SAN DIEGO – Patch testing may be in order for some patients with atopic dermatitis, according to Jonathan Silverberg, MD, PhD, of the department of dermatology, Northwestern University, Chicago.

Allergic contact dermatitis is a common comorbid condition in people with AD “and sometimes, can flare up the severity of the disease,” he said in a video interview at the American Academy of Dermatology annual meeting.

Patch testing can ferret out a trigger in atopic dermatitis patients with atypical disease distribution or refractory disease, and help avoid the need for systemic therapy, Dr. Silverman pointed out.

In the interview, he discussed these and other clinical scenarios, as well as how patch testing differs in these patients and what screening series to consider using.

Dr. Silverberg had no relevant financial disclosures.

[email protected]

SOURCE: Silverberg, J. et al, Session 061.

SAN DIEGO – Patch testing may be in order for some patients with atopic dermatitis, according to Jonathan Silverberg, MD, PhD, of the department of dermatology, Northwestern University, Chicago.

Allergic contact dermatitis is a common comorbid condition in people with AD “and sometimes, can flare up the severity of the disease,” he said in a video interview at the American Academy of Dermatology annual meeting.

Patch testing can ferret out a trigger in atopic dermatitis patients with atypical disease distribution or refractory disease, and help avoid the need for systemic therapy, Dr. Silverman pointed out.

In the interview, he discussed these and other clinical scenarios, as well as how patch testing differs in these patients and what screening series to consider using.

Dr. Silverberg had no relevant financial disclosures.

[email protected]

SOURCE: Silverberg, J. et al, Session 061.

SAN DIEGO – Patch testing may be in order for some patients with atopic dermatitis, according to Jonathan Silverberg, MD, PhD, of the department of dermatology, Northwestern University, Chicago.

Allergic contact dermatitis is a common comorbid condition in people with AD “and sometimes, can flare up the severity of the disease,” he said in a video interview at the American Academy of Dermatology annual meeting.

Patch testing can ferret out a trigger in atopic dermatitis patients with atypical disease distribution or refractory disease, and help avoid the need for systemic therapy, Dr. Silverman pointed out.

In the interview, he discussed these and other clinical scenarios, as well as how patch testing differs in these patients and what screening series to consider using.

Dr. Silverberg had no relevant financial disclosures.

[email protected]

SOURCE: Silverberg, J. et al, Session 061.

SAN DIEGO – Over the past few years, pediatric dermatologist Kalyani Marathe, MD, has been seeing young patients with vulvar diseases in a multidisciplinary vulvar dermatology clinic at Children’s National Health System, in Washington, DC.

When Dr. Marathe started, it was her first experience treating such patients and there still are not much data in this population. She and Veronica Gomez-Lobo, MD, a pediatric and adolescent gynecologist at Children’s, “have now been doing the clinic every month for the last three and a half years,” and counsel and treat patients together. With longitudinal follow-up, “we’re learning so much about these conditions in children,” most of whom are about ages 3-11 years.

In a video interview at the annual meeting of the American Academy of Dermatology, Dr. Marathe discussed some of what she and Dr. Gomez-Lobo have learned over the past 3 years, with algorithms for treatment for the most common conditions they encounter in the clinic: non-specific vulvovaginitis, lichen sclerosus, and vitiligo.

Dr. Marathe had no relevant disclosures. She is a Dermatology News editorial board advisor.

[email protected]

SOURCE: Marathe, K. et al, Session U018

SAN DIEGO – Over the past few years, pediatric dermatologist Kalyani Marathe, MD, has been seeing young patients with vulvar diseases in a multidisciplinary vulvar dermatology clinic at Children’s National Health System, in Washington, DC.

When Dr. Marathe started, it was her first experience treating such patients and there still are not much data in this population. She and Veronica Gomez-Lobo, MD, a pediatric and adolescent gynecologist at Children’s, “have now been doing the clinic every month for the last three and a half years,” and counsel and treat patients together. With longitudinal follow-up, “we’re learning so much about these conditions in children,” most of whom are about ages 3-11 years.

In a video interview at the annual meeting of the American Academy of Dermatology, Dr. Marathe discussed some of what she and Dr. Gomez-Lobo have learned over the past 3 years, with algorithms for treatment for the most common conditions they encounter in the clinic: non-specific vulvovaginitis, lichen sclerosus, and vitiligo.

Dr. Marathe had no relevant disclosures. She is a Dermatology News editorial board advisor.

[email protected]

SOURCE: Marathe, K. et al, Session U018

SAN DIEGO – Over the past few years, pediatric dermatologist Kalyani Marathe, MD, has been seeing young patients with vulvar diseases in a multidisciplinary vulvar dermatology clinic at Children’s National Health System, in Washington, DC.

When Dr. Marathe started, it was her first experience treating such patients and there still are not much data in this population. She and Veronica Gomez-Lobo, MD, a pediatric and adolescent gynecologist at Children’s, “have now been doing the clinic every month for the last three and a half years,” and counsel and treat patients together. With longitudinal follow-up, “we’re learning so much about these conditions in children,” most of whom are about ages 3-11 years.

In a video interview at the annual meeting of the American Academy of Dermatology, Dr. Marathe discussed some of what she and Dr. Gomez-Lobo have learned over the past 3 years, with algorithms for treatment for the most common conditions they encounter in the clinic: non-specific vulvovaginitis, lichen sclerosus, and vitiligo.

Dr. Marathe had no relevant disclosures. She is a Dermatology News editorial board advisor.

[email protected]

SOURCE: Marathe, K. et al, Session U018

SAN DIEGO – Risankizumab outperformed ustekinumab in two phase 3 trials investigating the IL-23 blocker for moderate to severe plaque psoriasis.

In two year-long studies, 56% and 59% of those taking risankizumab and 21% and 30% of those taking ustekinumab achieved completely clear skin, Kenneth B. Gordon, MD, said at the annual meeting of the American Academy of Dermatology.

Bruce Jancin/Frontline Medical News

“One of the things we are striving for now is complete skin clearance,” said Dr. Gordon, chair of the dermatology department the Medical College of Wisconsin, Milwaukee. “In the past, people have said that it wasn’t important to reach that, yet here we are, getting more than 50% of patients to that point.”

Risankizumab is an investigational monoclonal antibody that selectively blocks IL-23, a key inflammatory protein. The drug is also in phase 3 trials for Crohn's disease, and being investigated for psoriatic arthritis. AbbVie, which is developing risankizumab, plans future trials for treating ulcerative colitis.

Dr. Gordon reported the results of UltIMMa-1 and UltIMMa-2, identical three-armed studies that randomized a total of 797 patients with moderate to severe plaque psoriasis to risankizumab 150 mg, ustekinumab 45 mg or 90 mg (based on weight), or to a crossover group that took placebo for the first 16 weeks of the study and then were switched to risankizumab 150 mg for the remainder of the study. Study drugs were delivered at weeks 0, 4, 16, 28, and 40.

The coprimary endpoints were at least a 90% improvement in the Psoriasis Area Severity Index score (PASI 90) at week 16 and a score of 0 or 1 on the Static Physicians’ Assessment scale (sPGA 0/1) at week 16, compared with placebo. Key secondary endpoints compared risankizumab with ustekinumab: PASI 90, sPGA score of clear (sPGA 0), sPGA 0/1, and Dermatology Quality of Life (DLQI) score of 0/1 at week 16, and PASI 90, PASI 100 and sPGA 0 at week 52.

In both trials, patients were 48 years old on average; about 20% had severe plaque psoriasis. The mean PASI score was about 20 at trial entry. Prior therapy included biologics in 30%-43%, depending on the trial, and TNF-alpha inhibitors in about 25%.

Patient retention in the study was good, Dr. Gordon noted, with 95% of risankizumab patients still taking the drug at 52 weeks. Patients also stayed on ustekinumab, with 94% of UltIMMa-1 patients and 91% of UltIMMa-2 patients still taking the drug at 52 weeks.

At week 16, risankizumab was clearly superior to placebo in both endpoints. In both studies, 75% of actively treated patients achieved PASI 90, compared to 5% of those taking placebo. In UltIMMa-1, a clear or almost clear sPGA was seen in 88% of risankizumab patients as compared to 8% of those taking placebo. In UltIMMa-2, these numbers were 84% and 5%, respectively.

In the secondary comparison of the two active drugs, risankizumab significantly outperformed ustekinumab on PASI90 at 16 weeks in UltIMMa-1 (75% vs. 42%) and in UltIMMa-2 (75% vs. 47%). The PASI90 outcomes similarly favored risankizumab at 52 weeks in UltIMMa-1 (82% vs. 44%) and in UltIMMa-2 (81% vs. 50%).

As compared with ustekinumab, risankizumab aced the secondary endpoint of complete skin clearance in UltIMMa-1 and (36% vs. 12%) and UltIMMa-2 (51% vs. 24%). The results similarly favored risankizumab at 52 weeks in both trials (56% vs. 21% and 59% vs. 30%).

Another secondary endpoint looked at how the crossover group fared. At week 51, the PASI90 for this group was 78% in UltIMMa-1 and 85% in UltIMMa-2; the PASI100 at 52 weeks for these patients was 55% and 67%.

A responder time curve demonstrated just how quickly the crossover patients made up for lost time after switching to risankizumab. Although these patients made no progress toward disease clearance during their placebo period, they quickly caught up with the primary risankizumab group. At 16 weeks, 5% in this group had a PASI 90; by week 28, 51% did; and by week 52, PASI 90 topped out at 78%.

“The time course seen in this trial is very important,” Dr. Gordon said. “By 8 weeks, almost 44% [of the primary risankizumab group] was already at PASI90. They reached an extremely high level of response that was very consistent over 1 year. In the ustekinumab group, we saw some saw-toothing of response, indicating that people were losing effectiveness at the end of the dosing period. With risankizumab, we did not see that, indicating that the once every 12 weeks dosing period is effective.”

The DLQI 0/1 outcome occurred at 16 and 52 weeks in significantly more patients taking risankizumab in both studies. By week 52 in UltIMMa-1, 75% of patients on risankizumab had achieved a DLQ1 0/1, compared with 47% of the ustekinumab group. In UltIMMa-2, these numbers were 71% and 44%, with the crossover group posting scores similar to the primary risankizumab group in both studies (62% and 68%).

Risankizumab proved safe and well tolerated, Dr. Gordon said. Less than 1% of patients discontinued the medication due to an adverse event. In both the UltIMMa-1 and UltIMMa-2 trials, the most frequently reported treatment-emergent adverse event in the risankizumab groups was upper respiratory tract infection. In UltIMMa-1, one patient receiving risankizumab presented with latent tuberculosis and was treated with rifampicin. There were no new cases of tuberculosis.

The serious adverse event rate hovered between 2%-3% in both trials. The rate of serious infection was 1%. The rate of malignancy was 0.3%, but fell to 0 when nonmelanoma skin cancer was excluded. There were no major cardiovascular events.

"Not only do these data show significant rates of clear skin, but because we know the burden of psoriasis extends beyond the skin, we are encouraged by the patient-reported improvement in quality of life after one year of treatment," he said. "Given the significant impact of psoriasis, it is important to continue to investigate additional treatment options."

AbbVie sponsored the trials. Dr. Gordon is a consultant for the company.

[email protected]

SOURCE: Gordon et al. AAD, Abstract 6495

SAN DIEGO – Risankizumab outperformed ustekinumab in two phase 3 trials investigating the IL-23 blocker for moderate to severe plaque psoriasis.

In two year-long studies, 56% and 59% of those taking risankizumab and 21% and 30% of those taking ustekinumab achieved completely clear skin, Kenneth B. Gordon, MD, said at the annual meeting of the American Academy of Dermatology.

Bruce Jancin/Frontline Medical News

“One of the things we are striving for now is complete skin clearance,” said Dr. Gordon, chair of the dermatology department the Medical College of Wisconsin, Milwaukee. “In the past, people have said that it wasn’t important to reach that, yet here we are, getting more than 50% of patients to that point.”

Risankizumab is an investigational monoclonal antibody that selectively blocks IL-23, a key inflammatory protein. The drug is also in phase 3 trials for Crohn's disease, and being investigated for psoriatic arthritis. AbbVie, which is developing risankizumab, plans future trials for treating ulcerative colitis.

Dr. Gordon reported the results of UltIMMa-1 and UltIMMa-2, identical three-armed studies that randomized a total of 797 patients with moderate to severe plaque psoriasis to risankizumab 150 mg, ustekinumab 45 mg or 90 mg (based on weight), or to a crossover group that took placebo for the first 16 weeks of the study and then were switched to risankizumab 150 mg for the remainder of the study. Study drugs were delivered at weeks 0, 4, 16, 28, and 40.

The coprimary endpoints were at least a 90% improvement in the Psoriasis Area Severity Index score (PASI 90) at week 16 and a score of 0 or 1 on the Static Physicians’ Assessment scale (sPGA 0/1) at week 16, compared with placebo. Key secondary endpoints compared risankizumab with ustekinumab: PASI 90, sPGA score of clear (sPGA 0), sPGA 0/1, and Dermatology Quality of Life (DLQI) score of 0/1 at week 16, and PASI 90, PASI 100 and sPGA 0 at week 52.

In both trials, patients were 48 years old on average; about 20% had severe plaque psoriasis. The mean PASI score was about 20 at trial entry. Prior therapy included biologics in 30%-43%, depending on the trial, and TNF-alpha inhibitors in about 25%.

Patient retention in the study was good, Dr. Gordon noted, with 95% of risankizumab patients still taking the drug at 52 weeks. Patients also stayed on ustekinumab, with 94% of UltIMMa-1 patients and 91% of UltIMMa-2 patients still taking the drug at 52 weeks.

At week 16, risankizumab was clearly superior to placebo in both endpoints. In both studies, 75% of actively treated patients achieved PASI 90, compared to 5% of those taking placebo. In UltIMMa-1, a clear or almost clear sPGA was seen in 88% of risankizumab patients as compared to 8% of those taking placebo. In UltIMMa-2, these numbers were 84% and 5%, respectively.

In the secondary comparison of the two active drugs, risankizumab significantly outperformed ustekinumab on PASI90 at 16 weeks in UltIMMa-1 (75% vs. 42%) and in UltIMMa-2 (75% vs. 47%). The PASI90 outcomes similarly favored risankizumab at 52 weeks in UltIMMa-1 (82% vs. 44%) and in UltIMMa-2 (81% vs. 50%).

As compared with ustekinumab, risankizumab aced the secondary endpoint of complete skin clearance in UltIMMa-1 and (36% vs. 12%) and UltIMMa-2 (51% vs. 24%). The results similarly favored risankizumab at 52 weeks in both trials (56% vs. 21% and 59% vs. 30%).

Another secondary endpoint looked at how the crossover group fared. At week 51, the PASI90 for this group was 78% in UltIMMa-1 and 85% in UltIMMa-2; the PASI100 at 52 weeks for these patients was 55% and 67%.

A responder time curve demonstrated just how quickly the crossover patients made up for lost time after switching to risankizumab. Although these patients made no progress toward disease clearance during their placebo period, they quickly caught up with the primary risankizumab group. At 16 weeks, 5% in this group had a PASI 90; by week 28, 51% did; and by week 52, PASI 90 topped out at 78%.

“The time course seen in this trial is very important,” Dr. Gordon said. “By 8 weeks, almost 44% [of the primary risankizumab group] was already at PASI90. They reached an extremely high level of response that was very consistent over 1 year. In the ustekinumab group, we saw some saw-toothing of response, indicating that people were losing effectiveness at the end of the dosing period. With risankizumab, we did not see that, indicating that the once every 12 weeks dosing period is effective.”

The DLQI 0/1 outcome occurred at 16 and 52 weeks in significantly more patients taking risankizumab in both studies. By week 52 in UltIMMa-1, 75% of patients on risankizumab had achieved a DLQ1 0/1, compared with 47% of the ustekinumab group. In UltIMMa-2, these numbers were 71% and 44%, with the crossover group posting scores similar to the primary risankizumab group in both studies (62% and 68%).

Risankizumab proved safe and well tolerated, Dr. Gordon said. Less than 1% of patients discontinued the medication due to an adverse event. In both the UltIMMa-1 and UltIMMa-2 trials, the most frequently reported treatment-emergent adverse event in the risankizumab groups was upper respiratory tract infection. In UltIMMa-1, one patient receiving risankizumab presented with latent tuberculosis and was treated with rifampicin. There were no new cases of tuberculosis.

The serious adverse event rate hovered between 2%-3% in both trials. The rate of serious infection was 1%. The rate of malignancy was 0.3%, but fell to 0 when nonmelanoma skin cancer was excluded. There were no major cardiovascular events.

"Not only do these data show significant rates of clear skin, but because we know the burden of psoriasis extends beyond the skin, we are encouraged by the patient-reported improvement in quality of life after one year of treatment," he said. "Given the significant impact of psoriasis, it is important to continue to investigate additional treatment options."

AbbVie sponsored the trials. Dr. Gordon is a consultant for the company.

[email protected]

SOURCE: Gordon et al. AAD, Abstract 6495

SAN DIEGO – Risankizumab outperformed ustekinumab in two phase 3 trials investigating the IL-23 blocker for moderate to severe plaque psoriasis.

In two year-long studies, 56% and 59% of those taking risankizumab and 21% and 30% of those taking ustekinumab achieved completely clear skin, Kenneth B. Gordon, MD, said at the annual meeting of the American Academy of Dermatology.

Bruce Jancin/Frontline Medical News

“One of the things we are striving for now is complete skin clearance,” said Dr. Gordon, chair of the dermatology department the Medical College of Wisconsin, Milwaukee. “In the past, people have said that it wasn’t important to reach that, yet here we are, getting more than 50% of patients to that point.”

Risankizumab is an investigational monoclonal antibody that selectively blocks IL-23, a key inflammatory protein. The drug is also in phase 3 trials for Crohn's disease, and being investigated for psoriatic arthritis. AbbVie, which is developing risankizumab, plans future trials for treating ulcerative colitis.

Dr. Gordon reported the results of UltIMMa-1 and UltIMMa-2, identical three-armed studies that randomized a total of 797 patients with moderate to severe plaque psoriasis to risankizumab 150 mg, ustekinumab 45 mg or 90 mg (based on weight), or to a crossover group that took placebo for the first 16 weeks of the study and then were switched to risankizumab 150 mg for the remainder of the study. Study drugs were delivered at weeks 0, 4, 16, 28, and 40.

The coprimary endpoints were at least a 90% improvement in the Psoriasis Area Severity Index score (PASI 90) at week 16 and a score of 0 or 1 on the Static Physicians’ Assessment scale (sPGA 0/1) at week 16, compared with placebo. Key secondary endpoints compared risankizumab with ustekinumab: PASI 90, sPGA score of clear (sPGA 0), sPGA 0/1, and Dermatology Quality of Life (DLQI) score of 0/1 at week 16, and PASI 90, PASI 100 and sPGA 0 at week 52.

In both trials, patients were 48 years old on average; about 20% had severe plaque psoriasis. The mean PASI score was about 20 at trial entry. Prior therapy included biologics in 30%-43%, depending on the trial, and TNF-alpha inhibitors in about 25%.

Patient retention in the study was good, Dr. Gordon noted, with 95% of risankizumab patients still taking the drug at 52 weeks. Patients also stayed on ustekinumab, with 94% of UltIMMa-1 patients and 91% of UltIMMa-2 patients still taking the drug at 52 weeks.

At week 16, risankizumab was clearly superior to placebo in both endpoints. In both studies, 75% of actively treated patients achieved PASI 90, compared to 5% of those taking placebo. In UltIMMa-1, a clear or almost clear sPGA was seen in 88% of risankizumab patients as compared to 8% of those taking placebo. In UltIMMa-2, these numbers were 84% and 5%, respectively.

In the secondary comparison of the two active drugs, risankizumab significantly outperformed ustekinumab on PASI90 at 16 weeks in UltIMMa-1 (75% vs. 42%) and in UltIMMa-2 (75% vs. 47%). The PASI90 outcomes similarly favored risankizumab at 52 weeks in UltIMMa-1 (82% vs. 44%) and in UltIMMa-2 (81% vs. 50%).

As compared with ustekinumab, risankizumab aced the secondary endpoint of complete skin clearance in UltIMMa-1 and (36% vs. 12%) and UltIMMa-2 (51% vs. 24%). The results similarly favored risankizumab at 52 weeks in both trials (56% vs. 21% and 59% vs. 30%).

Another secondary endpoint looked at how the crossover group fared. At week 51, the PASI90 for this group was 78% in UltIMMa-1 and 85% in UltIMMa-2; the PASI100 at 52 weeks for these patients was 55% and 67%.

A responder time curve demonstrated just how quickly the crossover patients made up for lost time after switching to risankizumab. Although these patients made no progress toward disease clearance during their placebo period, they quickly caught up with the primary risankizumab group. At 16 weeks, 5% in this group had a PASI 90; by week 28, 51% did; and by week 52, PASI 90 topped out at 78%.

“The time course seen in this trial is very important,” Dr. Gordon said. “By 8 weeks, almost 44% [of the primary risankizumab group] was already at PASI90. They reached an extremely high level of response that was very consistent over 1 year. In the ustekinumab group, we saw some saw-toothing of response, indicating that people were losing effectiveness at the end of the dosing period. With risankizumab, we did not see that, indicating that the once every 12 weeks dosing period is effective.”

The DLQI 0/1 outcome occurred at 16 and 52 weeks in significantly more patients taking risankizumab in both studies. By week 52 in UltIMMa-1, 75% of patients on risankizumab had achieved a DLQ1 0/1, compared with 47% of the ustekinumab group. In UltIMMa-2, these numbers were 71% and 44%, with the crossover group posting scores similar to the primary risankizumab group in both studies (62% and 68%).

Risankizumab proved safe and well tolerated, Dr. Gordon said. Less than 1% of patients discontinued the medication due to an adverse event. In both the UltIMMa-1 and UltIMMa-2 trials, the most frequently reported treatment-emergent adverse event in the risankizumab groups was upper respiratory tract infection. In UltIMMa-1, one patient receiving risankizumab presented with latent tuberculosis and was treated with rifampicin. There were no new cases of tuberculosis.

The serious adverse event rate hovered between 2%-3% in both trials. The rate of serious infection was 1%. The rate of malignancy was 0.3%, but fell to 0 when nonmelanoma skin cancer was excluded. There were no major cardiovascular events.

"Not only do these data show significant rates of clear skin, but because we know the burden of psoriasis extends beyond the skin, we are encouraged by the patient-reported improvement in quality of life after one year of treatment," he said. "Given the significant impact of psoriasis, it is important to continue to investigate additional treatment options."

AbbVie sponsored the trials. Dr. Gordon is a consultant for the company.

[email protected]

SOURCE: Gordon et al. AAD, Abstract 6495

2017 was a busy year in the AGA Community, our member-only discussion forum. Some of our favorite discussions included challenging clinical cases you shared, remembering your colleague Dr. Marv Sleisenger and first-hand recaps of AGA’s Advocacy Day experiences.

Thank you to everyone who contributed to the conversations in 2017, making the AGA Community a hub for collaboration to ever-expand the field of GI.

Tied for the title of top contributor in 2017 were Dmitriy Kedrin, MD, PhD, of Elliot Hospital in Manchester, N.H., and Sunanda Kane, MD, MSPH, AGAF, of Mayo Clinic in Rochester, MN.

Both are key influencers in the forum, especially with helping colleagues manage challenging patient cases. Learn more about each contributor and why keeping up with the Community is an important part of their regular routines in this brief Q&A.

Thanks for being such an active member of the AGA Community! Why do you contribute?

Dr. Kane: “You are welcome! I contribute because I feel I have helpful suggestions and recommendations for managing difficult patient scenarios as well as for professional issues.”

Dr. Kedrin: “I think it is important for GI docs to be a part of a larger community, stay informed on latest guidelines, research publications and approaches to difficult cases, where more than one road can be taken. I feel that it is a great forum for someone like me, relatively junior gastroenterologist.”

Why do you enjoy being part of the AGA Community?

Kane: “I feel engaged with my colleagues who I otherwise do not see on a regular basis, and get to ‘meet’ new ones.”

Kedrin: “I find the case discussions informative. I learn a great deal about current trends and opinions on important topics in the GI world.”

What do you like to do in your free time?

Kane: “I enjoy cooking and binge-watching Netflix.”

Kedrin: “I bake bread and run a gastroenterology literature review podcast called ‘GI Pearls.’”

What’s your approach to handling a difficult patient case you come across in your practice?

Kane: “I reach out to as many of my colleagues as I think appropriate who may have some experience or thoughts about how to help a difficult patient.”

Kedrin: “I often seek advice of other clinicians, some with more expertise in a particular area. I also go to the literature and try to learn more that way, help expand my differential as well as figure out the best therapeutic approach.”

Was there a conversation in the AGA Community in 2017 that was your favorite?

Kane: “All conversations have merit, none stick out as a favorite.”

Kedrin: “Oh, there are several. I recall a patient case where there were several thought leaders in the field who had a disagreement about the best approach to treatment. The work-life balance conversation [Early Career Group members only] was also very good. I also enjoyed reading about different opinions regarding the values of randomized versus observational trials that happened a while back.”

View the top discussions and contributors from 2017 on the AGA Community homepage, for a limited time.

[email protected]

2017 was a busy year in the AGA Community, our member-only discussion forum. Some of our favorite discussions included challenging clinical cases you shared, remembering your colleague Dr. Marv Sleisenger and first-hand recaps of AGA’s Advocacy Day experiences.

Thank you to everyone who contributed to the conversations in 2017, making the AGA Community a hub for collaboration to ever-expand the field of GI.

Tied for the title of top contributor in 2017 were Dmitriy Kedrin, MD, PhD, of Elliot Hospital in Manchester, N.H., and Sunanda Kane, MD, MSPH, AGAF, of Mayo Clinic in Rochester, MN.

Both are key influencers in the forum, especially with helping colleagues manage challenging patient cases. Learn more about each contributor and why keeping up with the Community is an important part of their regular routines in this brief Q&A.

Thanks for being such an active member of the AGA Community! Why do you contribute?

Dr. Kane: “You are welcome! I contribute because I feel I have helpful suggestions and recommendations for managing difficult patient scenarios as well as for professional issues.”

Dr. Kedrin: “I think it is important for GI docs to be a part of a larger community, stay informed on latest guidelines, research publications and approaches to difficult cases, where more than one road can be taken. I feel that it is a great forum for someone like me, relatively junior gastroenterologist.”

Why do you enjoy being part of the AGA Community?

Kane: “I feel engaged with my colleagues who I otherwise do not see on a regular basis, and get to ‘meet’ new ones.”

Kedrin: “I find the case discussions informative. I learn a great deal about current trends and opinions on important topics in the GI world.”

What do you like to do in your free time?

Kane: “I enjoy cooking and binge-watching Netflix.”

Kedrin: “I bake bread and run a gastroenterology literature review podcast called ‘GI Pearls.’”

What’s your approach to handling a difficult patient case you come across in your practice?

Kane: “I reach out to as many of my colleagues as I think appropriate who may have some experience or thoughts about how to help a difficult patient.”

Kedrin: “I often seek advice of other clinicians, some with more expertise in a particular area. I also go to the literature and try to learn more that way, help expand my differential as well as figure out the best therapeutic approach.”

Was there a conversation in the AGA Community in 2017 that was your favorite?

Kane: “All conversations have merit, none stick out as a favorite.”

Kedrin: “Oh, there are several. I recall a patient case where there were several thought leaders in the field who had a disagreement about the best approach to treatment. The work-life balance conversation [Early Career Group members only] was also very good. I also enjoyed reading about different opinions regarding the values of randomized versus observational trials that happened a while back.”

View the top discussions and contributors from 2017 on the AGA Community homepage, for a limited time.

[email protected]

2017 was a busy year in the AGA Community, our member-only discussion forum. Some of our favorite discussions included challenging clinical cases you shared, remembering your colleague Dr. Marv Sleisenger and first-hand recaps of AGA’s Advocacy Day experiences.

Thank you to everyone who contributed to the conversations in 2017, making the AGA Community a hub for collaboration to ever-expand the field of GI.

Tied for the title of top contributor in 2017 were Dmitriy Kedrin, MD, PhD, of Elliot Hospital in Manchester, N.H., and Sunanda Kane, MD, MSPH, AGAF, of Mayo Clinic in Rochester, MN.

Both are key influencers in the forum, especially with helping colleagues manage challenging patient cases. Learn more about each contributor and why keeping up with the Community is an important part of their regular routines in this brief Q&A.

Thanks for being such an active member of the AGA Community! Why do you contribute?

Dr. Kane: “You are welcome! I contribute because I feel I have helpful suggestions and recommendations for managing difficult patient scenarios as well as for professional issues.”

Dr. Kedrin: “I think it is important for GI docs to be a part of a larger community, stay informed on latest guidelines, research publications and approaches to difficult cases, where more than one road can be taken. I feel that it is a great forum for someone like me, relatively junior gastroenterologist.”

Why do you enjoy being part of the AGA Community?

Kane: “I feel engaged with my colleagues who I otherwise do not see on a regular basis, and get to ‘meet’ new ones.”

Kedrin: “I find the case discussions informative. I learn a great deal about current trends and opinions on important topics in the GI world.”

What do you like to do in your free time?

Kane: “I enjoy cooking and binge-watching Netflix.”

Kedrin: “I bake bread and run a gastroenterology literature review podcast called ‘GI Pearls.’”

What’s your approach to handling a difficult patient case you come across in your practice?

Kane: “I reach out to as many of my colleagues as I think appropriate who may have some experience or thoughts about how to help a difficult patient.”

Kedrin: “I often seek advice of other clinicians, some with more expertise in a particular area. I also go to the literature and try to learn more that way, help expand my differential as well as figure out the best therapeutic approach.”

Was there a conversation in the AGA Community in 2017 that was your favorite?

Kane: “All conversations have merit, none stick out as a favorite.”

Kedrin: “Oh, there are several. I recall a patient case where there were several thought leaders in the field who had a disagreement about the best approach to treatment. The work-life balance conversation [Early Career Group members only] was also very good. I also enjoyed reading about different opinions regarding the values of randomized versus observational trials that happened a while back.”

View the top discussions and contributors from 2017 on the AGA Community homepage, for a limited time.

[email protected]

Fractionated, picosecond-domain neodymium:YAG laser therapy appears safe and effective at improving facial photodamage at all ages, wrote Eric F. Bernstein, MD, a laser surgeon in private practice in Ardmore, Pa., and his associates.

In the study, two fractionated lasers were each combined with a specially designed “holographic beam-splitting optic” to treat mild to moderate facial wrinkles in 24 patients aged 18-75 years with Fitzpatrick skin types I-VI; 14 patients received five monthly treatments with the 1,064 nm laser, while the other 10 patients received four monthly treatments with the 532 nm laser.

Blinded evaluators assessed images taken at baseline and at 12 weeks after treatment. The evaluators found improvements of greater than 20% in 56.9% of the evaluated images, with no statistically significant difference between the two wavelengths. Of those treated with the 1,064 nm laser, 12 of 14 patients were “satisfied” or “very satisfied”; of those treated with the 532 nm laser, 8 of the 10 were “satisfied” or “very satisfied,” Dr. Bernstein and his colleagues wrote in the Journal of Drugs in Dermatology.

Patients experienced only mild to moderate discomfort during the laser treatment. Side effects were mild and were limited to erythema and edema in almost all patients; fewer than half the patients developed petechiae. Side effects generally resolved within a few days of treatment.

Dr. Bernstein and some of the other authors reported relationships with Syneron Candela, which provided funding for and loaned equipment used in the study.

Source: Bernstein EF et al. J Drugs Dermatol. 2017 Nov 1;16(11):1077-82.

[email protected]

Fractionated, picosecond-domain neodymium:YAG laser therapy appears safe and effective at improving facial photodamage at all ages, wrote Eric F. Bernstein, MD, a laser surgeon in private practice in Ardmore, Pa., and his associates.

In the study, two fractionated lasers were each combined with a specially designed “holographic beam-splitting optic” to treat mild to moderate facial wrinkles in 24 patients aged 18-75 years with Fitzpatrick skin types I-VI; 14 patients received five monthly treatments with the 1,064 nm laser, while the other 10 patients received four monthly treatments with the 532 nm laser.

Blinded evaluators assessed images taken at baseline and at 12 weeks after treatment. The evaluators found improvements of greater than 20% in 56.9% of the evaluated images, with no statistically significant difference between the two wavelengths. Of those treated with the 1,064 nm laser, 12 of 14 patients were “satisfied” or “very satisfied”; of those treated with the 532 nm laser, 8 of the 10 were “satisfied” or “very satisfied,” Dr. Bernstein and his colleagues wrote in the Journal of Drugs in Dermatology.

Patients experienced only mild to moderate discomfort during the laser treatment. Side effects were mild and were limited to erythema and edema in almost all patients; fewer than half the patients developed petechiae. Side effects generally resolved within a few days of treatment.

Dr. Bernstein and some of the other authors reported relationships with Syneron Candela, which provided funding for and loaned equipment used in the study.

Source: Bernstein EF et al. J Drugs Dermatol. 2017 Nov 1;16(11):1077-82.

[email protected]

Fractionated, picosecond-domain neodymium:YAG laser therapy appears safe and effective at improving facial photodamage at all ages, wrote Eric F. Bernstein, MD, a laser surgeon in private practice in Ardmore, Pa., and his associates.

In the study, two fractionated lasers were each combined with a specially designed “holographic beam-splitting optic” to treat mild to moderate facial wrinkles in 24 patients aged 18-75 years with Fitzpatrick skin types I-VI; 14 patients received five monthly treatments with the 1,064 nm laser, while the other 10 patients received four monthly treatments with the 532 nm laser.

Blinded evaluators assessed images taken at baseline and at 12 weeks after treatment. The evaluators found improvements of greater than 20% in 56.9% of the evaluated images, with no statistically significant difference between the two wavelengths. Of those treated with the 1,064 nm laser, 12 of 14 patients were “satisfied” or “very satisfied”; of those treated with the 532 nm laser, 8 of the 10 were “satisfied” or “very satisfied,” Dr. Bernstein and his colleagues wrote in the Journal of Drugs in Dermatology.

Patients experienced only mild to moderate discomfort during the laser treatment. Side effects were mild and were limited to erythema and edema in almost all patients; fewer than half the patients developed petechiae. Side effects generally resolved within a few days of treatment.

Dr. Bernstein and some of the other authors reported relationships with Syneron Candela, which provided funding for and loaned equipment used in the study.

Source: Bernstein EF et al. J Drugs Dermatol. 2017 Nov 1;16(11):1077-82.

[email protected]

Chronic pain is a common health care problem that remains a significant burden for the VHA.^1,2 Some reports indicate that nearly 50% of VA patients report chronic pain.^3,4 Both within and outside the VHA, primary care providers (PCPs) generally manage patients with chronic pain.^5,6 Historically, a biomedical approach to chronic pain also included the use of opioid medications, which may have contributed to increased opioid-related morbidity and mortality especially among the veteran patient population.^7-9 The use of opioids also is controversial due to concerns about adverse effects (AEs), long-term efficacy, functional outcomes, and the potential for drug abuse and addiction.¹⁰ Consequently, alternative treatment options that incorporate an interdisciplinary approach have gained significant interest among pain care providers.¹¹ Interdisciplinary programs have been shown to improve functional status and psychological well-being and to reduce pain severity and opioid use.^12-14 These benefits may persist for a decade or longer.¹⁵

Background

The Stepped Care Model for Pain Management (SCM-PM) is a specific pain treatment approach promoted by the VA National Pain Management Directive.¹⁶ This systematically adjusted approach is associated with improved patient satisfaction and health outcomes for pain and depression.^17,18 At its core, the model promotes engaging patients as active participants in their care along with a team of doctors who can offer an integrated, evidence-based, multimodal, interdisciplinary treatment plan.

To successfully implement this strategy at the VA, patient aligned care teams (PACT) assess and manage patients with common pain conditions through collaboration with mental health, complementary and integrative health services, physical therapy, and other programs, such as opioid renewal clinics and pain schools.¹⁹ This collaborative care approach, which the PCP initiates, is step 1 of the SCM-PM. If initial treatment is not successful and patients are not improving as expected, specialty care consultation and collaborative comanagement through interdisciplinary pain specialty teams are sought (step 2). Finally, step 3 involves tertiary, interdisciplinary care, including access to advanced diagnostic and pain rehabilitation programs accredited by the Commission for Accreditation of Rehabilitation Facilities (CARF).

Although the advantages of interdisciplinary pain programs are clear, resource limitations as well as challenges related to competencies of the PCPs, nurses, and associated health care professionals in pain assessment and management can make implementation of these programs, including the SCM-PM, difficult for many clinics and facilities. Thus, identifying effective chronic pain models and strategies, incorporating the philosophy and key elements of interdisciplinary programs, and accounting for facility resources and capacity are all important.

At the Ann Arbor VAMC, development of a comprehensive interdisciplinary team started with the implementation of joint sessions with a clinical pharmacist and health psychologist embedded in primary care to enhance access to behavioral pain management interventions.²⁰ This program was subsequently expanded to include a pain physician, 2 pain-focused physical therapists (PTs) and a pain nurse.

This article describes a novel team approach for providing more comprehensive, interdisciplinary care for patients with chronic pain along with the initial results for the patients who were part of an outpatient pain group program (OPGP).

Methods

Developing a more interdisciplinary pain management program included integrating different services and creating a strategy for comprehensive evaluation and management of patients with chronic pain. After patients were referred to the interdisciplinary pain clinic by their PCP, they received a systematically structured multidimensional assessment. The primary focus of this assessment was to create an individually directed treatment approach based on the patient’s responses to previous treatments and information collected from several questionnaires administered prior to evaluation. This information helped guide individual patient decision making and actively engaged patients in their care, thus following one of the central tenants of the SCM-PM model. Moreover, functional restoration was at the core of each patient’s evaluation and management. The primary focus was on nonpharmacologic treatment options that included psychological, physical, and occupational therapy; self-management; education; and complementary and alternative therapies. These modalities were offered either individually or in a group setting.

The first step after referral was an evaluation that followed the main core principles for complex disease management described by Tauben and Theodore.²¹ All new patients were asked to complete a 2-question pain intensity and pain interference measure, the 4-question Patient Health Questionnaire (PHQ-4), 4-question Primary Care-PTSD screening tool (PC-PTSD), and the STOP-BANG questionnaire to assess the risk for obstructive sleep apnea.^22-24 Each measure allowed the physician to identify specific problem areas and formulate a treatment plan that would incorporate PTs or occupational therapists, psychologists and/or clinical specialists, and pharmacists if needed.

Patients who were found to have or expressed significant disability because of pain and who wished to learn pain self-management strategies could participate in an 8-week OPGP. This program included the use of cognitive behavioral therapy (CBT) strategies along with group physical therapy classes. Some patients also received individual therapies concurrently with the 8-week OPGP. Patients were excluded from participating in the OPGP only if their current medical or psychiatric status precluded them from full engagement and maximum benefit as determined by the pain physician and psychologist.

Participants and Intervention

Program participants were patients with a chronic pain diagnosis who enrolled in the interdisciplinary pain team OPGP between April 2016 and April 2017. Most patients were referred by their PCPs due to chronic low back, neck, joint or neuropathic pain, although many presented with multiple pain areas. The onset of pain often was a result of a service-related injury or overuse, or the etiology was unknown.

A board-certified pain physician, licensed clinical psychologist, 2 licensed PTs, and a clinical pharmacist led the OPGP sessions. The program was composed of 3-hour-long sessions held weekly for 8 consecutive weeks. Each week, a member of the team covered a specific topic (Table 1).

These sessions focused on the importance of exercise, movement, and physical therapy; appropriate use of medications for managing chronic pain; pacing activities and body mechanics; and the medical approach to managing chronic pain. In addition to didactic presentations, interaction and therapeutic dialogue was encouraged among patients. The education portion of each weekly session lasted about 90 minutes, including a short break. Then, following another short break, patients proceeded to the physical therapy area and engaged in an individualized, monitored exercise program, conducted by the team PTs. Patients also were issued pedometers and encouraged to track their steps each day. Education in improving posture and body mechanics was a key component of the exercise portion of the program so patients could resume their normal daily activities and regain enjoyment in their life. Pain outcomemeasures were collected at admission and immediately before discharge.

Medication management also was an important part of the program for some patients and included tapering off opioids and other drugs and implementing trials of adjuvant pain medications shown to help chronic pain. For some patients, this medication management continued after the patient completed the program.

Measures

The Pain Outcome Questionnaire (POQ) is a 19-item, self-report measure of pain treatment outcomes. Pain rating, mobility, activities of daily living, vitality, negative effect, and fear are the functioning domains evaluated, and the subscale scores are added to produce a total score. The POQ was developed from samples of veterans undergoing inpatient or outpatient pain treatment at VA facilities. For each of the subscales and the total score, higher values indicate poorer outcomes. In normative outpatient VA samples, a total score of 71 is at the 25th percentile, and 120 is at the 75th percentile. The POQ has been shown to have good reliability and validity among veterans in an outpatient setting.²⁵

The Pain Catastrophizing Scale (PCS) is a 13-item scale designed to measure various levels of pain catastrophizing.²⁶ Each item is rated on a 5-point Likert-type scale, from 0 (not at all) to 4 (all the time). The PCS consists of 3 subscale domains: rumination, 4 items; magnification, 3 items; and helplessness, 6 items. Responses to all items also can be added to produce a total score from 0 to 52, with higher scores indicating a higher level of catastrophic thinking related to pain. This project evaluated both the total score and the 3 subscale scores.

The Pain Self-Efficacy Questionnaire (PSEQ) is a 10-item questionnaire that assesses confidence in an individual’s ability to cope or to perform activities despite the pain.²⁷ The PSEQ covers a range of functions, including household chores, socializing, work, as well as coping with pain without medications. Each question has a 7-point Likert scale response: 0 = not at all confident, and 7 = completely confident, to produce a total score from 0 to 60. Higher scores indicate stronger pain self-efficacy, which has been shown to be associated with return to work and maintenance of functional gains.

The Patient Health Questionnaire-4 (PHQ-4) is a 4-item instrument used to screen for depression and anxiety in outpatient medical settings.²² Patients indicate how often they have been bothered by certain problems on a 4-point Likert scale, from 0 (not at all) to 3 (nearly every day). The PHQ-4 provides a total score (0-12) with scores of 6 to 8 indicating moderate and 9 to 12 indicating severe psychological distress; 2 subscale scores, 1 for anxiety (2 questions) and 1 for depression (2 questions). For this analysis, the total PHQ-4 score has been dichotomized with 1 indicating a score in the moderate or severe range vs 0 for a score of mild or no psychological distress. Likewise, each of the subscale scores have been dichotomized with 1 indicating a score of 3 or greater, which is considered a positive screen.

The 6-minute walk test (6MWT) measures the distance (in feet) an individual can walk over a total of 6 minutes on a hard, flat surface.²⁸ Even though the individual can walk at a self-selected pace and rest if needed during the test, the goal is for the patient to walk as far as possible over the course of 6 minutes. The 6MWT provides information regarding functional capacity, response to therapy, and prognosis across a range of chronic conditions, including pain.

Data Analysis

Data analysis included the use of both descriptive and comparative statistics. A descriptive analysis was conducted to examine the characteristics of patients who did and did not complete the OPGP. Specific outcomes for those individuals who completed the program, and thus had complete pre- and post-OPGP information, then were compared. Paired t tests were used to compare differences in continuous measures between baseline (pre-OPGP) and the 8-week follow-up (post-OPGP). Comparisons involving dichotomous measures were made using the Fisher exact test. A 2-sided α with a P value .05 was considered statistically significant. All statistical analyses were conducted using STATA version 14.1 (StataCorp, College Station, TX).

Results

A total of 36 patients enrolled, and 28 (77%) completed the OPGP. Patients who did not complete the program (n = 8) either self-discharged due to lack of interest or had difficulty in consistently making their appointments and decided not to continue (Table 2).

Outcomes for OPGP Completers

Improvements were observed for all outcome domains among patients who completed the program (eTable).

Discussion

This report describes the novel model for improving delivery of chronic pain management services implemented at the Ann Arbor VAMC through the development of a multidisciplinary pain PACT. The program included using a systematically structured multidimensional approach to identify appropriate treatments and delivery of interdisciplinary care for patients with chronic pain through an OPGP. The authors’ findings establish the feasibility and acceptability of the OPGP. More than 75% of those enrolled completed the program, indicating the promising potential of this approach with significant improvements observed for several pain-related outcomes among those who completed the 8-week program.

Stepped care is a well-established approach to managing complex chronic pain conditions. The approach adds increased levels of treatment intensity when there is no improvement after initial, simple measures are instituted (eg, over-the-counter pain medications, physical therapy, life style changes). Understanding the complexity of the pain experience while treating the patient and not simply the pain has the highest likelihood of helping patients with chronic pain. Given the prevalence of chronic pain among patients in primary care nationally, measurement-based pain care potentially could result in an earlier referral to appropriate care well before pain becomes intractable and chronic.

Growing evidence shows that multidisciplinary treatments reduce pain symptoms and intensity, medication, health care provider use, and improve quality of life.^11-15,29,30 A systematic review by van Tulder and colleagues, for example, noted improvements in physical parameters, such as range of motion and flexibility and behavioral health parameters, including anxiety, depression, and cognition.²⁹ Similarly, the cohort of patients who participated in the OPGP showed statistically significant improvements in several domains of pain-related distress and functioning following treatment, including pain catastrophizing, pain self-efficacy, and the multicomponent pain outcomes questionnaires. Functional improvement also was observed by comparing the distance walked in 6 minutes before and after program completion.

There is significant variation in duration of rehabilitation programs lasting from 2 weeks to 12 weeks or longer. These sessions consist of half days, daily sessions, weekly sessions, and monthly sessions. Inconsistencies also exist among programs that use 3 to 280 professional contact hours. Although it has been shown that programs with more than 100 hours of professional contact tended to have better outcomes than did those with less than 30 hours of contact, Stratton and colleagues reported that a 6-week group program was equivalent or better than a 12- and 10-week group program among veterans.^11,31 These findings along with staffing and resource constraints led to the implementation of the 8-week OPGP with fewer than 30 hours of contact time per group. These results have important practical implications, as shorter treatments may offer comparable therapeutic impact than do longer, more time-intensive protocols.

Limitations

These findings were derived from a quality improvement project within one institution, and several limitations exist. Although the broader purpose of the article was to show how the fundamentals of creating a cohesive multidisciplinary chronic pain team can be implemented within the VA setting, the highlighted outcomes were primarily from participants in the OPGP Since this was not a controlled or experimental study and given potential sample size and selections issues as well as the lack of longer-term follow-up information, further study is needed to draw definitive conclusions about program effectiveness, despite promising preliminary results. In addition, medication use, such as opioids either before or after completion of the program, was not included as part of this evaluation. As previously discussed, medication management for some patients continued beyond the 8-week time frame of the OPGP. Nonetheless, understanding the impact of this team approach on opioid use also is an important topic for future research.

Despite these limitations, the described model could be a feasible option for improving pain management in outpatient practices not only within the VA but in community settings.

Conclusion

These results suggest that the use of short-term, structured therapeutic protocols could be a potentially effective strategy for the behavioral treatment of chronic pain conditions among veterans. The development and implementation of effective, innovative, evidence-based practice to address the needs of patients with chronic pain is an important priority for maximizing clinical service delivery and meeting the needs of the nation’s veterans.

Acknowledgments
The authors thank the previous Associate Chief of Staff, Ambulatory Care, Clinton Greenstone, MD, and Director of Primary Care Adam Tremblay, MD, for their vision, leadership, and support of the team and its efforts.

This work was supported in part through a Department of Veterans Affairs Health Services Research and Development Service Research Career Scientist Award (RCS 11-222) awarded to Sarah Krein, PhD.

Chronic pain is a common health care problem that remains a significant burden for the VHA.^1,2 Some reports indicate that nearly 50% of VA patients report chronic pain.^3,4 Both within and outside the VHA, primary care providers (PCPs) generally manage patients with chronic pain.^5,6 Historically, a biomedical approach to chronic pain also included the use of opioid medications, which may have contributed to increased opioid-related morbidity and mortality especially among the veteran patient population.^7-9 The use of opioids also is controversial due to concerns about adverse effects (AEs), long-term efficacy, functional outcomes, and the potential for drug abuse and addiction.¹⁰ Consequently, alternative treatment options that incorporate an interdisciplinary approach have gained significant interest among pain care providers.¹¹ Interdisciplinary programs have been shown to improve functional status and psychological well-being and to reduce pain severity and opioid use.^12-14 These benefits may persist for a decade or longer.¹⁵

Background

The Stepped Care Model for Pain Management (SCM-PM) is a specific pain treatment approach promoted by the VA National Pain Management Directive.¹⁶ This systematically adjusted approach is associated with improved patient satisfaction and health outcomes for pain and depression.^17,18 At its core, the model promotes engaging patients as active participants in their care along with a team of doctors who can offer an integrated, evidence-based, multimodal, interdisciplinary treatment plan.

To successfully implement this strategy at the VA, patient aligned care teams (PACT) assess and manage patients with common pain conditions through collaboration with mental health, complementary and integrative health services, physical therapy, and other programs, such as opioid renewal clinics and pain schools.¹⁹ This collaborative care approach, which the PCP initiates, is step 1 of the SCM-PM. If initial treatment is not successful and patients are not improving as expected, specialty care consultation and collaborative comanagement through interdisciplinary pain specialty teams are sought (step 2). Finally, step 3 involves tertiary, interdisciplinary care, including access to advanced diagnostic and pain rehabilitation programs accredited by the Commission for Accreditation of Rehabilitation Facilities (CARF).

Although the advantages of interdisciplinary pain programs are clear, resource limitations as well as challenges related to competencies of the PCPs, nurses, and associated health care professionals in pain assessment and management can make implementation of these programs, including the SCM-PM, difficult for many clinics and facilities. Thus, identifying effective chronic pain models and strategies, incorporating the philosophy and key elements of interdisciplinary programs, and accounting for facility resources and capacity are all important.

At the Ann Arbor VAMC, development of a comprehensive interdisciplinary team started with the implementation of joint sessions with a clinical pharmacist and health psychologist embedded in primary care to enhance access to behavioral pain management interventions.²⁰ This program was subsequently expanded to include a pain physician, 2 pain-focused physical therapists (PTs) and a pain nurse.

This article describes a novel team approach for providing more comprehensive, interdisciplinary care for patients with chronic pain along with the initial results for the patients who were part of an outpatient pain group program (OPGP).

Methods

Developing a more interdisciplinary pain management program included integrating different services and creating a strategy for comprehensive evaluation and management of patients with chronic pain. After patients were referred to the interdisciplinary pain clinic by their PCP, they received a systematically structured multidimensional assessment. The primary focus of this assessment was to create an individually directed treatment approach based on the patient’s responses to previous treatments and information collected from several questionnaires administered prior to evaluation. This information helped guide individual patient decision making and actively engaged patients in their care, thus following one of the central tenants of the SCM-PM model. Moreover, functional restoration was at the core of each patient’s evaluation and management. The primary focus was on nonpharmacologic treatment options that included psychological, physical, and occupational therapy; self-management; education; and complementary and alternative therapies. These modalities were offered either individually or in a group setting.

The first step after referral was an evaluation that followed the main core principles for complex disease management described by Tauben and Theodore.²¹ All new patients were asked to complete a 2-question pain intensity and pain interference measure, the 4-question Patient Health Questionnaire (PHQ-4), 4-question Primary Care-PTSD screening tool (PC-PTSD), and the STOP-BANG questionnaire to assess the risk for obstructive sleep apnea.^22-24 Each measure allowed the physician to identify specific problem areas and formulate a treatment plan that would incorporate PTs or occupational therapists, psychologists and/or clinical specialists, and pharmacists if needed.

Patients who were found to have or expressed significant disability because of pain and who wished to learn pain self-management strategies could participate in an 8-week OPGP. This program included the use of cognitive behavioral therapy (CBT) strategies along with group physical therapy classes. Some patients also received individual therapies concurrently with the 8-week OPGP. Patients were excluded from participating in the OPGP only if their current medical or psychiatric status precluded them from full engagement and maximum benefit as determined by the pain physician and psychologist.

Participants and Intervention

Program participants were patients with a chronic pain diagnosis who enrolled in the interdisciplinary pain team OPGP between April 2016 and April 2017. Most patients were referred by their PCPs due to chronic low back, neck, joint or neuropathic pain, although many presented with multiple pain areas. The onset of pain often was a result of a service-related injury or overuse, or the etiology was unknown.

A board-certified pain physician, licensed clinical psychologist, 2 licensed PTs, and a clinical pharmacist led the OPGP sessions. The program was composed of 3-hour-long sessions held weekly for 8 consecutive weeks. Each week, a member of the team covered a specific topic (Table 1).

These sessions focused on the importance of exercise, movement, and physical therapy; appropriate use of medications for managing chronic pain; pacing activities and body mechanics; and the medical approach to managing chronic pain. In addition to didactic presentations, interaction and therapeutic dialogue was encouraged among patients. The education portion of each weekly session lasted about 90 minutes, including a short break. Then, following another short break, patients proceeded to the physical therapy area and engaged in an individualized, monitored exercise program, conducted by the team PTs. Patients also were issued pedometers and encouraged to track their steps each day. Education in improving posture and body mechanics was a key component of the exercise portion of the program so patients could resume their normal daily activities and regain enjoyment in their life. Pain outcomemeasures were collected at admission and immediately before discharge.

Medication management also was an important part of the program for some patients and included tapering off opioids and other drugs and implementing trials of adjuvant pain medications shown to help chronic pain. For some patients, this medication management continued after the patient completed the program.

Measures

The Pain Outcome Questionnaire (POQ) is a 19-item, self-report measure of pain treatment outcomes. Pain rating, mobility, activities of daily living, vitality, negative effect, and fear are the functioning domains evaluated, and the subscale scores are added to produce a total score. The POQ was developed from samples of veterans undergoing inpatient or outpatient pain treatment at VA facilities. For each of the subscales and the total score, higher values indicate poorer outcomes. In normative outpatient VA samples, a total score of 71 is at the 25th percentile, and 120 is at the 75th percentile. The POQ has been shown to have good reliability and validity among veterans in an outpatient setting.²⁵

The Pain Catastrophizing Scale (PCS) is a 13-item scale designed to measure various levels of pain catastrophizing.²⁶ Each item is rated on a 5-point Likert-type scale, from 0 (not at all) to 4 (all the time). The PCS consists of 3 subscale domains: rumination, 4 items; magnification, 3 items; and helplessness, 6 items. Responses to all items also can be added to produce a total score from 0 to 52, with higher scores indicating a higher level of catastrophic thinking related to pain. This project evaluated both the total score and the 3 subscale scores.

The Pain Self-Efficacy Questionnaire (PSEQ) is a 10-item questionnaire that assesses confidence in an individual’s ability to cope or to perform activities despite the pain.²⁷ The PSEQ covers a range of functions, including household chores, socializing, work, as well as coping with pain without medications. Each question has a 7-point Likert scale response: 0 = not at all confident, and 7 = completely confident, to produce a total score from 0 to 60. Higher scores indicate stronger pain self-efficacy, which has been shown to be associated with return to work and maintenance of functional gains.

The Patient Health Questionnaire-4 (PHQ-4) is a 4-item instrument used to screen for depression and anxiety in outpatient medical settings.²² Patients indicate how often they have been bothered by certain problems on a 4-point Likert scale, from 0 (not at all) to 3 (nearly every day). The PHQ-4 provides a total score (0-12) with scores of 6 to 8 indicating moderate and 9 to 12 indicating severe psychological distress; 2 subscale scores, 1 for anxiety (2 questions) and 1 for depression (2 questions). For this analysis, the total PHQ-4 score has been dichotomized with 1 indicating a score in the moderate or severe range vs 0 for a score of mild or no psychological distress. Likewise, each of the subscale scores have been dichotomized with 1 indicating a score of 3 or greater, which is considered a positive screen.

The 6-minute walk test (6MWT) measures the distance (in feet) an individual can walk over a total of 6 minutes on a hard, flat surface.²⁸ Even though the individual can walk at a self-selected pace and rest if needed during the test, the goal is for the patient to walk as far as possible over the course of 6 minutes. The 6MWT provides information regarding functional capacity, response to therapy, and prognosis across a range of chronic conditions, including pain.

Data Analysis

Data analysis included the use of both descriptive and comparative statistics. A descriptive analysis was conducted to examine the characteristics of patients who did and did not complete the OPGP. Specific outcomes for those individuals who completed the program, and thus had complete pre- and post-OPGP information, then were compared. Paired t tests were used to compare differences in continuous measures between baseline (pre-OPGP) and the 8-week follow-up (post-OPGP). Comparisons involving dichotomous measures were made using the Fisher exact test. A 2-sided α with a P value .05 was considered statistically significant. All statistical analyses were conducted using STATA version 14.1 (StataCorp, College Station, TX).

Results

A total of 36 patients enrolled, and 28 (77%) completed the OPGP. Patients who did not complete the program (n = 8) either self-discharged due to lack of interest or had difficulty in consistently making their appointments and decided not to continue (Table 2).

Outcomes for OPGP Completers

Improvements were observed for all outcome domains among patients who completed the program (eTable).

Discussion

This report describes the novel model for improving delivery of chronic pain management services implemented at the Ann Arbor VAMC through the development of a multidisciplinary pain PACT. The program included using a systematically structured multidimensional approach to identify appropriate treatments and delivery of interdisciplinary care for patients with chronic pain through an OPGP. The authors’ findings establish the feasibility and acceptability of the OPGP. More than 75% of those enrolled completed the program, indicating the promising potential of this approach with significant improvements observed for several pain-related outcomes among those who completed the 8-week program.

Stepped care is a well-established approach to managing complex chronic pain conditions. The approach adds increased levels of treatment intensity when there is no improvement after initial, simple measures are instituted (eg, over-the-counter pain medications, physical therapy, life style changes). Understanding the complexity of the pain experience while treating the patient and not simply the pain has the highest likelihood of helping patients with chronic pain. Given the prevalence of chronic pain among patients in primary care nationally, measurement-based pain care potentially could result in an earlier referral to appropriate care well before pain becomes intractable and chronic.

Growing evidence shows that multidisciplinary treatments reduce pain symptoms and intensity, medication, health care provider use, and improve quality of life.^11-15,29,30 A systematic review by van Tulder and colleagues, for example, noted improvements in physical parameters, such as range of motion and flexibility and behavioral health parameters, including anxiety, depression, and cognition.²⁹ Similarly, the cohort of patients who participated in the OPGP showed statistically significant improvements in several domains of pain-related distress and functioning following treatment, including pain catastrophizing, pain self-efficacy, and the multicomponent pain outcomes questionnaires. Functional improvement also was observed by comparing the distance walked in 6 minutes before and after program completion.

There is significant variation in duration of rehabilitation programs lasting from 2 weeks to 12 weeks or longer. These sessions consist of half days, daily sessions, weekly sessions, and monthly sessions. Inconsistencies also exist among programs that use 3 to 280 professional contact hours. Although it has been shown that programs with more than 100 hours of professional contact tended to have better outcomes than did those with less than 30 hours of contact, Stratton and colleagues reported that a 6-week group program was equivalent or better than a 12- and 10-week group program among veterans.^11,31 These findings along with staffing and resource constraints led to the implementation of the 8-week OPGP with fewer than 30 hours of contact time per group. These results have important practical implications, as shorter treatments may offer comparable therapeutic impact than do longer, more time-intensive protocols.

Limitations

These findings were derived from a quality improvement project within one institution, and several limitations exist. Although the broader purpose of the article was to show how the fundamentals of creating a cohesive multidisciplinary chronic pain team can be implemented within the VA setting, the highlighted outcomes were primarily from participants in the OPGP Since this was not a controlled or experimental study and given potential sample size and selections issues as well as the lack of longer-term follow-up information, further study is needed to draw definitive conclusions about program effectiveness, despite promising preliminary results. In addition, medication use, such as opioids either before or after completion of the program, was not included as part of this evaluation. As previously discussed, medication management for some patients continued beyond the 8-week time frame of the OPGP. Nonetheless, understanding the impact of this team approach on opioid use also is an important topic for future research.

Despite these limitations, the described model could be a feasible option for improving pain management in outpatient practices not only within the VA but in community settings.

Conclusion

These results suggest that the use of short-term, structured therapeutic protocols could be a potentially effective strategy for the behavioral treatment of chronic pain conditions among veterans. The development and implementation of effective, innovative, evidence-based practice to address the needs of patients with chronic pain is an important priority for maximizing clinical service delivery and meeting the needs of the nation’s veterans.

Acknowledgments
The authors thank the previous Associate Chief of Staff, Ambulatory Care, Clinton Greenstone, MD, and Director of Primary Care Adam Tremblay, MD, for their vision, leadership, and support of the team and its efforts.

This work was supported in part through a Department of Veterans Affairs Health Services Research and Development Service Research Career Scientist Award (RCS 11-222) awarded to Sarah Krein, PhD.

Chronic pain is a common health care problem that remains a significant burden for the VHA.^1,2 Some reports indicate that nearly 50% of VA patients report chronic pain.^3,4 Both within and outside the VHA, primary care providers (PCPs) generally manage patients with chronic pain.^5,6 Historically, a biomedical approach to chronic pain also included the use of opioid medications, which may have contributed to increased opioid-related morbidity and mortality especially among the veteran patient population.^7-9 The use of opioids also is controversial due to concerns about adverse effects (AEs), long-term efficacy, functional outcomes, and the potential for drug abuse and addiction.¹⁰ Consequently, alternative treatment options that incorporate an interdisciplinary approach have gained significant interest among pain care providers.¹¹ Interdisciplinary programs have been shown to improve functional status and psychological well-being and to reduce pain severity and opioid use.^12-14 These benefits may persist for a decade or longer.¹⁵

Background

The Stepped Care Model for Pain Management (SCM-PM) is a specific pain treatment approach promoted by the VA National Pain Management Directive.¹⁶ This systematically adjusted approach is associated with improved patient satisfaction and health outcomes for pain and depression.^17,18 At its core, the model promotes engaging patients as active participants in their care along with a team of doctors who can offer an integrated, evidence-based, multimodal, interdisciplinary treatment plan.

To successfully implement this strategy at the VA, patient aligned care teams (PACT) assess and manage patients with common pain conditions through collaboration with mental health, complementary and integrative health services, physical therapy, and other programs, such as opioid renewal clinics and pain schools.¹⁹ This collaborative care approach, which the PCP initiates, is step 1 of the SCM-PM. If initial treatment is not successful and patients are not improving as expected, specialty care consultation and collaborative comanagement through interdisciplinary pain specialty teams are sought (step 2). Finally, step 3 involves tertiary, interdisciplinary care, including access to advanced diagnostic and pain rehabilitation programs accredited by the Commission for Accreditation of Rehabilitation Facilities (CARF).

Although the advantages of interdisciplinary pain programs are clear, resource limitations as well as challenges related to competencies of the PCPs, nurses, and associated health care professionals in pain assessment and management can make implementation of these programs, including the SCM-PM, difficult for many clinics and facilities. Thus, identifying effective chronic pain models and strategies, incorporating the philosophy and key elements of interdisciplinary programs, and accounting for facility resources and capacity are all important.

At the Ann Arbor VAMC, development of a comprehensive interdisciplinary team started with the implementation of joint sessions with a clinical pharmacist and health psychologist embedded in primary care to enhance access to behavioral pain management interventions.²⁰ This program was subsequently expanded to include a pain physician, 2 pain-focused physical therapists (PTs) and a pain nurse.

This article describes a novel team approach for providing more comprehensive, interdisciplinary care for patients with chronic pain along with the initial results for the patients who were part of an outpatient pain group program (OPGP).

Methods

Developing a more interdisciplinary pain management program included integrating different services and creating a strategy for comprehensive evaluation and management of patients with chronic pain. After patients were referred to the interdisciplinary pain clinic by their PCP, they received a systematically structured multidimensional assessment. The primary focus of this assessment was to create an individually directed treatment approach based on the patient’s responses to previous treatments and information collected from several questionnaires administered prior to evaluation. This information helped guide individual patient decision making and actively engaged patients in their care, thus following one of the central tenants of the SCM-PM model. Moreover, functional restoration was at the core of each patient’s evaluation and management. The primary focus was on nonpharmacologic treatment options that included psychological, physical, and occupational therapy; self-management; education; and complementary and alternative therapies. These modalities were offered either individually or in a group setting.

The first step after referral was an evaluation that followed the main core principles for complex disease management described by Tauben and Theodore.²¹ All new patients were asked to complete a 2-question pain intensity and pain interference measure, the 4-question Patient Health Questionnaire (PHQ-4), 4-question Primary Care-PTSD screening tool (PC-PTSD), and the STOP-BANG questionnaire to assess the risk for obstructive sleep apnea.^22-24 Each measure allowed the physician to identify specific problem areas and formulate a treatment plan that would incorporate PTs or occupational therapists, psychologists and/or clinical specialists, and pharmacists if needed.

Patients who were found to have or expressed significant disability because of pain and who wished to learn pain self-management strategies could participate in an 8-week OPGP. This program included the use of cognitive behavioral therapy (CBT) strategies along with group physical therapy classes. Some patients also received individual therapies concurrently with the 8-week OPGP. Patients were excluded from participating in the OPGP only if their current medical or psychiatric status precluded them from full engagement and maximum benefit as determined by the pain physician and psychologist.

Participants and Intervention

Program participants were patients with a chronic pain diagnosis who enrolled in the interdisciplinary pain team OPGP between April 2016 and April 2017. Most patients were referred by their PCPs due to chronic low back, neck, joint or neuropathic pain, although many presented with multiple pain areas. The onset of pain often was a result of a service-related injury or overuse, or the etiology was unknown.

A board-certified pain physician, licensed clinical psychologist, 2 licensed PTs, and a clinical pharmacist led the OPGP sessions. The program was composed of 3-hour-long sessions held weekly for 8 consecutive weeks. Each week, a member of the team covered a specific topic (Table 1).

These sessions focused on the importance of exercise, movement, and physical therapy; appropriate use of medications for managing chronic pain; pacing activities and body mechanics; and the medical approach to managing chronic pain. In addition to didactic presentations, interaction and therapeutic dialogue was encouraged among patients. The education portion of each weekly session lasted about 90 minutes, including a short break. Then, following another short break, patients proceeded to the physical therapy area and engaged in an individualized, monitored exercise program, conducted by the team PTs. Patients also were issued pedometers and encouraged to track their steps each day. Education in improving posture and body mechanics was a key component of the exercise portion of the program so patients could resume their normal daily activities and regain enjoyment in their life. Pain outcomemeasures were collected at admission and immediately before discharge.

Medication management also was an important part of the program for some patients and included tapering off opioids and other drugs and implementing trials of adjuvant pain medications shown to help chronic pain. For some patients, this medication management continued after the patient completed the program.

Measures

The Pain Outcome Questionnaire (POQ) is a 19-item, self-report measure of pain treatment outcomes. Pain rating, mobility, activities of daily living, vitality, negative effect, and fear are the functioning domains evaluated, and the subscale scores are added to produce a total score. The POQ was developed from samples of veterans undergoing inpatient or outpatient pain treatment at VA facilities. For each of the subscales and the total score, higher values indicate poorer outcomes. In normative outpatient VA samples, a total score of 71 is at the 25th percentile, and 120 is at the 75th percentile. The POQ has been shown to have good reliability and validity among veterans in an outpatient setting.²⁵

The Pain Catastrophizing Scale (PCS) is a 13-item scale designed to measure various levels of pain catastrophizing.²⁶ Each item is rated on a 5-point Likert-type scale, from 0 (not at all) to 4 (all the time). The PCS consists of 3 subscale domains: rumination, 4 items; magnification, 3 items; and helplessness, 6 items. Responses to all items also can be added to produce a total score from 0 to 52, with higher scores indicating a higher level of catastrophic thinking related to pain. This project evaluated both the total score and the 3 subscale scores.

The Pain Self-Efficacy Questionnaire (PSEQ) is a 10-item questionnaire that assesses confidence in an individual’s ability to cope or to perform activities despite the pain.²⁷ The PSEQ covers a range of functions, including household chores, socializing, work, as well as coping with pain without medications. Each question has a 7-point Likert scale response: 0 = not at all confident, and 7 = completely confident, to produce a total score from 0 to 60. Higher scores indicate stronger pain self-efficacy, which has been shown to be associated with return to work and maintenance of functional gains.

The Patient Health Questionnaire-4 (PHQ-4) is a 4-item instrument used to screen for depression and anxiety in outpatient medical settings.²² Patients indicate how often they have been bothered by certain problems on a 4-point Likert scale, from 0 (not at all) to 3 (nearly every day). The PHQ-4 provides a total score (0-12) with scores of 6 to 8 indicating moderate and 9 to 12 indicating severe psychological distress; 2 subscale scores, 1 for anxiety (2 questions) and 1 for depression (2 questions). For this analysis, the total PHQ-4 score has been dichotomized with 1 indicating a score in the moderate or severe range vs 0 for a score of mild or no psychological distress. Likewise, each of the subscale scores have been dichotomized with 1 indicating a score of 3 or greater, which is considered a positive screen.

The 6-minute walk test (6MWT) measures the distance (in feet) an individual can walk over a total of 6 minutes on a hard, flat surface.²⁸ Even though the individual can walk at a self-selected pace and rest if needed during the test, the goal is for the patient to walk as far as possible over the course of 6 minutes. The 6MWT provides information regarding functional capacity, response to therapy, and prognosis across a range of chronic conditions, including pain.

Data Analysis

Data analysis included the use of both descriptive and comparative statistics. A descriptive analysis was conducted to examine the characteristics of patients who did and did not complete the OPGP. Specific outcomes for those individuals who completed the program, and thus had complete pre- and post-OPGP information, then were compared. Paired t tests were used to compare differences in continuous measures between baseline (pre-OPGP) and the 8-week follow-up (post-OPGP). Comparisons involving dichotomous measures were made using the Fisher exact test. A 2-sided α with a P value .05 was considered statistically significant. All statistical analyses were conducted using STATA version 14.1 (StataCorp, College Station, TX).

Results

A total of 36 patients enrolled, and 28 (77%) completed the OPGP. Patients who did not complete the program (n = 8) either self-discharged due to lack of interest or had difficulty in consistently making their appointments and decided not to continue (Table 2).

Outcomes for OPGP Completers

Improvements were observed for all outcome domains among patients who completed the program (eTable).

Discussion

This report describes the novel model for improving delivery of chronic pain management services implemented at the Ann Arbor VAMC through the development of a multidisciplinary pain PACT. The program included using a systematically structured multidimensional approach to identify appropriate treatments and delivery of interdisciplinary care for patients with chronic pain through an OPGP. The authors’ findings establish the feasibility and acceptability of the OPGP. More than 75% of those enrolled completed the program, indicating the promising potential of this approach with significant improvements observed for several pain-related outcomes among those who completed the 8-week program.

Stepped care is a well-established approach to managing complex chronic pain conditions. The approach adds increased levels of treatment intensity when there is no improvement after initial, simple measures are instituted (eg, over-the-counter pain medications, physical therapy, life style changes). Understanding the complexity of the pain experience while treating the patient and not simply the pain has the highest likelihood of helping patients with chronic pain. Given the prevalence of chronic pain among patients in primary care nationally, measurement-based pain care potentially could result in an earlier referral to appropriate care well before pain becomes intractable and chronic.

Growing evidence shows that multidisciplinary treatments reduce pain symptoms and intensity, medication, health care provider use, and improve quality of life.^11-15,29,30 A systematic review by van Tulder and colleagues, for example, noted improvements in physical parameters, such as range of motion and flexibility and behavioral health parameters, including anxiety, depression, and cognition.²⁹ Similarly, the cohort of patients who participated in the OPGP showed statistically significant improvements in several domains of pain-related distress and functioning following treatment, including pain catastrophizing, pain self-efficacy, and the multicomponent pain outcomes questionnaires. Functional improvement also was observed by comparing the distance walked in 6 minutes before and after program completion.

There is significant variation in duration of rehabilitation programs lasting from 2 weeks to 12 weeks or longer. These sessions consist of half days, daily sessions, weekly sessions, and monthly sessions. Inconsistencies also exist among programs that use 3 to 280 professional contact hours. Although it has been shown that programs with more than 100 hours of professional contact tended to have better outcomes than did those with less than 30 hours of contact, Stratton and colleagues reported that a 6-week group program was equivalent or better than a 12- and 10-week group program among veterans.^11,31 These findings along with staffing and resource constraints led to the implementation of the 8-week OPGP with fewer than 30 hours of contact time per group. These results have important practical implications, as shorter treatments may offer comparable therapeutic impact than do longer, more time-intensive protocols.

Limitations

These findings were derived from a quality improvement project within one institution, and several limitations exist. Although the broader purpose of the article was to show how the fundamentals of creating a cohesive multidisciplinary chronic pain team can be implemented within the VA setting, the highlighted outcomes were primarily from participants in the OPGP Since this was not a controlled or experimental study and given potential sample size and selections issues as well as the lack of longer-term follow-up information, further study is needed to draw definitive conclusions about program effectiveness, despite promising preliminary results. In addition, medication use, such as opioids either before or after completion of the program, was not included as part of this evaluation. As previously discussed, medication management for some patients continued beyond the 8-week time frame of the OPGP. Nonetheless, understanding the impact of this team approach on opioid use also is an important topic for future research.

Despite these limitations, the described model could be a feasible option for improving pain management in outpatient practices not only within the VA but in community settings.

Conclusion

These results suggest that the use of short-term, structured therapeutic protocols could be a potentially effective strategy for the behavioral treatment of chronic pain conditions among veterans. The development and implementation of effective, innovative, evidence-based practice to address the needs of patients with chronic pain is an important priority for maximizing clinical service delivery and meeting the needs of the nation’s veterans.

Acknowledgments
The authors thank the previous Associate Chief of Staff, Ambulatory Care, Clinton Greenstone, MD, and Director of Primary Care Adam Tremblay, MD, for their vision, leadership, and support of the team and its efforts.

This work was supported in part through a Department of Veterans Affairs Health Services Research and Development Service Research Career Scientist Award (RCS 11-222) awarded to Sarah Krein, PhD.

There’s now a blood test to diagnose concussion, elderly people are getting the least protection from the flu vaccine, concerns about VTE from JAK inhibitors may be overblown, and black women are much more likely to have a certain risk factor for preterm birth.

Listen to the MDedge Daily News podcast for all the details on today’s top news.

There’s now a blood test to diagnose concussion, elderly people are getting the least protection from the flu vaccine, concerns about VTE from JAK inhibitors may be overblown, and black women are much more likely to have a certain risk factor for preterm birth.

Listen to the MDedge Daily News podcast for all the details on today’s top news.

There’s now a blood test to diagnose concussion, elderly people are getting the least protection from the flu vaccine, concerns about VTE from JAK inhibitors may be overblown, and black women are much more likely to have a certain risk factor for preterm birth.

Listen to the MDedge Daily News podcast for all the details on today’s top news.

It has been well established that metastatic disease to bone has major significance in the morbidity associated with the diagnosis of cancer.¹ More than 75% of patients with metastatic cancer will have bone involvement at the time of death.^2-4 Moreover, there is a reported 8% incidence of a pathologic fracture in patients who carry the diagnosis of cancer.⁵ Common sites of involvement include the spine, ribs, pelvis, and long bones such as humerus and femur.⁶ Pathologic fracture is fracture caused by disease rather than injury or trauma (referred to here as nonpathologic). In any bone, pathologic fracture will be associated with increased morbidity for the patient, but it is the spine and long bones that frequently require surgical intervention and are associated with high mortality and morbidity. Advanced cancer can also increase fracture risk through increasing falls; in one prospective study of patients with advanced cancer, more than half the patients experienced a fall.⁷

Based on historical studies of patients who have died from common cancers,^4,6 it is commonly believed that breast, lung, thyroid, kidney, and prostate cancers are the most common sources of metastasis to bone, and that other common cancers, such as colorectal carcinoma (CRC), have lower rates of metastasis to bone.^6,8,9 It has been inferred from this data that cancers such as CRC thereby have lower rates of pathologic fracture.

Presence of bone metastasis at time of death may be less clinically relevant than occurrence of pathologic fracture and, especially, pathologic fracture requiring hospitalization. The authors are aware of no studies that have determined the number of patients hospitalized as a result of pathologic fracture from common tumors. Despite cadaveric findings, clinical experience dictates that colorectal carcinoma is not an uncommon primary tumor in patients presenting with metastatic disease and pathologic fracture, whereas thyroid carcinoma is more rare.

Despite lower rates of metastasis to bone from CRC, progression to advanced disease is common, with projected 50,000 deaths in the United States in 2014, and tumor progression is associated with metastasis to bone.¹⁰ Patterns of health care use and costs associated with skeletal-related events in more common metastatic prostate and breast cancer are well documented.^11-13 The authors are aware of no population-based studies examining the burden from metastatic fractures or hospitalization incidence attributed to CRC.
 

Methods

This is a retrospective study of patients hospitalized in the United States with metastatic disease. Data for this study were obtained from the 2003-2010 National (Nationwide) Inpatient Sample (NIS), the Healthcare Cost and Utilization Project (HCUP), and the Agency for Healthcare Research and Quality.¹⁴ The NIS is a stratified sample of approximately 20% of inpatient hospitalization discharges in the United States with more than 7 million hospital stays each year. The dataset contains basic patient demographics, dates of admission, discharge, and procedures, as well as diagnosis and procedure codes for unique hospitalizations. The numbers of new cases of each type of cancer diagnosed in the United States during 2003-2010 were determined from fact sheets published by the American Cancer Society.¹⁵

In all, 1,008,641 patients with metastatic disease in the NIS database, were identified by the presence of International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) diagnosis codes 196.0-199.1. Patients were then classified by primary cancer type based on the presence of additional ICD-9-CM codes for a specific cancer type (140.x-189.x) or for a history of a specific cancer type (V10.00 – V10.91). The analysis was limited to the 10 most common types of cancer. Multiple myeloma, leukemia, lymphoma, and primary cancers of bone also cause pathologic fractures, but they were purposefully excluded from the analysis because they do not represent truly metastatic disease. Patients were excluded if they were younger than 18 years (n = 9,425), had been admitted with major significant trauma (Major Diagnostic Category 24; n = 287), or if the cancer type was either not listed in discharge billing data or not one of the 10 most common types (n = 324,249). Therefore, the final study sample consisted of 674,680 hospitalizations.

The primary outcome assessed was pathologic fracture, identified with ICD-9-CM codes 733.10-733,19. Fractures not due to bone metastasis can occur in patients with metastatic disease owing to falls and general debility; therefore, the secondary outcome was nonpathologic fracture, identified with ICD-9-CM codes for fracture (805.0-829.0) in the absence of a code for pathologic fracture. Fractures classified as a “stress fracture” (ICD-9-CM code 733.9x) or where there was a concomitant diagnosis of osteoporosis (ICD-9-CM cod 733.0x) were also considered nonpathologic for the purpose of this study. Thus there were 3 groups of hospitalized patients identified: metastatic disease without fracture (No Fracture); Pathologic Fracture; and Nonpathologic Fracture. The study was limited to the 10 types of cancer with the highest numbers of pathologic fracture, leaving 647,680 hospitalizations for analysis.

Univariate analyses comparing the Pathologic, Nonpathologic, and No Fracture groups were performed with the Student t test for continuous characteristics and chi-square test for categorical characteristics. All analyses were performed with use of Stata 13.1 (StataCorp, College Station, TX).

This study protocol (RSRB00055625) was reviewed by the Office for Human Subject Protection Research Subjects Review Board at the University of Rochester and was determined to meet exemption criteria.

Results

From 2003-2010 there were 674,680 hospitalizations in patients with metastatic cancer that met the inclusion criteria. Hospitalization was most frequent for lung cancer (187,059 admissions), colorectal cancer (172,039), and breast cancer (124,303; Table 1).

There were 17,303 hospitalizations with pathologic fracture and 12,770 hospitalizations with nonpathologic fracture (Figure 1).

Discussion

Other investigators have looked at risk factors for pathologic fracture, such as degree of bone involvement, location, and the presence of lytic versus blastic disease, as well as the optimal management of such patients.^16-20 In those analyses, there is an emphasis on large, lytic lesions with cortical destruction in weight-bearing long bones, and on functional pain as a key determinant of fracture risk. Although the guidelines outlined by Mirel and others are helpful in predicting fractures, they are not widely applied by practicing oncologists.¹⁸ Oncologists and surgeons lack foolproof criteria to predict impending pathologic fracture despite evidence that the pathologic fracture event greatly increases mortality and morbidity.^1,4,21,22 As far as we know, this is the first study to determine which types of primary carcinomas were most associated with pathologic fracture requiring hospitalization. This finding will hopefully raise awareness among doctors who care for these patients to be particularly conscientious with patients who present with symptoms of bone pain with activity (functional bone pain) or with lytic disease in the long bones. The results of the present study are similar to those from cadaveric studies, which emphasize the importance of lung, breast, prostate, and kidney cancers as primary tumors that metastasize to bone and lead to pathologic fracture. A novel finding is the nearly 4-fold greater number of pathologic fractures from colorectal carcinoma than thyroid carcinoma.

The importance of detecting patients at risk for pathologic fracture is now more relevant than ever because there are treatment modalities that are readily available to patients with metastatic bone involvement. Two classes of medications, the RANK-ligand inhibitors and bisphosphonates, reduce the number of skeletal events, such as pathologic fracture, in patients with metastatic disease to bone.^23-26 However, most of those studies focused on the 3 most common carcinomas (breast, lung, and prostate) to metastasize to bone and cause pathologic fracture. There is greater variability in the prophylactic treatment of other forms of cancer that have metastasized to bone amongst oncologists.

Despite a lower proportion of hospitalizations for fracture in patients with CRC than for thyroid carcinoma (0.5% vs 1.6%, respectively), there were more pathologic fractures from CRC than from thyroid carcinoma because there are far more cases of CRC. SEER data estimate that in 2014 there were 62,000 cases of thyroid cancer and 1,890 deaths, compared with 136,000 cases of CRC and 50,000 deaths.¹⁰ Previous findings have shown that bone metastasis from CRC is more common than originally thought, based on autopsies of CRC patients.³ However, the lower rate of bone metastasis in CRC compared with other malignancies has led to a decreased focus on skeletal-related events in CRC. Our results suggest vigilance to bone health is warranted in patients with metastatic CRC. A novel finding is that patients with metastatic CRC also have a high number of hospital admissions for nonpathologic fracture. In establishing that patients with metastatic CRC with bone involvement have a real and significant risk of developing both pathologic and nonpathologic fractures, it may alter the treatment practice for these patients going forward, with greater consideration for an antiresorptive therapy, fall prevention education, or other preventive modalities, such as external-beam radiation therapy after it has been established that patients have metastatic bone disease.

There were some demographic differences between patients with metastatic disease who sustain pathologic fractures and those who do not fracture or sustain nonpathologic fractures. Patients with pathologic fracture were younger than those with nonpathologic fractures, and patients who sustained any fracture were more likely to be white than were patients in the no-fracture group. Known osteoporosis risk factors including older, female, and white with Northern European descent.²⁷ Those findings emphasize the importance of osteoporosis screening and fracture prevention in patients with metastatic disease in general, regardless of the presence of bony metastasis. The present study found that patients who reside in zip codes areas with higher incomes were at slightly increased risk of hospitalization for pathologic fracture. Economic disparities in access to health care and cancer care are well documented,²⁸ and the basis for this finding is a direction for future research.

Both mean billed costs and length of stay were greatest in the pathologic fracture group. The large number of admissions for no-fractured patients may be a final opportunity for intervention and preventative measures in this fragile population. Improved surveillance for bony lesions and attention to pain, especially at night, or unexplained hypercalcemia may help with early diagnosis and prevent some pathologic fractures. Patients with pathologic fracture often undergo additional treatments such as radiation therapy or chemotherapy. These additional treatments may partially explain the higher billed costs associated with inpatient hospitalization; future studies may be able to elucidate treatment differences or other reasons for the increased costs associated with pathologic fractures and identify targets to reduce expenditures.
 

Limitations

This study is subject to the limitations of a retrospective analysis based on hospital administrative discharge data. It evaluates only billed charges and does not account for costs associated with rehabilitation stays. However, it represents a stratified cross-sample of hospitalizations in the United States, in both teaching and nonteaching hospitals, and is the largest study to date that the authors are aware of looking at the burden of pathologic fractures in patients with metastatic disease.

This study specifically included only patients with metastatic disease, which therefore limits comparisons with the rate of hospitalization for nonpathologic fracture in patients without metastatic disease. Patients with metastatic disease who were not hospitalized during the study period are nevertheless at risk for fracture but would not have been captured in this study. It is also likely that some patients with metastatic disease had multiple hospitalizations, including some that were not for fracture; therefore, this study likely underestimates the percentage of patients with metastatic disease who sustain pathologic and nonpathologic fracture.

Some patients were excluded because we were not able to identify a primary cancer from hospital discharge records. The lack of an included diagnosis may be a result of indeterminate primary during the fracture admission or may represent a failure to accurately code a primary, known cancer. Although the NIS does not permit identification of these patients to determine if a primary cancer was subsequently identified, future studies using other databases may target patients presenting with pathologic fracture and an unknown primary tumor to evaluate subsequent cancer diagnosis.
 

Summary

The significance of bone metastasis in causing pathologic fractures in lung, breast, prostate, and kidney cancers was confirmed. Colorectal carcinoma has been established as the fifth most common primary cancer in patients with metastatic disease who are hospitalized with pathologic fracture, and a large number of patients with metastatic CRC sustain nonpathologic fractures requiring hospitalization. In patients with metastatic CRC or new skeletal pain, education on fall prevention and increased vigilance should be considered. Further studies are needed to determine the best method for prevention of pathologic fractures in all highly prevalent cancers, with previous hospitalizations without fracture as an appropriate target. Previous paradigms about which cancers metastasize to bone should be reconsidered in the context of which lead to clinically important fractures and hospitalization.

It has been well established that metastatic disease to bone has major significance in the morbidity associated with the diagnosis of cancer.¹ More than 75% of patients with metastatic cancer will have bone involvement at the time of death.^2-4 Moreover, there is a reported 8% incidence of a pathologic fracture in patients who carry the diagnosis of cancer.⁵ Common sites of involvement include the spine, ribs, pelvis, and long bones such as humerus and femur.⁶ Pathologic fracture is fracture caused by disease rather than injury or trauma (referred to here as nonpathologic). In any bone, pathologic fracture will be associated with increased morbidity for the patient, but it is the spine and long bones that frequently require surgical intervention and are associated with high mortality and morbidity. Advanced cancer can also increase fracture risk through increasing falls; in one prospective study of patients with advanced cancer, more than half the patients experienced a fall.⁷

Based on historical studies of patients who have died from common cancers,^4,6 it is commonly believed that breast, lung, thyroid, kidney, and prostate cancers are the most common sources of metastasis to bone, and that other common cancers, such as colorectal carcinoma (CRC), have lower rates of metastasis to bone.^6,8,9 It has been inferred from this data that cancers such as CRC thereby have lower rates of pathologic fracture.

Presence of bone metastasis at time of death may be less clinically relevant than occurrence of pathologic fracture and, especially, pathologic fracture requiring hospitalization. The authors are aware of no studies that have determined the number of patients hospitalized as a result of pathologic fracture from common tumors. Despite cadaveric findings, clinical experience dictates that colorectal carcinoma is not an uncommon primary tumor in patients presenting with metastatic disease and pathologic fracture, whereas thyroid carcinoma is more rare.

Despite lower rates of metastasis to bone from CRC, progression to advanced disease is common, with projected 50,000 deaths in the United States in 2014, and tumor progression is associated with metastasis to bone.¹⁰ Patterns of health care use and costs associated with skeletal-related events in more common metastatic prostate and breast cancer are well documented.^11-13 The authors are aware of no population-based studies examining the burden from metastatic fractures or hospitalization incidence attributed to CRC.
 

Methods

This is a retrospective study of patients hospitalized in the United States with metastatic disease. Data for this study were obtained from the 2003-2010 National (Nationwide) Inpatient Sample (NIS), the Healthcare Cost and Utilization Project (HCUP), and the Agency for Healthcare Research and Quality.¹⁴ The NIS is a stratified sample of approximately 20% of inpatient hospitalization discharges in the United States with more than 7 million hospital stays each year. The dataset contains basic patient demographics, dates of admission, discharge, and procedures, as well as diagnosis and procedure codes for unique hospitalizations. The numbers of new cases of each type of cancer diagnosed in the United States during 2003-2010 were determined from fact sheets published by the American Cancer Society.¹⁵

In all, 1,008,641 patients with metastatic disease in the NIS database, were identified by the presence of International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) diagnosis codes 196.0-199.1. Patients were then classified by primary cancer type based on the presence of additional ICD-9-CM codes for a specific cancer type (140.x-189.x) or for a history of a specific cancer type (V10.00 – V10.91). The analysis was limited to the 10 most common types of cancer. Multiple myeloma, leukemia, lymphoma, and primary cancers of bone also cause pathologic fractures, but they were purposefully excluded from the analysis because they do not represent truly metastatic disease. Patients were excluded if they were younger than 18 years (n = 9,425), had been admitted with major significant trauma (Major Diagnostic Category 24; n = 287), or if the cancer type was either not listed in discharge billing data or not one of the 10 most common types (n = 324,249). Therefore, the final study sample consisted of 674,680 hospitalizations.

The primary outcome assessed was pathologic fracture, identified with ICD-9-CM codes 733.10-733,19. Fractures not due to bone metastasis can occur in patients with metastatic disease owing to falls and general debility; therefore, the secondary outcome was nonpathologic fracture, identified with ICD-9-CM codes for fracture (805.0-829.0) in the absence of a code for pathologic fracture. Fractures classified as a “stress fracture” (ICD-9-CM code 733.9x) or where there was a concomitant diagnosis of osteoporosis (ICD-9-CM cod 733.0x) were also considered nonpathologic for the purpose of this study. Thus there were 3 groups of hospitalized patients identified: metastatic disease without fracture (No Fracture); Pathologic Fracture; and Nonpathologic Fracture. The study was limited to the 10 types of cancer with the highest numbers of pathologic fracture, leaving 647,680 hospitalizations for analysis.

Univariate analyses comparing the Pathologic, Nonpathologic, and No Fracture groups were performed with the Student t test for continuous characteristics and chi-square test for categorical characteristics. All analyses were performed with use of Stata 13.1 (StataCorp, College Station, TX).

This study protocol (RSRB00055625) was reviewed by the Office for Human Subject Protection Research Subjects Review Board at the University of Rochester and was determined to meet exemption criteria.

Results

From 2003-2010 there were 674,680 hospitalizations in patients with metastatic cancer that met the inclusion criteria. Hospitalization was most frequent for lung cancer (187,059 admissions), colorectal cancer (172,039), and breast cancer (124,303; Table 1).

There were 17,303 hospitalizations with pathologic fracture and 12,770 hospitalizations with nonpathologic fracture (Figure 1).

Discussion

Other investigators have looked at risk factors for pathologic fracture, such as degree of bone involvement, location, and the presence of lytic versus blastic disease, as well as the optimal management of such patients.^16-20 In those analyses, there is an emphasis on large, lytic lesions with cortical destruction in weight-bearing long bones, and on functional pain as a key determinant of fracture risk. Although the guidelines outlined by Mirel and others are helpful in predicting fractures, they are not widely applied by practicing oncologists.¹⁸ Oncologists and surgeons lack foolproof criteria to predict impending pathologic fracture despite evidence that the pathologic fracture event greatly increases mortality and morbidity.^1,4,21,22 As far as we know, this is the first study to determine which types of primary carcinomas were most associated with pathologic fracture requiring hospitalization. This finding will hopefully raise awareness among doctors who care for these patients to be particularly conscientious with patients who present with symptoms of bone pain with activity (functional bone pain) or with lytic disease in the long bones. The results of the present study are similar to those from cadaveric studies, which emphasize the importance of lung, breast, prostate, and kidney cancers as primary tumors that metastasize to bone and lead to pathologic fracture. A novel finding is the nearly 4-fold greater number of pathologic fractures from colorectal carcinoma than thyroid carcinoma.

The importance of detecting patients at risk for pathologic fracture is now more relevant than ever because there are treatment modalities that are readily available to patients with metastatic bone involvement. Two classes of medications, the RANK-ligand inhibitors and bisphosphonates, reduce the number of skeletal events, such as pathologic fracture, in patients with metastatic disease to bone.^23-26 However, most of those studies focused on the 3 most common carcinomas (breast, lung, and prostate) to metastasize to bone and cause pathologic fracture. There is greater variability in the prophylactic treatment of other forms of cancer that have metastasized to bone amongst oncologists.

Despite a lower proportion of hospitalizations for fracture in patients with CRC than for thyroid carcinoma (0.5% vs 1.6%, respectively), there were more pathologic fractures from CRC than from thyroid carcinoma because there are far more cases of CRC. SEER data estimate that in 2014 there were 62,000 cases of thyroid cancer and 1,890 deaths, compared with 136,000 cases of CRC and 50,000 deaths.¹⁰ Previous findings have shown that bone metastasis from CRC is more common than originally thought, based on autopsies of CRC patients.³ However, the lower rate of bone metastasis in CRC compared with other malignancies has led to a decreased focus on skeletal-related events in CRC. Our results suggest vigilance to bone health is warranted in patients with metastatic CRC. A novel finding is that patients with metastatic CRC also have a high number of hospital admissions for nonpathologic fracture. In establishing that patients with metastatic CRC with bone involvement have a real and significant risk of developing both pathologic and nonpathologic fractures, it may alter the treatment practice for these patients going forward, with greater consideration for an antiresorptive therapy, fall prevention education, or other preventive modalities, such as external-beam radiation therapy after it has been established that patients have metastatic bone disease.

There were some demographic differences between patients with metastatic disease who sustain pathologic fractures and those who do not fracture or sustain nonpathologic fractures. Patients with pathologic fracture were younger than those with nonpathologic fractures, and patients who sustained any fracture were more likely to be white than were patients in the no-fracture group. Known osteoporosis risk factors including older, female, and white with Northern European descent.²⁷ Those findings emphasize the importance of osteoporosis screening and fracture prevention in patients with metastatic disease in general, regardless of the presence of bony metastasis. The present study found that patients who reside in zip codes areas with higher incomes were at slightly increased risk of hospitalization for pathologic fracture. Economic disparities in access to health care and cancer care are well documented,²⁸ and the basis for this finding is a direction for future research.

Both mean billed costs and length of stay were greatest in the pathologic fracture group. The large number of admissions for no-fractured patients may be a final opportunity for intervention and preventative measures in this fragile population. Improved surveillance for bony lesions and attention to pain, especially at night, or unexplained hypercalcemia may help with early diagnosis and prevent some pathologic fractures. Patients with pathologic fracture often undergo additional treatments such as radiation therapy or chemotherapy. These additional treatments may partially explain the higher billed costs associated with inpatient hospitalization; future studies may be able to elucidate treatment differences or other reasons for the increased costs associated with pathologic fractures and identify targets to reduce expenditures.
 

Limitations

This study is subject to the limitations of a retrospective analysis based on hospital administrative discharge data. It evaluates only billed charges and does not account for costs associated with rehabilitation stays. However, it represents a stratified cross-sample of hospitalizations in the United States, in both teaching and nonteaching hospitals, and is the largest study to date that the authors are aware of looking at the burden of pathologic fractures in patients with metastatic disease.

This study specifically included only patients with metastatic disease, which therefore limits comparisons with the rate of hospitalization for nonpathologic fracture in patients without metastatic disease. Patients with metastatic disease who were not hospitalized during the study period are nevertheless at risk for fracture but would not have been captured in this study. It is also likely that some patients with metastatic disease had multiple hospitalizations, including some that were not for fracture; therefore, this study likely underestimates the percentage of patients with metastatic disease who sustain pathologic and nonpathologic fracture.

Some patients were excluded because we were not able to identify a primary cancer from hospital discharge records. The lack of an included diagnosis may be a result of indeterminate primary during the fracture admission or may represent a failure to accurately code a primary, known cancer. Although the NIS does not permit identification of these patients to determine if a primary cancer was subsequently identified, future studies using other databases may target patients presenting with pathologic fracture and an unknown primary tumor to evaluate subsequent cancer diagnosis.
 

Summary

The significance of bone metastasis in causing pathologic fractures in lung, breast, prostate, and kidney cancers was confirmed. Colorectal carcinoma has been established as the fifth most common primary cancer in patients with metastatic disease who are hospitalized with pathologic fracture, and a large number of patients with metastatic CRC sustain nonpathologic fractures requiring hospitalization. In patients with metastatic CRC or new skeletal pain, education on fall prevention and increased vigilance should be considered. Further studies are needed to determine the best method for prevention of pathologic fractures in all highly prevalent cancers, with previous hospitalizations without fracture as an appropriate target. Previous paradigms about which cancers metastasize to bone should be reconsidered in the context of which lead to clinically important fractures and hospitalization.

It has been well established that metastatic disease to bone has major significance in the morbidity associated with the diagnosis of cancer.¹ More than 75% of patients with metastatic cancer will have bone involvement at the time of death.^2-4 Moreover, there is a reported 8% incidence of a pathologic fracture in patients who carry the diagnosis of cancer.⁵ Common sites of involvement include the spine, ribs, pelvis, and long bones such as humerus and femur.⁶ Pathologic fracture is fracture caused by disease rather than injury or trauma (referred to here as nonpathologic). In any bone, pathologic fracture will be associated with increased morbidity for the patient, but it is the spine and long bones that frequently require surgical intervention and are associated with high mortality and morbidity. Advanced cancer can also increase fracture risk through increasing falls; in one prospective study of patients with advanced cancer, more than half the patients experienced a fall.⁷

Based on historical studies of patients who have died from common cancers,^4,6 it is commonly believed that breast, lung, thyroid, kidney, and prostate cancers are the most common sources of metastasis to bone, and that other common cancers, such as colorectal carcinoma (CRC), have lower rates of metastasis to bone.^6,8,9 It has been inferred from this data that cancers such as CRC thereby have lower rates of pathologic fracture.

Presence of bone metastasis at time of death may be less clinically relevant than occurrence of pathologic fracture and, especially, pathologic fracture requiring hospitalization. The authors are aware of no studies that have determined the number of patients hospitalized as a result of pathologic fracture from common tumors. Despite cadaveric findings, clinical experience dictates that colorectal carcinoma is not an uncommon primary tumor in patients presenting with metastatic disease and pathologic fracture, whereas thyroid carcinoma is more rare.

Despite lower rates of metastasis to bone from CRC, progression to advanced disease is common, with projected 50,000 deaths in the United States in 2014, and tumor progression is associated with metastasis to bone.¹⁰ Patterns of health care use and costs associated with skeletal-related events in more common metastatic prostate and breast cancer are well documented.^11-13 The authors are aware of no population-based studies examining the burden from metastatic fractures or hospitalization incidence attributed to CRC.
 

Methods

This is a retrospective study of patients hospitalized in the United States with metastatic disease. Data for this study were obtained from the 2003-2010 National (Nationwide) Inpatient Sample (NIS), the Healthcare Cost and Utilization Project (HCUP), and the Agency for Healthcare Research and Quality.¹⁴ The NIS is a stratified sample of approximately 20% of inpatient hospitalization discharges in the United States with more than 7 million hospital stays each year. The dataset contains basic patient demographics, dates of admission, discharge, and procedures, as well as diagnosis and procedure codes for unique hospitalizations. The numbers of new cases of each type of cancer diagnosed in the United States during 2003-2010 were determined from fact sheets published by the American Cancer Society.¹⁵

In all, 1,008,641 patients with metastatic disease in the NIS database, were identified by the presence of International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) diagnosis codes 196.0-199.1. Patients were then classified by primary cancer type based on the presence of additional ICD-9-CM codes for a specific cancer type (140.x-189.x) or for a history of a specific cancer type (V10.00 – V10.91). The analysis was limited to the 10 most common types of cancer. Multiple myeloma, leukemia, lymphoma, and primary cancers of bone also cause pathologic fractures, but they were purposefully excluded from the analysis because they do not represent truly metastatic disease. Patients were excluded if they were younger than 18 years (n = 9,425), had been admitted with major significant trauma (Major Diagnostic Category 24; n = 287), or if the cancer type was either not listed in discharge billing data or not one of the 10 most common types (n = 324,249). Therefore, the final study sample consisted of 674,680 hospitalizations.

The primary outcome assessed was pathologic fracture, identified with ICD-9-CM codes 733.10-733,19. Fractures not due to bone metastasis can occur in patients with metastatic disease owing to falls and general debility; therefore, the secondary outcome was nonpathologic fracture, identified with ICD-9-CM codes for fracture (805.0-829.0) in the absence of a code for pathologic fracture. Fractures classified as a “stress fracture” (ICD-9-CM code 733.9x) or where there was a concomitant diagnosis of osteoporosis (ICD-9-CM cod 733.0x) were also considered nonpathologic for the purpose of this study. Thus there were 3 groups of hospitalized patients identified: metastatic disease without fracture (No Fracture); Pathologic Fracture; and Nonpathologic Fracture. The study was limited to the 10 types of cancer with the highest numbers of pathologic fracture, leaving 647,680 hospitalizations for analysis.

Univariate analyses comparing the Pathologic, Nonpathologic, and No Fracture groups were performed with the Student t test for continuous characteristics and chi-square test for categorical characteristics. All analyses were performed with use of Stata 13.1 (StataCorp, College Station, TX).

This study protocol (RSRB00055625) was reviewed by the Office for Human Subject Protection Research Subjects Review Board at the University of Rochester and was determined to meet exemption criteria.

Results

From 2003-2010 there were 674,680 hospitalizations in patients with metastatic cancer that met the inclusion criteria. Hospitalization was most frequent for lung cancer (187,059 admissions), colorectal cancer (172,039), and breast cancer (124,303; Table 1).

There were 17,303 hospitalizations with pathologic fracture and 12,770 hospitalizations with nonpathologic fracture (Figure 1).

Discussion

Other investigators have looked at risk factors for pathologic fracture, such as degree of bone involvement, location, and the presence of lytic versus blastic disease, as well as the optimal management of such patients.^16-20 In those analyses, there is an emphasis on large, lytic lesions with cortical destruction in weight-bearing long bones, and on functional pain as a key determinant of fracture risk. Although the guidelines outlined by Mirel and others are helpful in predicting fractures, they are not widely applied by practicing oncologists.¹⁸ Oncologists and surgeons lack foolproof criteria to predict impending pathologic fracture despite evidence that the pathologic fracture event greatly increases mortality and morbidity.^1,4,21,22 As far as we know, this is the first study to determine which types of primary carcinomas were most associated with pathologic fracture requiring hospitalization. This finding will hopefully raise awareness among doctors who care for these patients to be particularly conscientious with patients who present with symptoms of bone pain with activity (functional bone pain) or with lytic disease in the long bones. The results of the present study are similar to those from cadaveric studies, which emphasize the importance of lung, breast, prostate, and kidney cancers as primary tumors that metastasize to bone and lead to pathologic fracture. A novel finding is the nearly 4-fold greater number of pathologic fractures from colorectal carcinoma than thyroid carcinoma.

The importance of detecting patients at risk for pathologic fracture is now more relevant than ever because there are treatment modalities that are readily available to patients with metastatic bone involvement. Two classes of medications, the RANK-ligand inhibitors and bisphosphonates, reduce the number of skeletal events, such as pathologic fracture, in patients with metastatic disease to bone.^23-26 However, most of those studies focused on the 3 most common carcinomas (breast, lung, and prostate) to metastasize to bone and cause pathologic fracture. There is greater variability in the prophylactic treatment of other forms of cancer that have metastasized to bone amongst oncologists.

Despite a lower proportion of hospitalizations for fracture in patients with CRC than for thyroid carcinoma (0.5% vs 1.6%, respectively), there were more pathologic fractures from CRC than from thyroid carcinoma because there are far more cases of CRC. SEER data estimate that in 2014 there were 62,000 cases of thyroid cancer and 1,890 deaths, compared with 136,000 cases of CRC and 50,000 deaths.¹⁰ Previous findings have shown that bone metastasis from CRC is more common than originally thought, based on autopsies of CRC patients.³ However, the lower rate of bone metastasis in CRC compared with other malignancies has led to a decreased focus on skeletal-related events in CRC. Our results suggest vigilance to bone health is warranted in patients with metastatic CRC. A novel finding is that patients with metastatic CRC also have a high number of hospital admissions for nonpathologic fracture. In establishing that patients with metastatic CRC with bone involvement have a real and significant risk of developing both pathologic and nonpathologic fractures, it may alter the treatment practice for these patients going forward, with greater consideration for an antiresorptive therapy, fall prevention education, or other preventive modalities, such as external-beam radiation therapy after it has been established that patients have metastatic bone disease.

There were some demographic differences between patients with metastatic disease who sustain pathologic fractures and those who do not fracture or sustain nonpathologic fractures. Patients with pathologic fracture were younger than those with nonpathologic fractures, and patients who sustained any fracture were more likely to be white than were patients in the no-fracture group. Known osteoporosis risk factors including older, female, and white with Northern European descent.²⁷ Those findings emphasize the importance of osteoporosis screening and fracture prevention in patients with metastatic disease in general, regardless of the presence of bony metastasis. The present study found that patients who reside in zip codes areas with higher incomes were at slightly increased risk of hospitalization for pathologic fracture. Economic disparities in access to health care and cancer care are well documented,²⁸ and the basis for this finding is a direction for future research.

Both mean billed costs and length of stay were greatest in the pathologic fracture group. The large number of admissions for no-fractured patients may be a final opportunity for intervention and preventative measures in this fragile population. Improved surveillance for bony lesions and attention to pain, especially at night, or unexplained hypercalcemia may help with early diagnosis and prevent some pathologic fractures. Patients with pathologic fracture often undergo additional treatments such as radiation therapy or chemotherapy. These additional treatments may partially explain the higher billed costs associated with inpatient hospitalization; future studies may be able to elucidate treatment differences or other reasons for the increased costs associated with pathologic fractures and identify targets to reduce expenditures.
 

Limitations

This study is subject to the limitations of a retrospective analysis based on hospital administrative discharge data. It evaluates only billed charges and does not account for costs associated with rehabilitation stays. However, it represents a stratified cross-sample of hospitalizations in the United States, in both teaching and nonteaching hospitals, and is the largest study to date that the authors are aware of looking at the burden of pathologic fractures in patients with metastatic disease.

This study specifically included only patients with metastatic disease, which therefore limits comparisons with the rate of hospitalization for nonpathologic fracture in patients without metastatic disease. Patients with metastatic disease who were not hospitalized during the study period are nevertheless at risk for fracture but would not have been captured in this study. It is also likely that some patients with metastatic disease had multiple hospitalizations, including some that were not for fracture; therefore, this study likely underestimates the percentage of patients with metastatic disease who sustain pathologic and nonpathologic fracture.

Some patients were excluded because we were not able to identify a primary cancer from hospital discharge records. The lack of an included diagnosis may be a result of indeterminate primary during the fracture admission or may represent a failure to accurately code a primary, known cancer. Although the NIS does not permit identification of these patients to determine if a primary cancer was subsequently identified, future studies using other databases may target patients presenting with pathologic fracture and an unknown primary tumor to evaluate subsequent cancer diagnosis.
 

Summary

The significance of bone metastasis in causing pathologic fractures in lung, breast, prostate, and kidney cancers was confirmed. Colorectal carcinoma has been established as the fifth most common primary cancer in patients with metastatic disease who are hospitalized with pathologic fracture, and a large number of patients with metastatic CRC sustain nonpathologic fractures requiring hospitalization. In patients with metastatic CRC or new skeletal pain, education on fall prevention and increased vigilance should be considered. Further studies are needed to determine the best method for prevention of pathologic fractures in all highly prevalent cancers, with previous hospitalizations without fracture as an appropriate target. Previous paradigms about which cancers metastasize to bone should be reconsidered in the context of which lead to clinically important fractures and hospitalization.

Physical activity has numerous physical, mental, and psychosocial benefits for cancer survivors, such as a reduction in the risk of mobility disability, depression, and anxiety, and improved patient quality of life.^1,2 In addition, higher levels of physical activity are associated with reduced cancer-specific and all-causes mortality as well as cancer-specific outcomes including reduced risk of cancer progression and recurrence and new primary cancers.^3-5 However, fewer than one-third of cancer survivors are meeting government and cancer-specific recommendations of 150 minutes a week of moderate to vigorous physical activity (MPVA; ≥3 metabolic equivalents [METs]).^6,7 Growing evidence also demonstrates a significant association between higher levels of sedentary behavior and many deleterious health effects after cancer, including an increased risk for decreased physical functioning and development of other chronic diseases such as cardiovascular disease or diabetes.⁸ Distinct from physical activity, sedentary behavior is defined as any waking activity resulting in low levels of energy expenditure (≤1.5 METs) while in a seated or reclined position.⁹ Increased sedentary behavior, even when controlling for moderate and vigorous physical activity (MVPA), is associated with poor quality of life and increased all-cause mortality in cancer survivors.^10,11 Given the associations observed between higher levels of physical activity, lower levels of sedentary behavior, and improved health and disease outcomes among the large and increasing number of cancer survivors in the United States, it is important to identify low-cost methods that can be used in a in a variety of settings (ie, research, clinical, community) to accurately and efficiently measure survivors’ lifestyle behaviors to identify high-risk survivors for early intervention, better understand the effects of these behaviors on survivors’ health outcomes and disease trajectories, and ultimately, improve survivors’ health and quality of life.^12,13

Two methods commonly used to capture physical activity and sedentary behavior across the lifespan are accelerometry (Actigraph, Pensacola, FL) and self-report questionnaires such as the Godin Leisure-Time Questionnaire (GLTEQ), International Physical Activity Questionnaire (IPAQ), and Sitting Time Questionnaire (STQ).^14-17 Each method has unique strengths and weaknesses. Sending accelerometers to multiple individuals at a single time point can be costly, particularly in large-scale epidemiological studies, and the accelerometer’s waist-worn, nonwaterproof design may prevent researchers from capturing certain activities such as swimming and resistance training. However, the accelerometer provides objective, precise assessments of most physical activities and may help remove response bias.¹⁸ Conversely, self-report questionnaires rely solely on individuals’ memories and often result in recall bias, inaccurate reporting, and under- or overestimation of physical activity engagement.^19,20 Nevertheless, these questionnaires can be widely disseminated at low cost in a variety of settings (eg, clinical, research, community) and are less of a burden to participants.

Recent studies comparing objective (eg, accelerometer) with subjective (eg, self-report) methods of measuring physical activity and sedentary behavior in healthy middle-aged adults and older adults have demonstrated mixed findings with no distinct trends in the degree to which these methods differ.^19,21,22 To date, little consideration has been given to the measurement of these lifestyle behaviors in cancer survivors. Boyle and colleagues recently investigated the concurrent validity of an accelerometer to the GLTEQ in colon cancer survivors, finding significant differences in estimated MVPA (~11 minutes). However, no studies, to our knowledge, have compared accelerometer and self-report measures in breast cancer survivors, so it remains unclear how these different measurement tools relate to each another in this population.

It is particularly important to compare these measurement tools among breast cancer survivors because evidence indicates this population’s behavioral habits, self-perceived activity, and sitting time and movement patterns may differ significantly from the general population and other survivor groups across the lifespan.^23,24 Further, previous studies examining these behaviors in cancer survivors focused primarily on sitting time and MVPA.^15,25,26 Examining other lower-intensity intensities (eg, light activity or lifestyle) in cancer survivors may also be important given that increased levels of activity are associated with health benefits, ranging from reduced disability and fatigue to improved cardiovascular health and quality of life, and that breast cancer survivors engage in fewer of these activities compared with noncancer controls.²³ These lower levels of physical activity may be more prevalent among cancer survivors of their high levels of fatigue and propensity toward increased sitting time during the first year of treatment,¹¹ so it is important to be able to accurately assess these activities in this population. The purpose of the present study was to compare estimates of time spent in light physical activity (LPA), MVPA, and sitting time (ST) obtained from an accelerometer and 3 self-report measurement tools (GLTEQ, IPAQ, STQ) in a large, US-based sample of breast cancer survivors. A secondary purpose was to determine whether estimate comparisons among measurements changed by participant characteristics.
 

Methods

Participants and procedures

This study consisted of a subsample of women who participated in a larger study whose findings have been reported elsewhere by Phillips and McAuley.²⁷ In that study, breast cancer survivors (n = 1,631) were recruited nationally to participate in a 6-month prospective study on quality of life. Eligibility criteria included being aged 18 years or older, having had a diagnosis of breast cancer, being English speaking, and having access to the internet. Once consented to participate in the study, 500 women were randomly selected to wear the accelerometer.

Participants in this group were mailed an accelerometer, an activity log, instructions for use, and a self-addressed stamped envelope to return the monitor. They were asked to wear the accelerometer during all waking hours for 7 consecutive days of usual activity. They were also sent a secure link to complete 3 activity questionnaires online. The questionnaires were to be completed by the end of the 7-day monitoring period. Only women with 3 or more valid days of accelerometer data and complete data on variables of interest (n = 414) were included in the present analyses. All of the participants consented to the study procedures approved by the University of Illinois Institutional Review Board.
 

Measures

Demographics. The participants self-reported their age, level of education, height, and weight. Their body mass index (BMI; kg/m²) was estimated using the standard equation. They also self-reported their health and cancer history, detailing breast cancer disease stage, time since diagnosis, treatment type, and whether they had had a cancer recurrence. They were also asked to report whether they had ever been diagnosed (Yes/No) with 18 chronic conditions (eg, diabetes, arthritis).

Godin Leisure-Time Exercise Questionnaire.¹⁶ The GLTEQ assessed participants’ weekly frequency and mean amount of time performing MVPA (moderate exercise, such as fast walking, combined with vigorous exercise, such as jogging), and LPA (light/mild exercise, eg, easy walking) during the previous 7 days. The mean daily duration (in minutes) for each intensity category (MVPA, LPA) was calculated using activity frequencies and the amount of time spent in each activity presented as minutes/day.

The International Physical Activity Questionnaire.¹⁴ The IPAQ evaluated participants’ physical activity of at least moderate intensity in 4 domains of everyday life: job-related physical activity, transportation, housework/caring for family, and leisure-time activity. Within each domain, participants were asked the number of days per week and time per day (hours and minutes) spent performing MVPA. To estimate sitting time, the questionnaire asks participants to report the total amount of time spent sitting per day in 2 conditions, during weekdays and during weekends. The present analysis averaged sitting time for a typical 7-day (5 week days, 2 weekend days) period. We multiplied reported minutes per day and frequency per week of each activity category (MVPA and ST) to calculate the mean number of minutes per day.^29,30

Sitting Time Questionnaire.^17,28 The STQ estimated the mean time (hours and minutes) participants spent sitting each day on weekdays and at weekends within 5 domains: while traveling to and from places, at work, watching television, using a computer at home, and at leisure, not including watching television (eg, visiting friends, movies, dining out). Mean minutes per day of ST were calculated using all sitting domains.

Actigraph accelerometer (model GT1M, Health One Technology, Fort Walton Beach, FL). The Actigraph GT1M is a reliable and objective measure of physical activity.^31-33 Participants wore the monitor on the right hip for 7 consecutive days during all waking hours, except when bathing or swimming. Activity data was analyzed in 1-minute intervals. A valid day of accelerometer wear time was defined as ≥600 minutes with no more than 60 minutes of consecutive zero-values, with allowance of 2 minutes or fewer of observations <100 counts/minute within the nonwear interval.³⁴ Each minute of wear time was classified according to intensity (counts/min) using the following cut-points:³⁴ sedentary, <100 counts/min; LPA, 100-2,019 counts/min; and MVPA, ≥2,020 count/min. Mean daily durations (min/day) spent in each behavior were estimated by dividing the number of minutes in each category by the number of valid days.

Statistical analysis

All statistical analyses were completed in SPSS Statistics 23 (IBM, Chicago, IL). Descriptive statistics were used to define participant characteristics. Rank-order correlation between the methods was assessed using Spearman’s rho (r_s) and results were interpreted as follows: r_s = 0.10, small; 0.30, moderate; and 0.50, strong.³⁵ Within each activity intensity group, we jointly modeled daily minutes of self-report and accelerometer data using a random-intercept mixed-effects regression model. Differences between measurement tools were assessed based on regression coefficients with accelerometer as the reference category. Finally, we did a post hoc analysis of leisure-time–only MVPA from the IPAQ to compare with other estimates of MVPA.

We calculated the measurement tool difference scores for each estimated intensity category (ST, LPA, MVPA), that is, accelerometer estimated ST minus STQ estimated ST, and GLTEQ estimated MVPA minus IPAQ estimated MVPA. We used these data in an exploratory analysis to examine whether there were statistically significant differences between measurement difference scores by demographic or disease characteristics using linear regression stratified analyses. For example, we were interested in whether there was a significant difference in measurement tool estimates for sitting time in older compared with younger survivors. Analyses were stratified by age (<60/≥60 years), body mass index (<25 kg/m²/≥25 kg/m²), race (white/people of color), disease stage (I and II/III and IV), years since diagnosis (≤5 years/>5 years), recurrence (Yes/No), received chemotherapy (Yes/No ), received radiation (Yes/No ), and the presence of 1 or more chronic diseases (Yes/No ).

Results

Participants

The mean age of the participants was 56.8 years [9.2], they were overweight (BMI, 26.2 kg/m² [5.4]), and predominantly white (96.7%; Table 1). Table 2 provides a summary of mean daily duration of activity estimates for ST, LPA, and MVPA and the estimate mean difference scores between measurements.

Moderate and vigorous physical activity

Accelerometer−GLTEQ. The mean difference in MVPA estimates between the accelerometer and GLTEQ was less than 5 minutes (M_{accelerometer} = 20.2 minutes; M_GLTEQ = 23.6 minutes), even though the difference was statistically significant (P = .02). Estimates of MVPA from the accelerometer and GLTEQ (r_s = 0.564, P < .001) showed a strong relationship. Stratified analyses showed that the difference scores between the GLTEQ and accelerometer were lower for older survivors (≥60 years) compared with younger survivors such that older survivors reported significantly less time in MVPA on the GLTEQ compared with accelerometer estimates (difference score [D] = 6.8 minutes less, P = .001).

Accelerometer−IPAQ. The accelerometer estimated significantly fewer minutes of MVPA per day when compared with the IPAQ (M_diff = -67.4; 95% confidence interval [CI], -78.6, -55.8; P < .001). Estimates of MVPA from the accelerometer and IPAQ (r_s = 0.011, P = .680) were poorly related. Differences between the IPAQ and accelerometer were greater for later-stage breast cancer, compared with early-stage diagnoses such that participants with late-stage disease reported significantly less MVPA on the IPAQ compared with accelerometer estimates (D = 41.8 minutes less than early-stage disease, P = .018). Finally, participants of color reported a greater difference in MVPA between the accelerometer and the IPAQ than did their white counterparts (D = 47.5 minutes, P = .033).

GLTEQ−IPAQ. GLTEQ estimated significantly fewer minutes of MVPA per day compared with the IPAQ (M_diff = -64.6; 95% CI, -76.6, -52.5; P < .001). The estimates of MVPA from the GLTEQ had a small correlation with IPAQ estimates (r_s = 0.128, P = .011).

IPAQ estimates showed almost triple the MVPA minutes per day as were estimated by the accelerometer and GLTEQ. As the MVPA estimate for the IPAQ include nonleisure activities, we conducted a post hoc analyses that only included the leisure-time items from the IPAQ. Leisure-time only IPAQ items, estimates indicated survivors spent a mean 18.5 [SD, 14.2] min/day in MVPA. Although the magnitude of the difference between the accelerometer and GLTEQ estimates (~10 minutes) was much smaller using the leisure-time only IPAQ items, a repeated measures analysis of variance revealed there was still a significant difference between these estimates (P < .05 for both) and negligible correlation.

Light intensity physical activity

Accelerometer−GLTEQ. There was a large and significant difference between LPA estimates from the GLTEQ and accelerometer (M_diff = 224.5; 95% CI, 218.2, 230.7; P < .001) with estimates from the accelerometer being higher than those for the GLTEQ. Additionally, the measurements showed a negligible correlation (r_s = 0.004, P = .94). Difference scores for GLTEQ and accelerometer estimated LPA were significantly different by age, with survivors aged 60 years or older demonstrating a difference that was 18.3 minutes shorter (P = .005) than the difference in younger survivors (<60 years).

Sitting time

Accelerometer−IPAQ. Mean IPAQ estimates were significantly lower (M = 303.8 [63.4]) than accelerometer estimates (M = 603.9 [78.0]). Rank-order correlations between IPAQ and accelerometer estimated ST was small (r_s =0.26, P < .001). Difference scores between IPAQ and accelerometer estimates were significantly greater for survivors who were 60 years or older, compared with those younger than 60 years (D = 47.6 minutes, P = .006), indicating that older survivors tended to self-report significantly more ST than estimated by the accelerometer.

Accelerometer−STQ. There was no significant difference in estimated mean ST minutes per day between the STQ and the accelerometer, but the correlation between estimates was low (r_s = 0.30, P < .001). Stratified analyses revealed estimates for the difference scores for mean daily ST between the STQ and accelerometer were greater for participants who were diagnosed with later-stage breast cancer (D= -158.3 minutes, P < .001) and those who had received chemotherapy (D= -61.7 minutes, P = .028; Table 2) than for those who were diagnosed with early-stage breast cancer or had not received chemotherapy. Women who had later-stage disease reported significantly less ST than did women diagnosed with early-stage disease, when compared with estimates by the accelerometer.

IPAQ−STQ. The estimated mean ST was significantly lower for IPAQ (M = 303.8 minutes [163.4]) than for the STQ (M = 605.2 minutes [296.2]). There were no significant estimate differences among the stratified groups.

Discussion

The purpose of the present study was to compare 4 measurement tools, an accelerometer-based activity monitor and 3 self-report questionnaires, to estimate ST, LPA, and MVPA in breast cancer survivors. Developing and evaluating accurate and precise measurement tools to assess physical activity and ST in breast cancer survivors remains a critical step toward better understanding the role of physical activity in cancer survivorship. Our results indicate that the congruency of the measurement tools examined was highly dependent on the activity intensity of interest and participants’ demographic or disease characteristics. Overall, the accelerometer estimated a greater amount of time spent sitting and engaging in LPA and less time in MVPA than was estimated on the STQ, GLTEQ, and IPAQ. In addition, our findings suggest significant subgroup differences that will be important in future development and implementation of physical activity measurement for breast cancer survivors.

MVPA has been the most commonly measured activity intensity among cancer survivors to date.^15,25,26 The present results indicate mean daily MVPA estimates were significantly higher for the GLTEQ compared with the accelerometer (M_diff = 2.8 min/d, P = .019), although the magnitude of these differences was relatively small. This difference is lower than in another study that compared these measures in colon cancer survivors and found the GLTEQ over-estimated MVPA by 10.6 min/day compared with the accelerometer (P < .01).¹⁵ However, the correlation between the 2 tools in our study was similar to that of Boyle and colleagues (r_s = 0.56 and r_s = 0.51, respectively). A possible explanation for the equivocal findings across these studies may lie in the difference in study sample demographics; a previous study results finding breast cancer survivors may be better at recalling their physical activities because they may be more attentive to activities they perform daily.²⁶

The IPAQ significantly estimated more than an hour more of MVPA minutes per day compared with the accelerometer and GLTEQ. There are a number of limitations to the reporting of MVPA on the IPAQ. These limitations have been previously reported in the literature and include cross-cultural differences as well as overreporting of nonleisure-time MVPA (eg, occupational or household activities). However, the IPAQ has consistently been shown to be a valid and reliable tool for physical activity surveillance in different populations across the world.^29,36,37 This shows that although MVPA was overestimated in our population, we do not mean to undermine the IPAQ value in other populations in which it has shown great utility for overall physical activity surveillance. When we excluded nonleisure-time MVPA, MVPA equated to about 18 min/day, which was closer in magnitude to the GLTEQ and accelerometer. These data highlight the importance of identifying the specific activity parameters of interest when selecting a measurement tool to ensure congruency between the tool and construct of interest.

The differences in MVPA estimation from the 3 tools have significant translational consequences, notably the potential for misclassification of meeting physical activity guidelines. For example, the percentage of women in the present sample that met physical activity guidelines ranged from 0% (using the accelerometer) to 19.5% (using the IPAQ), depending on the measurement tool used. These findings have meaningful implications for future physical activity assessment because multiple measurement tools are currently being used to estimate physical activity in breast cancer survivors and would provide useful information regarding how breast cancer survivors report their physical activity time.

For example, scores from the IPAQ may result in a survivor being classified as meeting physical activity guidelines when in fact they are not, and thereby missing the opportunity for intervention; or the accelerometer may classify an active survivor as inactive, which could result in using time and resources for a behavior change intervention that is not necessary. The clinical significance of these findings is to provide providers with data-based information on the strengths and limitations of the measurement tools so that they can accurately estimate physical activity and ST and appropriately optimize resources and treatments.

The degree of measurement tool congruence is likely influenced by a number of factors. First, survivors’ perceptions of the intensity of their activity are relative and subjective to their state of feeling during the activity. For example, breast cancer survivors with lower functional capacity may perceive activities with lower absolute intensity as having a higher relative intensity (ie, they think they are working at a moderate intensity so record an activity as such, but the activity is classified as light by the accelerometer). Second, although our self-report measures asked survivors to record the time they had spent active over the previous 7 days, survivors might report on what they consider a “usual” week, which may reflect the ideal rather than the reality. Third, the accelerometer cut-points used were derived from young, healthy adults on a treadmill. Thus, generalization to an older, sick, less active population that could be experiencing treatment-related side effects could lead to underestimation of time spent in MVPA. To better understand measurement congruency in breast cancer survivors, future research should investigate how functional capacity and activity intensity perceptions are influenced by a breast cancer diagnosis and how those factors may influence subjective and objective physical activity measurement. If those factors were found to have significant influence on activity in breast cancer survivors, it would warrant future development of breast-cancer–specific accelerometer reduction techniques.

The comparison of LPA presented another interesting significant contrast between self-report (GLTEQ) and accelerometry. Results indicated the GLTEQ underestimated LPA by 224.5 [3.2] min/day compared with the accelerometer. This equates to over 3.5 h/day of active time (or about 280 kcal/day) that was potentially unaccounted for by the GLTEQ. The difference between these estimates could be due to the fact that the GLTEQ was designed to measure exercise time and therefore may not be as sensitive as the accelerometer to nonexercise-related LPA. Light intensity activities typically span a large range of domains (ie, occupational, leisure time, household) and tend to occur in higher volumes than MVPA, which may lead to some challenges with recall. Expanding existing LPA questionnaires to encompass these domains would likely provide increased congruency between self-reported and accelerometer-derived estimates for LPA, as it may provide a better trigger for recalling these high volume activities. With increasing literature advocating the important role of LPA in adults’ health in concert with data suggesting survivors may engage in lower levels of LPA than healthy controls,²³, accurately accounting for these lower intensity activities to provide a “whole picture” of a survivor’s active day remains an important future research direction. Combining accelerometer and self-report data using ecological momentary assessment to capture these behaviors in real-time in the real world could provide a better understanding of the context in which LPA occurs as well as survivors’ perceptions of intensity to build more accurate and scalable measurement tools for LPA.

Our ST results indicate nonsignificant difference estimates from the accelerometer and the STQ (M_diff = 1.3 [15.3] min/day) with slightly higher estimates for the STQ versus accelerometer. This finding is consistent with the one other study that has examined these relationships in cancer survivors.¹⁵ However, our findings also indicate the IPAQ significantly underestimated ST compared with the accelerometer and the STQ by about half (Table 1). These differences may be because both the STQ and Marshall questionnaire used in the previous study measure multiple domains of sitting (ie, computer, television, travel) on both weekdays and weekends whereas the IPAQ uses only two recall items of overall sitting time (for weekday and weekend separately). The domain-specific, structured approach has been shown to improve recall and may help to prevent underestimation and general underreporting of the high volume, ubiquitous behavior of sitting.^17,38 Finally, we would be remiss to not acknowledge the known limitations to estimating ST using the count-based approach on the waist-worn accelerometer. Due to the monitor’s orientation at the hip, the accelerometer may misrepresent total ST by misclassifying standing still as sitting. However, Kozey-Keadle and colleagues have previously examined estimation of ST using waist-worn accelerometers and have shown the 100 count per minute cut off yields ST estimates within 5% range of accuracy for a seated position compared with direct observation.³⁹

Of further interest are our exploratory results indicating that age and disease stage may modify the congruency between activity and ST measures. Specifically, older survivors and those with more advanced disease stage generally reported more PA and less ST than were measured by the accelerometer. These differences raise the question of whether these subgroups are systematically reporting more time physically active, overestimating their intensity, or the accelerometer is misclassifying their activity intensity. These misclassifications could be due to their age, disease stage, fatigue status, functional status, cognitive function, occupational status, etc. and would be important next steps for exploration of measurement of physical activity in breast cancer survivors. Finally, the difference score for MVPA was greater for survivors of color than for white survivors, with survivors of color overreporting MVPA compared with accelerometer-derived estimates. This may be due in part to cultural differences between white survivors and survivors of color. Previous research has suggested that people of color may accumulate a majority of their activity in occupational or household-related domains, thus explaining lower levels of leisure-time MVPA but high levels of reported total MVPA from other nonleisure domains.²⁰ However, given the small number of survivors of color in the present study, these results should be interpreted with caution.

With the multitude of physical activity and ST measurement tools available, many factors including cost, sample size, primary outcome of interest, and activity characteristics of interest (eg, duration, intensity, energy expenditure) need to be considered⁴⁰ when choosing a tool. Our findings may help inform these decisions for breast cancer survivors. For example, if LPA is of interest, an accelerometer may provide a more comprehensive assessment of these activities than the GLTEQ. In contrast, if MVPA is the activity of interest, our results suggest the GLTEQ and accelerometer were more congruent than the IPAQ was with either measure, therefore, if budgetary constraints are a concern, the more cost-efficient GLTEQ could provide similar results to an accelerometer. In addition to considering measurement congruency, it is also critically important to carefully consider the population (breast cancer survivors) and subsequent burden that accompanies the measurement tool of choice. Overall, our results indicate, when choosing a questionnaire for ST or LPA for breast cancer survivors, the more comprehensive the questions, to encompass multiple domains or time of day, the greater amount of time that will be captured within that activity category. Conversely, since the majority of MVPA is completed in leisure-time, dependent on the age and race of the population, a shorter questionnaire may be sufficient. Additionally, dependent on time since diagnosis and treatment received, activity recall or body movement patterns may be affected which could influence measurement tool selection.^23,24 Finally, it is also important to consider the setting in which measurement is taking place. In busy clinical settings, shorter, self-report measures may have a greater chance of being implemented than accelerometers or longer self-report measures and would still provide useful information regarding an overall snapshot of survivors’ MVPA or ST that could be used to initiate a conversation or referral for a program to help survivors positively change one or both of these behaviors.

Limitations

There were a few limitations within the current study that should be taken into account. First, the accelerometer cut-points used were developed with healthy, young adults; therefore using different cut-points may have yielded different results.³⁴ Given the large age range in our participants (23-84 years), we believe the use of these cut-points was justified, in lieu of population-specific (ie, older adults) cut-points. In addition, limitations to estimating activity from an accelerometer include the inability to capture certain activities such as swimming and cycling and the aforementioned inability to distinguish between body postures (ie, sitting vs standing).⁴¹ The participants were predominantly white, highly educated, and high earners (85.2% earned ≥$40,000 per year), therefore, the present results may not be generalizable to survivors from more diverse backgrounds. However, as far as we know, this is the first study to report the congruency of estimated ST, LPA, and MVPA across multiple measurement tools in a nationwide sample of breast cancer survivors who were heterogeneous in terms of disease characteristics (ie, stage, treatment, time since diagnosis).

Conclusions

Our findings suggest that physical activity and ST estimates in breast cancer survivors may be dependent on the measurement tool used. In addition, congruency of measurement tools was dependent on activity intensity of interest, and participant age, race, and disease history may also influence these factors. Therefore, researchers should consider the intended outcomes of interest, the context in which the tool is being used (ie, clinical versus research), the available resources, and the participant population before they select a measurement tool for estimating physical activity and sitting time in breast cancer survivors.

Acknowledgment
This work was supported by grant #F31AG034025 from the National Institute on Aging (Dr Phillips); Shahid and Ann Carlson Khan endowed professorship and grant #AG020118 from the National Institute on Aging (Dr McAuley). Dr Phillips is supported by the National Cancer Institute #K07CA196840, and Dr Welch is supported by National Institute of Health/National Cancer Institute training grant CA193193. All data for this study were collected at the University of Illinois Urbana Champaign.

Physical activity has numerous physical, mental, and psychosocial benefits for cancer survivors, such as a reduction in the risk of mobility disability, depression, and anxiety, and improved patient quality of life.^1,2 In addition, higher levels of physical activity are associated with reduced cancer-specific and all-causes mortality as well as cancer-specific outcomes including reduced risk of cancer progression and recurrence and new primary cancers.^3-5 However, fewer than one-third of cancer survivors are meeting government and cancer-specific recommendations of 150 minutes a week of moderate to vigorous physical activity (MPVA; ≥3 metabolic equivalents [METs]).^6,7 Growing evidence also demonstrates a significant association between higher levels of sedentary behavior and many deleterious health effects after cancer, including an increased risk for decreased physical functioning and development of other chronic diseases such as cardiovascular disease or diabetes.⁸ Distinct from physical activity, sedentary behavior is defined as any waking activity resulting in low levels of energy expenditure (≤1.5 METs) while in a seated or reclined position.⁹ Increased sedentary behavior, even when controlling for moderate and vigorous physical activity (MVPA), is associated with poor quality of life and increased all-cause mortality in cancer survivors.^10,11 Given the associations observed between higher levels of physical activity, lower levels of sedentary behavior, and improved health and disease outcomes among the large and increasing number of cancer survivors in the United States, it is important to identify low-cost methods that can be used in a in a variety of settings (ie, research, clinical, community) to accurately and efficiently measure survivors’ lifestyle behaviors to identify high-risk survivors for early intervention, better understand the effects of these behaviors on survivors’ health outcomes and disease trajectories, and ultimately, improve survivors’ health and quality of life.^12,13

Two methods commonly used to capture physical activity and sedentary behavior across the lifespan are accelerometry (Actigraph, Pensacola, FL) and self-report questionnaires such as the Godin Leisure-Time Questionnaire (GLTEQ), International Physical Activity Questionnaire (IPAQ), and Sitting Time Questionnaire (STQ).^14-17 Each method has unique strengths and weaknesses. Sending accelerometers to multiple individuals at a single time point can be costly, particularly in large-scale epidemiological studies, and the accelerometer’s waist-worn, nonwaterproof design may prevent researchers from capturing certain activities such as swimming and resistance training. However, the accelerometer provides objective, precise assessments of most physical activities and may help remove response bias.¹⁸ Conversely, self-report questionnaires rely solely on individuals’ memories and often result in recall bias, inaccurate reporting, and under- or overestimation of physical activity engagement.^19,20 Nevertheless, these questionnaires can be widely disseminated at low cost in a variety of settings (eg, clinical, research, community) and are less of a burden to participants.

Recent studies comparing objective (eg, accelerometer) with subjective (eg, self-report) methods of measuring physical activity and sedentary behavior in healthy middle-aged adults and older adults have demonstrated mixed findings with no distinct trends in the degree to which these methods differ.^19,21,22 To date, little consideration has been given to the measurement of these lifestyle behaviors in cancer survivors. Boyle and colleagues recently investigated the concurrent validity of an accelerometer to the GLTEQ in colon cancer survivors, finding significant differences in estimated MVPA (~11 minutes). However, no studies, to our knowledge, have compared accelerometer and self-report measures in breast cancer survivors, so it remains unclear how these different measurement tools relate to each another in this population.

It is particularly important to compare these measurement tools among breast cancer survivors because evidence indicates this population’s behavioral habits, self-perceived activity, and sitting time and movement patterns may differ significantly from the general population and other survivor groups across the lifespan.^23,24 Further, previous studies examining these behaviors in cancer survivors focused primarily on sitting time and MVPA.^15,25,26 Examining other lower-intensity intensities (eg, light activity or lifestyle) in cancer survivors may also be important given that increased levels of activity are associated with health benefits, ranging from reduced disability and fatigue to improved cardiovascular health and quality of life, and that breast cancer survivors engage in fewer of these activities compared with noncancer controls.²³ These lower levels of physical activity may be more prevalent among cancer survivors of their high levels of fatigue and propensity toward increased sitting time during the first year of treatment,¹¹ so it is important to be able to accurately assess these activities in this population. The purpose of the present study was to compare estimates of time spent in light physical activity (LPA), MVPA, and sitting time (ST) obtained from an accelerometer and 3 self-report measurement tools (GLTEQ, IPAQ, STQ) in a large, US-based sample of breast cancer survivors. A secondary purpose was to determine whether estimate comparisons among measurements changed by participant characteristics.
 

Methods

Participants and procedures

This study consisted of a subsample of women who participated in a larger study whose findings have been reported elsewhere by Phillips and McAuley.²⁷ In that study, breast cancer survivors (n = 1,631) were recruited nationally to participate in a 6-month prospective study on quality of life. Eligibility criteria included being aged 18 years or older, having had a diagnosis of breast cancer, being English speaking, and having access to the internet. Once consented to participate in the study, 500 women were randomly selected to wear the accelerometer.

Participants in this group were mailed an accelerometer, an activity log, instructions for use, and a self-addressed stamped envelope to return the monitor. They were asked to wear the accelerometer during all waking hours for 7 consecutive days of usual activity. They were also sent a secure link to complete 3 activity questionnaires online. The questionnaires were to be completed by the end of the 7-day monitoring period. Only women with 3 or more valid days of accelerometer data and complete data on variables of interest (n = 414) were included in the present analyses. All of the participants consented to the study procedures approved by the University of Illinois Institutional Review Board.
 

Measures

Demographics. The participants self-reported their age, level of education, height, and weight. Their body mass index (BMI; kg/m²) was estimated using the standard equation. They also self-reported their health and cancer history, detailing breast cancer disease stage, time since diagnosis, treatment type, and whether they had had a cancer recurrence. They were also asked to report whether they had ever been diagnosed (Yes/No) with 18 chronic conditions (eg, diabetes, arthritis).

Godin Leisure-Time Exercise Questionnaire.¹⁶ The GLTEQ assessed participants’ weekly frequency and mean amount of time performing MVPA (moderate exercise, such as fast walking, combined with vigorous exercise, such as jogging), and LPA (light/mild exercise, eg, easy walking) during the previous 7 days. The mean daily duration (in minutes) for each intensity category (MVPA, LPA) was calculated using activity frequencies and the amount of time spent in each activity presented as minutes/day.

The International Physical Activity Questionnaire.¹⁴ The IPAQ evaluated participants’ physical activity of at least moderate intensity in 4 domains of everyday life: job-related physical activity, transportation, housework/caring for family, and leisure-time activity. Within each domain, participants were asked the number of days per week and time per day (hours and minutes) spent performing MVPA. To estimate sitting time, the questionnaire asks participants to report the total amount of time spent sitting per day in 2 conditions, during weekdays and during weekends. The present analysis averaged sitting time for a typical 7-day (5 week days, 2 weekend days) period. We multiplied reported minutes per day and frequency per week of each activity category (MVPA and ST) to calculate the mean number of minutes per day.^29,30

Sitting Time Questionnaire.^17,28 The STQ estimated the mean time (hours and minutes) participants spent sitting each day on weekdays and at weekends within 5 domains: while traveling to and from places, at work, watching television, using a computer at home, and at leisure, not including watching television (eg, visiting friends, movies, dining out). Mean minutes per day of ST were calculated using all sitting domains.

Actigraph accelerometer (model GT1M, Health One Technology, Fort Walton Beach, FL). The Actigraph GT1M is a reliable and objective measure of physical activity.^31-33 Participants wore the monitor on the right hip for 7 consecutive days during all waking hours, except when bathing or swimming. Activity data was analyzed in 1-minute intervals. A valid day of accelerometer wear time was defined as ≥600 minutes with no more than 60 minutes of consecutive zero-values, with allowance of 2 minutes or fewer of observations <100 counts/minute within the nonwear interval.³⁴ Each minute of wear time was classified according to intensity (counts/min) using the following cut-points:³⁴ sedentary, <100 counts/min; LPA, 100-2,019 counts/min; and MVPA, ≥2,020 count/min. Mean daily durations (min/day) spent in each behavior were estimated by dividing the number of minutes in each category by the number of valid days.

Statistical analysis

All statistical analyses were completed in SPSS Statistics 23 (IBM, Chicago, IL). Descriptive statistics were used to define participant characteristics. Rank-order correlation between the methods was assessed using Spearman’s rho (r_s) and results were interpreted as follows: r_s = 0.10, small; 0.30, moderate; and 0.50, strong.³⁵ Within each activity intensity group, we jointly modeled daily minutes of self-report and accelerometer data using a random-intercept mixed-effects regression model. Differences between measurement tools were assessed based on regression coefficients with accelerometer as the reference category. Finally, we did a post hoc analysis of leisure-time–only MVPA from the IPAQ to compare with other estimates of MVPA.

We calculated the measurement tool difference scores for each estimated intensity category (ST, LPA, MVPA), that is, accelerometer estimated ST minus STQ estimated ST, and GLTEQ estimated MVPA minus IPAQ estimated MVPA. We used these data in an exploratory analysis to examine whether there were statistically significant differences between measurement difference scores by demographic or disease characteristics using linear regression stratified analyses. For example, we were interested in whether there was a significant difference in measurement tool estimates for sitting time in older compared with younger survivors. Analyses were stratified by age (<60/≥60 years), body mass index (<25 kg/m²/≥25 kg/m²), race (white/people of color), disease stage (I and II/III and IV), years since diagnosis (≤5 years/>5 years), recurrence (Yes/No), received chemotherapy (Yes/No ), received radiation (Yes/No ), and the presence of 1 or more chronic diseases (Yes/No ).

Results

Participants

The mean age of the participants was 56.8 years [9.2], they were overweight (BMI, 26.2 kg/m² [5.4]), and predominantly white (96.7%; Table 1). Table 2 provides a summary of mean daily duration of activity estimates for ST, LPA, and MVPA and the estimate mean difference scores between measurements.

Moderate and vigorous physical activity

Accelerometer−GLTEQ. The mean difference in MVPA estimates between the accelerometer and GLTEQ was less than 5 minutes (M_{accelerometer} = 20.2 minutes; M_GLTEQ = 23.6 minutes), even though the difference was statistically significant (P = .02). Estimates of MVPA from the accelerometer and GLTEQ (r_s = 0.564, P < .001) showed a strong relationship. Stratified analyses showed that the difference scores between the GLTEQ and accelerometer were lower for older survivors (≥60 years) compared with younger survivors such that older survivors reported significantly less time in MVPA on the GLTEQ compared with accelerometer estimates (difference score [D] = 6.8 minutes less, P = .001).

Accelerometer−IPAQ. The accelerometer estimated significantly fewer minutes of MVPA per day when compared with the IPAQ (M_diff = -67.4; 95% confidence interval [CI], -78.6, -55.8; P < .001). Estimates of MVPA from the accelerometer and IPAQ (r_s = 0.011, P = .680) were poorly related. Differences between the IPAQ and accelerometer were greater for later-stage breast cancer, compared with early-stage diagnoses such that participants with late-stage disease reported significantly less MVPA on the IPAQ compared with accelerometer estimates (D = 41.8 minutes less than early-stage disease, P = .018). Finally, participants of color reported a greater difference in MVPA between the accelerometer and the IPAQ than did their white counterparts (D = 47.5 minutes, P = .033).

GLTEQ−IPAQ. GLTEQ estimated significantly fewer minutes of MVPA per day compared with the IPAQ (M_diff = -64.6; 95% CI, -76.6, -52.5; P < .001). The estimates of MVPA from the GLTEQ had a small correlation with IPAQ estimates (r_s = 0.128, P = .011).

IPAQ estimates showed almost triple the MVPA minutes per day as were estimated by the accelerometer and GLTEQ. As the MVPA estimate for the IPAQ include nonleisure activities, we conducted a post hoc analyses that only included the leisure-time items from the IPAQ. Leisure-time only IPAQ items, estimates indicated survivors spent a mean 18.5 [SD, 14.2] min/day in MVPA. Although the magnitude of the difference between the accelerometer and GLTEQ estimates (~10 minutes) was much smaller using the leisure-time only IPAQ items, a repeated measures analysis of variance revealed there was still a significant difference between these estimates (P < .05 for both) and negligible correlation.

Light intensity physical activity

Accelerometer−GLTEQ. There was a large and significant difference between LPA estimates from the GLTEQ and accelerometer (M_diff = 224.5; 95% CI, 218.2, 230.7; P < .001) with estimates from the accelerometer being higher than those for the GLTEQ. Additionally, the measurements showed a negligible correlation (r_s = 0.004, P = .94). Difference scores for GLTEQ and accelerometer estimated LPA were significantly different by age, with survivors aged 60 years or older demonstrating a difference that was 18.3 minutes shorter (P = .005) than the difference in younger survivors (<60 years).

Sitting time

Accelerometer−IPAQ. Mean IPAQ estimates were significantly lower (M = 303.8 [63.4]) than accelerometer estimates (M = 603.9 [78.0]). Rank-order correlations between IPAQ and accelerometer estimated ST was small (r_s =0.26, P < .001). Difference scores between IPAQ and accelerometer estimates were significantly greater for survivors who were 60 years or older, compared with those younger than 60 years (D = 47.6 minutes, P = .006), indicating that older survivors tended to self-report significantly more ST than estimated by the accelerometer.

Accelerometer−STQ. There was no significant difference in estimated mean ST minutes per day between the STQ and the accelerometer, but the correlation between estimates was low (r_s = 0.30, P < .001). Stratified analyses revealed estimates for the difference scores for mean daily ST between the STQ and accelerometer were greater for participants who were diagnosed with later-stage breast cancer (D= -158.3 minutes, P < .001) and those who had received chemotherapy (D= -61.7 minutes, P = .028; Table 2) than for those who were diagnosed with early-stage breast cancer or had not received chemotherapy. Women who had later-stage disease reported significantly less ST than did women diagnosed with early-stage disease, when compared with estimates by the accelerometer.

IPAQ−STQ. The estimated mean ST was significantly lower for IPAQ (M = 303.8 minutes [163.4]) than for the STQ (M = 605.2 minutes [296.2]). There were no significant estimate differences among the stratified groups.

Discussion

The purpose of the present study was to compare 4 measurement tools, an accelerometer-based activity monitor and 3 self-report questionnaires, to estimate ST, LPA, and MVPA in breast cancer survivors. Developing and evaluating accurate and precise measurement tools to assess physical activity and ST in breast cancer survivors remains a critical step toward better understanding the role of physical activity in cancer survivorship. Our results indicate that the congruency of the measurement tools examined was highly dependent on the activity intensity of interest and participants’ demographic or disease characteristics. Overall, the accelerometer estimated a greater amount of time spent sitting and engaging in LPA and less time in MVPA than was estimated on the STQ, GLTEQ, and IPAQ. In addition, our findings suggest significant subgroup differences that will be important in future development and implementation of physical activity measurement for breast cancer survivors.

MVPA has been the most commonly measured activity intensity among cancer survivors to date.^15,25,26 The present results indicate mean daily MVPA estimates were significantly higher for the GLTEQ compared with the accelerometer (M_diff = 2.8 min/d, P = .019), although the magnitude of these differences was relatively small. This difference is lower than in another study that compared these measures in colon cancer survivors and found the GLTEQ over-estimated MVPA by 10.6 min/day compared with the accelerometer (P < .01).¹⁵ However, the correlation between the 2 tools in our study was similar to that of Boyle and colleagues (r_s = 0.56 and r_s = 0.51, respectively). A possible explanation for the equivocal findings across these studies may lie in the difference in study sample demographics; a previous study results finding breast cancer survivors may be better at recalling their physical activities because they may be more attentive to activities they perform daily.²⁶

The IPAQ significantly estimated more than an hour more of MVPA minutes per day compared with the accelerometer and GLTEQ. There are a number of limitations to the reporting of MVPA on the IPAQ. These limitations have been previously reported in the literature and include cross-cultural differences as well as overreporting of nonleisure-time MVPA (eg, occupational or household activities). However, the IPAQ has consistently been shown to be a valid and reliable tool for physical activity surveillance in different populations across the world.^29,36,37 This shows that although MVPA was overestimated in our population, we do not mean to undermine the IPAQ value in other populations in which it has shown great utility for overall physical activity surveillance. When we excluded nonleisure-time MVPA, MVPA equated to about 18 min/day, which was closer in magnitude to the GLTEQ and accelerometer. These data highlight the importance of identifying the specific activity parameters of interest when selecting a measurement tool to ensure congruency between the tool and construct of interest.

The differences in MVPA estimation from the 3 tools have significant translational consequences, notably the potential for misclassification of meeting physical activity guidelines. For example, the percentage of women in the present sample that met physical activity guidelines ranged from 0% (using the accelerometer) to 19.5% (using the IPAQ), depending on the measurement tool used. These findings have meaningful implications for future physical activity assessment because multiple measurement tools are currently being used to estimate physical activity in breast cancer survivors and would provide useful information regarding how breast cancer survivors report their physical activity time.

For example, scores from the IPAQ may result in a survivor being classified as meeting physical activity guidelines when in fact they are not, and thereby missing the opportunity for intervention; or the accelerometer may classify an active survivor as inactive, which could result in using time and resources for a behavior change intervention that is not necessary. The clinical significance of these findings is to provide providers with data-based information on the strengths and limitations of the measurement tools so that they can accurately estimate physical activity and ST and appropriately optimize resources and treatments.

The degree of measurement tool congruence is likely influenced by a number of factors. First, survivors’ perceptions of the intensity of their activity are relative and subjective to their state of feeling during the activity. For example, breast cancer survivors with lower functional capacity may perceive activities with lower absolute intensity as having a higher relative intensity (ie, they think they are working at a moderate intensity so record an activity as such, but the activity is classified as light by the accelerometer). Second, although our self-report measures asked survivors to record the time they had spent active over the previous 7 days, survivors might report on what they consider a “usual” week, which may reflect the ideal rather than the reality. Third, the accelerometer cut-points used were derived from young, healthy adults on a treadmill. Thus, generalization to an older, sick, less active population that could be experiencing treatment-related side effects could lead to underestimation of time spent in MVPA. To better understand measurement congruency in breast cancer survivors, future research should investigate how functional capacity and activity intensity perceptions are influenced by a breast cancer diagnosis and how those factors may influence subjective and objective physical activity measurement. If those factors were found to have significant influence on activity in breast cancer survivors, it would warrant future development of breast-cancer–specific accelerometer reduction techniques.

The comparison of LPA presented another interesting significant contrast between self-report (GLTEQ) and accelerometry. Results indicated the GLTEQ underestimated LPA by 224.5 [3.2] min/day compared with the accelerometer. This equates to over 3.5 h/day of active time (or about 280 kcal/day) that was potentially unaccounted for by the GLTEQ. The difference between these estimates could be due to the fact that the GLTEQ was designed to measure exercise time and therefore may not be as sensitive as the accelerometer to nonexercise-related LPA. Light intensity activities typically span a large range of domains (ie, occupational, leisure time, household) and tend to occur in higher volumes than MVPA, which may lead to some challenges with recall. Expanding existing LPA questionnaires to encompass these domains would likely provide increased congruency between self-reported and accelerometer-derived estimates for LPA, as it may provide a better trigger for recalling these high volume activities. With increasing literature advocating the important role of LPA in adults’ health in concert with data suggesting survivors may engage in lower levels of LPA than healthy controls,²³, accurately accounting for these lower intensity activities to provide a “whole picture” of a survivor’s active day remains an important future research direction. Combining accelerometer and self-report data using ecological momentary assessment to capture these behaviors in real-time in the real world could provide a better understanding of the context in which LPA occurs as well as survivors’ perceptions of intensity to build more accurate and scalable measurement tools for LPA.

Our ST results indicate nonsignificant difference estimates from the accelerometer and the STQ (M_diff = 1.3 [15.3] min/day) with slightly higher estimates for the STQ versus accelerometer. This finding is consistent with the one other study that has examined these relationships in cancer survivors.¹⁵ However, our findings also indicate the IPAQ significantly underestimated ST compared with the accelerometer and the STQ by about half (Table 1). These differences may be because both the STQ and Marshall questionnaire used in the previous study measure multiple domains of sitting (ie, computer, television, travel) on both weekdays and weekends whereas the IPAQ uses only two recall items of overall sitting time (for weekday and weekend separately). The domain-specific, structured approach has been shown to improve recall and may help to prevent underestimation and general underreporting of the high volume, ubiquitous behavior of sitting.^17,38 Finally, we would be remiss to not acknowledge the known limitations to estimating ST using the count-based approach on the waist-worn accelerometer. Due to the monitor’s orientation at the hip, the accelerometer may misrepresent total ST by misclassifying standing still as sitting. However, Kozey-Keadle and colleagues have previously examined estimation of ST using waist-worn accelerometers and have shown the 100 count per minute cut off yields ST estimates within 5% range of accuracy for a seated position compared with direct observation.³⁹

Of further interest are our exploratory results indicating that age and disease stage may modify the congruency between activity and ST measures. Specifically, older survivors and those with more advanced disease stage generally reported more PA and less ST than were measured by the accelerometer. These differences raise the question of whether these subgroups are systematically reporting more time physically active, overestimating their intensity, or the accelerometer is misclassifying their activity intensity. These misclassifications could be due to their age, disease stage, fatigue status, functional status, cognitive function, occupational status, etc. and would be important next steps for exploration of measurement of physical activity in breast cancer survivors. Finally, the difference score for MVPA was greater for survivors of color than for white survivors, with survivors of color overreporting MVPA compared with accelerometer-derived estimates. This may be due in part to cultural differences between white survivors and survivors of color. Previous research has suggested that people of color may accumulate a majority of their activity in occupational or household-related domains, thus explaining lower levels of leisure-time MVPA but high levels of reported total MVPA from other nonleisure domains.²⁰ However, given the small number of survivors of color in the present study, these results should be interpreted with caution.

With the multitude of physical activity and ST measurement tools available, many factors including cost, sample size, primary outcome of interest, and activity characteristics of interest (eg, duration, intensity, energy expenditure) need to be considered⁴⁰ when choosing a tool. Our findings may help inform these decisions for breast cancer survivors. For example, if LPA is of interest, an accelerometer may provide a more comprehensive assessment of these activities than the GLTEQ. In contrast, if MVPA is the activity of interest, our results suggest the GLTEQ and accelerometer were more congruent than the IPAQ was with either measure, therefore, if budgetary constraints are a concern, the more cost-efficient GLTEQ could provide similar results to an accelerometer. In addition to considering measurement congruency, it is also critically important to carefully consider the population (breast cancer survivors) and subsequent burden that accompanies the measurement tool of choice. Overall, our results indicate, when choosing a questionnaire for ST or LPA for breast cancer survivors, the more comprehensive the questions, to encompass multiple domains or time of day, the greater amount of time that will be captured within that activity category. Conversely, since the majority of MVPA is completed in leisure-time, dependent on the age and race of the population, a shorter questionnaire may be sufficient. Additionally, dependent on time since diagnosis and treatment received, activity recall or body movement patterns may be affected which could influence measurement tool selection.^23,24 Finally, it is also important to consider the setting in which measurement is taking place. In busy clinical settings, shorter, self-report measures may have a greater chance of being implemented than accelerometers or longer self-report measures and would still provide useful information regarding an overall snapshot of survivors’ MVPA or ST that could be used to initiate a conversation or referral for a program to help survivors positively change one or both of these behaviors.

Limitations

There were a few limitations within the current study that should be taken into account. First, the accelerometer cut-points used were developed with healthy, young adults; therefore using different cut-points may have yielded different results.³⁴ Given the large age range in our participants (23-84 years), we believe the use of these cut-points was justified, in lieu of population-specific (ie, older adults) cut-points. In addition, limitations to estimating activity from an accelerometer include the inability to capture certain activities such as swimming and cycling and the aforementioned inability to distinguish between body postures (ie, sitting vs standing).⁴¹ The participants were predominantly white, highly educated, and high earners (85.2% earned ≥$40,000 per year), therefore, the present results may not be generalizable to survivors from more diverse backgrounds. However, as far as we know, this is the first study to report the congruency of estimated ST, LPA, and MVPA across multiple measurement tools in a nationwide sample of breast cancer survivors who were heterogeneous in terms of disease characteristics (ie, stage, treatment, time since diagnosis).

Conclusions

Our findings suggest that physical activity and ST estimates in breast cancer survivors may be dependent on the measurement tool used. In addition, congruency of measurement tools was dependent on activity intensity of interest, and participant age, race, and disease history may also influence these factors. Therefore, researchers should consider the intended outcomes of interest, the context in which the tool is being used (ie, clinical versus research), the available resources, and the participant population before they select a measurement tool for estimating physical activity and sitting time in breast cancer survivors.

Acknowledgment
This work was supported by grant #F31AG034025 from the National Institute on Aging (Dr Phillips); Shahid and Ann Carlson Khan endowed professorship and grant #AG020118 from the National Institute on Aging (Dr McAuley). Dr Phillips is supported by the National Cancer Institute #K07CA196840, and Dr Welch is supported by National Institute of Health/National Cancer Institute training grant CA193193. All data for this study were collected at the University of Illinois Urbana Champaign.

Physical activity has numerous physical, mental, and psychosocial benefits for cancer survivors, such as a reduction in the risk of mobility disability, depression, and anxiety, and improved patient quality of life.^1,2 In addition, higher levels of physical activity are associated with reduced cancer-specific and all-causes mortality as well as cancer-specific outcomes including reduced risk of cancer progression and recurrence and new primary cancers.^3-5 However, fewer than one-third of cancer survivors are meeting government and cancer-specific recommendations of 150 minutes a week of moderate to vigorous physical activity (MPVA; ≥3 metabolic equivalents [METs]).^6,7 Growing evidence also demonstrates a significant association between higher levels of sedentary behavior and many deleterious health effects after cancer, including an increased risk for decreased physical functioning and development of other chronic diseases such as cardiovascular disease or diabetes.⁸ Distinct from physical activity, sedentary behavior is defined as any waking activity resulting in low levels of energy expenditure (≤1.5 METs) while in a seated or reclined position.⁹ Increased sedentary behavior, even when controlling for moderate and vigorous physical activity (MVPA), is associated with poor quality of life and increased all-cause mortality in cancer survivors.^10,11 Given the associations observed between higher levels of physical activity, lower levels of sedentary behavior, and improved health and disease outcomes among the large and increasing number of cancer survivors in the United States, it is important to identify low-cost methods that can be used in a in a variety of settings (ie, research, clinical, community) to accurately and efficiently measure survivors’ lifestyle behaviors to identify high-risk survivors for early intervention, better understand the effects of these behaviors on survivors’ health outcomes and disease trajectories, and ultimately, improve survivors’ health and quality of life.^12,13

Two methods commonly used to capture physical activity and sedentary behavior across the lifespan are accelerometry (Actigraph, Pensacola, FL) and self-report questionnaires such as the Godin Leisure-Time Questionnaire (GLTEQ), International Physical Activity Questionnaire (IPAQ), and Sitting Time Questionnaire (STQ).^14-17 Each method has unique strengths and weaknesses. Sending accelerometers to multiple individuals at a single time point can be costly, particularly in large-scale epidemiological studies, and the accelerometer’s waist-worn, nonwaterproof design may prevent researchers from capturing certain activities such as swimming and resistance training. However, the accelerometer provides objective, precise assessments of most physical activities and may help remove response bias.¹⁸ Conversely, self-report questionnaires rely solely on individuals’ memories and often result in recall bias, inaccurate reporting, and under- or overestimation of physical activity engagement.^19,20 Nevertheless, these questionnaires can be widely disseminated at low cost in a variety of settings (eg, clinical, research, community) and are less of a burden to participants.

Recent studies comparing objective (eg, accelerometer) with subjective (eg, self-report) methods of measuring physical activity and sedentary behavior in healthy middle-aged adults and older adults have demonstrated mixed findings with no distinct trends in the degree to which these methods differ.^19,21,22 To date, little consideration has been given to the measurement of these lifestyle behaviors in cancer survivors. Boyle and colleagues recently investigated the concurrent validity of an accelerometer to the GLTEQ in colon cancer survivors, finding significant differences in estimated MVPA (~11 minutes). However, no studies, to our knowledge, have compared accelerometer and self-report measures in breast cancer survivors, so it remains unclear how these different measurement tools relate to each another in this population.

It is particularly important to compare these measurement tools among breast cancer survivors because evidence indicates this population’s behavioral habits, self-perceived activity, and sitting time and movement patterns may differ significantly from the general population and other survivor groups across the lifespan.^23,24 Further, previous studies examining these behaviors in cancer survivors focused primarily on sitting time and MVPA.^15,25,26 Examining other lower-intensity intensities (eg, light activity or lifestyle) in cancer survivors may also be important given that increased levels of activity are associated with health benefits, ranging from reduced disability and fatigue to improved cardiovascular health and quality of life, and that breast cancer survivors engage in fewer of these activities compared with noncancer controls.²³ These lower levels of physical activity may be more prevalent among cancer survivors of their high levels of fatigue and propensity toward increased sitting time during the first year of treatment,¹¹ so it is important to be able to accurately assess these activities in this population. The purpose of the present study was to compare estimates of time spent in light physical activity (LPA), MVPA, and sitting time (ST) obtained from an accelerometer and 3 self-report measurement tools (GLTEQ, IPAQ, STQ) in a large, US-based sample of breast cancer survivors. A secondary purpose was to determine whether estimate comparisons among measurements changed by participant characteristics.
 

Methods

Participants and procedures

This study consisted of a subsample of women who participated in a larger study whose findings have been reported elsewhere by Phillips and McAuley.²⁷ In that study, breast cancer survivors (n = 1,631) were recruited nationally to participate in a 6-month prospective study on quality of life. Eligibility criteria included being aged 18 years or older, having had a diagnosis of breast cancer, being English speaking, and having access to the internet. Once consented to participate in the study, 500 women were randomly selected to wear the accelerometer.

Participants in this group were mailed an accelerometer, an activity log, instructions for use, and a self-addressed stamped envelope to return the monitor. They were asked to wear the accelerometer during all waking hours for 7 consecutive days of usual activity. They were also sent a secure link to complete 3 activity questionnaires online. The questionnaires were to be completed by the end of the 7-day monitoring period. Only women with 3 or more valid days of accelerometer data and complete data on variables of interest (n = 414) were included in the present analyses. All of the participants consented to the study procedures approved by the University of Illinois Institutional Review Board.
 

Measures

Demographics. The participants self-reported their age, level of education, height, and weight. Their body mass index (BMI; kg/m²) was estimated using the standard equation. They also self-reported their health and cancer history, detailing breast cancer disease stage, time since diagnosis, treatment type, and whether they had had a cancer recurrence. They were also asked to report whether they had ever been diagnosed (Yes/No) with 18 chronic conditions (eg, diabetes, arthritis).

Godin Leisure-Time Exercise Questionnaire.¹⁶ The GLTEQ assessed participants’ weekly frequency and mean amount of time performing MVPA (moderate exercise, such as fast walking, combined with vigorous exercise, such as jogging), and LPA (light/mild exercise, eg, easy walking) during the previous 7 days. The mean daily duration (in minutes) for each intensity category (MVPA, LPA) was calculated using activity frequencies and the amount of time spent in each activity presented as minutes/day.

The International Physical Activity Questionnaire.¹⁴ The IPAQ evaluated participants’ physical activity of at least moderate intensity in 4 domains of everyday life: job-related physical activity, transportation, housework/caring for family, and leisure-time activity. Within each domain, participants were asked the number of days per week and time per day (hours and minutes) spent performing MVPA. To estimate sitting time, the questionnaire asks participants to report the total amount of time spent sitting per day in 2 conditions, during weekdays and during weekends. The present analysis averaged sitting time for a typical 7-day (5 week days, 2 weekend days) period. We multiplied reported minutes per day and frequency per week of each activity category (MVPA and ST) to calculate the mean number of minutes per day.^29,30

Sitting Time Questionnaire.^17,28 The STQ estimated the mean time (hours and minutes) participants spent sitting each day on weekdays and at weekends within 5 domains: while traveling to and from places, at work, watching television, using a computer at home, and at leisure, not including watching television (eg, visiting friends, movies, dining out). Mean minutes per day of ST were calculated using all sitting domains.

Actigraph accelerometer (model GT1M, Health One Technology, Fort Walton Beach, FL). The Actigraph GT1M is a reliable and objective measure of physical activity.^31-33 Participants wore the monitor on the right hip for 7 consecutive days during all waking hours, except when bathing or swimming. Activity data was analyzed in 1-minute intervals. A valid day of accelerometer wear time was defined as ≥600 minutes with no more than 60 minutes of consecutive zero-values, with allowance of 2 minutes or fewer of observations <100 counts/minute within the nonwear interval.³⁴ Each minute of wear time was classified according to intensity (counts/min) using the following cut-points:³⁴ sedentary, <100 counts/min; LPA, 100-2,019 counts/min; and MVPA, ≥2,020 count/min. Mean daily durations (min/day) spent in each behavior were estimated by dividing the number of minutes in each category by the number of valid days.

Statistical analysis

All statistical analyses were completed in SPSS Statistics 23 (IBM, Chicago, IL). Descriptive statistics were used to define participant characteristics. Rank-order correlation between the methods was assessed using Spearman’s rho (r_s) and results were interpreted as follows: r_s = 0.10, small; 0.30, moderate; and 0.50, strong.³⁵ Within each activity intensity group, we jointly modeled daily minutes of self-report and accelerometer data using a random-intercept mixed-effects regression model. Differences between measurement tools were assessed based on regression coefficients with accelerometer as the reference category. Finally, we did a post hoc analysis of leisure-time–only MVPA from the IPAQ to compare with other estimates of MVPA.

We calculated the measurement tool difference scores for each estimated intensity category (ST, LPA, MVPA), that is, accelerometer estimated ST minus STQ estimated ST, and GLTEQ estimated MVPA minus IPAQ estimated MVPA. We used these data in an exploratory analysis to examine whether there were statistically significant differences between measurement difference scores by demographic or disease characteristics using linear regression stratified analyses. For example, we were interested in whether there was a significant difference in measurement tool estimates for sitting time in older compared with younger survivors. Analyses were stratified by age (<60/≥60 years), body mass index (<25 kg/m²/≥25 kg/m²), race (white/people of color), disease stage (I and II/III and IV), years since diagnosis (≤5 years/>5 years), recurrence (Yes/No), received chemotherapy (Yes/No ), received radiation (Yes/No ), and the presence of 1 or more chronic diseases (Yes/No ).

Results

Participants

The mean age of the participants was 56.8 years [9.2], they were overweight (BMI, 26.2 kg/m² [5.4]), and predominantly white (96.7%; Table 1). Table 2 provides a summary of mean daily duration of activity estimates for ST, LPA, and MVPA and the estimate mean difference scores between measurements.

Moderate and vigorous physical activity

Accelerometer−GLTEQ. The mean difference in MVPA estimates between the accelerometer and GLTEQ was less than 5 minutes (M_{accelerometer} = 20.2 minutes; M_GLTEQ = 23.6 minutes), even though the difference was statistically significant (P = .02). Estimates of MVPA from the accelerometer and GLTEQ (r_s = 0.564, P < .001) showed a strong relationship. Stratified analyses showed that the difference scores between the GLTEQ and accelerometer were lower for older survivors (≥60 years) compared with younger survivors such that older survivors reported significantly less time in MVPA on the GLTEQ compared with accelerometer estimates (difference score [D] = 6.8 minutes less, P = .001).

Accelerometer−IPAQ. The accelerometer estimated significantly fewer minutes of MVPA per day when compared with the IPAQ (M_diff = -67.4; 95% confidence interval [CI], -78.6, -55.8; P < .001). Estimates of MVPA from the accelerometer and IPAQ (r_s = 0.011, P = .680) were poorly related. Differences between the IPAQ and accelerometer were greater for later-stage breast cancer, compared with early-stage diagnoses such that participants with late-stage disease reported significantly less MVPA on the IPAQ compared with accelerometer estimates (D = 41.8 minutes less than early-stage disease, P = .018). Finally, participants of color reported a greater difference in MVPA between the accelerometer and the IPAQ than did their white counterparts (D = 47.5 minutes, P = .033).

GLTEQ−IPAQ. GLTEQ estimated significantly fewer minutes of MVPA per day compared with the IPAQ (M_diff = -64.6; 95% CI, -76.6, -52.5; P < .001). The estimates of MVPA from the GLTEQ had a small correlation with IPAQ estimates (r_s = 0.128, P = .011).

IPAQ estimates showed almost triple the MVPA minutes per day as were estimated by the accelerometer and GLTEQ. As the MVPA estimate for the IPAQ include nonleisure activities, we conducted a post hoc analyses that only included the leisure-time items from the IPAQ. Leisure-time only IPAQ items, estimates indicated survivors spent a mean 18.5 [SD, 14.2] min/day in MVPA. Although the magnitude of the difference between the accelerometer and GLTEQ estimates (~10 minutes) was much smaller using the leisure-time only IPAQ items, a repeated measures analysis of variance revealed there was still a significant difference between these estimates (P < .05 for both) and negligible correlation.

Light intensity physical activity

Accelerometer−GLTEQ. There was a large and significant difference between LPA estimates from the GLTEQ and accelerometer (M_diff = 224.5; 95% CI, 218.2, 230.7; P < .001) with estimates from the accelerometer being higher than those for the GLTEQ. Additionally, the measurements showed a negligible correlation (r_s = 0.004, P = .94). Difference scores for GLTEQ and accelerometer estimated LPA were significantly different by age, with survivors aged 60 years or older demonstrating a difference that was 18.3 minutes shorter (P = .005) than the difference in younger survivors (<60 years).

Sitting time

Accelerometer−IPAQ. Mean IPAQ estimates were significantly lower (M = 303.8 [63.4]) than accelerometer estimates (M = 603.9 [78.0]). Rank-order correlations between IPAQ and accelerometer estimated ST was small (r_s =0.26, P < .001). Difference scores between IPAQ and accelerometer estimates were significantly greater for survivors who were 60 years or older, compared with those younger than 60 years (D = 47.6 minutes, P = .006), indicating that older survivors tended to self-report significantly more ST than estimated by the accelerometer.

Accelerometer−STQ. There was no significant difference in estimated mean ST minutes per day between the STQ and the accelerometer, but the correlation between estimates was low (r_s = 0.30, P < .001). Stratified analyses revealed estimates for the difference scores for mean daily ST between the STQ and accelerometer were greater for participants who were diagnosed with later-stage breast cancer (D= -158.3 minutes, P < .001) and those who had received chemotherapy (D= -61.7 minutes, P = .028; Table 2) than for those who were diagnosed with early-stage breast cancer or had not received chemotherapy. Women who had later-stage disease reported significantly less ST than did women diagnosed with early-stage disease, when compared with estimates by the accelerometer.

IPAQ−STQ. The estimated mean ST was significantly lower for IPAQ (M = 303.8 minutes [163.4]) than for the STQ (M = 605.2 minutes [296.2]). There were no significant estimate differences among the stratified groups.

Discussion

The purpose of the present study was to compare 4 measurement tools, an accelerometer-based activity monitor and 3 self-report questionnaires, to estimate ST, LPA, and MVPA in breast cancer survivors. Developing and evaluating accurate and precise measurement tools to assess physical activity and ST in breast cancer survivors remains a critical step toward better understanding the role of physical activity in cancer survivorship. Our results indicate that the congruency of the measurement tools examined was highly dependent on the activity intensity of interest and participants’ demographic or disease characteristics. Overall, the accelerometer estimated a greater amount of time spent sitting and engaging in LPA and less time in MVPA than was estimated on the STQ, GLTEQ, and IPAQ. In addition, our findings suggest significant subgroup differences that will be important in future development and implementation of physical activity measurement for breast cancer survivors.

MVPA has been the most commonly measured activity intensity among cancer survivors to date.^15,25,26 The present results indicate mean daily MVPA estimates were significantly higher for the GLTEQ compared with the accelerometer (M_diff = 2.8 min/d, P = .019), although the magnitude of these differences was relatively small. This difference is lower than in another study that compared these measures in colon cancer survivors and found the GLTEQ over-estimated MVPA by 10.6 min/day compared with the accelerometer (P < .01).¹⁵ However, the correlation between the 2 tools in our study was similar to that of Boyle and colleagues (r_s = 0.56 and r_s = 0.51, respectively). A possible explanation for the equivocal findings across these studies may lie in the difference in study sample demographics; a previous study results finding breast cancer survivors may be better at recalling their physical activities because they may be more attentive to activities they perform daily.²⁶

The IPAQ significantly estimated more than an hour more of MVPA minutes per day compared with the accelerometer and GLTEQ. There are a number of limitations to the reporting of MVPA on the IPAQ. These limitations have been previously reported in the literature and include cross-cultural differences as well as overreporting of nonleisure-time MVPA (eg, occupational or household activities). However, the IPAQ has consistently been shown to be a valid and reliable tool for physical activity surveillance in different populations across the world.^29,36,37 This shows that although MVPA was overestimated in our population, we do not mean to undermine the IPAQ value in other populations in which it has shown great utility for overall physical activity surveillance. When we excluded nonleisure-time MVPA, MVPA equated to about 18 min/day, which was closer in magnitude to the GLTEQ and accelerometer. These data highlight the importance of identifying the specific activity parameters of interest when selecting a measurement tool to ensure congruency between the tool and construct of interest.

The differences in MVPA estimation from the 3 tools have significant translational consequences, notably the potential for misclassification of meeting physical activity guidelines. For example, the percentage of women in the present sample that met physical activity guidelines ranged from 0% (using the accelerometer) to 19.5% (using the IPAQ), depending on the measurement tool used. These findings have meaningful implications for future physical activity assessment because multiple measurement tools are currently being used to estimate physical activity in breast cancer survivors and would provide useful information regarding how breast cancer survivors report their physical activity time.

For example, scores from the IPAQ may result in a survivor being classified as meeting physical activity guidelines when in fact they are not, and thereby missing the opportunity for intervention; or the accelerometer may classify an active survivor as inactive, which could result in using time and resources for a behavior change intervention that is not necessary. The clinical significance of these findings is to provide providers with data-based information on the strengths and limitations of the measurement tools so that they can accurately estimate physical activity and ST and appropriately optimize resources and treatments.

The degree of measurement tool congruence is likely influenced by a number of factors. First, survivors’ perceptions of the intensity of their activity are relative and subjective to their state of feeling during the activity. For example, breast cancer survivors with lower functional capacity may perceive activities with lower absolute intensity as having a higher relative intensity (ie, they think they are working at a moderate intensity so record an activity as such, but the activity is classified as light by the accelerometer). Second, although our self-report measures asked survivors to record the time they had spent active over the previous 7 days, survivors might report on what they consider a “usual” week, which may reflect the ideal rather than the reality. Third, the accelerometer cut-points used were derived from young, healthy adults on a treadmill. Thus, generalization to an older, sick, less active population that could be experiencing treatment-related side effects could lead to underestimation of time spent in MVPA. To better understand measurement congruency in breast cancer survivors, future research should investigate how functional capacity and activity intensity perceptions are influenced by a breast cancer diagnosis and how those factors may influence subjective and objective physical activity measurement. If those factors were found to have significant influence on activity in breast cancer survivors, it would warrant future development of breast-cancer–specific accelerometer reduction techniques.

The comparison of LPA presented another interesting significant contrast between self-report (GLTEQ) and accelerometry. Results indicated the GLTEQ underestimated LPA by 224.5 [3.2] min/day compared with the accelerometer. This equates to over 3.5 h/day of active time (or about 280 kcal/day) that was potentially unaccounted for by the GLTEQ. The difference between these estimates could be due to the fact that the GLTEQ was designed to measure exercise time and therefore may not be as sensitive as the accelerometer to nonexercise-related LPA. Light intensity activities typically span a large range of domains (ie, occupational, leisure time, household) and tend to occur in higher volumes than MVPA, which may lead to some challenges with recall. Expanding existing LPA questionnaires to encompass these domains would likely provide increased congruency between self-reported and accelerometer-derived estimates for LPA, as it may provide a better trigger for recalling these high volume activities. With increasing literature advocating the important role of LPA in adults’ health in concert with data suggesting survivors may engage in lower levels of LPA than healthy controls,²³, accurately accounting for these lower intensity activities to provide a “whole picture” of a survivor’s active day remains an important future research direction. Combining accelerometer and self-report data using ecological momentary assessment to capture these behaviors in real-time in the real world could provide a better understanding of the context in which LPA occurs as well as survivors’ perceptions of intensity to build more accurate and scalable measurement tools for LPA.

Our ST results indicate nonsignificant difference estimates from the accelerometer and the STQ (M_diff = 1.3 [15.3] min/day) with slightly higher estimates for the STQ versus accelerometer. This finding is consistent with the one other study that has examined these relationships in cancer survivors.¹⁵ However, our findings also indicate the IPAQ significantly underestimated ST compared with the accelerometer and the STQ by about half (Table 1). These differences may be because both the STQ and Marshall questionnaire used in the previous study measure multiple domains of sitting (ie, computer, television, travel) on both weekdays and weekends whereas the IPAQ uses only two recall items of overall sitting time (for weekday and weekend separately). The domain-specific, structured approach has been shown to improve recall and may help to prevent underestimation and general underreporting of the high volume, ubiquitous behavior of sitting.^17,38 Finally, we would be remiss to not acknowledge the known limitations to estimating ST using the count-based approach on the waist-worn accelerometer. Due to the monitor’s orientation at the hip, the accelerometer may misrepresent total ST by misclassifying standing still as sitting. However, Kozey-Keadle and colleagues have previously examined estimation of ST using waist-worn accelerometers and have shown the 100 count per minute cut off yields ST estimates within 5% range of accuracy for a seated position compared with direct observation.³⁹

Of further interest are our exploratory results indicating that age and disease stage may modify the congruency between activity and ST measures. Specifically, older survivors and those with more advanced disease stage generally reported more PA and less ST than were measured by the accelerometer. These differences raise the question of whether these subgroups are systematically reporting more time physically active, overestimating their intensity, or the accelerometer is misclassifying their activity intensity. These misclassifications could be due to their age, disease stage, fatigue status, functional status, cognitive function, occupational status, etc. and would be important next steps for exploration of measurement of physical activity in breast cancer survivors. Finally, the difference score for MVPA was greater for survivors of color than for white survivors, with survivors of color overreporting MVPA compared with accelerometer-derived estimates. This may be due in part to cultural differences between white survivors and survivors of color. Previous research has suggested that people of color may accumulate a majority of their activity in occupational or household-related domains, thus explaining lower levels of leisure-time MVPA but high levels of reported total MVPA from other nonleisure domains.²⁰ However, given the small number of survivors of color in the present study, these results should be interpreted with caution.

With the multitude of physical activity and ST measurement tools available, many factors including cost, sample size, primary outcome of interest, and activity characteristics of interest (eg, duration, intensity, energy expenditure) need to be considered⁴⁰ when choosing a tool. Our findings may help inform these decisions for breast cancer survivors. For example, if LPA is of interest, an accelerometer may provide a more comprehensive assessment of these activities than the GLTEQ. In contrast, if MVPA is the activity of interest, our results suggest the GLTEQ and accelerometer were more congruent than the IPAQ was with either measure, therefore, if budgetary constraints are a concern, the more cost-efficient GLTEQ could provide similar results to an accelerometer. In addition to considering measurement congruency, it is also critically important to carefully consider the population (breast cancer survivors) and subsequent burden that accompanies the measurement tool of choice. Overall, our results indicate, when choosing a questionnaire for ST or LPA for breast cancer survivors, the more comprehensive the questions, to encompass multiple domains or time of day, the greater amount of time that will be captured within that activity category. Conversely, since the majority of MVPA is completed in leisure-time, dependent on the age and race of the population, a shorter questionnaire may be sufficient. Additionally, dependent on time since diagnosis and treatment received, activity recall or body movement patterns may be affected which could influence measurement tool selection.^23,24 Finally, it is also important to consider the setting in which measurement is taking place. In busy clinical settings, shorter, self-report measures may have a greater chance of being implemented than accelerometers or longer self-report measures and would still provide useful information regarding an overall snapshot of survivors’ MVPA or ST that could be used to initiate a conversation or referral for a program to help survivors positively change one or both of these behaviors.

Limitations

There were a few limitations within the current study that should be taken into account. First, the accelerometer cut-points used were developed with healthy, young adults; therefore using different cut-points may have yielded different results.³⁴ Given the large age range in our participants (23-84 years), we believe the use of these cut-points was justified, in lieu of population-specific (ie, older adults) cut-points. In addition, limitations to estimating activity from an accelerometer include the inability to capture certain activities such as swimming and cycling and the aforementioned inability to distinguish between body postures (ie, sitting vs standing).⁴¹ The participants were predominantly white, highly educated, and high earners (85.2% earned ≥$40,000 per year), therefore, the present results may not be generalizable to survivors from more diverse backgrounds. However, as far as we know, this is the first study to report the congruency of estimated ST, LPA, and MVPA across multiple measurement tools in a nationwide sample of breast cancer survivors who were heterogeneous in terms of disease characteristics (ie, stage, treatment, time since diagnosis).

Conclusions

Our findings suggest that physical activity and ST estimates in breast cancer survivors may be dependent on the measurement tool used. In addition, congruency of measurement tools was dependent on activity intensity of interest, and participant age, race, and disease history may also influence these factors. Therefore, researchers should consider the intended outcomes of interest, the context in which the tool is being used (ie, clinical versus research), the available resources, and the participant population before they select a measurement tool for estimating physical activity and sitting time in breast cancer survivors.

Acknowledgment
This work was supported by grant #F31AG034025 from the National Institute on Aging (Dr Phillips); Shahid and Ann Carlson Khan endowed professorship and grant #AG020118 from the National Institute on Aging (Dr McAuley). Dr Phillips is supported by the National Cancer Institute #K07CA196840, and Dr Welch is supported by National Institute of Health/National Cancer Institute training grant CA193193. All data for this study were collected at the University of Illinois Urbana Champaign.

The fall 2017 approval by the US Food and Drug Administration (FDA) of abemaciclib made it the third cyclin-dependent kinase (CDK) inhibitor approved for the treatment of hormone receptor (HR)-positive breast cancer, and the first to receive an approved indication as monotherapy in that setting. Abemaciclib is a small-molecule inhibitor of the CDK4 and CDK6 proteins, which are key gatekeepers of the cell cycle and frequently dysregulated in HR-positive breast cancer. On the basis of the randomized, placebo-controlled, multicenter phase 3 MONARCH-2 trial, it was approved in combination with fulvestrant for the treatment of women with HR-positive, HER2-negative advanced or metastatic breast cancer who had progressed during endocrine therapy.¹

A total of 669 women aged 18 years and older, with any menopausal status, an Eastern Cooperative Oncology Group (ECOG) Performance Status of 0 or 1, measurable disease per Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1) or nonmeasurable bone-only disease, were enrolled. Patients had progressed during neoadjuvant or adjuvant endocrine therapy, within 12 months of adjuvant endocrine therapy, or during frontline endocrine treatment for metastatic disease.

Those who had received more than 1 endocrine therapy or any prior chemotherapy for metastatic breast cancer or prior treatment with everolimus or CDK4/6 inhibitors, as well as those with the presence of visceral crisis or evidence or history of central nervous system (CNS) metastases, were excluded from the study.

Patients were randomized 2:1 to receive 150 mg abemaciclib or placebo, both in combination with 500 mg fulvestrant. The initial dose of abemaciclib was 200 mg, but this was amended to 150mg after enrollment of the first 178 patients to alleviate diarrhea-related toxicity concerns. Randomization was stratified according to metastatic site (visceral, bone only, or other) and endocrine therapy resistance (primary or secondary).

Tumors were measured by computed tomography (CT) and magnetic-resonance imaging (MRI) according to RECIST-1.1 within 28 days before random assignment, every 8 weeks for the first year, every 12 weeks thereafter, and then within 2 weeks of clinical progression. Bone scintigraphy was also performed at baseline and then every 6th cycle starting with cycle 7. Hematologic and blood chemistry laboratory tests were performed centrally on days 1 and 15 of the first cycle and day 1 of all remaining cycles.

The primary endpoint was progression-free survival (PFS); median PFS was 16.4 months in the abemaciclib arm, compared with 9.3 months in the placebo arm in the intent-to-treat population (hazard ratio [HR], 0.553;P < .0000001), translating to a 45% reduction in the risk of disease progression or death with the combination. Objective response rate in the 2 groups among patients with measurable disease was 48.1% and 21.3%, respectively, which included a complete response rate of 3.5% in the abemaciclib arm. The median duration of response was not yet reached in the study group, compared with 25.6 months for placebo. Overall survival data were not yet mature.

The agency also approved abemaciclib as monotherapy for women and men with HR-positive, HER2-negative advanced or metastatic breast cancer with disease progression following endocrine therapy and prior chemotherapy in the metastatic setting. That approval was based on data from the single-arm MONARCH-1 trial of 132 patients who received 200 mg abemaciclib twice daily on a continuous schedule.²

Patients had adequate organ function, measurable disease per RECIST-1.1, and an ECOG performance status of 0 or 1. Patients must have progressed on or after previous endocrine therapy and have received prior treatment with at least 2 chemotherapy regimens, at least 1 of them, but no more than 2, having been administered in the metastatic setting. Exclusion criteria included prior receipt of a CDK inhibitor, major surgery within 14 days of the start of the study, and CNS metastases.

Tumor assessments were performed by CT or MRI according to RECIST-1.1 within the 4 weeks prior to the first dose of study drug and then subsequently at every other cycle. Responses were confirmed at least 4 weeks after the initial observation. The overall response rate was 19.7%, made up completely of partial responses. Median duration of response was 8.6 months, median PFS was 6 months and median OS was 17.7 months.
 

Adverse events

The most common adverse events experienced with the combination of abemaciclib and fulvestrant were neutropenia (23.6%) and diarrhea (13.4%). The rate of grade 4 neutropenia was higher in the combination arm (2.9% vs 0.4%) and there were 3 deaths with the combination that were linked to treatment-related AEs. In the monotherapy trial, abemaciclib treatment most commonly caused diarrhea (90.2%), fatigue (65.2%), nausea (64.4%), decreased appetite (45.5%), and abdominal pain (38.6%). Grade 3 diarrhea and fatigue occurred in 19.7% and 12.9% of patients, respectively. Serious AEs occurred in 24.2% of patients and AEs led to treatment discontinuation in 7.6% of patients.

Warnings and precautions

Abemaciclib is marketed as Verzenio by Eli Lilly and Company. Warnings and precautions relating to diarrhea, neutropenia, hepatotoxicity, venous thromboembolism (VTE), and embryofetal toxicity are detailed in the prescribing information. In the event of diarrhea, patients should be treated with antidiarrheal therapy and should increase oral fluids and notify their health care provider. Treatment should be interrupted for grade 3 or 4 diarrhea and then resumed at a lower dose upon return to grade 1.

To guard against neutropenia, complete blood counts should be performed prior to starting therapy, every 2 weeks for the first 2 months, monthly for the subsequent 2 months, and then as clinically indicated. Treatment should be interrupted or delayed or the dose reduced for grade 3 or 4 neutropenia and patients should report episodes of fever.

Liver function tests should be performed before starting abemaciclib, every 2 weeks for the first 2 months, monthly for the next 2 months, and then as clinically indicated. For patients who develop persistent or recurrent grade 2, 3 or 4 hepatic transaminase elevation, dose interruption, reduction, discontinuation, or delay should be considered.

Patients should be monitored for signs and symptoms of VTE and pulmonary embolism, and treated appropriately. Pregnant women should be advised of the potential risk to a fetus, and those of reproductive potential should be counselled on the importance of using effective contraception during treatment and for at least 3 weeks after the last dose.³

The fall 2017 approval by the US Food and Drug Administration (FDA) of abemaciclib made it the third cyclin-dependent kinase (CDK) inhibitor approved for the treatment of hormone receptor (HR)-positive breast cancer, and the first to receive an approved indication as monotherapy in that setting. Abemaciclib is a small-molecule inhibitor of the CDK4 and CDK6 proteins, which are key gatekeepers of the cell cycle and frequently dysregulated in HR-positive breast cancer. On the basis of the randomized, placebo-controlled, multicenter phase 3 MONARCH-2 trial, it was approved in combination with fulvestrant for the treatment of women with HR-positive, HER2-negative advanced or metastatic breast cancer who had progressed during endocrine therapy.¹

A total of 669 women aged 18 years and older, with any menopausal status, an Eastern Cooperative Oncology Group (ECOG) Performance Status of 0 or 1, measurable disease per Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1) or nonmeasurable bone-only disease, were enrolled. Patients had progressed during neoadjuvant or adjuvant endocrine therapy, within 12 months of adjuvant endocrine therapy, or during frontline endocrine treatment for metastatic disease.

Those who had received more than 1 endocrine therapy or any prior chemotherapy for metastatic breast cancer or prior treatment with everolimus or CDK4/6 inhibitors, as well as those with the presence of visceral crisis or evidence or history of central nervous system (CNS) metastases, were excluded from the study.

Patients were randomized 2:1 to receive 150 mg abemaciclib or placebo, both in combination with 500 mg fulvestrant. The initial dose of abemaciclib was 200 mg, but this was amended to 150mg after enrollment of the first 178 patients to alleviate diarrhea-related toxicity concerns. Randomization was stratified according to metastatic site (visceral, bone only, or other) and endocrine therapy resistance (primary or secondary).

Tumors were measured by computed tomography (CT) and magnetic-resonance imaging (MRI) according to RECIST-1.1 within 28 days before random assignment, every 8 weeks for the first year, every 12 weeks thereafter, and then within 2 weeks of clinical progression. Bone scintigraphy was also performed at baseline and then every 6th cycle starting with cycle 7. Hematologic and blood chemistry laboratory tests were performed centrally on days 1 and 15 of the first cycle and day 1 of all remaining cycles.

The primary endpoint was progression-free survival (PFS); median PFS was 16.4 months in the abemaciclib arm, compared with 9.3 months in the placebo arm in the intent-to-treat population (hazard ratio [HR], 0.553;P < .0000001), translating to a 45% reduction in the risk of disease progression or death with the combination. Objective response rate in the 2 groups among patients with measurable disease was 48.1% and 21.3%, respectively, which included a complete response rate of 3.5% in the abemaciclib arm. The median duration of response was not yet reached in the study group, compared with 25.6 months for placebo. Overall survival data were not yet mature.

The agency also approved abemaciclib as monotherapy for women and men with HR-positive, HER2-negative advanced or metastatic breast cancer with disease progression following endocrine therapy and prior chemotherapy in the metastatic setting. That approval was based on data from the single-arm MONARCH-1 trial of 132 patients who received 200 mg abemaciclib twice daily on a continuous schedule.²

Patients had adequate organ function, measurable disease per RECIST-1.1, and an ECOG performance status of 0 or 1. Patients must have progressed on or after previous endocrine therapy and have received prior treatment with at least 2 chemotherapy regimens, at least 1 of them, but no more than 2, having been administered in the metastatic setting. Exclusion criteria included prior receipt of a CDK inhibitor, major surgery within 14 days of the start of the study, and CNS metastases.

Tumor assessments were performed by CT or MRI according to RECIST-1.1 within the 4 weeks prior to the first dose of study drug and then subsequently at every other cycle. Responses were confirmed at least 4 weeks after the initial observation. The overall response rate was 19.7%, made up completely of partial responses. Median duration of response was 8.6 months, median PFS was 6 months and median OS was 17.7 months.
 

Adverse events

The most common adverse events experienced with the combination of abemaciclib and fulvestrant were neutropenia (23.6%) and diarrhea (13.4%). The rate of grade 4 neutropenia was higher in the combination arm (2.9% vs 0.4%) and there were 3 deaths with the combination that were linked to treatment-related AEs. In the monotherapy trial, abemaciclib treatment most commonly caused diarrhea (90.2%), fatigue (65.2%), nausea (64.4%), decreased appetite (45.5%), and abdominal pain (38.6%). Grade 3 diarrhea and fatigue occurred in 19.7% and 12.9% of patients, respectively. Serious AEs occurred in 24.2% of patients and AEs led to treatment discontinuation in 7.6% of patients.

Warnings and precautions

Abemaciclib is marketed as Verzenio by Eli Lilly and Company. Warnings and precautions relating to diarrhea, neutropenia, hepatotoxicity, venous thromboembolism (VTE), and embryofetal toxicity are detailed in the prescribing information. In the event of diarrhea, patients should be treated with antidiarrheal therapy and should increase oral fluids and notify their health care provider. Treatment should be interrupted for grade 3 or 4 diarrhea and then resumed at a lower dose upon return to grade 1.

To guard against neutropenia, complete blood counts should be performed prior to starting therapy, every 2 weeks for the first 2 months, monthly for the subsequent 2 months, and then as clinically indicated. Treatment should be interrupted or delayed or the dose reduced for grade 3 or 4 neutropenia and patients should report episodes of fever.

Liver function tests should be performed before starting abemaciclib, every 2 weeks for the first 2 months, monthly for the next 2 months, and then as clinically indicated. For patients who develop persistent or recurrent grade 2, 3 or 4 hepatic transaminase elevation, dose interruption, reduction, discontinuation, or delay should be considered.

Patients should be monitored for signs and symptoms of VTE and pulmonary embolism, and treated appropriately. Pregnant women should be advised of the potential risk to a fetus, and those of reproductive potential should be counselled on the importance of using effective contraception during treatment and for at least 3 weeks after the last dose.³

The fall 2017 approval by the US Food and Drug Administration (FDA) of abemaciclib made it the third cyclin-dependent kinase (CDK) inhibitor approved for the treatment of hormone receptor (HR)-positive breast cancer, and the first to receive an approved indication as monotherapy in that setting. Abemaciclib is a small-molecule inhibitor of the CDK4 and CDK6 proteins, which are key gatekeepers of the cell cycle and frequently dysregulated in HR-positive breast cancer. On the basis of the randomized, placebo-controlled, multicenter phase 3 MONARCH-2 trial, it was approved in combination with fulvestrant for the treatment of women with HR-positive, HER2-negative advanced or metastatic breast cancer who had progressed during endocrine therapy.¹

A total of 669 women aged 18 years and older, with any menopausal status, an Eastern Cooperative Oncology Group (ECOG) Performance Status of 0 or 1, measurable disease per Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1) or nonmeasurable bone-only disease, were enrolled. Patients had progressed during neoadjuvant or adjuvant endocrine therapy, within 12 months of adjuvant endocrine therapy, or during frontline endocrine treatment for metastatic disease.

Those who had received more than 1 endocrine therapy or any prior chemotherapy for metastatic breast cancer or prior treatment with everolimus or CDK4/6 inhibitors, as well as those with the presence of visceral crisis or evidence or history of central nervous system (CNS) metastases, were excluded from the study.

Patients were randomized 2:1 to receive 150 mg abemaciclib or placebo, both in combination with 500 mg fulvestrant. The initial dose of abemaciclib was 200 mg, but this was amended to 150mg after enrollment of the first 178 patients to alleviate diarrhea-related toxicity concerns. Randomization was stratified according to metastatic site (visceral, bone only, or other) and endocrine therapy resistance (primary or secondary).

Tumors were measured by computed tomography (CT) and magnetic-resonance imaging (MRI) according to RECIST-1.1 within 28 days before random assignment, every 8 weeks for the first year, every 12 weeks thereafter, and then within 2 weeks of clinical progression. Bone scintigraphy was also performed at baseline and then every 6th cycle starting with cycle 7. Hematologic and blood chemistry laboratory tests were performed centrally on days 1 and 15 of the first cycle and day 1 of all remaining cycles.

The primary endpoint was progression-free survival (PFS); median PFS was 16.4 months in the abemaciclib arm, compared with 9.3 months in the placebo arm in the intent-to-treat population (hazard ratio [HR], 0.553;P < .0000001), translating to a 45% reduction in the risk of disease progression or death with the combination. Objective response rate in the 2 groups among patients with measurable disease was 48.1% and 21.3%, respectively, which included a complete response rate of 3.5% in the abemaciclib arm. The median duration of response was not yet reached in the study group, compared with 25.6 months for placebo. Overall survival data were not yet mature.

The agency also approved abemaciclib as monotherapy for women and men with HR-positive, HER2-negative advanced or metastatic breast cancer with disease progression following endocrine therapy and prior chemotherapy in the metastatic setting. That approval was based on data from the single-arm MONARCH-1 trial of 132 patients who received 200 mg abemaciclib twice daily on a continuous schedule.²

Patients had adequate organ function, measurable disease per RECIST-1.1, and an ECOG performance status of 0 or 1. Patients must have progressed on or after previous endocrine therapy and have received prior treatment with at least 2 chemotherapy regimens, at least 1 of them, but no more than 2, having been administered in the metastatic setting. Exclusion criteria included prior receipt of a CDK inhibitor, major surgery within 14 days of the start of the study, and CNS metastases.

Tumor assessments were performed by CT or MRI according to RECIST-1.1 within the 4 weeks prior to the first dose of study drug and then subsequently at every other cycle. Responses were confirmed at least 4 weeks after the initial observation. The overall response rate was 19.7%, made up completely of partial responses. Median duration of response was 8.6 months, median PFS was 6 months and median OS was 17.7 months.
 

Adverse events

The most common adverse events experienced with the combination of abemaciclib and fulvestrant were neutropenia (23.6%) and diarrhea (13.4%). The rate of grade 4 neutropenia was higher in the combination arm (2.9% vs 0.4%) and there were 3 deaths with the combination that were linked to treatment-related AEs. In the monotherapy trial, abemaciclib treatment most commonly caused diarrhea (90.2%), fatigue (65.2%), nausea (64.4%), decreased appetite (45.5%), and abdominal pain (38.6%). Grade 3 diarrhea and fatigue occurred in 19.7% and 12.9% of patients, respectively. Serious AEs occurred in 24.2% of patients and AEs led to treatment discontinuation in 7.6% of patients.

Warnings and precautions

Abemaciclib is marketed as Verzenio by Eli Lilly and Company. Warnings and precautions relating to diarrhea, neutropenia, hepatotoxicity, venous thromboembolism (VTE), and embryofetal toxicity are detailed in the prescribing information. In the event of diarrhea, patients should be treated with antidiarrheal therapy and should increase oral fluids and notify their health care provider. Treatment should be interrupted for grade 3 or 4 diarrhea and then resumed at a lower dose upon return to grade 1.

To guard against neutropenia, complete blood counts should be performed prior to starting therapy, every 2 weeks for the first 2 months, monthly for the subsequent 2 months, and then as clinically indicated. Treatment should be interrupted or delayed or the dose reduced for grade 3 or 4 neutropenia and patients should report episodes of fever.

Liver function tests should be performed before starting abemaciclib, every 2 weeks for the first 2 months, monthly for the next 2 months, and then as clinically indicated. For patients who develop persistent or recurrent grade 2, 3 or 4 hepatic transaminase elevation, dose interruption, reduction, discontinuation, or delay should be considered.

Patients should be monitored for signs and symptoms of VTE and pulmonary embolism, and treated appropriately. Pregnant women should be advised of the potential risk to a fetus, and those of reproductive potential should be counselled on the importance of using effective contraception during treatment and for at least 3 weeks after the last dose.³