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Updated: Jun 21, 2026

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
Published on: April 21, 2017
Comparison of Consumer Smartwatch and Research-Grade Accelerometer-Derived Step Counts in Amyotrophic Lateral
Gordon Jewett1,2,3, Reed Ferber4,5, Colin B Josephson2,3,6,7,8,9
1Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Introduction/Aims:
Objective, scalable measures of function are needed in amyotrophic lateral sclerosis (ALS). Research-grade accelerometers are promising but may be difficult to deploy for prolonged remote monitoring, whereas consumer smartwatches offer a practical alternative. We evaluated the feasibility of long-term smartwatch monitoring in ALS and compared smartwatch- and accelerometer-derived daily step counts.
Methods:
In this single-centre prospective observational study, participants wore a Fitbit Sense (smartwatch) on the wrist longitudinally and an ActiGraph GT9X Link (accelerometer) on the opposite wrist during clinic-aligned assessment periods. Device comparison was restricted to concurrent valid days (≥ 10 h wear) and included paired comparison of mean daily step counts, Pearson correlation, Bland-Altman analysis, and proportional bias assessment.
Results:
Forty participants were enrolled and 39 contributed data across 8093 smartwatch and 915 accelerometer days. Step count comparison included 503 concurrent valid days from 34 participants. Mean daily step counts were 3480 (SD 4365) for smartwatch and 4066 (SD 4797) for accelerometer (p < 0.0001), with strong correlation (r = 0.91, p < 0.0001). Mean bias was -586 steps for smartwatch, with 95% limits of agreement from -4193 to 3021. Among participants with ≥ 6 months of concurrent data, devices showed similar decline over time (interaction p = 0.76).
Discussion:
Long-term remote monitoring using a smartwatch was feasible in ALS. Smartwatch-derived step counts were strongly correlated with accelerometer-derived estimates but were systematically lower and showed wide day-level limits of agreement. Smartwatch-derived step counts may be useful for group-level analyses and longitudinal monitoring, though device-specific bias should be considered in interpretation.

