A reproducible pipeline for processing commercial wearable step-count data in aging cohorts: Application and...

Wearable devices offer the ability to objectively characterize free-living physical activity; however, raw step-count data generated by commercial devices require systematic processing before they can support rigorous inference. We describe a transparent, reproducible standard operating procedure (SOP) for transforming epoch-level step-count data from commercial Garmin devices into participant-level analytic variables and demonstrate its application in the STRRIDE-PD Reunion study: a long-term follow-up of older adults originally enrolled in a supervised exercise intervention trial. This data pipeline standardizes timestamps, reconstructs daily epoch grids, infers wear time from observed step patterns, and applies a prespecified valid-day threshold (≥10 h inferred wear time) to generate participant-level summaries. Among 67 participants (mean age 71.4 years, 65.7% women), the median valid-day count was 10 days, median average daily steps were 5794, and participant-level estimates were identical across ≥10-h and ≥ 6-h valid-day thresholds. Wearable-derived step counts were significantly associated with 9 of 16 cardiometabolic and fitness outcomes, including cardiorespiratory fitness, body composition, and lipid profiles. By contrast, self-reported exercise - assessed via a frequency-by-duration composite ranked into deciles - was not significantly associated with any outcome. Regression calibration suggested that measurement limitations in self-reported exercise may have contributed to attenuated associations compared with Garmin-derived and calibration-corrected estimates. Limitations include the modest analytic sample size and potential measurement error assumption violations, relatively brief monitoring duration, and indirect wear-time inference. These findings suggest that measurement approach can meaningfully shape physical activity-health conclusions, and that reproducible wearable data pipelines are essential infrastructure for aging epidemiology.