Background & Aims
Functional status—an index of physical, emotional, and social wellbeing—is difficult to assess in older patients.1 Standard tools like the PROMIS-29 offer brief, cross-sectional insights that rely on patient recall and, therefore, cognitive state. Limited healthcare access can leave functional decline undetected, making real-time, precise assessment crucial. Chronic pain, particularly prevalent in older adults, exemplifies this challenge. Up to 28% of U.S. adults suffer from chronic pain.2 Older adults are a vulnerable group with: higher rates and multiple pain-related diagnoses, age-related physiologic susceptibility to adverse drug effects,3 and implicit biases related to race, socioeconomic status, and cognitive state.4 Pain in older adults is frequently under or untreated.4 Smartphones, widely used by older Americans, might offer a practical way to track real-time mobility, emotion, and sociability and detect changes in functional status, as opposed to a pain score.5
Methods
The Pain Intervention and Digital Research Program (Pain-IDR) seeks to develop new tools to measure functional status and quality of care while providing standard care to patients with chronic pain.5 We used the Beiwe Research Platform to collect active (PROMIS-29 measures of physical function, depression, and anxiety) and passive (accelerometer and GPS location) data from 46 people (20M, 25F, 1TG; mean age 55) with chronic musculoskeletal pain for up to 32 weeks. Accelerometer data was summarized as walking time, daily step count, and cadence. GPS data was summarized as distance traveled, and maximum distance from home. We performed Pearson’s correlation coefficients between active and passive data variables as a preliminary test to understand the data structure. Ordinary least squares (OLS) regression models were fitted to examine whether passive accelerometer and GPS measures were related to active PROMIS-29 subscores of physical function (PF), depression (DEP), or anxiety (ANX).
Results
Accelerometer measures of walking time (r=0.11, p=4.13e-03) and daily step count (r=0.1, p=0.0101) were positively correlated with PF, while cadence (r=-0.17, p=6.06e-06) was negative correlated with PF. Only cadence was negatively correlated with DEP (r=-0.18, p=1.76e-06). ANX results were comparable. GPS measures of traveled distance was non-significant re: PF, DEP and ANX. Maximum distance from home was negatively correlated with ANX (r=-0.08, p=0.0366). Given accelerometer walking time was highly correlated with daily step count (r=1.00) and GPS traveled distance was highly correlated with maximum distance from home (r=0.66), we limited the OLS regression model independent variables to walking time, cadence, and maximum distance from home. PF was associated with walking time (?=0.00, p=0.017), cadence (?=-1.76, p=0), and maximum distance from home (?=-0.00, p=0.04), with comparable results for ANX. DEP was associated with walking time (?=0.00, p=0.003) and cadence (?=-1.59, p=0).
Conclusions
Longitudinal, remote data collection is feasible in older patients with chronic musculoskeletal pain. We further show that passive data traces active measures of physical function, depression, or anxiety collected in the same participants continuously, albeit without the need for patient engagement and at greater response rates. Future work further explore the role of passive data collection in clinical decision making and quality of patient care.
References
1. Applegate WB, Blass JP, Williams TF. Instruments for the Functional Assessment of Older Patients. New Engl J Medicine 1990;322(17):1207–14.
2. Dahlhamer J, Lucas J, Zelaya, C, et al. Prevalence of Chronic Pain and High-Impact Chronic Pain Among Adults — United States, 2016. Morbidity Mortal Wkly Rep 2018;67(36):1001–6.
3. Bruckenthal P, Reid MC, Reisner L. Special Issues in the Management of Chronic Pain in Older Adults. Pain Med 2009;10(S2):S67–78.
4. Arnstein PM, Herr K. Pain in the Older Person. In: Ballantyne JC, Fishman SM, Rathmell JP, editors. Bonica’s Management of Pain, 5th Edition. 2019. p. 929–39.
5. Fu M, Shen J, Gu C, et al. The Pain Intervention & Digital Research Program: an operational report on combining digital research with outpatient chronic disease management. Frontiers in Pain Research 2024;1–11.
Presenting Author
Daniel Barron
Poster Authors
Daniel Barron
MD PhD
Mass General Brigham
Lead Author
Joanna Shen BS
Lead Author
Ellina Oliveira MPH
Lead Author
Zacharia Isaac MD
Spaulding Rehabilitation Hospital
Lead Author
Danielle L. Sarno
MD
Brigham and Women's Hospital, Spaulding Rehabilitation Hospital, Harvard Medical School
Lead Author
Jennifer Kurz MD
Spaulding Rehabilitation Hospital
Lead Author
David Silbersweig MD
Brigham & Women's Hospital
Lead Author
Jukka-Pekka Onnela PhD
Harvard T.H. Chan School of Public Health
Lead Author
Daniel Barron
Brigham & Women's Hospital
Lead Author
Topics
- Novel Experimental/Analytic Approaches/Tools