavorable outcomes at the population level.

This study was funded by the Dutch Organization for Scientific Research, which had no role in study design, data collection, analysis, interpretation, or writing. The authors declared no competing interests. Levin disclosed no competing interests relevant to his comments.

A version of this article first appeared on Medscape.com. 

The risk of detecting colorectal cancer (CRC) increases by up to 13-fold in the presence of prior fecal hemoglobin (f-Hb) concentrations in fecal immunochemical tests (FIT), especially negative ones, according to a large international dose-response meta-analysis.

Although the association with neoplasia decreased as f-Hb levels rose, the findings support the development of risk-stratified screening strategies based on these concentrations, according to researchers led by Danica M.N. van den Berg, MSc, a PhD candidate and econometrics researcher in the department of public health at Erasmus University Medical Center in Rotterdam, the Netherlands.

Higher f-Hb concentrations in prior negative screening tests are strongly associated with an increased risk of detecting colorectal neoplasia in subsequent screenings, van den Berg said in an interview. “Gastroenterologists and other clinicians should consider the value of f-Hb concentrations in refining screening protocols and personalizing patient care to detect colorectal neoplasia earlier and more accurately.”

Published in Gastroenterology, the study was prompted by prior research showing individuals with f-Hb concentrations just below the positivity cutoff had an elevated CRC risk vs those with low or no f-Hb. “However, global variations in FIT positivity cutoffs and f-Hb category definitions complicated cross-study comparisons,” van den Berg said. 

Given the lack of an established dose-response relationship, the study aimed to clarify how f-Hb levels in previous screenings correlate with colorectal neoplasia detection. “Understanding this relationship is crucial for developing risk-stratified colorectal cancer screening strategies based on prior FIT results, which could improve the harm-benefit balance of screening,” she said.

According to van den Berg, f-Hb concentrations could help determine optimal CRC screening intervals by identifying higher-risk individuals who could benefit from more frequent testing, while those with lower concentrations could be screened less frequently.

 

Study Details

The systematic review and meta-analysis are the first to focus on the dose-response relationship between f-Hb levels in prior FIT screenings and colorectal neoplasia detection, van den Berg said. It included 13 ethnically diverse studies published during 2011-2023 with 4,493,223 individuals from Spain, France, the Netherlands, Taiwan, Denmark, Scotland, Ireland, Korea, Italy, and Norway. Most studies were cohort-based, and one was a randomized controlled trial.

All studies demonstrated a positive association between f-Hb in previous screenings and colorectal neoplasia detection. Almost all reported the f-Hb concentration measured in the prior screening round, while one study combined the f-Hb concentration of two previous screening rounds by using the cumulative f-Hb value. There was, however, wide variability in the stool positivity cut-offs in the included studies, ranging from 10 μg f-Hb/g to 80 μg f-Hb/g.

With an overall effect size of 0.69 (95% CI, 0.59-0.79), pooled analysis revealed that in the next screening round, individuals with f-Hb concentrations in stool of 5, 10, 20, and 40 μg/g had a threefold, fivefold, eightfold, and 13-fold higher risk for colorectal neoplasia, respectively, vs individuals showing 0 μg/g. Although there was significant study heterogeneity (I² = 97.5%, P < .001), sensitivity analyses confirmed the consistency of findings. Interestingly, subgroup analyses indicated that f-Hb concentrations from a previous negative test were especially predictive of advanced neoplasia in subsequent screenings.

“This is a strategy worth pursuing and evaluating in the United States,” said gastroenterologist Theodore R. Levin, MD, a research scientist at Kaiser Permanente Division of Research in Northern California, commenting on the study but not involved in it. “However, there is no currently available FIT brand in the US that reports f-Hb concentration. All FITs in the United States report as a qualitative positive-negative result.”

The Dutch investigation aligns with prior studies demonstrating a positive association between f-Hb concentrations in previous screenings and the detection of colorectal neoplasia. “Our working hypothesis was that risk increases in a decreasing manner as f-Hb concentrations rise, and the findings supported this hypothesis,” van den Berg said.

Other research has projected f-Hb level risk stratification to be effective and perhaps cost-effective in reducing delayed diagnosis of CRC.

 

Feasibility of Implementation

In large national screening programs in Europe, Asia, and Australia, as well as those of Kaiser Permanente and the Veterans Health Administration in the United States, information on f-Hb concentrations is already available.

“Therefore, incorporating an Hb-based approach should be relatively easy and affordable,” van den Berg said, and may help to optimize resource use while maintaining high detection rates. “However, the more critical question is whether such an approach would be acceptable to the target population.” To that end, randomized controlled trials in Italy and the Netherlands are offering tailored invitation intervals based on prior f-Hb concentrations and may provide insight into the real-world application of risk-stratified screening.

Among the many variables to be considered in the context of population-wide screening are cost-effectiveness, acceptability, and practicality, as well as invitation intervals, positivity cut-off levels, and start and stop ages for screening. “A key focus will be understanding the acceptability of risk-stratified colorectal cancer screening based on f-Hb among the target population and addressing any information needs they may have, as these are critical factors for successful implementation,” said van den Berg. Her group is currently studying the most effective and cost-effective risk-based strategy for CRC screening based on f-Hb levels.

The authors cautioned that since individuals with undetectable f-Hb levels make up the majority of those with negative FIT results, care must be taken that reducing screening frequency for this low-risk group does not lead to unfavorable outcomes at the population level.

This study was funded by the Dutch Organization for Scientific Research, which had no role in study design, data collection, analysis, interpretation, or writing. The authors declared no competing interests. Levin disclosed no competing interests relevant to his comments.

A version of this article first appeared on Medscape.com. 

Quitting smoking is challenging, particularly when resources are limited. A recent study in the United States confirmed that an intensive program combining behavioral therapy and medication, linked to a lung cancer screening program, offers the highest success rate. However, its long-term success was similar to that of telephone counseling and drug therapy.

Pulmonologist and experienced smoking cessation specialist from Stuttgart, Germany, Alexander Rupp, MD, emphasized the importance of leveraging routine healthcare interactions to encourage smoking cessation. “Although every doctor-patient contact offers the opportunity to discuss the risks of smoking and the opportunities for smoking cessation, the ‘window of opportunity’ is very wide, especially during lung cancer screening,” he said.

Germany is preparing to launch a lung cancer screening program for high-risk individuals, primarily current smokers and former smokers. Following the establishment of radiation protection regulations for such a program last year, the German Federal Joint Committee is currently working on its design. The initiative could be a game-changer for smoking cessation.

Lung cancer screening has been available for smokers in the United States for some time. Paul M. Cinciripini, PhD, and colleagues from the University of Texas MD Anderson Cancer Center, Houston, examined three smoking cessation strategies with decreasing treatment intensity among screening participants.

 

Unique Opportunity

Previous studies have shown that participation in a lung cancer screening program — typically offered only to high-risk individuals — significantly increases motivation to quit smoking.

“Repeated contact with doctors, repeated CT scans, and especially the findings that require monitoring all contribute to this effect,” explained Rupp, who regularly offers smoking cessation courses.

It has long been known how smoking cessation works best. “The gold standard is a combination of behavioral therapy support and drug treatment — if there is an addiction and withdrawal symptoms occur after quitting, which is the case for the majority of smokers,” Rupp explained.

The US study reinforced what is already well known: More intensive treatment approaches lead to higher quit rates.

“We know that the more intensively we look after smokers, the higher the quit rate. This applies in both areas: The more therapy sessions we do and the more often we prescribe medication, the more likely the patients are to succeed in remaining abstinent,” Rupp said.

However, resources for intensive smoking cessation programs are limited. A database maintained by the German Cancer Research Center and the German Federal Center for Health Education lists only 455 providers of smoking cessation courses in Germany, “not all of which even work on an evidence-based basis,” Rupp emphasized. Given that there are around 16 million smokers in Germany, there is an urgent need for smoking cessation programs that are less resource-intensive.

 

Intensity Variations

The US study compared three smoking cessation strategies of varying intensities, integrating behavioral counseling and medication.

Group 1: An integrated program with eight behavioral therapy sessions and 10-12 weeks of nicotine replacement therapy or medication (bupropion or varenicline).

Group 2: Lighter version of the integrated program. It consisted of four telephone consultations, written materials, online support, and 12 weeks of nicotine replacement therapy or medication prescribed by a radiologist.

Group 3: The least intensive approach, with 12 weeks of nicotine replacement therapy alone.

Each strategy was evaluated in 210 lung cancer screening participants aged 55-64 years who smoked an average of 15-20 cigarettes per day.

After 3 months, significantly more participants in the most intensive program (Group 1, 37.1%) had quit smoking than those in the other two groups (Group 2, 27.1%; Group 3, 25.2%).

But after 6 months, the difference between Groups 1 and 2 was not significant. The quit rates were as follows: Group 1, 32.4%; Group 2, 27.6%; and Group 3, 20.5%.

“It can be concluded from these results that the intensity of smoking cessation can be reduced to a certain extent as long as the combination of behavioral counseling and medication is given,” Rupp concluded.

 

Digital Solutions

Another new possibility, which was not examined in the US study, is digital health applications.

Smoke Free is a digital health application that provides behavioral therapy support for smoking cessation and is available in both German and English. Designed to replicate structured smoking cessation programs and offers an accessible alternative for individuals seeking to quit smoking.

Rupp emphasized the potential of digital tools like Smoke Free to expand access to effective smoking cessation strategies, particularly for those unable to attend in-person programs. While traditional cessation programs are limited in availability, digital apps can increase engagement in and adherence to smoking cessation efforts.

However, the biggest hurdle is smokers’ procrastination: “If you make smokers an offer, they usually do not take action afterward because they are caught in their ambivalence about whether they should quit or not.”

 

Policy Implications

This makes smoking cessation a mandatory component of lung cancer screening in the future. “It’s about cancer, and patients are really afraid of that,” Rupp advocated.

In a position paper, the German Respiratory Society, supported by multiple medical societies, has called for smoking cessation to be integrated into lung cancer screening protocols, with full coverage of counseling and medication by health insurance.

“Smoking cessation must be a mandatory component. If a participant in the lung cancer screening does not want this, then he or she must actively object,” stressed Rupp, lead author of the position paper. Also, the costs of smoking cessation, including those of withdrawal-inhibiting medication, must be fully covered by statutory health insurance, which has not been the case to date.

“That’s the only thing that makes sense. You can’t deny an addict access to proven treatments, especially when we know that a smoker who quits spontaneously without support has a relapse rate of 95%-97%, and the medication per se increases the quit rate by a factor of two or three,” Rupp concluded.

This story was translated and adapted from Medscape’s German edition using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article appeared on Medscape.com.

Quitting smoking is challenging, particularly when resources are limited. A recent study in the United States confirmed that an intensive program combining behavioral therapy and medication, linked to a lung cancer screening program, offers the highest success rate. However, its long-term success was similar to that of telephone counseling and drug therapy.

Pulmonologist and experienced smoking cessation specialist from Stuttgart, Germany, Alexander Rupp, MD, emphasized the importance of leveraging routine healthcare interactions to encourage smoking cessation. “Although every doctor-patient contact offers the opportunity to discuss the risks of smoking and the opportunities for smoking cessation, the ‘window of opportunity’ is very wide, especially during lung cancer screening,” he said.

Germany is preparing to launch a lung cancer screening program for high-risk individuals, primarily current smokers and former smokers. Following the establishment of radiation protection regulations for such a program last year, the German Federal Joint Committee is currently working on its design. The initiative could be a game-changer for smoking cessation.

Lung cancer screening has been available for smokers in the United States for some time. Paul M. Cinciripini, PhD, and colleagues from the University of Texas MD Anderson Cancer Center, Houston, examined three smoking cessation strategies with decreasing treatment intensity among screening participants.

 

Unique Opportunity

Previous studies have shown that participation in a lung cancer screening program — typically offered only to high-risk individuals — significantly increases motivation to quit smoking.

“Repeated contact with doctors, repeated CT scans, and especially the findings that require monitoring all contribute to this effect,” explained Rupp, who regularly offers smoking cessation courses.

It has long been known how smoking cessation works best. “The gold standard is a combination of behavioral therapy support and drug treatment — if there is an addiction and withdrawal symptoms occur after quitting, which is the case for the majority of smokers,” Rupp explained.

The US study reinforced what is already well known: More intensive treatment approaches lead to higher quit rates.

“We know that the more intensively we look after smokers, the higher the quit rate. This applies in both areas: The more therapy sessions we do and the more often we prescribe medication, the more likely the patients are to succeed in remaining abstinent,” Rupp said.

However, resources for intensive smoking cessation programs are limited. A database maintained by the German Cancer Research Center and the German Federal Center for Health Education lists only 455 providers of smoking cessation courses in Germany, “not all of which even work on an evidence-based basis,” Rupp emphasized. Given that there are around 16 million smokers in Germany, there is an urgent need for smoking cessation programs that are less resource-intensive.

 

Intensity Variations

The US study compared three smoking cessation strategies of varying intensities, integrating behavioral counseling and medication.

Group 1: An integrated program with eight behavioral therapy sessions and 10-12 weeks of nicotine replacement therapy or medication (bupropion or varenicline).

Group 2: Lighter version of the integrated program. It consisted of four telephone consultations, written materials, online support, and 12 weeks of nicotine replacement therapy or medication prescribed by a radiologist.

Group 3: The least intensive approach, with 12 weeks of nicotine replacement therapy alone.

Each strategy was evaluated in 210 lung cancer screening participants aged 55-64 years who smoked an average of 15-20 cigarettes per day.

After 3 months, significantly more participants in the most intensive program (Group 1, 37.1%) had quit smoking than those in the other two groups (Group 2, 27.1%; Group 3, 25.2%).

But after 6 months, the difference between Groups 1 and 2 was not significant. The quit rates were as follows: Group 1, 32.4%; Group 2, 27.6%; and Group 3, 20.5%.

“It can be concluded from these results that the intensity of smoking cessation can be reduced to a certain extent as long as the combination of behavioral counseling and medication is given,” Rupp concluded.

 

Digital Solutions

Another new possibility, which was not examined in the US study, is digital health applications.

Smoke Free is a digital health application that provides behavioral therapy support for smoking cessation and is available in both German and English. Designed to replicate structured smoking cessation programs and offers an accessible alternative for individuals seeking to quit smoking.

Rupp emphasized the potential of digital tools like Smoke Free to expand access to effective smoking cessation strategies, particularly for those unable to attend in-person programs. While traditional cessation programs are limited in availability, digital apps can increase engagement in and adherence to smoking cessation efforts.

However, the biggest hurdle is smokers’ procrastination: “If you make smokers an offer, they usually do not take action afterward because they are caught in their ambivalence about whether they should quit or not.”

 

Policy Implications

This makes smoking cessation a mandatory component of lung cancer screening in the future. “It’s about cancer, and patients are really afraid of that,” Rupp advocated.

In a position paper, the German Respiratory Society, supported by multiple medical societies, has called for smoking cessation to be integrated into lung cancer screening protocols, with full coverage of counseling and medication by health insurance.

“Smoking cessation must be a mandatory component. If a participant in the lung cancer screening does not want this, then he or she must actively object,” stressed Rupp, lead author of the position paper. Also, the costs of smoking cessation, including those of withdrawal-inhibiting medication, must be fully covered by statutory health insurance, which has not been the case to date.

“That’s the only thing that makes sense. You can’t deny an addict access to proven treatments, especially when we know that a smoker who quits spontaneously without support has a relapse rate of 95%-97%, and the medication per se increases the quit rate by a factor of two or three,” Rupp concluded.

This story was translated and adapted from Medscape’s German edition using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article appeared on Medscape.com.

Quitting smoking is challenging, particularly when resources are limited. A recent study in the United States confirmed that an intensive program combining behavioral therapy and medication, linked to a lung cancer screening program, offers the highest success rate. However, its long-term success was similar to that of telephone counseling and drug therapy.

Pulmonologist and experienced smoking cessation specialist from Stuttgart, Germany, Alexander Rupp, MD, emphasized the importance of leveraging routine healthcare interactions to encourage smoking cessation. “Although every doctor-patient contact offers the opportunity to discuss the risks of smoking and the opportunities for smoking cessation, the ‘window of opportunity’ is very wide, especially during lung cancer screening,” he said.

Germany is preparing to launch a lung cancer screening program for high-risk individuals, primarily current smokers and former smokers. Following the establishment of radiation protection regulations for such a program last year, the German Federal Joint Committee is currently working on its design. The initiative could be a game-changer for smoking cessation.

Lung cancer screening has been available for smokers in the United States for some time. Paul M. Cinciripini, PhD, and colleagues from the University of Texas MD Anderson Cancer Center, Houston, examined three smoking cessation strategies with decreasing treatment intensity among screening participants.

 

Unique Opportunity

Previous studies have shown that participation in a lung cancer screening program — typically offered only to high-risk individuals — significantly increases motivation to quit smoking.

“Repeated contact with doctors, repeated CT scans, and especially the findings that require monitoring all contribute to this effect,” explained Rupp, who regularly offers smoking cessation courses.

It has long been known how smoking cessation works best. “The gold standard is a combination of behavioral therapy support and drug treatment — if there is an addiction and withdrawal symptoms occur after quitting, which is the case for the majority of smokers,” Rupp explained.

The US study reinforced what is already well known: More intensive treatment approaches lead to higher quit rates.

“We know that the more intensively we look after smokers, the higher the quit rate. This applies in both areas: The more therapy sessions we do and the more often we prescribe medication, the more likely the patients are to succeed in remaining abstinent,” Rupp said.

However, resources for intensive smoking cessation programs are limited. A database maintained by the German Cancer Research Center and the German Federal Center for Health Education lists only 455 providers of smoking cessation courses in Germany, “not all of which even work on an evidence-based basis,” Rupp emphasized. Given that there are around 16 million smokers in Germany, there is an urgent need for smoking cessation programs that are less resource-intensive.

 

Intensity Variations

The US study compared three smoking cessation strategies of varying intensities, integrating behavioral counseling and medication.

Group 1: An integrated program with eight behavioral therapy sessions and 10-12 weeks of nicotine replacement therapy or medication (bupropion or varenicline).

Group 2: Lighter version of the integrated program. It consisted of four telephone consultations, written materials, online support, and 12 weeks of nicotine replacement therapy or medication prescribed by a radiologist.

Group 3: The least intensive approach, with 12 weeks of nicotine replacement therapy alone.

Each strategy was evaluated in 210 lung cancer screening participants aged 55-64 years who smoked an average of 15-20 cigarettes per day.

After 3 months, significantly more participants in the most intensive program (Group 1, 37.1%) had quit smoking than those in the other two groups (Group 2, 27.1%; Group 3, 25.2%).

But after 6 months, the difference between Groups 1 and 2 was not significant. The quit rates were as follows: Group 1, 32.4%; Group 2, 27.6%; and Group 3, 20.5%.

“It can be concluded from these results that the intensity of smoking cessation can be reduced to a certain extent as long as the combination of behavioral counseling and medication is given,” Rupp concluded.

 

Digital Solutions

Another new possibility, which was not examined in the US study, is digital health applications.

Smoke Free is a digital health application that provides behavioral therapy support for smoking cessation and is available in both German and English. Designed to replicate structured smoking cessation programs and offers an accessible alternative for individuals seeking to quit smoking.

Rupp emphasized the potential of digital tools like Smoke Free to expand access to effective smoking cessation strategies, particularly for those unable to attend in-person programs. While traditional cessation programs are limited in availability, digital apps can increase engagement in and adherence to smoking cessation efforts.

However, the biggest hurdle is smokers’ procrastination: “If you make smokers an offer, they usually do not take action afterward because they are caught in their ambivalence about whether they should quit or not.”

 

Policy Implications

This makes smoking cessation a mandatory component of lung cancer screening in the future. “It’s about cancer, and patients are really afraid of that,” Rupp advocated.

In a position paper, the German Respiratory Society, supported by multiple medical societies, has called for smoking cessation to be integrated into lung cancer screening protocols, with full coverage of counseling and medication by health insurance.

“Smoking cessation must be a mandatory component. If a participant in the lung cancer screening does not want this, then he or she must actively object,” stressed Rupp, lead author of the position paper. Also, the costs of smoking cessation, including those of withdrawal-inhibiting medication, must be fully covered by statutory health insurance, which has not been the case to date.

“That’s the only thing that makes sense. You can’t deny an addict access to proven treatments, especially when we know that a smoker who quits spontaneously without support has a relapse rate of 95%-97%, and the medication per se increases the quit rate by a factor of two or three,” Rupp concluded.

This story was translated and adapted from Medscape’s German edition using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article appeared on Medscape.com.

Headache, including migraine, tension-type, trigeminal autonomic cephalalgia (TAC), and posttraumatic stress headache are significantly associated with both attempted and completed suicide, results of a large study suggested.

The risk for attempted and completed suicide was more than threefold higher for individuals with posttraumatic headache and about twofold higher for those with TAC than their counterparts without headache.

Even those with tension-type headache, one of the milder headache types, carried nearly a twofold increased risk for attempted suicide vs the comparison group with no headache.

First author Holly Elser, MD, MPH, PhD, a resident physician in the Department of Neurology at the University of Pennsylvania, Philadelphia, told Medscape Medical News that the findings were “quite striking” and underscore the importance of screening for suicide risk even in patients with mild headache.

The findings were published online on February 3 in JAMA Neurology.

Common, Disabling 

With an estimated global lifetime prevalence of 67%, headache disorders are a leading cause of productivity loss, work absences, and short-term disability.

The mechanisms linking headache disorders to suicide remain unclear for several reasons, the investigators noted.

First, the relationship between headache and psychiatric comorbidities may be complex and bidirectional, with psychiatric symptoms potentially exacerbating headache severity and frequency, the investigators noted.

Secondly, research has shown a consistent link between chronic pain and suicidality, even after adjusting for comorbid psychiatric conditions. Finally, disruptions in serotonergic pathways and increased production of inflammatory cytokines may contribute to both headache disorders and psychiatric symptoms, suggesting a shared biological basis.

The mechanisms linking headache disorders to suicide remain unclear for several reasons, the investigators noted.

First, the relationship between headache and psychiatric comorbidities may be complex and bidirectional, with psychiatric symptoms potentially exacerbating headache severity and frequency, the investigators noted.

Secondly, research has shown a consistent link between chronic pain and suicidality, even after adjusting for comorbid psychiatric conditions. Finally, disruptions in serotonergic pathways and increased production of inflammatory cytokines may contribute to both headache disorders and psychiatric symptoms, suggesting a shared biological basis.

“Patients diagnosed with headache with comorbid psychiatric symptoms may benefit in particular from comanagement with behavioral health specialists,” she added.

Not ‘Just Headaches’

In an interview with Medscape Medical News, Fred Cohen, MD, an assistant professor of medicine and neurology at the Icahn School of Medicine at Mount Sinai Hospital, New York City, shared his perspective on the findings. Cohen, who was not involved in the study agreed with Elser’s point and incorporated screening into his practice.

“As part of my routine at every new patient appointment, I conduct screenings for depression and suicide risk. If a patient responds affirmatively to any of these questions, I make sure they get connected to the mental health resources they need,” Cohen said.

At least one of his patients per week screens positive for depression, he noted.

“Primary headaches, including migraine and trigeminal autonomic cephalalgias, are a significant source of disability and suffering,” said Cohen, adding that migraine, in particular, is the leading cause of disability worldwide among women aged 18-50 years. “These conditions are often misunderstood and dismissed as ‘just headaches,’ when in reality, they are much more complex and debilitating.”

Given that depression and anxiety are common co-occurring conditions with primary headache disorders, he said, “depression screenings should be standard practice when evaluating patients with headaches.”

The study’s limitations include dependence on diagnosis codes, which are prone to misclassification, and lack of information about headache chronicity and severity, which could have affected the findings.

There was no information provided about study funding. Elser and Cohen reported no relevant financial relationships.

Headache, including migraine, tension-type, trigeminal autonomic cephalalgia (TAC), and posttraumatic stress headache are significantly associated with both attempted and completed suicide, results of a large study suggested.

The risk for attempted and completed suicide was more than threefold higher for individuals with posttraumatic headache and about twofold higher for those with TAC than their counterparts without headache.

Even those with tension-type headache, one of the milder headache types, carried nearly a twofold increased risk for attempted suicide vs the comparison group with no headache.

First author Holly Elser, MD, MPH, PhD, a resident physician in the Department of Neurology at the University of Pennsylvania, Philadelphia, told Medscape Medical News that the findings were “quite striking” and underscore the importance of screening for suicide risk even in patients with mild headache.

The findings were published online on February 3 in JAMA Neurology.

Common, Disabling 

With an estimated global lifetime prevalence of 67%, headache disorders are a leading cause of productivity loss, work absences, and short-term disability.

The mechanisms linking headache disorders to suicide remain unclear for several reasons, the investigators noted.

First, the relationship between headache and psychiatric comorbidities may be complex and bidirectional, with psychiatric symptoms potentially exacerbating headache severity and frequency, the investigators noted.

Secondly, research has shown a consistent link between chronic pain and suicidality, even after adjusting for comorbid psychiatric conditions. Finally, disruptions in serotonergic pathways and increased production of inflammatory cytokines may contribute to both headache disorders and psychiatric symptoms, suggesting a shared biological basis.

The mechanisms linking headache disorders to suicide remain unclear for several reasons, the investigators noted.

First, the relationship between headache and psychiatric comorbidities may be complex and bidirectional, with psychiatric symptoms potentially exacerbating headache severity and frequency, the investigators noted.

Secondly, research has shown a consistent link between chronic pain and suicidality, even after adjusting for comorbid psychiatric conditions. Finally, disruptions in serotonergic pathways and increased production of inflammatory cytokines may contribute to both headache disorders and psychiatric symptoms, suggesting a shared biological basis.

“Patients diagnosed with headache with comorbid psychiatric symptoms may benefit in particular from comanagement with behavioral health specialists,” she added.

Not ‘Just Headaches’

In an interview with Medscape Medical News, Fred Cohen, MD, an assistant professor of medicine and neurology at the Icahn School of Medicine at Mount Sinai Hospital, New York City, shared his perspective on the findings. Cohen, who was not involved in the study agreed with Elser’s point and incorporated screening into his practice.

“As part of my routine at every new patient appointment, I conduct screenings for depression and suicide risk. If a patient responds affirmatively to any of these questions, I make sure they get connected to the mental health resources they need,” Cohen said.

At least one of his patients per week screens positive for depression, he noted.

“Primary headaches, including migraine and trigeminal autonomic cephalalgias, are a significant source of disability and suffering,” said Cohen, adding that migraine, in particular, is the leading cause of disability worldwide among women aged 18-50 years. “These conditions are often misunderstood and dismissed as ‘just headaches,’ when in reality, they are much more complex and debilitating.”

Given that depression and anxiety are common co-occurring conditions with primary headache disorders, he said, “depression screenings should be standard practice when evaluating patients with headaches.”

The study’s limitations include dependence on diagnosis codes, which are prone to misclassification, and lack of information about headache chronicity and severity, which could have affected the findings.

There was no information provided about study funding. Elser and Cohen reported no relevant financial relationships.

Headache, including migraine, tension-type, trigeminal autonomic cephalalgia (TAC), and posttraumatic stress headache are significantly associated with both attempted and completed suicide, results of a large study suggested.

The risk for attempted and completed suicide was more than threefold higher for individuals with posttraumatic headache and about twofold higher for those with TAC than their counterparts without headache.

Even those with tension-type headache, one of the milder headache types, carried nearly a twofold increased risk for attempted suicide vs the comparison group with no headache.

First author Holly Elser, MD, MPH, PhD, a resident physician in the Department of Neurology at the University of Pennsylvania, Philadelphia, told Medscape Medical News that the findings were “quite striking” and underscore the importance of screening for suicide risk even in patients with mild headache.

The findings were published online on February 3 in JAMA Neurology.

Common, Disabling 

With an estimated global lifetime prevalence of 67%, headache disorders are a leading cause of productivity loss, work absences, and short-term disability.

The mechanisms linking headache disorders to suicide remain unclear for several reasons, the investigators noted.

First, the relationship between headache and psychiatric comorbidities may be complex and bidirectional, with psychiatric symptoms potentially exacerbating headache severity and frequency, the investigators noted.

Secondly, research has shown a consistent link between chronic pain and suicidality, even after adjusting for comorbid psychiatric conditions. Finally, disruptions in serotonergic pathways and increased production of inflammatory cytokines may contribute to both headache disorders and psychiatric symptoms, suggesting a shared biological basis.

The mechanisms linking headache disorders to suicide remain unclear for several reasons, the investigators noted.

First, the relationship between headache and psychiatric comorbidities may be complex and bidirectional, with psychiatric symptoms potentially exacerbating headache severity and frequency, the investigators noted.

Secondly, research has shown a consistent link between chronic pain and suicidality, even after adjusting for comorbid psychiatric conditions. Finally, disruptions in serotonergic pathways and increased production of inflammatory cytokines may contribute to both headache disorders and psychiatric symptoms, suggesting a shared biological basis.

“Patients diagnosed with headache with comorbid psychiatric symptoms may benefit in particular from comanagement with behavioral health specialists,” she added.

Not ‘Just Headaches’

In an interview with Medscape Medical News, Fred Cohen, MD, an assistant professor of medicine and neurology at the Icahn School of Medicine at Mount Sinai Hospital, New York City, shared his perspective on the findings. Cohen, who was not involved in the study agreed with Elser’s point and incorporated screening into his practice.

“As part of my routine at every new patient appointment, I conduct screenings for depression and suicide risk. If a patient responds affirmatively to any of these questions, I make sure they get connected to the mental health resources they need,” Cohen said.

At least one of his patients per week screens positive for depression, he noted.

“Primary headaches, including migraine and trigeminal autonomic cephalalgias, are a significant source of disability and suffering,” said Cohen, adding that migraine, in particular, is the leading cause of disability worldwide among women aged 18-50 years. “These conditions are often misunderstood and dismissed as ‘just headaches,’ when in reality, they are much more complex and debilitating.”

Given that depression and anxiety are common co-occurring conditions with primary headache disorders, he said, “depression screenings should be standard practice when evaluating patients with headaches.”

The study’s limitations include dependence on diagnosis codes, which are prone to misclassification, and lack of information about headache chronicity and severity, which could have affected the findings.

There was no information provided about study funding. Elser and Cohen reported no relevant financial relationships.

About half of US adults aged ≥ 65 years report arthritis, and of those, 44% have an arthritis-attributable activity limitation.^1,2 Arthritis is a significant health issue for veterans, with veterans reporting higher rates of disability compared with the civilian population.³

Osteoarthritis (OA) is the most common type of arthritis.⁴ Among individuals aged ≥ 40 years, the incidence of OA is nearly twice as high among veterans compared with civilians and is a leading cause of separation from military service and disability.^5,6 OA pain and disability have been shown to be associated with increases in health care and medication use, including opioids, nonsteroidal anti-inflammatory medications, and muscle relaxants.^7,8 Because OA is chronic and has no cure, safe and effective management strategies—such as exercise— are critical to minimize pain and maintain physical function.⁹

Exercise can reduce pain and disability associated with OA and is a first-line recommendation in guidelines for the treatment of knee and hip OA.⁹ Given the limited exercise and high levels of physical inactivity among veterans with OA, there is a need to identify opportunities that support veterans with OA engaging in regular exercise.

Gerofit, an outpatient clinical exercise program available at 30 Veterans Health Administration (VHA) sites, may provide an opportunity for older veterans with arthritis to engage in exercise.¹⁰ Gerofit is specifically designed for veterans aged ≥ 65 years. It is not disease-specific and supports older veterans with multiple chronic conditions, including OA. Veterans aged ≥ 65 years with a referral from a VA clinician are eligible for Gerofit. Those who are unable to perform activities of daily living; unable to independently function without assistance; have a history of unstable angina, proliferative diabetic retinopathy, oxygen dependence, volatile behavioral issues, or are unable to work successfully in a group environment/setting; experience active substance abuse, homelessness, or uncontrolled incontinence; and have open wounds that cannot be appropriately dressed are excluded from Gerofit. Exercise sessions are held 3 times per week and last from 60 to 90 minutes. Sessions are supervised by Gerofit staff and include personalized exercise prescriptions based on functional assessments. Exercise prescriptions include aerobic, resistance, and balance/flexibility components and are modified by the Gerofit program staff as needed. Gerofit adopts a functional fitness approach and includes individual progression as appropriate according to evidence-based guidelines, using the Borg ratings of perceived exertion. ¹¹ Assessments are performed at baseline, 3 months, 6 months, and annually thereafter. Clinical staff conduct all assessments, including physical function testing, and record them in a database. Assessments are reviewed with the veteran to chart progress and identify future goals or needs. Veterans perform personalized self-paced exercises in the Gerofit group setting. Exercise prescriptions are continuously modified to meet individualized needs and goals. Veterans may participate continuously with no end date.

Participation in supervised exercise is associated with improved physical function and individuals with arthritis can improve function even though their baseline functional status is lower than individuals without arthritis. ¹² In this analysis, we examine the impact of exercise on the status and location of arthritis (upper body, lower body, or both). Lower body arthritis is more common than upper body arthritis and lower extremity function is associated with increased ability to perform activities of daily living, resulting in independence among older adults.^13,14 We also include upper body strength measures to capture important functional movements such as reaching and pulling.¹⁵ Among those who participate in Gerofit, the greatest gains in physical function occur during the initial 3 months, which tend to be sustained over 12 months.¹⁶ For this reason, this study focused on the initial 3 months of the program.

Older adults with arthritis may have pain and functional limitations that exceed those of the general older adult population. Exercise programs for older adults that do not specifically target arthritis but are able to improve physical function among those with arthritis could potentially increase access to exercise for older adults living with arthritis. Therefore, the purpose of this study was to determine whether change in physical function with participation in Gerofit for 3 months varies by arthritis status, including no arthritis, any arthritis, lower body arthritis, or both upper and lower body arthritis compared with no arthritis.

Methods

This is a secondary analysis of previously collected data from 10 VHA Gerofit sites (Ann Arbor, Baltimore, Greater Los Angeles, Canandaigua, Cincinnati, Miami, Honolulu, Denver, Durham, and Pittsburgh) from 2002 to 2019. Implementation data regarding the consistency of the program delivery at Gerofit expansion sites have been previously published.¹⁶ Although the delivery of Gerofit transitioned to telehealth due to COVID-19, data for this analysis were collected from in-person exercise sessions prior to the pandemic.¹⁷ Data were collected for clinical purposes. This project was part of the Gerofit quality improvement initiative and was reviewed and approved by the Durham Institutional Review Board as quality improvement.

Participants in Gerofit who completed baseline and 3-month assessments were included to analyze the effects of exercise on physical function. At each of the time points, physical functional assessments included: (1) usual gait speed (> 10 meters [m/s], or 10- meter walk test [10MWT]); (2) lower body strength (chair stands [number completed in 30 seconds]); (3) upper body strength (number of arm curls [5-lb for females/8-lb for males] completed in 30 seconds); and (4) 6-minute walk distance [6MWD] in meters to measure aerobic endurance). These measures have been validated in older adults.^18-21 Arm curls were added to the physical function assessments after the 10MWT, chair stands, and 6MWD; therefore, fewer participants had data for this measure. Participants self-reported at baseline on 45 common medical conditions, including arthritis or rheumatism (both upper body and lower body were offered as choices). Self-reporting has been shown to be an acceptable method of identifying arthritis in adults.²²

Descriptive statistics at baseline were calculated for all participants. One-way analysis of variance and X² tests were used to determine differences in baseline characteristics across arthritis status. The primary outcomes were changes in physical function measures from baseline to 3 months by arthritis status. Arthritis status was defined as: any arthritis, which includes individuals who reported upper body arthritis, lower body arthritis, or both; and arthritis status individuals reporting either upper body arthritis, lower body arthritis, or both. Categories of arthritis for arthritis status were mutually exclusive. Two separate linear models were constructed for each of the 4 physical function measures, with change from baseline to 3 months as the outcome (dependent variable) and arthritis status, age, and body mass index (BMI) as predictors (independent variables). The first model compared any arthritis with no arthritis and the second model compared arthritis status (both upper and lower body arthritis vs lower body arthritis) with no arthritis. These models were used to obtain mean changes and 95% CIs in physical function and to test for differences in the change in physical function measures by arthritis status. Statistical analyses were performed using R software, version 4.0.3.

Results

Baseline and 3-month data were available for 737 Gerofit participants and included in the analysis. The mean (SD) age was 73.5 (7.1) years. A total of 707 participants were male (95.9%) and 322 (43.6%) reported some arthritis, with arthritis in both the upper and lower body being reported by 168 participants (52.2%) (Table 1). There were no differences in age, sex, or race for those with any arthritis compared with those with no arthritis, but BMI was significantly higher in those reporting any arthritis compared with no arthritis. For the baseline functional measures, statistically significant differences were observed between those with no arthritis and those reporting any arthritis for the 10MWT (P = .001), chair stands (P = .046), and 6MWD (P = .001), but not for arm curls (P = .77), with those with no arthritis performing better.

All 4 arthritis status groups showed improvements in each of the physical function measures over 3 months. For the 10MWT the mean change (95% CI) in gait speed (m/s) was 0.06 (0.04-0.08) for patients with no arthritis, 0.07 (0.05- 0.08) for any arthritis, 0.07 (0.04-0.11) for lower body arthritis, and 0.07 (0.04- 0.09) for both lower and upper body arthritis. For the number of arm curls in 30 seconds the mean change (95% CI) was 2.3 (1.8-2.8) for patients with no arthritis, 2.1 (1.5-2.6) for any arthritis, 2.0 (1.1-3.0) for lower body arthritis, and 1.9 (1.1-2.7) for both lower and upper body arthritis. For the number of chair stands in 30 seconds the mean change (95% CI) was 2.1 (1.7-2.4) for patients with no arthritis, 2.2 (1.8-2.6) for any arthritis, 2.3 (1.6-2.9), for lower body arthritis, and 2.0 (1.5-2.5) for both lower and upper body arthritis. For the 6MWD distance in meters the mean change (95% CI) was 21.5 (15.5-27.4) for patients with no arthritis, 28.6 (21.9-35.3) for any arthritis, 30.4 (19.5-41.3) for lower body arthritis, and 28.6 (19.2-38.0) for both lower and upper body arthritis (Figure).

We used 2 models to measure the change from baseline to 3 months for each of the arthritis groups. Model 1 compared any arthritis vs no arthritis and model 2 compared lower body arthritis and both upper and lower body arthritis vs no arthritis for each physical function measure (Table 2). There were no statistically significant differences in 3-month change in physical function for any of the physical function measures between arthritis groups after adjusting for age and BMI.

Discussion

Participation in Gerofit was associated with functional gains among all participants over 3 months, regardless of arthritis status. Older veterans reporting any arthritis had significantly lower physical function scores upon enrollment into Gerofit compared with those veterans reporting no arthritis. However, compared with individuals who reported no arthritis, individuals who reported arthritis (any arthritis, lower body arthritis only, or both lower and upper body arthritis) experienced similar improvements (ie, no statistically significant differences in mean change from baseline to follow-up among those with and without arthritis). This study suggests that progressive, multicomponent exercise programs for older adults may be beneficial for those with arthritis.

Involvement of multiple sites of arthritis is associated with moderate to severe functional limitations as well as lower healthrelated quality of life.²³ While it has been found that individuals with arthritis can improve function with supervised exercise, even though their baseline functional status is lower than individuals without arthritis, it was not clear whether individuals with multiple joint involvement also would benefit.¹² The results of this study suggest that these individuals can improve across various domains of physical function despite variation in arthritis location and status. As incidence of arthritis increases with age, targeting older adults for exercise programs such as Gerofit may improve functional limitations and health-related quality of life associated with arthritis.²

We evaluated physical function using multiple measures to assess upper (arm curls) and lower (chair stands, 10MWT) extremity physical function and aerobic endurance (6MWD). Participants in this study reached clinically meaningful changes with 3 months of participation in Gerofit for most of the physical function measures. Gerofit participants had a mean gait speed improvement of 0.05 to 0.07 m/s compared with 0.10 to 0.30 m/s, which was reported previously. ^24,25 In this study, nearly all groups achieved the clinically important improvements in the chair stand in 30 seconds (2.0 to 2.6) and the 6MWD (21.8 to 59.1 m) that have been reported in the literature.^24-26

The Osteoarthritis Research Society International recommends the chair stand and 6MWD performance-based tests for individuals with hip and knee arthritis because they align with patient-reported outcomes and represent the types of activities relevant to this population.²⁷ The findings of this study suggest that improvement in these physical function measures with participation in exercise align with data from arthritis-specific exercise programs designed for wide implementation. Hughes and colleagues reported improvements in the 6MWD after the 8-week Fit and Strong exercise intervention, which included walking and lower body resistance training.²⁸ The Arthritis Foundation’s Walk With Ease program is a 6-week walking program that has shown improvements in chair stands and gait speed.²⁹ Another Arthritis Foundation program, People with Arthritis Can Exercise, is an 8-week course consisting of a variety of resistance, aerobic, and balance activities. This program has been associated with increases in chair stands but not gait speed or 6MWD.^30,31

This study found that participation in a VHA outpatient clinical supervised exercise program results in improvements in physical function that can be realized by older adults regardless of arthritis burden. Gerofit programs typically require 1.5 to 2.0 dedicated full-time equivalent employees to run the program effectively and additional administrative support, depending on size of the program.³² The cost savings generated by the program include reductions in hospitalization rates, emergency department visits, days in hospital, and medication use and provide a compelling argument for the program’s financial viability to health care systems through long-term savings and improved health outcomes for older adults.^33-36

While evidenced-based arthritis programs exist, this study illustrates that an exercise program without a focus on arthritis also improves physical function, potentially reducing the risk of disability related to arthritis. The clinical implication for these findings is that arthritis-specific exercise programs may not be needed to achieve functional improvements in individuals with arthritis. This is critical for under-resourced or exercise- limited health care systems or communities. Therefore, if exercise programming is limited, or arthritis-specific programs and interventions are not available, nonspecific exercise programs will also be beneficial to individuals with arthritis. Thus, individuals with arthritis should be encouraged to participate in any available exercise programming to achieve improvements in physical function. In addition, many older adults have multiple comorbidities, most of which improve with participation in exercise. ³⁷ Disease-specific exercise programs can offer tailored exercises and coaching related to common barriers in participation, such as joint pain for arthritis.³¹ It is unclear whether these additional programmatic components are associated with greater improvements in outcomes, such as physical function. More research is needed to explore the benefits of disease-specific tailored exercise programs compared with general exercise programs.

Strengths and Limitations

This study demonstrated the effect of participation in a clinical, supervised exercise program in a real-world setting. It suggests that even exercise programs not specifically targeted for arthritis populations can improve physical function among those with arthritis.

As a VHA clinical supervised exercise program, Gerofit may not be generalizable to all older adults or other exercise programs. In addition, this analysis only included a veteran population that was > 95% male and may not be generalizable to other populations. Arthritis status was defined by self-report and not verified in the health record. However, this approach has been shown to be acceptable in this setting and the most common type of arthritis in this population (OA) is a painful musculoskeletal condition associated with functional limitations.^4,22,38,39 Self-reported arthritis or rheumatism is associated with functional limitations.¹ Therefore, it is unlikely that the results would differ for physician-diagnosed or radiographically defined OA. Additionally, the study did not have data on the total number of joints with arthritis or arthritis severity but rather used upper body, lower body, and both upper and lower body arthritis as a proxy for arthritis status. While our models were adjusted for age and BMI, 2 known confounding factors for the association between arthritis and physical function, there are other potential confounding factors that were not included in the models. ^40,41 Finally, this study only included individuals with completed baseline and 3-month follow-up assessments, and the individuals who participated for longer or shorter periods may have had different physical function outcomes than individuals included in this study.

Conclusions

Participation in 3 months VHA Gerofit outpatient supervised exercise programs can improve physical function for all older adults, regardless of arthritis status. These programs may increase access to exercise programming that is beneficial for common conditions affecting older adults, such as arthritis.

About half of US adults aged ≥ 65 years report arthritis, and of those, 44% have an arthritis-attributable activity limitation.^1,2 Arthritis is a significant health issue for veterans, with veterans reporting higher rates of disability compared with the civilian population.³

Osteoarthritis (OA) is the most common type of arthritis.⁴ Among individuals aged ≥ 40 years, the incidence of OA is nearly twice as high among veterans compared with civilians and is a leading cause of separation from military service and disability.^5,6 OA pain and disability have been shown to be associated with increases in health care and medication use, including opioids, nonsteroidal anti-inflammatory medications, and muscle relaxants.^7,8 Because OA is chronic and has no cure, safe and effective management strategies—such as exercise— are critical to minimize pain and maintain physical function.⁹

Exercise can reduce pain and disability associated with OA and is a first-line recommendation in guidelines for the treatment of knee and hip OA.⁹ Given the limited exercise and high levels of physical inactivity among veterans with OA, there is a need to identify opportunities that support veterans with OA engaging in regular exercise.

Gerofit, an outpatient clinical exercise program available at 30 Veterans Health Administration (VHA) sites, may provide an opportunity for older veterans with arthritis to engage in exercise.¹⁰ Gerofit is specifically designed for veterans aged ≥ 65 years. It is not disease-specific and supports older veterans with multiple chronic conditions, including OA. Veterans aged ≥ 65 years with a referral from a VA clinician are eligible for Gerofit. Those who are unable to perform activities of daily living; unable to independently function without assistance; have a history of unstable angina, proliferative diabetic retinopathy, oxygen dependence, volatile behavioral issues, or are unable to work successfully in a group environment/setting; experience active substance abuse, homelessness, or uncontrolled incontinence; and have open wounds that cannot be appropriately dressed are excluded from Gerofit. Exercise sessions are held 3 times per week and last from 60 to 90 minutes. Sessions are supervised by Gerofit staff and include personalized exercise prescriptions based on functional assessments. Exercise prescriptions include aerobic, resistance, and balance/flexibility components and are modified by the Gerofit program staff as needed. Gerofit adopts a functional fitness approach and includes individual progression as appropriate according to evidence-based guidelines, using the Borg ratings of perceived exertion. ¹¹ Assessments are performed at baseline, 3 months, 6 months, and annually thereafter. Clinical staff conduct all assessments, including physical function testing, and record them in a database. Assessments are reviewed with the veteran to chart progress and identify future goals or needs. Veterans perform personalized self-paced exercises in the Gerofit group setting. Exercise prescriptions are continuously modified to meet individualized needs and goals. Veterans may participate continuously with no end date.

Participation in supervised exercise is associated with improved physical function and individuals with arthritis can improve function even though their baseline functional status is lower than individuals without arthritis. ¹² In this analysis, we examine the impact of exercise on the status and location of arthritis (upper body, lower body, or both). Lower body arthritis is more common than upper body arthritis and lower extremity function is associated with increased ability to perform activities of daily living, resulting in independence among older adults.^13,14 We also include upper body strength measures to capture important functional movements such as reaching and pulling.¹⁵ Among those who participate in Gerofit, the greatest gains in physical function occur during the initial 3 months, which tend to be sustained over 12 months.¹⁶ For this reason, this study focused on the initial 3 months of the program.

Older adults with arthritis may have pain and functional limitations that exceed those of the general older adult population. Exercise programs for older adults that do not specifically target arthritis but are able to improve physical function among those with arthritis could potentially increase access to exercise for older adults living with arthritis. Therefore, the purpose of this study was to determine whether change in physical function with participation in Gerofit for 3 months varies by arthritis status, including no arthritis, any arthritis, lower body arthritis, or both upper and lower body arthritis compared with no arthritis.

Methods

This is a secondary analysis of previously collected data from 10 VHA Gerofit sites (Ann Arbor, Baltimore, Greater Los Angeles, Canandaigua, Cincinnati, Miami, Honolulu, Denver, Durham, and Pittsburgh) from 2002 to 2019. Implementation data regarding the consistency of the program delivery at Gerofit expansion sites have been previously published.¹⁶ Although the delivery of Gerofit transitioned to telehealth due to COVID-19, data for this analysis were collected from in-person exercise sessions prior to the pandemic.¹⁷ Data were collected for clinical purposes. This project was part of the Gerofit quality improvement initiative and was reviewed and approved by the Durham Institutional Review Board as quality improvement.

Participants in Gerofit who completed baseline and 3-month assessments were included to analyze the effects of exercise on physical function. At each of the time points, physical functional assessments included: (1) usual gait speed (> 10 meters [m/s], or 10- meter walk test [10MWT]); (2) lower body strength (chair stands [number completed in 30 seconds]); (3) upper body strength (number of arm curls [5-lb for females/8-lb for males] completed in 30 seconds); and (4) 6-minute walk distance [6MWD] in meters to measure aerobic endurance). These measures have been validated in older adults.^18-21 Arm curls were added to the physical function assessments after the 10MWT, chair stands, and 6MWD; therefore, fewer participants had data for this measure. Participants self-reported at baseline on 45 common medical conditions, including arthritis or rheumatism (both upper body and lower body were offered as choices). Self-reporting has been shown to be an acceptable method of identifying arthritis in adults.²²

Descriptive statistics at baseline were calculated for all participants. One-way analysis of variance and X² tests were used to determine differences in baseline characteristics across arthritis status. The primary outcomes were changes in physical function measures from baseline to 3 months by arthritis status. Arthritis status was defined as: any arthritis, which includes individuals who reported upper body arthritis, lower body arthritis, or both; and arthritis status individuals reporting either upper body arthritis, lower body arthritis, or both. Categories of arthritis for arthritis status were mutually exclusive. Two separate linear models were constructed for each of the 4 physical function measures, with change from baseline to 3 months as the outcome (dependent variable) and arthritis status, age, and body mass index (BMI) as predictors (independent variables). The first model compared any arthritis with no arthritis and the second model compared arthritis status (both upper and lower body arthritis vs lower body arthritis) with no arthritis. These models were used to obtain mean changes and 95% CIs in physical function and to test for differences in the change in physical function measures by arthritis status. Statistical analyses were performed using R software, version 4.0.3.

Results

Baseline and 3-month data were available for 737 Gerofit participants and included in the analysis. The mean (SD) age was 73.5 (7.1) years. A total of 707 participants were male (95.9%) and 322 (43.6%) reported some arthritis, with arthritis in both the upper and lower body being reported by 168 participants (52.2%) (Table 1). There were no differences in age, sex, or race for those with any arthritis compared with those with no arthritis, but BMI was significantly higher in those reporting any arthritis compared with no arthritis. For the baseline functional measures, statistically significant differences were observed between those with no arthritis and those reporting any arthritis for the 10MWT (P = .001), chair stands (P = .046), and 6MWD (P = .001), but not for arm curls (P = .77), with those with no arthritis performing better.

All 4 arthritis status groups showed improvements in each of the physical function measures over 3 months. For the 10MWT the mean change (95% CI) in gait speed (m/s) was 0.06 (0.04-0.08) for patients with no arthritis, 0.07 (0.05- 0.08) for any arthritis, 0.07 (0.04-0.11) for lower body arthritis, and 0.07 (0.04- 0.09) for both lower and upper body arthritis. For the number of arm curls in 30 seconds the mean change (95% CI) was 2.3 (1.8-2.8) for patients with no arthritis, 2.1 (1.5-2.6) for any arthritis, 2.0 (1.1-3.0) for lower body arthritis, and 1.9 (1.1-2.7) for both lower and upper body arthritis. For the number of chair stands in 30 seconds the mean change (95% CI) was 2.1 (1.7-2.4) for patients with no arthritis, 2.2 (1.8-2.6) for any arthritis, 2.3 (1.6-2.9), for lower body arthritis, and 2.0 (1.5-2.5) for both lower and upper body arthritis. For the 6MWD distance in meters the mean change (95% CI) was 21.5 (15.5-27.4) for patients with no arthritis, 28.6 (21.9-35.3) for any arthritis, 30.4 (19.5-41.3) for lower body arthritis, and 28.6 (19.2-38.0) for both lower and upper body arthritis (Figure).

We used 2 models to measure the change from baseline to 3 months for each of the arthritis groups. Model 1 compared any arthritis vs no arthritis and model 2 compared lower body arthritis and both upper and lower body arthritis vs no arthritis for each physical function measure (Table 2). There were no statistically significant differences in 3-month change in physical function for any of the physical function measures between arthritis groups after adjusting for age and BMI.

Discussion

Participation in Gerofit was associated with functional gains among all participants over 3 months, regardless of arthritis status. Older veterans reporting any arthritis had significantly lower physical function scores upon enrollment into Gerofit compared with those veterans reporting no arthritis. However, compared with individuals who reported no arthritis, individuals who reported arthritis (any arthritis, lower body arthritis only, or both lower and upper body arthritis) experienced similar improvements (ie, no statistically significant differences in mean change from baseline to follow-up among those with and without arthritis). This study suggests that progressive, multicomponent exercise programs for older adults may be beneficial for those with arthritis.

Involvement of multiple sites of arthritis is associated with moderate to severe functional limitations as well as lower healthrelated quality of life.²³ While it has been found that individuals with arthritis can improve function with supervised exercise, even though their baseline functional status is lower than individuals without arthritis, it was not clear whether individuals with multiple joint involvement also would benefit.¹² The results of this study suggest that these individuals can improve across various domains of physical function despite variation in arthritis location and status. As incidence of arthritis increases with age, targeting older adults for exercise programs such as Gerofit may improve functional limitations and health-related quality of life associated with arthritis.²

We evaluated physical function using multiple measures to assess upper (arm curls) and lower (chair stands, 10MWT) extremity physical function and aerobic endurance (6MWD). Participants in this study reached clinically meaningful changes with 3 months of participation in Gerofit for most of the physical function measures. Gerofit participants had a mean gait speed improvement of 0.05 to 0.07 m/s compared with 0.10 to 0.30 m/s, which was reported previously. ^24,25 In this study, nearly all groups achieved the clinically important improvements in the chair stand in 30 seconds (2.0 to 2.6) and the 6MWD (21.8 to 59.1 m) that have been reported in the literature.^24-26

The Osteoarthritis Research Society International recommends the chair stand and 6MWD performance-based tests for individuals with hip and knee arthritis because they align with patient-reported outcomes and represent the types of activities relevant to this population.²⁷ The findings of this study suggest that improvement in these physical function measures with participation in exercise align with data from arthritis-specific exercise programs designed for wide implementation. Hughes and colleagues reported improvements in the 6MWD after the 8-week Fit and Strong exercise intervention, which included walking and lower body resistance training.²⁸ The Arthritis Foundation’s Walk With Ease program is a 6-week walking program that has shown improvements in chair stands and gait speed.²⁹ Another Arthritis Foundation program, People with Arthritis Can Exercise, is an 8-week course consisting of a variety of resistance, aerobic, and balance activities. This program has been associated with increases in chair stands but not gait speed or 6MWD.^30,31

This study found that participation in a VHA outpatient clinical supervised exercise program results in improvements in physical function that can be realized by older adults regardless of arthritis burden. Gerofit programs typically require 1.5 to 2.0 dedicated full-time equivalent employees to run the program effectively and additional administrative support, depending on size of the program.³² The cost savings generated by the program include reductions in hospitalization rates, emergency department visits, days in hospital, and medication use and provide a compelling argument for the program’s financial viability to health care systems through long-term savings and improved health outcomes for older adults.^33-36

While evidenced-based arthritis programs exist, this study illustrates that an exercise program without a focus on arthritis also improves physical function, potentially reducing the risk of disability related to arthritis. The clinical implication for these findings is that arthritis-specific exercise programs may not be needed to achieve functional improvements in individuals with arthritis. This is critical for under-resourced or exercise- limited health care systems or communities. Therefore, if exercise programming is limited, or arthritis-specific programs and interventions are not available, nonspecific exercise programs will also be beneficial to individuals with arthritis. Thus, individuals with arthritis should be encouraged to participate in any available exercise programming to achieve improvements in physical function. In addition, many older adults have multiple comorbidities, most of which improve with participation in exercise. ³⁷ Disease-specific exercise programs can offer tailored exercises and coaching related to common barriers in participation, such as joint pain for arthritis.³¹ It is unclear whether these additional programmatic components are associated with greater improvements in outcomes, such as physical function. More research is needed to explore the benefits of disease-specific tailored exercise programs compared with general exercise programs.

Strengths and Limitations

This study demonstrated the effect of participation in a clinical, supervised exercise program in a real-world setting. It suggests that even exercise programs not specifically targeted for arthritis populations can improve physical function among those with arthritis.

As a VHA clinical supervised exercise program, Gerofit may not be generalizable to all older adults or other exercise programs. In addition, this analysis only included a veteran population that was > 95% male and may not be generalizable to other populations. Arthritis status was defined by self-report and not verified in the health record. However, this approach has been shown to be acceptable in this setting and the most common type of arthritis in this population (OA) is a painful musculoskeletal condition associated with functional limitations.^4,22,38,39 Self-reported arthritis or rheumatism is associated with functional limitations.¹ Therefore, it is unlikely that the results would differ for physician-diagnosed or radiographically defined OA. Additionally, the study did not have data on the total number of joints with arthritis or arthritis severity but rather used upper body, lower body, and both upper and lower body arthritis as a proxy for arthritis status. While our models were adjusted for age and BMI, 2 known confounding factors for the association between arthritis and physical function, there are other potential confounding factors that were not included in the models. ^40,41 Finally, this study only included individuals with completed baseline and 3-month follow-up assessments, and the individuals who participated for longer or shorter periods may have had different physical function outcomes than individuals included in this study.

Conclusions

Participation in 3 months VHA Gerofit outpatient supervised exercise programs can improve physical function for all older adults, regardless of arthritis status. These programs may increase access to exercise programming that is beneficial for common conditions affecting older adults, such as arthritis.

About half of US adults aged ≥ 65 years report arthritis, and of those, 44% have an arthritis-attributable activity limitation.^1,2 Arthritis is a significant health issue for veterans, with veterans reporting higher rates of disability compared with the civilian population.³

Osteoarthritis (OA) is the most common type of arthritis.⁴ Among individuals aged ≥ 40 years, the incidence of OA is nearly twice as high among veterans compared with civilians and is a leading cause of separation from military service and disability.^5,6 OA pain and disability have been shown to be associated with increases in health care and medication use, including opioids, nonsteroidal anti-inflammatory medications, and muscle relaxants.^7,8 Because OA is chronic and has no cure, safe and effective management strategies—such as exercise— are critical to minimize pain and maintain physical function.⁹

Exercise can reduce pain and disability associated with OA and is a first-line recommendation in guidelines for the treatment of knee and hip OA.⁹ Given the limited exercise and high levels of physical inactivity among veterans with OA, there is a need to identify opportunities that support veterans with OA engaging in regular exercise.

Gerofit, an outpatient clinical exercise program available at 30 Veterans Health Administration (VHA) sites, may provide an opportunity for older veterans with arthritis to engage in exercise.¹⁰ Gerofit is specifically designed for veterans aged ≥ 65 years. It is not disease-specific and supports older veterans with multiple chronic conditions, including OA. Veterans aged ≥ 65 years with a referral from a VA clinician are eligible for Gerofit. Those who are unable to perform activities of daily living; unable to independently function without assistance; have a history of unstable angina, proliferative diabetic retinopathy, oxygen dependence, volatile behavioral issues, or are unable to work successfully in a group environment/setting; experience active substance abuse, homelessness, or uncontrolled incontinence; and have open wounds that cannot be appropriately dressed are excluded from Gerofit. Exercise sessions are held 3 times per week and last from 60 to 90 minutes. Sessions are supervised by Gerofit staff and include personalized exercise prescriptions based on functional assessments. Exercise prescriptions include aerobic, resistance, and balance/flexibility components and are modified by the Gerofit program staff as needed. Gerofit adopts a functional fitness approach and includes individual progression as appropriate according to evidence-based guidelines, using the Borg ratings of perceived exertion. ¹¹ Assessments are performed at baseline, 3 months, 6 months, and annually thereafter. Clinical staff conduct all assessments, including physical function testing, and record them in a database. Assessments are reviewed with the veteran to chart progress and identify future goals or needs. Veterans perform personalized self-paced exercises in the Gerofit group setting. Exercise prescriptions are continuously modified to meet individualized needs and goals. Veterans may participate continuously with no end date.

Participation in supervised exercise is associated with improved physical function and individuals with arthritis can improve function even though their baseline functional status is lower than individuals without arthritis. ¹² In this analysis, we examine the impact of exercise on the status and location of arthritis (upper body, lower body, or both). Lower body arthritis is more common than upper body arthritis and lower extremity function is associated with increased ability to perform activities of daily living, resulting in independence among older adults.^13,14 We also include upper body strength measures to capture important functional movements such as reaching and pulling.¹⁵ Among those who participate in Gerofit, the greatest gains in physical function occur during the initial 3 months, which tend to be sustained over 12 months.¹⁶ For this reason, this study focused on the initial 3 months of the program.

Older adults with arthritis may have pain and functional limitations that exceed those of the general older adult population. Exercise programs for older adults that do not specifically target arthritis but are able to improve physical function among those with arthritis could potentially increase access to exercise for older adults living with arthritis. Therefore, the purpose of this study was to determine whether change in physical function with participation in Gerofit for 3 months varies by arthritis status, including no arthritis, any arthritis, lower body arthritis, or both upper and lower body arthritis compared with no arthritis.

Methods

This is a secondary analysis of previously collected data from 10 VHA Gerofit sites (Ann Arbor, Baltimore, Greater Los Angeles, Canandaigua, Cincinnati, Miami, Honolulu, Denver, Durham, and Pittsburgh) from 2002 to 2019. Implementation data regarding the consistency of the program delivery at Gerofit expansion sites have been previously published.¹⁶ Although the delivery of Gerofit transitioned to telehealth due to COVID-19, data for this analysis were collected from in-person exercise sessions prior to the pandemic.¹⁷ Data were collected for clinical purposes. This project was part of the Gerofit quality improvement initiative and was reviewed and approved by the Durham Institutional Review Board as quality improvement.

Participants in Gerofit who completed baseline and 3-month assessments were included to analyze the effects of exercise on physical function. At each of the time points, physical functional assessments included: (1) usual gait speed (> 10 meters [m/s], or 10- meter walk test [10MWT]); (2) lower body strength (chair stands [number completed in 30 seconds]); (3) upper body strength (number of arm curls [5-lb for females/8-lb for males] completed in 30 seconds); and (4) 6-minute walk distance [6MWD] in meters to measure aerobic endurance). These measures have been validated in older adults.^18-21 Arm curls were added to the physical function assessments after the 10MWT, chair stands, and 6MWD; therefore, fewer participants had data for this measure. Participants self-reported at baseline on 45 common medical conditions, including arthritis or rheumatism (both upper body and lower body were offered as choices). Self-reporting has been shown to be an acceptable method of identifying arthritis in adults.²²

Descriptive statistics at baseline were calculated for all participants. One-way analysis of variance and X² tests were used to determine differences in baseline characteristics across arthritis status. The primary outcomes were changes in physical function measures from baseline to 3 months by arthritis status. Arthritis status was defined as: any arthritis, which includes individuals who reported upper body arthritis, lower body arthritis, or both; and arthritis status individuals reporting either upper body arthritis, lower body arthritis, or both. Categories of arthritis for arthritis status were mutually exclusive. Two separate linear models were constructed for each of the 4 physical function measures, with change from baseline to 3 months as the outcome (dependent variable) and arthritis status, age, and body mass index (BMI) as predictors (independent variables). The first model compared any arthritis with no arthritis and the second model compared arthritis status (both upper and lower body arthritis vs lower body arthritis) with no arthritis. These models were used to obtain mean changes and 95% CIs in physical function and to test for differences in the change in physical function measures by arthritis status. Statistical analyses were performed using R software, version 4.0.3.

Results

Baseline and 3-month data were available for 737 Gerofit participants and included in the analysis. The mean (SD) age was 73.5 (7.1) years. A total of 707 participants were male (95.9%) and 322 (43.6%) reported some arthritis, with arthritis in both the upper and lower body being reported by 168 participants (52.2%) (Table 1). There were no differences in age, sex, or race for those with any arthritis compared with those with no arthritis, but BMI was significantly higher in those reporting any arthritis compared with no arthritis. For the baseline functional measures, statistically significant differences were observed between those with no arthritis and those reporting any arthritis for the 10MWT (P = .001), chair stands (P = .046), and 6MWD (P = .001), but not for arm curls (P = .77), with those with no arthritis performing better.

All 4 arthritis status groups showed improvements in each of the physical function measures over 3 months. For the 10MWT the mean change (95% CI) in gait speed (m/s) was 0.06 (0.04-0.08) for patients with no arthritis, 0.07 (0.05- 0.08) for any arthritis, 0.07 (0.04-0.11) for lower body arthritis, and 0.07 (0.04- 0.09) for both lower and upper body arthritis. For the number of arm curls in 30 seconds the mean change (95% CI) was 2.3 (1.8-2.8) for patients with no arthritis, 2.1 (1.5-2.6) for any arthritis, 2.0 (1.1-3.0) for lower body arthritis, and 1.9 (1.1-2.7) for both lower and upper body arthritis. For the number of chair stands in 30 seconds the mean change (95% CI) was 2.1 (1.7-2.4) for patients with no arthritis, 2.2 (1.8-2.6) for any arthritis, 2.3 (1.6-2.9), for lower body arthritis, and 2.0 (1.5-2.5) for both lower and upper body arthritis. For the 6MWD distance in meters the mean change (95% CI) was 21.5 (15.5-27.4) for patients with no arthritis, 28.6 (21.9-35.3) for any arthritis, 30.4 (19.5-41.3) for lower body arthritis, and 28.6 (19.2-38.0) for both lower and upper body arthritis (Figure).

We used 2 models to measure the change from baseline to 3 months for each of the arthritis groups. Model 1 compared any arthritis vs no arthritis and model 2 compared lower body arthritis and both upper and lower body arthritis vs no arthritis for each physical function measure (Table 2). There were no statistically significant differences in 3-month change in physical function for any of the physical function measures between arthritis groups after adjusting for age and BMI.

Discussion

Participation in Gerofit was associated with functional gains among all participants over 3 months, regardless of arthritis status. Older veterans reporting any arthritis had significantly lower physical function scores upon enrollment into Gerofit compared with those veterans reporting no arthritis. However, compared with individuals who reported no arthritis, individuals who reported arthritis (any arthritis, lower body arthritis only, or both lower and upper body arthritis) experienced similar improvements (ie, no statistically significant differences in mean change from baseline to follow-up among those with and without arthritis). This study suggests that progressive, multicomponent exercise programs for older adults may be beneficial for those with arthritis.

Involvement of multiple sites of arthritis is associated with moderate to severe functional limitations as well as lower healthrelated quality of life.²³ While it has been found that individuals with arthritis can improve function with supervised exercise, even though their baseline functional status is lower than individuals without arthritis, it was not clear whether individuals with multiple joint involvement also would benefit.¹² The results of this study suggest that these individuals can improve across various domains of physical function despite variation in arthritis location and status. As incidence of arthritis increases with age, targeting older adults for exercise programs such as Gerofit may improve functional limitations and health-related quality of life associated with arthritis.²

We evaluated physical function using multiple measures to assess upper (arm curls) and lower (chair stands, 10MWT) extremity physical function and aerobic endurance (6MWD). Participants in this study reached clinically meaningful changes with 3 months of participation in Gerofit for most of the physical function measures. Gerofit participants had a mean gait speed improvement of 0.05 to 0.07 m/s compared with 0.10 to 0.30 m/s, which was reported previously. ^24,25 In this study, nearly all groups achieved the clinically important improvements in the chair stand in 30 seconds (2.0 to 2.6) and the 6MWD (21.8 to 59.1 m) that have been reported in the literature.^24-26

The Osteoarthritis Research Society International recommends the chair stand and 6MWD performance-based tests for individuals with hip and knee arthritis because they align with patient-reported outcomes and represent the types of activities relevant to this population.²⁷ The findings of this study suggest that improvement in these physical function measures with participation in exercise align with data from arthritis-specific exercise programs designed for wide implementation. Hughes and colleagues reported improvements in the 6MWD after the 8-week Fit and Strong exercise intervention, which included walking and lower body resistance training.²⁸ The Arthritis Foundation’s Walk With Ease program is a 6-week walking program that has shown improvements in chair stands and gait speed.²⁹ Another Arthritis Foundation program, People with Arthritis Can Exercise, is an 8-week course consisting of a variety of resistance, aerobic, and balance activities. This program has been associated with increases in chair stands but not gait speed or 6MWD.^30,31

This study found that participation in a VHA outpatient clinical supervised exercise program results in improvements in physical function that can be realized by older adults regardless of arthritis burden. Gerofit programs typically require 1.5 to 2.0 dedicated full-time equivalent employees to run the program effectively and additional administrative support, depending on size of the program.³² The cost savings generated by the program include reductions in hospitalization rates, emergency department visits, days in hospital, and medication use and provide a compelling argument for the program’s financial viability to health care systems through long-term savings and improved health outcomes for older adults.^33-36

While evidenced-based arthritis programs exist, this study illustrates that an exercise program without a focus on arthritis also improves physical function, potentially reducing the risk of disability related to arthritis. The clinical implication for these findings is that arthritis-specific exercise programs may not be needed to achieve functional improvements in individuals with arthritis. This is critical for under-resourced or exercise- limited health care systems or communities. Therefore, if exercise programming is limited, or arthritis-specific programs and interventions are not available, nonspecific exercise programs will also be beneficial to individuals with arthritis. Thus, individuals with arthritis should be encouraged to participate in any available exercise programming to achieve improvements in physical function. In addition, many older adults have multiple comorbidities, most of which improve with participation in exercise. ³⁷ Disease-specific exercise programs can offer tailored exercises and coaching related to common barriers in participation, such as joint pain for arthritis.³¹ It is unclear whether these additional programmatic components are associated with greater improvements in outcomes, such as physical function. More research is needed to explore the benefits of disease-specific tailored exercise programs compared with general exercise programs.

Strengths and Limitations

This study demonstrated the effect of participation in a clinical, supervised exercise program in a real-world setting. It suggests that even exercise programs not specifically targeted for arthritis populations can improve physical function among those with arthritis.

As a VHA clinical supervised exercise program, Gerofit may not be generalizable to all older adults or other exercise programs. In addition, this analysis only included a veteran population that was > 95% male and may not be generalizable to other populations. Arthritis status was defined by self-report and not verified in the health record. However, this approach has been shown to be acceptable in this setting and the most common type of arthritis in this population (OA) is a painful musculoskeletal condition associated with functional limitations.^4,22,38,39 Self-reported arthritis or rheumatism is associated with functional limitations.¹ Therefore, it is unlikely that the results would differ for physician-diagnosed or radiographically defined OA. Additionally, the study did not have data on the total number of joints with arthritis or arthritis severity but rather used upper body, lower body, and both upper and lower body arthritis as a proxy for arthritis status. While our models were adjusted for age and BMI, 2 known confounding factors for the association between arthritis and physical function, there are other potential confounding factors that were not included in the models. ^40,41 Finally, this study only included individuals with completed baseline and 3-month follow-up assessments, and the individuals who participated for longer or shorter periods may have had different physical function outcomes than individuals included in this study.

Conclusions

Participation in 3 months VHA Gerofit outpatient supervised exercise programs can improve physical function for all older adults, regardless of arthritis status. These programs may increase access to exercise programming that is beneficial for common conditions affecting older adults, such as arthritis.

Osteoporotic fragility fractures constitute a significant public health concern, with 1 in 2 women and 1 in 5 men aged > 50 years sustaining an osteoporotic fracture.¹ Osteoporotic fractures are costly and associated with reduced quality of life and impaired survival.^2-6 Many interventions including fall mitigation, calcium, vitamin D supplementation, and osteoporosis—specific medications reduce fracture risk.⁷ New medications for treating osteoporosis, including anabolic therapies, are costly and require clinical oversight to ensure safe delivery. This includes laboratory monitoring, timing of in-clinic dosing and provision of sequence therapy.^8,9 COVID-19 introduced numerous barriers to osteoporosis care, raising concerns for medication interruption and patients lost to follow-up, which made monitoring these high risk and costly medications even more important.

The US Department of Veterans Affairs (VA) was an early adopter of using the electronic health record to analyze and implement system-wide processes for population management and quality improvement.¹⁰ This enabled the creation of clinical dashboards to display key performance indicator data that support quality improvement and patient care initiatives.^11-15 The VA Puget Sound Health Care System (VAPSHCS) has a dedicated osteoporosis clinic focused on preventing and treating veterans at high risk for fracture. Considering the growing utilization of osteoporosis medications, particularly those requiring timed sequential therapy to prevent bone mineral density loss and rebound osteoporotic fractures, close monitoring and follow-up is required. The COVID-19 pandemic made clear the need for proactive osteoporosis management. This article describes the creation and use of an automated clinic dashboard to identify and contact veterans with osteoporosis-related care needs, such as prescription refills, laboratory tests, and clinical visits.

Methods

An automated dashboard was created in partnership with VA pharmacy clinical informatics to display the osteoporosis medication prescription (including last refill), monitoring laboratory test values and most recent osteoporosis clinic visit for each clinic patient. Data from the VA Corporate Data Warehouse were extracted. The resulting tables were used to create a patient cohort with ≥ 1 active medication for alendronate, zoledronic acid, the parathyroid hormone analogues (PTH) teriparatide or abaloparatide, denosumab, or romosozumab. Notably, alendronate was the only oral bisphosphonate prescribed in the clinic. These data were formatted and displayed using Microsoft SQL Server Reporting Services. The secure and encrypted dashboard alerts the clinic staff when prescriptions, appointments, or laboratory tests, such as estimated glomerular filtration rate, 25-hydroxy vitamin D, calcium, and PTH are overdue or out of reference range. The dashboard tracked the most recent clinic visit or dual-energy X-ray absorptiometry (DXA) scan if performed within the VA. Overdue laboratory test alerts for bisphosphonates were flagged if delayed 12 months and 6 months for all other medications.

On March 20, 2021, the VAPSHCS osteoporosis clinic was staffed by 1 endocrinologist, 1 geriatrician, 1 rheumatologist, and 1 registered nurse (RN) coordinator. Overdue or out-of-range alerts were reviewed weekly by the RN coordinator, who addressed alerts. For any overdue laboratory work or prescription refills, the RN coordinator alerted the primary osteoporosis physician via the electronic health record for updated orders. Patients were contacted by phone to schedule a clinic visit, complete ordered laboratory work, or discuss osteoporosis medication refills based on the need identified by the dashboard. A letter was mailed to the patient requesting they contact the osteoporosis clinic for patients who could not be reached by phone after 2 attempts. If 3 attempts (2 phone calls and a letter) were unsuccessful, the osteoporosis physician was alerted so they could either call the patient, alert the primary referring clinician, or discontinue the osteoporosis medication.

Results

As of March 20, 2021, 139 patients were included on the dashboard. Ninety-two patients (66%) had unmet care needs and 29% were female. Ages ranged from 40 to 100 years (Table). The dashboard alerted the team to 3 patients lost to follow-up, all of whom had transferred to care outside the clinic. Twenty-three patients (17%) had overdue medications, including 2 (9%) who had not refilled oral bisphosphonate and 18 (78%) who were overdue for intravenous bisphosphonate treatment. One veteran flagged as overdue for their denosumab injection was unable to receive it due to a significant change in health status. Two veterans were overdue for a PTH analogue refill, 1 of whom had completed their course and transitioned to bisphosphonate.

The most common alert was 40 patients (29%) with overdue laboratory tests, 37 of which were receiving bisphosphonates. One patient included on the dashboard was taking romosozumab and all their monitoring parameters were up to date, thus their data were not included in the Table to prevent possible identification.

Discussion

A dashboard alerted the osteoporosis clinic team to veterans who were overdue for visits, laboratory work, and prescription renewals. Overall, 92 patients (66%) had unmet care needs identified by the dashboard, all of which were addressed with phone calls and/or letters. Most of the overdue medication refills and laboratory tests were for patients taking bisphosphonates avoiding VAPSHCS during the COVID-19 pandemic. The dashboard enabled the RN coordinator to promptly contact the patient, facilitate coordination of care requirements, and guarantee the safe and efficient delivery of osteoporosis care.

The VA has historically been a leader in the creation of clinical dashboards to support health campaigns.^11,12 These dashboards have successfully improved quality metrics towards the treatment of hepatitis C virus, heart failure, and highrisk opioid prescribing.^13-15 Data have shown that successful clinical dashboard implementation must be done in conjunction with protected time or staff to support care improvements.¹⁶ Additionally, the time required for clinical dashboards can limit their sustainability and feasibility.¹⁷ A study aimed at improving osteoporosis care for patients with Parkinson disease found that weekly multidisciplinary review of at-risk patients resulted in all new patients and 91% of follow-up patients receiving evidence- based osteoporosis treatments.¹⁷ However, despite the benefits, the intervention required significant time and resources. In contrast, the osteoporosis dashboard implemented at VAPSHCS was not time or resource intensive, requiring about 1 hour per week for the RN coordinator to review the dashboard and coordinate patient care needs.

Limitations

This study setting is unique from other health care organizations or VA health care systems. Implementation of a similar dashboard in other clinical settings where patients receive medical care in multiple health care systems may differ. The VA dedicates resources to support veteran population health management, which may not be available in other health care systems.^11,12 These issues may pose a barrier to implementing a similar osteoporosis dashboard in non-VA facilities. In addition, it is significant that while the dashboard can be reconfigured and adapted to track veterans across different VA facilities, certain complexities arise if essential data, such as laboratory tests and DXA imaging, are conducted outside of VA facilities. In such cases, manual entry of this information into the dashboard would be necessary. Because the dashboard was quickly developed during the COVID-19 pandemic, this study lacked preimplementation data on laboratory testing, medication refills, and DXA imaging, which would have enabled a comparison of adherence before and after dashboard implementation. Finally, we acknowledge the delay in publishing these findings; however, we believe sharing innovative approaches to providing care for high-risk populations is essential, as demonstrated during the COVID-19 pandemic.

Conclusions

An osteoporosis clinic dashboard served as a valuable clinical support tool to ensure safe and effective osteoporosis medication delivery at VAPSHCS. Considering the growing utilization of osteoporosis medications, this dashboard plays a vital role in facilitating care coordination for patients receiving these high-risk treatments.¹⁸ Use of the dashboard supported the effective use of high-cost osteoporosis medications and is likely to improve clinical osteoporosis outcomes.

Despite the known fracture risk reduction, osteoporosis medication adherence is low.^19,20 Maintaining consistent pharmacotherapy for osteoporosis is essential not only for fracture prevention but also reducing health care costs related to osteoporosis and preserving patient independence and functionality.^21-24 While initially developed in response to the COVID-19 pandemic, the dashboard remains useful. The VAPSHCS osteoporosis clinic is now staffed by 2 physicians (endocrine and rheumatology) and the dashboard is still in use. The RN coordinator spends about 15 minutes per week using the dashboard and managing the 67 veterans on osteoporosis therapy. This dashboard represents a sustainable clinical tool with the capacity to minimize osteoporosis care gaps and improve outcomes.

Osteoporotic fragility fractures constitute a significant public health concern, with 1 in 2 women and 1 in 5 men aged > 50 years sustaining an osteoporotic fracture.¹ Osteoporotic fractures are costly and associated with reduced quality of life and impaired survival.^2-6 Many interventions including fall mitigation, calcium, vitamin D supplementation, and osteoporosis—specific medications reduce fracture risk.⁷ New medications for treating osteoporosis, including anabolic therapies, are costly and require clinical oversight to ensure safe delivery. This includes laboratory monitoring, timing of in-clinic dosing and provision of sequence therapy.^8,9 COVID-19 introduced numerous barriers to osteoporosis care, raising concerns for medication interruption and patients lost to follow-up, which made monitoring these high risk and costly medications even more important.

The US Department of Veterans Affairs (VA) was an early adopter of using the electronic health record to analyze and implement system-wide processes for population management and quality improvement.¹⁰ This enabled the creation of clinical dashboards to display key performance indicator data that support quality improvement and patient care initiatives.^11-15 The VA Puget Sound Health Care System (VAPSHCS) has a dedicated osteoporosis clinic focused on preventing and treating veterans at high risk for fracture. Considering the growing utilization of osteoporosis medications, particularly those requiring timed sequential therapy to prevent bone mineral density loss and rebound osteoporotic fractures, close monitoring and follow-up is required. The COVID-19 pandemic made clear the need for proactive osteoporosis management. This article describes the creation and use of an automated clinic dashboard to identify and contact veterans with osteoporosis-related care needs, such as prescription refills, laboratory tests, and clinical visits.

Methods

An automated dashboard was created in partnership with VA pharmacy clinical informatics to display the osteoporosis medication prescription (including last refill), monitoring laboratory test values and most recent osteoporosis clinic visit for each clinic patient. Data from the VA Corporate Data Warehouse were extracted. The resulting tables were used to create a patient cohort with ≥ 1 active medication for alendronate, zoledronic acid, the parathyroid hormone analogues (PTH) teriparatide or abaloparatide, denosumab, or romosozumab. Notably, alendronate was the only oral bisphosphonate prescribed in the clinic. These data were formatted and displayed using Microsoft SQL Server Reporting Services. The secure and encrypted dashboard alerts the clinic staff when prescriptions, appointments, or laboratory tests, such as estimated glomerular filtration rate, 25-hydroxy vitamin D, calcium, and PTH are overdue or out of reference range. The dashboard tracked the most recent clinic visit or dual-energy X-ray absorptiometry (DXA) scan if performed within the VA. Overdue laboratory test alerts for bisphosphonates were flagged if delayed 12 months and 6 months for all other medications.

On March 20, 2021, the VAPSHCS osteoporosis clinic was staffed by 1 endocrinologist, 1 geriatrician, 1 rheumatologist, and 1 registered nurse (RN) coordinator. Overdue or out-of-range alerts were reviewed weekly by the RN coordinator, who addressed alerts. For any overdue laboratory work or prescription refills, the RN coordinator alerted the primary osteoporosis physician via the electronic health record for updated orders. Patients were contacted by phone to schedule a clinic visit, complete ordered laboratory work, or discuss osteoporosis medication refills based on the need identified by the dashboard. A letter was mailed to the patient requesting they contact the osteoporosis clinic for patients who could not be reached by phone after 2 attempts. If 3 attempts (2 phone calls and a letter) were unsuccessful, the osteoporosis physician was alerted so they could either call the patient, alert the primary referring clinician, or discontinue the osteoporosis medication.

Results

As of March 20, 2021, 139 patients were included on the dashboard. Ninety-two patients (66%) had unmet care needs and 29% were female. Ages ranged from 40 to 100 years (Table). The dashboard alerted the team to 3 patients lost to follow-up, all of whom had transferred to care outside the clinic. Twenty-three patients (17%) had overdue medications, including 2 (9%) who had not refilled oral bisphosphonate and 18 (78%) who were overdue for intravenous bisphosphonate treatment. One veteran flagged as overdue for their denosumab injection was unable to receive it due to a significant change in health status. Two veterans were overdue for a PTH analogue refill, 1 of whom had completed their course and transitioned to bisphosphonate.

The most common alert was 40 patients (29%) with overdue laboratory tests, 37 of which were receiving bisphosphonates. One patient included on the dashboard was taking romosozumab and all their monitoring parameters were up to date, thus their data were not included in the Table to prevent possible identification.

Discussion

A dashboard alerted the osteoporosis clinic team to veterans who were overdue for visits, laboratory work, and prescription renewals. Overall, 92 patients (66%) had unmet care needs identified by the dashboard, all of which were addressed with phone calls and/or letters. Most of the overdue medication refills and laboratory tests were for patients taking bisphosphonates avoiding VAPSHCS during the COVID-19 pandemic. The dashboard enabled the RN coordinator to promptly contact the patient, facilitate coordination of care requirements, and guarantee the safe and efficient delivery of osteoporosis care.

The VA has historically been a leader in the creation of clinical dashboards to support health campaigns.^11,12 These dashboards have successfully improved quality metrics towards the treatment of hepatitis C virus, heart failure, and highrisk opioid prescribing.^13-15 Data have shown that successful clinical dashboard implementation must be done in conjunction with protected time or staff to support care improvements.¹⁶ Additionally, the time required for clinical dashboards can limit their sustainability and feasibility.¹⁷ A study aimed at improving osteoporosis care for patients with Parkinson disease found that weekly multidisciplinary review of at-risk patients resulted in all new patients and 91% of follow-up patients receiving evidence- based osteoporosis treatments.¹⁷ However, despite the benefits, the intervention required significant time and resources. In contrast, the osteoporosis dashboard implemented at VAPSHCS was not time or resource intensive, requiring about 1 hour per week for the RN coordinator to review the dashboard and coordinate patient care needs.

Limitations

This study setting is unique from other health care organizations or VA health care systems. Implementation of a similar dashboard in other clinical settings where patients receive medical care in multiple health care systems may differ. The VA dedicates resources to support veteran population health management, which may not be available in other health care systems.^11,12 These issues may pose a barrier to implementing a similar osteoporosis dashboard in non-VA facilities. In addition, it is significant that while the dashboard can be reconfigured and adapted to track veterans across different VA facilities, certain complexities arise if essential data, such as laboratory tests and DXA imaging, are conducted outside of VA facilities. In such cases, manual entry of this information into the dashboard would be necessary. Because the dashboard was quickly developed during the COVID-19 pandemic, this study lacked preimplementation data on laboratory testing, medication refills, and DXA imaging, which would have enabled a comparison of adherence before and after dashboard implementation. Finally, we acknowledge the delay in publishing these findings; however, we believe sharing innovative approaches to providing care for high-risk populations is essential, as demonstrated during the COVID-19 pandemic.

Conclusions

An osteoporosis clinic dashboard served as a valuable clinical support tool to ensure safe and effective osteoporosis medication delivery at VAPSHCS. Considering the growing utilization of osteoporosis medications, this dashboard plays a vital role in facilitating care coordination for patients receiving these high-risk treatments.¹⁸ Use of the dashboard supported the effective use of high-cost osteoporosis medications and is likely to improve clinical osteoporosis outcomes.

Despite the known fracture risk reduction, osteoporosis medication adherence is low.^19,20 Maintaining consistent pharmacotherapy for osteoporosis is essential not only for fracture prevention but also reducing health care costs related to osteoporosis and preserving patient independence and functionality.^21-24 While initially developed in response to the COVID-19 pandemic, the dashboard remains useful. The VAPSHCS osteoporosis clinic is now staffed by 2 physicians (endocrine and rheumatology) and the dashboard is still in use. The RN coordinator spends about 15 minutes per week using the dashboard and managing the 67 veterans on osteoporosis therapy. This dashboard represents a sustainable clinical tool with the capacity to minimize osteoporosis care gaps and improve outcomes.

Osteoporotic fragility fractures constitute a significant public health concern, with 1 in 2 women and 1 in 5 men aged > 50 years sustaining an osteoporotic fracture.¹ Osteoporotic fractures are costly and associated with reduced quality of life and impaired survival.^2-6 Many interventions including fall mitigation, calcium, vitamin D supplementation, and osteoporosis—specific medications reduce fracture risk.⁷ New medications for treating osteoporosis, including anabolic therapies, are costly and require clinical oversight to ensure safe delivery. This includes laboratory monitoring, timing of in-clinic dosing and provision of sequence therapy.^8,9 COVID-19 introduced numerous barriers to osteoporosis care, raising concerns for medication interruption and patients lost to follow-up, which made monitoring these high risk and costly medications even more important.

The US Department of Veterans Affairs (VA) was an early adopter of using the electronic health record to analyze and implement system-wide processes for population management and quality improvement.¹⁰ This enabled the creation of clinical dashboards to display key performance indicator data that support quality improvement and patient care initiatives.^11-15 The VA Puget Sound Health Care System (VAPSHCS) has a dedicated osteoporosis clinic focused on preventing and treating veterans at high risk for fracture. Considering the growing utilization of osteoporosis medications, particularly those requiring timed sequential therapy to prevent bone mineral density loss and rebound osteoporotic fractures, close monitoring and follow-up is required. The COVID-19 pandemic made clear the need for proactive osteoporosis management. This article describes the creation and use of an automated clinic dashboard to identify and contact veterans with osteoporosis-related care needs, such as prescription refills, laboratory tests, and clinical visits.

Methods

An automated dashboard was created in partnership with VA pharmacy clinical informatics to display the osteoporosis medication prescription (including last refill), monitoring laboratory test values and most recent osteoporosis clinic visit for each clinic patient. Data from the VA Corporate Data Warehouse were extracted. The resulting tables were used to create a patient cohort with ≥ 1 active medication for alendronate, zoledronic acid, the parathyroid hormone analogues (PTH) teriparatide or abaloparatide, denosumab, or romosozumab. Notably, alendronate was the only oral bisphosphonate prescribed in the clinic. These data were formatted and displayed using Microsoft SQL Server Reporting Services. The secure and encrypted dashboard alerts the clinic staff when prescriptions, appointments, or laboratory tests, such as estimated glomerular filtration rate, 25-hydroxy vitamin D, calcium, and PTH are overdue or out of reference range. The dashboard tracked the most recent clinic visit or dual-energy X-ray absorptiometry (DXA) scan if performed within the VA. Overdue laboratory test alerts for bisphosphonates were flagged if delayed 12 months and 6 months for all other medications.

On March 20, 2021, the VAPSHCS osteoporosis clinic was staffed by 1 endocrinologist, 1 geriatrician, 1 rheumatologist, and 1 registered nurse (RN) coordinator. Overdue or out-of-range alerts were reviewed weekly by the RN coordinator, who addressed alerts. For any overdue laboratory work or prescription refills, the RN coordinator alerted the primary osteoporosis physician via the electronic health record for updated orders. Patients were contacted by phone to schedule a clinic visit, complete ordered laboratory work, or discuss osteoporosis medication refills based on the need identified by the dashboard. A letter was mailed to the patient requesting they contact the osteoporosis clinic for patients who could not be reached by phone after 2 attempts. If 3 attempts (2 phone calls and a letter) were unsuccessful, the osteoporosis physician was alerted so they could either call the patient, alert the primary referring clinician, or discontinue the osteoporosis medication.

Results

As of March 20, 2021, 139 patients were included on the dashboard. Ninety-two patients (66%) had unmet care needs and 29% were female. Ages ranged from 40 to 100 years (Table). The dashboard alerted the team to 3 patients lost to follow-up, all of whom had transferred to care outside the clinic. Twenty-three patients (17%) had overdue medications, including 2 (9%) who had not refilled oral bisphosphonate and 18 (78%) who were overdue for intravenous bisphosphonate treatment. One veteran flagged as overdue for their denosumab injection was unable to receive it due to a significant change in health status. Two veterans were overdue for a PTH analogue refill, 1 of whom had completed their course and transitioned to bisphosphonate.

The most common alert was 40 patients (29%) with overdue laboratory tests, 37 of which were receiving bisphosphonates. One patient included on the dashboard was taking romosozumab and all their monitoring parameters were up to date, thus their data were not included in the Table to prevent possible identification.

Discussion

A dashboard alerted the osteoporosis clinic team to veterans who were overdue for visits, laboratory work, and prescription renewals. Overall, 92 patients (66%) had unmet care needs identified by the dashboard, all of which were addressed with phone calls and/or letters. Most of the overdue medication refills and laboratory tests were for patients taking bisphosphonates avoiding VAPSHCS during the COVID-19 pandemic. The dashboard enabled the RN coordinator to promptly contact the patient, facilitate coordination of care requirements, and guarantee the safe and efficient delivery of osteoporosis care.

The VA has historically been a leader in the creation of clinical dashboards to support health campaigns.^11,12 These dashboards have successfully improved quality metrics towards the treatment of hepatitis C virus, heart failure, and highrisk opioid prescribing.^13-15 Data have shown that successful clinical dashboard implementation must be done in conjunction with protected time or staff to support care improvements.¹⁶ Additionally, the time required for clinical dashboards can limit their sustainability and feasibility.¹⁷ A study aimed at improving osteoporosis care for patients with Parkinson disease found that weekly multidisciplinary review of at-risk patients resulted in all new patients and 91% of follow-up patients receiving evidence- based osteoporosis treatments.¹⁷ However, despite the benefits, the intervention required significant time and resources. In contrast, the osteoporosis dashboard implemented at VAPSHCS was not time or resource intensive, requiring about 1 hour per week for the RN coordinator to review the dashboard and coordinate patient care needs.

Limitations

This study setting is unique from other health care organizations or VA health care systems. Implementation of a similar dashboard in other clinical settings where patients receive medical care in multiple health care systems may differ. The VA dedicates resources to support veteran population health management, which may not be available in other health care systems.^11,12 These issues may pose a barrier to implementing a similar osteoporosis dashboard in non-VA facilities. In addition, it is significant that while the dashboard can be reconfigured and adapted to track veterans across different VA facilities, certain complexities arise if essential data, such as laboratory tests and DXA imaging, are conducted outside of VA facilities. In such cases, manual entry of this information into the dashboard would be necessary. Because the dashboard was quickly developed during the COVID-19 pandemic, this study lacked preimplementation data on laboratory testing, medication refills, and DXA imaging, which would have enabled a comparison of adherence before and after dashboard implementation. Finally, we acknowledge the delay in publishing these findings; however, we believe sharing innovative approaches to providing care for high-risk populations is essential, as demonstrated during the COVID-19 pandemic.

Conclusions

An osteoporosis clinic dashboard served as a valuable clinical support tool to ensure safe and effective osteoporosis medication delivery at VAPSHCS. Considering the growing utilization of osteoporosis medications, this dashboard plays a vital role in facilitating care coordination for patients receiving these high-risk treatments.¹⁸ Use of the dashboard supported the effective use of high-cost osteoporosis medications and is likely to improve clinical osteoporosis outcomes.

Despite the known fracture risk reduction, osteoporosis medication adherence is low.^19,20 Maintaining consistent pharmacotherapy for osteoporosis is essential not only for fracture prevention but also reducing health care costs related to osteoporosis and preserving patient independence and functionality.^21-24 While initially developed in response to the COVID-19 pandemic, the dashboard remains useful. The VAPSHCS osteoporosis clinic is now staffed by 2 physicians (endocrine and rheumatology) and the dashboard is still in use. The RN coordinator spends about 15 minutes per week using the dashboard and managing the 67 veterans on osteoporosis therapy. This dashboard represents a sustainable clinical tool with the capacity to minimize osteoporosis care gaps and improve outcomes.

The impact of obesity in the United States is significant. Between August 2021 and August 2023, the prevalence of obesity (body mass index ≥ 30) in US adults was 40.3%.¹ The prevalence of obesity in adults aged 40 to 59 years was 46.4%, higher than the prevalence in adults aged 20 to 39 years (35.5%) and those aged ≥ 60 years (38.9%).¹ The excess annual medical costs associated with obesity in the US are estimated at nearly $173 billion.²

The first-line treatment for obesity is lifestyle modifications, including a healthy diet and exercise. When lifestyle modifications are not enough to achieve weight-loss goals, bariatric surgery and anti-obesity medications (AOMs) are often considered. Five medications were approved for the long-term tretament of obesity by the US Food and Drug Administration (FDA) between 2021 and 2023, when this study was conducted: semaglutide (Wegovy), liraglutide (Saxenda), phentermine and topiramate, naltrexone and bupropion, and orlistat. The clinically meaningful (and commonly accepted) weight-loss target for these medications is ≥ 5% from baseline by week 12 of the maximally tolerated dose of therapy. A 5% weight loss has been shown to be clinically significant in improving cardiometabolic risk factors.^3,4 These medications are intended to be used as an adjunct to healthy diet and exercise. Of note, semaglutide and liraglutide carry brand names, which are associated with different dosing for the treatment of type 2 diabetes mellitus (T2DM).

All 5 FDA-approved AOMs were available at the Veterans Affairs Sioux Falls Health Care System (VASFHCS) for the treatment of obesity at the time of the study. To qualify for an AOM, a veteran at VASFHCS must first work with a dietitian or be enrolled in the MOVE! clinic to participate in the weight management program, which focuses on dietary, exercise, and behavioral changes. At VASFHCS, AOMs are prescribed by primary care practitioners, clinical pharmacy providers, and advanced practitioners within the MOVE! program.

Ample data exist for the efficacy of AOMs. However, no published research has reported on AOM efficacy by age group (Appendix).^5-11 While most of the AOM clinical trials included older adults, the average age of participants was typically between 40 and 50 years. It is well-known that pharmacokinetic and pharmacodynamic changes occur as age increases. Renal and hepatic clearance is reduced while the volume distribution and sensitivities to some medications may increase. ¹² Although this study did not focus on specific pharmacokinetic and pharmacodynamic changes with respect to AOM, it is important to recognize that this may play a role in the efficacy and safety of AOMs in older adults.

Methods

This retrospective single-center chart review was performed using the VASFHCS Computerized Patient Record System to compare the efficacy of AOMs in older adults (aged ≥ 65 years) vs adults (aged < 65 years). The primary endpoint was the percent change in body weight from baseline to 6 and 12 months after initiation of AOM therapy in the older adult vs adult population. Secondary endpoints included changes in low-density lipoprotein (LDL), hemoglobin A_1c (HbA_1c), and blood pressure (BP) from baseline compared to 12 months on AOM therapy. HbA_1c was assessed in patients with T2DM or prediabetes at the time of AOM initiation. Two safety endpoints were also explored to determine the incidence of medication adverse events (AEs) and subsequent discontinuation of AOM. A subset analysis was performed to determine whether there was a difference in percent change in body weight between patients in 3 age groups: 18 to 40 years, 41 to 64 years, and ≥ 65 years.

The study population included patients who were prescribed an AOM between January 1, 2021, and June 30, 2023. Patients were excluded if they did not continue AOM therapy for ≥ 6 months after initiation or if they underwent gastric bypass surgery while undergoing AOM therapy. Patients taking semaglutide (Ozempic) or liraglutide (Victoza) for both T2DM and weight loss who were eventually switched to the weight loss formulations (Wegovy or Saxenda) were included. Patients who switched between semaglutide and liraglutide for weight loss were also included. Those taking semaglutide or liraglutide solely for T2DM treatment were excluded because they are dosed differently.

Collected data included age, gender, race, weight (baseline, 6 and 12 months after initiation of AOM), metabolic laboratory values/vital signs (HbA_1c, LDL, and BP at baseline and 12 months after initiation of AOM), diagnosis of T2DM or prediabetes, reported AEs associated with AOM therapy, and date of AOM initiation and discontinuation (if applicable). Baseline values were defined at the time of medication initiation or values documented within 6 months prior to medication initiation if true baseline data were not reported. If values were not recorded at months 6 and 12 after AOM initiation, values documented closest to those targets were used. Weights were used for baseline, 6-, and 12-month data unless they were unavailable due to use of virtual care modalities. In these cases, patient-reported weights were used. Patients were included in the 6-month data, but not the 12-month data, if they were taking AOMs for > 6 months but not for 12 months. If patients had been on multiple AOMs, baseline data were recorded at the start of the first medication that was used for 6 months or longer. Twelve-month data were recorded after subsequent medication change. Twelve-month metabolic laboratory values/vital signs were recorded for patients included in the study even if they did not complete ≥ 12 months of AOM therapy.

Statistical Analysis

Data from patients who were prescribed an AOM from January 2021 to June 2023 and who remained on the medication for ≥ 6 months were analyzed. Baseline characteristics were analyzed using descriptive statistics. The primary and secondary endpoints were evaluated using the t test. The safety endpoints were analyzed using descriptive statistics. An analysis of variance test was used for the subset analysis. Results with P < .05 were statistically significant.

Results

A total of 144 participants were included in this study, 116 in the adult group (aged < 65 years) and 28 in the older adult group (aged ≥ 65 years). Sixty-seven patients were excluded due to prespecified inclusion and exclusion criteria.

Other than the predetermined mean age differences (48 years vs 71 years), there were multiple differences in patient baseline characteristics. When comparing older adults and adults, average weight (283 lb vs 269 lb) and White race (89% vs 87%) were slightly higher in the older adult group. Also, a higher prevalence of T2DM (54% and 18%) and a lower prevalence of prediabetes (21% and 33%) was noted in the older adult group. HbA_1c and BP were similar between both groups at baseline, while LDL was slightly lower in the older adult group (Table 1).

Patients in the adult group lost a mean 7.0% and 8.7% of body weight at 6 and 12 months, respectively, while the older adult group lost 5.0% and 6.6% body weight at 6 and 12 months, respectively. The difference in percent change in body weight was not statistically different at 6 (P = .08) or 12 (P = .26) months between patients in the adult group vs the older adult group or in the specific age groups (18-40 years, 41-64 years, ≥ 65 years) at 6 months (P = .24) or 12 months (P = .53) (Figure).

At 12 months, the difference between the adult group vs the older adult group was not statistically significant for HbA_1c in patients with T2DM or prediabetes (P = .73), LDL (P = .95), systolic BP (P = .58), or diastolic BP (P = .51) (Table 2).

For the safety endpoint, the incidence of AEs was found to be different between groups. There were more reported AEs (61.2% vs 39.3%) and a greater increase in therapy discontinuation due to AEs (6.0% vs 0%) in the adult group compared to the older adult group (Table 3).

Discussion

Patients taking AOMs revealed no statistically significant difference in percent change in body weight at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. The subset analysis also showed no statistically significant difference in change in percent body weight between more narrowly defined age groups of 18 to 40 years, 41 to 64 years, and ≥ 65 years. This suggests that AOM may have similar efficacy for weight loss in all ages of adults.

Secondary endpoint findings showed no statistically significant difference in HbA_1c (in patients with T2DM or prediabetes), LDL, or BP at 12 months between the 2 groups. Although this study did not differentiate secondary outcomes based on the individual AOM, the change in HbA_1c in both groups was expected, given that 70% of the patients included in this study were taking a glucagon-like peptide-1 agonist (liraglutide and semaglutide) at some point during the study. It’s also worth noting that secondary endpoints were collected for patients who discontinued the AOM between 6 and 12 months. Therefore, the patients’ HbA_1c, LDL, and BP may not have accurately reflected the change that could have been expected if they had continued AOM therapy beyond the 12-month period.

Due to the different mechanisms and range in efficacy that AOMs have in regard to weight loss, changes in all outcomes, including weight, HbA_1c, LDL, and BP were expected to vary as patients were included even after switching AOM (collection of data started after ≥ 6 months on a single AOM). Switching of AOM after the first 6 months of therapy was recorded in 25% of the patients in the ≥ 65 years group and 330% of the patients in the < 65 years group.

The incidence of AEs and subsequent discontinuation of AOMs in this study was higher in the adult group. This study excluded patients who did not continue taking an AOM for at least 6 months. As a result, the incidence of AEs between the 2 groups within the first 6 months of AOM therapy remains unknown. It is possible that during the first 6 months of therapy, patients aged < 65 years were more willing to tolerate or had fewer severe AEs compared with the older adult group. It’s also possible that the smaller number of patients in the older adult group was due to increased AEs that led them to discontinue early (before completion of 6 months of therapy) and/or prescriber discomfort in using AOMs in the older adult population. In addition, because the specific medication(s) taken by patients in each group were not detailed, it is unknown whether the adult group was taking AOMs associated with a greater number of AEs.

Limitations

This was a retrospective study with a relatively small sample size. A larger sample size may have shown more precise differences between age groups and may be more representative of the general population. Additionally, data were reliant on appropriate documentation, and adherence to AOM therapy was not assessed due to the retrospective nature of this study. At times, the study relied on patient reported data points, such as weight, if a clinic weight was not available. Also, this study did not account for many potential confounding factors such as other medications taken by the patient, which can affect outcomes including weight, HbA_1c, LDL, blood pressure, and AEs.

Conclusions

This retrospective study of patients taking AOMs showed no statistically significant difference in weight loss at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. A subset analysis found no statistically significant difference in change in body weight between specific age groups (18-40 years, 41-64 years, and ≥ 65 years). There was also no statistically significant difference in secondary outcomes, including change in HbA_1c (in patients with T2DM or prediabetes), LDL or BP between age groups. The safety endpoints showed a higher incidence of medication AEs in the adult group, with more of these adults discontinuing therapy due to AEs. This study indicates that AOM may have similar outcomes for weight loss and metabolic laboratory values/vital sign changes between adults and older adults. Also, our findings suggest that patients aged < 65 years may experience more AEs than patients aged ≥ 65 years after ≥ 6 months of AOM therapy. Larger studies are needed to further evaluate these age-specific findings.

The impact of obesity in the United States is significant. Between August 2021 and August 2023, the prevalence of obesity (body mass index ≥ 30) in US adults was 40.3%.¹ The prevalence of obesity in adults aged 40 to 59 years was 46.4%, higher than the prevalence in adults aged 20 to 39 years (35.5%) and those aged ≥ 60 years (38.9%).¹ The excess annual medical costs associated with obesity in the US are estimated at nearly $173 billion.²

The first-line treatment for obesity is lifestyle modifications, including a healthy diet and exercise. When lifestyle modifications are not enough to achieve weight-loss goals, bariatric surgery and anti-obesity medications (AOMs) are often considered. Five medications were approved for the long-term tretament of obesity by the US Food and Drug Administration (FDA) between 2021 and 2023, when this study was conducted: semaglutide (Wegovy), liraglutide (Saxenda), phentermine and topiramate, naltrexone and bupropion, and orlistat. The clinically meaningful (and commonly accepted) weight-loss target for these medications is ≥ 5% from baseline by week 12 of the maximally tolerated dose of therapy. A 5% weight loss has been shown to be clinically significant in improving cardiometabolic risk factors.^3,4 These medications are intended to be used as an adjunct to healthy diet and exercise. Of note, semaglutide and liraglutide carry brand names, which are associated with different dosing for the treatment of type 2 diabetes mellitus (T2DM).

All 5 FDA-approved AOMs were available at the Veterans Affairs Sioux Falls Health Care System (VASFHCS) for the treatment of obesity at the time of the study. To qualify for an AOM, a veteran at VASFHCS must first work with a dietitian or be enrolled in the MOVE! clinic to participate in the weight management program, which focuses on dietary, exercise, and behavioral changes. At VASFHCS, AOMs are prescribed by primary care practitioners, clinical pharmacy providers, and advanced practitioners within the MOVE! program.

Ample data exist for the efficacy of AOMs. However, no published research has reported on AOM efficacy by age group (Appendix).^5-11 While most of the AOM clinical trials included older adults, the average age of participants was typically between 40 and 50 years. It is well-known that pharmacokinetic and pharmacodynamic changes occur as age increases. Renal and hepatic clearance is reduced while the volume distribution and sensitivities to some medications may increase. ¹² Although this study did not focus on specific pharmacokinetic and pharmacodynamic changes with respect to AOM, it is important to recognize that this may play a role in the efficacy and safety of AOMs in older adults.

Methods

This retrospective single-center chart review was performed using the VASFHCS Computerized Patient Record System to compare the efficacy of AOMs in older adults (aged ≥ 65 years) vs adults (aged < 65 years). The primary endpoint was the percent change in body weight from baseline to 6 and 12 months after initiation of AOM therapy in the older adult vs adult population. Secondary endpoints included changes in low-density lipoprotein (LDL), hemoglobin A_1c (HbA_1c), and blood pressure (BP) from baseline compared to 12 months on AOM therapy. HbA_1c was assessed in patients with T2DM or prediabetes at the time of AOM initiation. Two safety endpoints were also explored to determine the incidence of medication adverse events (AEs) and subsequent discontinuation of AOM. A subset analysis was performed to determine whether there was a difference in percent change in body weight between patients in 3 age groups: 18 to 40 years, 41 to 64 years, and ≥ 65 years.

The study population included patients who were prescribed an AOM between January 1, 2021, and June 30, 2023. Patients were excluded if they did not continue AOM therapy for ≥ 6 months after initiation or if they underwent gastric bypass surgery while undergoing AOM therapy. Patients taking semaglutide (Ozempic) or liraglutide (Victoza) for both T2DM and weight loss who were eventually switched to the weight loss formulations (Wegovy or Saxenda) were included. Patients who switched between semaglutide and liraglutide for weight loss were also included. Those taking semaglutide or liraglutide solely for T2DM treatment were excluded because they are dosed differently.

Collected data included age, gender, race, weight (baseline, 6 and 12 months after initiation of AOM), metabolic laboratory values/vital signs (HbA_1c, LDL, and BP at baseline and 12 months after initiation of AOM), diagnosis of T2DM or prediabetes, reported AEs associated with AOM therapy, and date of AOM initiation and discontinuation (if applicable). Baseline values were defined at the time of medication initiation or values documented within 6 months prior to medication initiation if true baseline data were not reported. If values were not recorded at months 6 and 12 after AOM initiation, values documented closest to those targets were used. Weights were used for baseline, 6-, and 12-month data unless they were unavailable due to use of virtual care modalities. In these cases, patient-reported weights were used. Patients were included in the 6-month data, but not the 12-month data, if they were taking AOMs for > 6 months but not for 12 months. If patients had been on multiple AOMs, baseline data were recorded at the start of the first medication that was used for 6 months or longer. Twelve-month data were recorded after subsequent medication change. Twelve-month metabolic laboratory values/vital signs were recorded for patients included in the study even if they did not complete ≥ 12 months of AOM therapy.

Statistical Analysis

Data from patients who were prescribed an AOM from January 2021 to June 2023 and who remained on the medication for ≥ 6 months were analyzed. Baseline characteristics were analyzed using descriptive statistics. The primary and secondary endpoints were evaluated using the t test. The safety endpoints were analyzed using descriptive statistics. An analysis of variance test was used for the subset analysis. Results with P < .05 were statistically significant.

Results

A total of 144 participants were included in this study, 116 in the adult group (aged < 65 years) and 28 in the older adult group (aged ≥ 65 years). Sixty-seven patients were excluded due to prespecified inclusion and exclusion criteria.

Other than the predetermined mean age differences (48 years vs 71 years), there were multiple differences in patient baseline characteristics. When comparing older adults and adults, average weight (283 lb vs 269 lb) and White race (89% vs 87%) were slightly higher in the older adult group. Also, a higher prevalence of T2DM (54% and 18%) and a lower prevalence of prediabetes (21% and 33%) was noted in the older adult group. HbA_1c and BP were similar between both groups at baseline, while LDL was slightly lower in the older adult group (Table 1).

Patients in the adult group lost a mean 7.0% and 8.7% of body weight at 6 and 12 months, respectively, while the older adult group lost 5.0% and 6.6% body weight at 6 and 12 months, respectively. The difference in percent change in body weight was not statistically different at 6 (P = .08) or 12 (P = .26) months between patients in the adult group vs the older adult group or in the specific age groups (18-40 years, 41-64 years, ≥ 65 years) at 6 months (P = .24) or 12 months (P = .53) (Figure).

At 12 months, the difference between the adult group vs the older adult group was not statistically significant for HbA_1c in patients with T2DM or prediabetes (P = .73), LDL (P = .95), systolic BP (P = .58), or diastolic BP (P = .51) (Table 2).

For the safety endpoint, the incidence of AEs was found to be different between groups. There were more reported AEs (61.2% vs 39.3%) and a greater increase in therapy discontinuation due to AEs (6.0% vs 0%) in the adult group compared to the older adult group (Table 3).

Discussion

Patients taking AOMs revealed no statistically significant difference in percent change in body weight at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. The subset analysis also showed no statistically significant difference in change in percent body weight between more narrowly defined age groups of 18 to 40 years, 41 to 64 years, and ≥ 65 years. This suggests that AOM may have similar efficacy for weight loss in all ages of adults.

Secondary endpoint findings showed no statistically significant difference in HbA_1c (in patients with T2DM or prediabetes), LDL, or BP at 12 months between the 2 groups. Although this study did not differentiate secondary outcomes based on the individual AOM, the change in HbA_1c in both groups was expected, given that 70% of the patients included in this study were taking a glucagon-like peptide-1 agonist (liraglutide and semaglutide) at some point during the study. It’s also worth noting that secondary endpoints were collected for patients who discontinued the AOM between 6 and 12 months. Therefore, the patients’ HbA_1c, LDL, and BP may not have accurately reflected the change that could have been expected if they had continued AOM therapy beyond the 12-month period.

Due to the different mechanisms and range in efficacy that AOMs have in regard to weight loss, changes in all outcomes, including weight, HbA_1c, LDL, and BP were expected to vary as patients were included even after switching AOM (collection of data started after ≥ 6 months on a single AOM). Switching of AOM after the first 6 months of therapy was recorded in 25% of the patients in the ≥ 65 years group and 330% of the patients in the < 65 years group.

The incidence of AEs and subsequent discontinuation of AOMs in this study was higher in the adult group. This study excluded patients who did not continue taking an AOM for at least 6 months. As a result, the incidence of AEs between the 2 groups within the first 6 months of AOM therapy remains unknown. It is possible that during the first 6 months of therapy, patients aged < 65 years were more willing to tolerate or had fewer severe AEs compared with the older adult group. It’s also possible that the smaller number of patients in the older adult group was due to increased AEs that led them to discontinue early (before completion of 6 months of therapy) and/or prescriber discomfort in using AOMs in the older adult population. In addition, because the specific medication(s) taken by patients in each group were not detailed, it is unknown whether the adult group was taking AOMs associated with a greater number of AEs.

Limitations

This was a retrospective study with a relatively small sample size. A larger sample size may have shown more precise differences between age groups and may be more representative of the general population. Additionally, data were reliant on appropriate documentation, and adherence to AOM therapy was not assessed due to the retrospective nature of this study. At times, the study relied on patient reported data points, such as weight, if a clinic weight was not available. Also, this study did not account for many potential confounding factors such as other medications taken by the patient, which can affect outcomes including weight, HbA_1c, LDL, blood pressure, and AEs.

Conclusions

This retrospective study of patients taking AOMs showed no statistically significant difference in weight loss at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. A subset analysis found no statistically significant difference in change in body weight between specific age groups (18-40 years, 41-64 years, and ≥ 65 years). There was also no statistically significant difference in secondary outcomes, including change in HbA_1c (in patients with T2DM or prediabetes), LDL or BP between age groups. The safety endpoints showed a higher incidence of medication AEs in the adult group, with more of these adults discontinuing therapy due to AEs. This study indicates that AOM may have similar outcomes for weight loss and metabolic laboratory values/vital sign changes between adults and older adults. Also, our findings suggest that patients aged < 65 years may experience more AEs than patients aged ≥ 65 years after ≥ 6 months of AOM therapy. Larger studies are needed to further evaluate these age-specific findings.

The impact of obesity in the United States is significant. Between August 2021 and August 2023, the prevalence of obesity (body mass index ≥ 30) in US adults was 40.3%.¹ The prevalence of obesity in adults aged 40 to 59 years was 46.4%, higher than the prevalence in adults aged 20 to 39 years (35.5%) and those aged ≥ 60 years (38.9%).¹ The excess annual medical costs associated with obesity in the US are estimated at nearly $173 billion.²

The first-line treatment for obesity is lifestyle modifications, including a healthy diet and exercise. When lifestyle modifications are not enough to achieve weight-loss goals, bariatric surgery and anti-obesity medications (AOMs) are often considered. Five medications were approved for the long-term tretament of obesity by the US Food and Drug Administration (FDA) between 2021 and 2023, when this study was conducted: semaglutide (Wegovy), liraglutide (Saxenda), phentermine and topiramate, naltrexone and bupropion, and orlistat. The clinically meaningful (and commonly accepted) weight-loss target for these medications is ≥ 5% from baseline by week 12 of the maximally tolerated dose of therapy. A 5% weight loss has been shown to be clinically significant in improving cardiometabolic risk factors.^3,4 These medications are intended to be used as an adjunct to healthy diet and exercise. Of note, semaglutide and liraglutide carry brand names, which are associated with different dosing for the treatment of type 2 diabetes mellitus (T2DM).

All 5 FDA-approved AOMs were available at the Veterans Affairs Sioux Falls Health Care System (VASFHCS) for the treatment of obesity at the time of the study. To qualify for an AOM, a veteran at VASFHCS must first work with a dietitian or be enrolled in the MOVE! clinic to participate in the weight management program, which focuses on dietary, exercise, and behavioral changes. At VASFHCS, AOMs are prescribed by primary care practitioners, clinical pharmacy providers, and advanced practitioners within the MOVE! program.

Ample data exist for the efficacy of AOMs. However, no published research has reported on AOM efficacy by age group (Appendix).^5-11 While most of the AOM clinical trials included older adults, the average age of participants was typically between 40 and 50 years. It is well-known that pharmacokinetic and pharmacodynamic changes occur as age increases. Renal and hepatic clearance is reduced while the volume distribution and sensitivities to some medications may increase. ¹² Although this study did not focus on specific pharmacokinetic and pharmacodynamic changes with respect to AOM, it is important to recognize that this may play a role in the efficacy and safety of AOMs in older adults.

Methods

This retrospective single-center chart review was performed using the VASFHCS Computerized Patient Record System to compare the efficacy of AOMs in older adults (aged ≥ 65 years) vs adults (aged < 65 years). The primary endpoint was the percent change in body weight from baseline to 6 and 12 months after initiation of AOM therapy in the older adult vs adult population. Secondary endpoints included changes in low-density lipoprotein (LDL), hemoglobin A_1c (HbA_1c), and blood pressure (BP) from baseline compared to 12 months on AOM therapy. HbA_1c was assessed in patients with T2DM or prediabetes at the time of AOM initiation. Two safety endpoints were also explored to determine the incidence of medication adverse events (AEs) and subsequent discontinuation of AOM. A subset analysis was performed to determine whether there was a difference in percent change in body weight between patients in 3 age groups: 18 to 40 years, 41 to 64 years, and ≥ 65 years.

The study population included patients who were prescribed an AOM between January 1, 2021, and June 30, 2023. Patients were excluded if they did not continue AOM therapy for ≥ 6 months after initiation or if they underwent gastric bypass surgery while undergoing AOM therapy. Patients taking semaglutide (Ozempic) or liraglutide (Victoza) for both T2DM and weight loss who were eventually switched to the weight loss formulations (Wegovy or Saxenda) were included. Patients who switched between semaglutide and liraglutide for weight loss were also included. Those taking semaglutide or liraglutide solely for T2DM treatment were excluded because they are dosed differently.

Collected data included age, gender, race, weight (baseline, 6 and 12 months after initiation of AOM), metabolic laboratory values/vital signs (HbA_1c, LDL, and BP at baseline and 12 months after initiation of AOM), diagnosis of T2DM or prediabetes, reported AEs associated with AOM therapy, and date of AOM initiation and discontinuation (if applicable). Baseline values were defined at the time of medication initiation or values documented within 6 months prior to medication initiation if true baseline data were not reported. If values were not recorded at months 6 and 12 after AOM initiation, values documented closest to those targets were used. Weights were used for baseline, 6-, and 12-month data unless they were unavailable due to use of virtual care modalities. In these cases, patient-reported weights were used. Patients were included in the 6-month data, but not the 12-month data, if they were taking AOMs for > 6 months but not for 12 months. If patients had been on multiple AOMs, baseline data were recorded at the start of the first medication that was used for 6 months or longer. Twelve-month data were recorded after subsequent medication change. Twelve-month metabolic laboratory values/vital signs were recorded for patients included in the study even if they did not complete ≥ 12 months of AOM therapy.

Statistical Analysis

Data from patients who were prescribed an AOM from January 2021 to June 2023 and who remained on the medication for ≥ 6 months were analyzed. Baseline characteristics were analyzed using descriptive statistics. The primary and secondary endpoints were evaluated using the t test. The safety endpoints were analyzed using descriptive statistics. An analysis of variance test was used for the subset analysis. Results with P < .05 were statistically significant.

Results

A total of 144 participants were included in this study, 116 in the adult group (aged < 65 years) and 28 in the older adult group (aged ≥ 65 years). Sixty-seven patients were excluded due to prespecified inclusion and exclusion criteria.

Other than the predetermined mean age differences (48 years vs 71 years), there were multiple differences in patient baseline characteristics. When comparing older adults and adults, average weight (283 lb vs 269 lb) and White race (89% vs 87%) were slightly higher in the older adult group. Also, a higher prevalence of T2DM (54% and 18%) and a lower prevalence of prediabetes (21% and 33%) was noted in the older adult group. HbA_1c and BP were similar between both groups at baseline, while LDL was slightly lower in the older adult group (Table 1).

Patients in the adult group lost a mean 7.0% and 8.7% of body weight at 6 and 12 months, respectively, while the older adult group lost 5.0% and 6.6% body weight at 6 and 12 months, respectively. The difference in percent change in body weight was not statistically different at 6 (P = .08) or 12 (P = .26) months between patients in the adult group vs the older adult group or in the specific age groups (18-40 years, 41-64 years, ≥ 65 years) at 6 months (P = .24) or 12 months (P = .53) (Figure).

At 12 months, the difference between the adult group vs the older adult group was not statistically significant for HbA_1c in patients with T2DM or prediabetes (P = .73), LDL (P = .95), systolic BP (P = .58), or diastolic BP (P = .51) (Table 2).

For the safety endpoint, the incidence of AEs was found to be different between groups. There were more reported AEs (61.2% vs 39.3%) and a greater increase in therapy discontinuation due to AEs (6.0% vs 0%) in the adult group compared to the older adult group (Table 3).

Discussion

Patients taking AOMs revealed no statistically significant difference in percent change in body weight at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. The subset analysis also showed no statistically significant difference in change in percent body weight between more narrowly defined age groups of 18 to 40 years, 41 to 64 years, and ≥ 65 years. This suggests that AOM may have similar efficacy for weight loss in all ages of adults.

Secondary endpoint findings showed no statistically significant difference in HbA_1c (in patients with T2DM or prediabetes), LDL, or BP at 12 months between the 2 groups. Although this study did not differentiate secondary outcomes based on the individual AOM, the change in HbA_1c in both groups was expected, given that 70% of the patients included in this study were taking a glucagon-like peptide-1 agonist (liraglutide and semaglutide) at some point during the study. It’s also worth noting that secondary endpoints were collected for patients who discontinued the AOM between 6 and 12 months. Therefore, the patients’ HbA_1c, LDL, and BP may not have accurately reflected the change that could have been expected if they had continued AOM therapy beyond the 12-month period.

Due to the different mechanisms and range in efficacy that AOMs have in regard to weight loss, changes in all outcomes, including weight, HbA_1c, LDL, and BP were expected to vary as patients were included even after switching AOM (collection of data started after ≥ 6 months on a single AOM). Switching of AOM after the first 6 months of therapy was recorded in 25% of the patients in the ≥ 65 years group and 330% of the patients in the < 65 years group.

The incidence of AEs and subsequent discontinuation of AOMs in this study was higher in the adult group. This study excluded patients who did not continue taking an AOM for at least 6 months. As a result, the incidence of AEs between the 2 groups within the first 6 months of AOM therapy remains unknown. It is possible that during the first 6 months of therapy, patients aged < 65 years were more willing to tolerate or had fewer severe AEs compared with the older adult group. It’s also possible that the smaller number of patients in the older adult group was due to increased AEs that led them to discontinue early (before completion of 6 months of therapy) and/or prescriber discomfort in using AOMs in the older adult population. In addition, because the specific medication(s) taken by patients in each group were not detailed, it is unknown whether the adult group was taking AOMs associated with a greater number of AEs.

Limitations

This was a retrospective study with a relatively small sample size. A larger sample size may have shown more precise differences between age groups and may be more representative of the general population. Additionally, data were reliant on appropriate documentation, and adherence to AOM therapy was not assessed due to the retrospective nature of this study. At times, the study relied on patient reported data points, such as weight, if a clinic weight was not available. Also, this study did not account for many potential confounding factors such as other medications taken by the patient, which can affect outcomes including weight, HbA_1c, LDL, blood pressure, and AEs.

Conclusions

This retrospective study of patients taking AOMs showed no statistically significant difference in weight loss at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. A subset analysis found no statistically significant difference in change in body weight between specific age groups (18-40 years, 41-64 years, and ≥ 65 years). There was also no statistically significant difference in secondary outcomes, including change in HbA_1c (in patients with T2DM or prediabetes), LDL or BP between age groups. The safety endpoints showed a higher incidence of medication AEs in the adult group, with more of these adults discontinuing therapy due to AEs. This study indicates that AOM may have similar outcomes for weight loss and metabolic laboratory values/vital sign changes between adults and older adults. Also, our findings suggest that patients aged < 65 years may experience more AEs than patients aged ≥ 65 years after ≥ 6 months of AOM therapy. Larger studies are needed to further evaluate these age-specific findings.