Filtering for productive activity changes outcomes in step-based monitoring among children

Abstract

Wearable activity monitors are increasingly prevalent in health research, but there is as yet no data-driven study of artefact removal in datasets collected from typically developing children across childhood. Here, stride count data were collected via a commercially available activity monitor (StepWatch), which employs an internal filter for sub-threshold accelerations, but does not post-process supra-threshold activity data. We observed 428 typically developing children, ages 2–15, wearing the Step Watch for 5 consecutive days. We developed a minimum per-minute stride-count below which the data outputted from the StepWatch could be considered ‘idle’ and not ‘productive’. We found that a threshold stride count of 10 steps per minute captured 90% of samples in a weighted average among isolated non-zero stride-count samples offset by inactivity. This threshold did not vary by age, gender, or by an age-gender interaction. Filtering the activity data according to this threshold reduced overall stride count by 8–10% by age group, from 8177 ± 2659 to 7432 ± 2641 strides per day. The impact on number of bouts per day decreased from an overall average of 79.3 ± 17.2 to 72.7 ± 12.1; this effect varied by age group. This study delivers the first data-driven estimate of a minimum activity threshold in step- or stride units that may extend to other studies. We conclude that the impact of production-idle filtering on activity data is substantial and suggests a possible impetus for re-contextualizing extant studies and guidelines reported without such filtering.

Unexplained Variance in Hydration Study

“…Should water intake be adjusted according to body composition, daily max ambient temperature, Mean temperature, or a composite measure accounting for time spent outdoors, total amount of direct sunlight, temperature and wind speed? Wearable sensors capable of recording climatic parameters are becoming increasingly prevalent [68,69,70,71], and perhaps large population investigations will soon have access to more accurate estimations of internal body temperature [72,73], sweat rate [74], and physical activity due to an increasingly robust infrastructure to support analysis of pedometers [75]. Furthermore, we recommend reporting the model goodness of fit as is conventional in modeling [76,77,78].…”