Validation of a markerless motion capture app for automated scoring of sit-to-stand, timed up and go, and short physical performance battery tests in adults with chronic disease

Jennifer K Bertrand, Margaret L McNeely, Jack Bates, Joshua Joy, Jenil Kanani, Victor E Ezeugwu and Puneeta Tandon

PLOS Digital Health, 2026, vol. 5, issue 1, 1-19

Abstract: Physical performance tests such as the 30-second Sit-to-Stand (30s-STS), Timed Up and Go (TUG), and Short Physical Performance Battery (SPPB) are widely used to assess physical function in older adults and are predictive of key health outcomes. However, their routine use in clinical practice is limited by time, resource, and personnel constraints. This study aimed to validate the automated scoring of physical performance assessments using a mobile, markerless motion capture (MMC) app compared to scoring by a certified exercise physiologist (CEP), and to quantify the rate and reasons for technology-related data loss. 228 adults (mean age = 61.6 ± 11.9 years) with at least one chronic medical condition were enrolled. Participants completed seven performance assessments: 30s-STS, TUG, and all components of the SPPB (Side-by-Side, Semi-Tandem and Tandem balance stands, 5-times Sit-to-Stand (5xSTS), and Gait Speed). All tests were scored simultaneously by a CEP and the MMC app using a Light Detection and Ranging (LiDAR)-enabled iPad. Agreement was assessed using intraclass correlation coefficients (ICCs) and weighted Cohen’s kappa. Agreement between the MMC app and CEP was good to excellent for all assessments. ICCs ranged from 0.812 (Tandem Stand) to 0.995 (5xSTS). The overall SPPB score showed almost perfect agreement (κ = 0.808). Perfect agreement with no variability was observed for the Side-by-Side and Semi-Tandem balance tests. The overall tech-related data loss rate was low (3.1%), with the most common issue being poor motion tracking quality (1.3%). Automated scoring of physical performance tests using a self-contained MMC app demonstrated high agreement with expert scoring and low data loss in a cohort of participants with a range of chronic medical conditions. These findings support the use of MMC-enabled mobile applications for scalable, accessible, and objective assessment of physical function in clinical settings, with future potential for remote and asynchronous use.Author summary: Tests of physical function—like standing up from a chair or walking a short distance—are powerful tools for understanding mobility, frailty, and future health risks in older adults. However, these tests are rarely used in routine care due to time, space, and staff constraints. In our study, we evaluated a mobile application that uses the iPad’s built-in camera to automatically measure how a person performs common movement-based tests. We tested the app with 228 older adults living with chronic illness and compared its automated scores to those from a trained health professional. We found strong agreement between the two, suggesting that accurate scoring can be achieved without a human rater. The app also performed reliably, with very few technical failures or lost data. By reducing the need for trained observers or specialized equipment, our tool could help make mobility assessments more accessible—both in clinics and at home. In future work, we aim to expand the technology for use with standard smartphone cameras and support fully self-directed testing. Ultimately, our goal is to enable broader, earlier detection of functional decline, and to support more personalized care through digital health tools that are scalable, affordable, and easy to use.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001172

DOI: 10.1371/journal.pdig.0001172

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