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Updated: May 18, 2026
Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
Published on: February 21, 2025
Investigating the comparability of wearable accelerometer methods in the association between physical activity and
Yacine Lapointe1, Aayush Kapur1, Abhinav Sharma2
1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1200, 2100 McGill College, Montreal, QC H3A 1G1, Canada.
Objective:
The selection of accelerometer processing methods may influence the shape of the dose-response association between wearable-measured physical activity and health outcomes. We aimed to compare the association of stroke and myocardial infarction with Moderate-Vigorous Physical Activity (MVPA) assessed by three accelerometer-generated metrics: Low-pass Filtered Euclidean Norm Minus One (LFENMO), machine-learning, and activity counts.
Methods:
We computed MVPA durations in the UK Biobank accelerometer sub-cohort recruited between 2013 and 2015 in the UK. The outcomes, incident stroke and myocardial infarction, were followed up until December 2022. We used Cox regression and a restricted cubic spline to estimate the dose-response association for each of the three MVPA metrics.
Results:
There were 90,237 cardiovascular disease-free participants at baseline. We observed 1298 incident strokes and 2031 myocardial infarctions. For stroke, a linear decrease in hazard ratio was observed with machine-learning, but not with LFENMO and activity counts. For myocardial infarction, machine-learning and LFENMO showed a curvilinear decrease in hazard ratios, whereas activity counts showed a linear decrease.
Conclusions:
The dose-response associations between MVPA and cardiovascular disease varied markedly across the three accelerometer-derived MVPA metrics. Research using a single accelerometer metric may caution about the interpretation of the association.
