Validity of Functional Performance Outcomes and Gait Metrics Measured by Smartphone Application in Patients with Chronic Stroke

Accurate assessment of impaired functional mobility is crucial to track recovery and guide interventions after stroke. Conventionally, physical therapists administer semi-objective rating scales or manually record performance on various tests. Technological developments, particularly automated algorithms that use motion data from inertial sensors, offer a promising solution for more efficient and objective evaluations. This cross-sectional analysis examined the agreement between therapist-recorded measures and a smartphone application (OneStep) for the Chair Stand Test (CST), Timed Up and Go (TUG), and the Six-Minute Walk Test (6MWT) in 42 participants with chronic stroke (mean age 54 years, 50% male). Analysis was measured via a two-way random-effect intraclass correlation coefficient analysis ICC(2,1) (absolute agreement) and Bland–Altman limits of agreement. ICC(2,1) was 0.989 [0.980, 0.994] for the CST, 0.995 [0.985, 0.998] for the TUG, and 0.921 [0.776, 0.967] for the 6MWT. Mean differences (therapist–app score) were −0.08 repetitions (CST), −1.03 s (TUG). and −69 feet (6MWT). Within-session repeatability was not detectably different between the app and therapist for the CST (SEM ratio app/therapist 0.91 [0.76, 1.05]) or the TUG (1.36 [0.92, 2.13]); the TUG estimates are imprecise and do not establish equivalence. The 6MWT was not considered in within-session repeatability as multiple tests were not completed due to patient fatigue. App failure rates were relatively low for the CST (2/80), TUG (3/76), and 6MWT (5/36). Correlations with patient-reported outcomes, clinician-rated outcomes, and gait metrics were exploratory (89 of 115 significant after false discovery rate correction). The results suggest close agreement for the CST, a small systematic difference for the TUG, and larger differences for the 6MWT. Future research should address the longitudinal predictive utility and broader implementation throughout the health care system of mobile applications like OneStep.

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Journal
Sensors
Published
2026-11-03
DOI
https://doi.org/10.3390/s26216947
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
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article

Validity of Functional Performance Outcomes and Gait Metrics Measured by Smartphone Application in Patients with Chronic Stroke

David F. Putrino, Gabriela Rozanski, Aidan Rogers, Jessica Polizzi et al.
Sensors
Stroke Rehabilitation and Recovery
article

Validity of Functional Performance Outcomes and Gait Metrics Measured by Smartphone Application in Patients with Chronic Stroke

David F. Putrino, Gabriela Rozanski, Aidan Rogers, Jessica Polizzi, Jenna M. Tosto-Mancuso, Karolina Roszkowski, Wilber Parada Iraheta, Megha Amin
article en

Abstract

Accurate assessment of impaired functional mobility is crucial to track recovery and guide interventions after stroke. Conventionally, physical therapists administer semi-objective rating scales or manually record performance on various tests. Technological developments, particularly automated algorithms that use motion data from inertial sensors, offer a promising solution for more efficient and objective evaluations. This cross-sectional analysis examined the agreement between therapist-recorded measures and a smartphone application (OneStep) for the Chair Stand Test (CST), Timed Up and Go (TUG), and the Six-Minute Walk Test (6MWT) in 42 participants with chronic stroke (mean age 54 years, 50% male). Analysis was measured via a two-way random-effect intraclass correlation coefficient analysis ICC(2,1) (absolute agreement) and Bland–Altman limits of agreement. ICC(2,1) was 0.989 [0.980, 0.994] for the CST, 0.995 [0.985, 0.998] for the TUG, and 0.921 [0.776, 0.967] for the 6MWT. Mean differences (therapist–app score) were −0.08 repetitions (CST), −1.03 s (TUG). and −69 feet (6MWT). Within-session repeatability was not detectably different between the app and therapist for the CST (SEM ratio app/therapist 0.91 [0.76, 1.05]) or the TUG (1.36 [0.92, 2.13]); the TUG estimates are imprecise and do not establish equivalence. The 6MWT was not considered in within-session repeatability as multiple tests were not completed due to patient fatigue. App failure rates were relatively low for the CST (2/80), TUG (3/76), and 6MWT (5/36). Correlations with patient-reported outcomes, clinician-rated outcomes, and gait metrics were exploratory (89 of 115 significant after false discovery rate correction). The results suggest close agreement for the CST, a small systematic difference for the TUG, and larger differences for the 6MWT. Future research should address the longitudinal predictive utility and broader implementation throughout the health care system of mobile applications like OneStep.

SensorsVol. 26(21)
Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 18%
Stroke Rehabilitation and Recovery
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