Wearable devices have revolutionized the way physical activity is monitored, providing objective data on free-living activity levels. However, the raw step-count data generated by these devices often require systematic processing to ensure that the results can support rigorous scientific inference. This article discusses a reproducible standard operating procedure (SOP) developed for transforming epoch-level step-count data from commercial Garmin devices into participant-level analytic variables.
The application of this data processing pipeline was demonstrated in the STRRIDE-PD Reunion study, which involved a long-term follow-up of older adults who were originally enrolled in a supervised exercise intervention trial. The study included 67 participants with a mean age of 71.4 years, of which 65.7% were women. The median valid-day count for participants was 10 days, and the median average daily steps recorded were 5794.
One of the key findings of the study was that wearable-derived step counts were significantly associated with 9 out of 16 cardiometabolic and fitness outcomes, including cardiorespiratory fitness, body composition, and lipid profiles. In contrast, self-reported exercise, assessed through a frequency-by-duration composite ranked into deciles, did not show significant associations with any of the outcomes measured.
The study also highlighted that measurement limitations in self-reported exercise could have contributed to the weaker associations observed compared to the Garmin-derived step counts. The authors noted several limitations in their analysis, including the modest sample size, potential violations of measurement error assumptions, a relatively brief monitoring duration, and indirect inference of wear time.
These findings underscore the importance of the measurement approach in shaping physical activity and health conclusions. The authors advocate for the development of reproducible wearable data pipelines as essential infrastructure for aging epidemiology, emphasizing that such methodologies can enhance the reliability of research outcomes.
What this study does not show
This study does not establish a direct causal relationship between step counts and health outcomes, nor does it provide evidence for the effectiveness of specific interventions based on the data processed through the pipeline.
Evidence scorecard
- Analytic validity: Established – The study provides a clear methodology for data processing.
- Clinical validity: Partial – The associations with health outcomes are noted but not causally linked.
- Clinical utility: Not established – The study does not measure health outcomes after acting on the test result.