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Abstract Number: 0456

Use of Machine Learning to Evaluate Incremental Value of Actigraphy Data for Classifying Treatment Response in Patients with Rheumatoid Arthritis

Jeffrey Curtis1, Yujie Su2, Cassie Clinton3, David Curtis4, Laura Stradford5, Patrick Zueger6, William Benjamin Nowell7, Pankaj Patel8, Esteban Rivera9, Kelly Gavigan4, Shilpa Venkatachalam10 and Fenglong Xie3, 1FASTER Medicine, Hoover, AL, 2Illumination Health, Hoover, AL, 3University of Alabama at Birmingham, Birmingham, AL, 4Global Healthy Living Foundation, Upper Nyack, NY, 5Global Healthy Living Foundation, Nyack, NY, 6AbbVie Inc, North Chicago, IL, 7Regeneron, New York, NY, 8AbbVie Inc., North Chicago, IL, 9Global Healthy Living Foundation, Long Island City, NY, 10Global Healthy Living Foundation, New York, NY

Meeting: ACR Convergence 2024

Keywords: informatics, Measurement Instrument, Patient reported outcomes, physical activity, rheumatoid arthritis

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Session Information

Date: Saturday, November 16, 2024

Title: RA – Diagnosis, Manifestations, & Outcomes Poster I

Session Type: Poster Session A

Session Time: 10:30AM-12:30PM

Background/Purpose: Digital health technology to collect electronic patient reported outcomes (ePRO) and biosensor data are increasingly used to generate real-world data in pharmacoepidemiology. However, the utility of passive biosensor data from actigraphy devices incremental to collecting ePRO data to classify treatment response in rheumatoid arthritis (RA) or other musculoskeletal conditions is not clear. The aim of this analysis was to determine the incremental value of actigraphy data in classifying treatment response among RA patients.

Methods: We conducted a prospective observational study at 28 US community rheumatology clinics among RA patients initiating upadacitinib or adalimumab. Patients contributed multiple ePROs at home on a  smartphone app (PatientSpot, formerly ArthritisPower) on a daily, weekly and monthly basis (mean duration 2 min/day) from baseline to a follow-up visit occurring about 3-4 months later. We incorporated ePRO data, with and without actigraphy data, from a study-provided device (Fitbit Versa3) into machine learning (ML) models to classify whether patients achieved low disease activity (LDA) or remission (REM; Clinical Disease Activity Index < 10) as assessed by a rheumatologist  at the follow-up visit. Feature engineering was performed on both ePRO and actigraphy raw data to derive within-person slope and variance of longitudinal change. A 2/3:1/3 random split sample approach was used to create separate training and testing samples; precision (positive predictive value) and recall (sensitivity) from the testing dataset were used to compare model accuracy.

Results: A total of 150 RA patients completed the study, and 96 contributed actigraphy data. Mean(SD) age was 52(12) years and 79% were women. A total of 62% (test sample) achieved LDA or REM. With only ePRO data, the best-performing ML generalized linear model achieved 86% precision and 75% recall to classify LDA and required 6 ePROs longitudinally to obtain adequate model performance. Ensemble ML methods attained higher recall (97%; 67% precision). The most important ePROs for classifying patients in LDA or REM were PROMIS fatigue, pain intensity, RADAI5, PROMIS physical function, PROMIS anxiety, and PROMIS pain interference. With the addition of passive actigraphy data, performance was similar or better (86% precision, 83% recall), and required fewer actively collected ePROs (pt global, RADAI5, PROMIS anxiety).

Conclusion: Longitudinal ePRO data collected at home accurately classified treatment response to new RA therapies. Passively contributed actigraphy data increased model performance slightly and reduced participant burden for data collection. Remote actigraphy data capture may be useful to develop a better understanding of real-world patient experience and treatment response.


Disclosures: J. Curtis: Abbvie, 2, 5, Amgen, 2, 5, Bristol Myers Squibb, 2, 5, CorEvitas, 2, 5, Janssen, 2, 5, Labcorp, 2, 5, Lilly, 2, 5, Novartis, 2, 5, Pfizer, 2, 5, Sanofi/Regeneron, 2, 5, UCB, 2, 5; Y. Su: None; C. Clinton: None; D. Curtis: None; L. Stradford: AbbVie, 5, Amgen, 5, BMS, 5, Eli Lilly, 5, Global Healthy Living Foundation, 3, Pfizer, 5; P. Zueger: AbbVie, 3, 11; W. Nowell: AbbVie, 2, 5, Amgen, 5, Global Healthy Living Foundation, 12, former employee, Janssen, 2, 5, Regeneron Pharmaceuticals, 3, Scipher Medicine, 5; P. Patel: AbbVie, 3, 11; E. Rivera: Global Healthy Living Foundation, 3; K. Gavigan: Global Healthy Living Foundation, 3; S. Venkatachalam: None; F. Xie: None.

To cite this abstract in AMA style:

Curtis J, Su Y, Clinton C, Curtis D, Stradford L, Zueger P, Nowell W, Patel P, Rivera E, Gavigan K, Venkatachalam S, Xie F. Use of Machine Learning to Evaluate Incremental Value of Actigraphy Data for Classifying Treatment Response in Patients with Rheumatoid Arthritis [abstract]. Arthritis Rheumatol. 2024; 76 (suppl 9). https://acrabstracts.org/abstract/use-of-machine-learning-to-evaluate-incremental-value-of-actigraphy-data-for-classifying-treatment-response-in-patients-with-rheumatoid-arthritis/. Accessed .
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