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Filtering for productive activity changes outcomes in step-based monitoring among children
Michael Wininger1, Kristie Bjornson
1Department of Rehabilitation Sciences, University of Hartford, 200 Bloomfield Avenue, West Hartford, CT 06117, USA. Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. Cooperative Studies Program, Department of Veterans Affairs, West Haven, CT, USA.
Insights
This study establishes a data-driven method for filtering "idle" data from wearable activity monitors in children. A threshold of 10 steps per minute effectively distinguishes productive activity, improving data accuracy for health research.
Area of Science:
- Pediatric Health Research
- Biomedical Engineering
- Human Movement Science
Background:
- Wearable activity monitors are common in health research, but lack standardized methods for artefact removal in pediatric datasets.
- Existing monitors like StepWatch filter some data but do not post-process all activity, potentially including non-productive movement.
Purpose of the Study:
- To establish a data-driven, minimum per-minute stride count threshold for identifying "productive" activity in typically developing children.
- To assess the impact of implementing this threshold on stride count and activity bout data across different age groups.
Main Methods:
- Collected stride count data from 428 typically developing children (ages 2-15) using StepWatch monitors over 5 days.
- Developed and validated a minimum per-minute stride count threshold to differentiate "idle" from "productive" activity.
- Analyzed the reduction in total stride count and activity bouts after applying the filtering threshold.
Main Results:
- A threshold of 10 steps per minute captured 90% of productive activity samples, consistent across age and gender.
- Filtering reduced daily stride counts by 8-10% and the number of activity bouts per day from 79.3 to 72.7.
- The effect of filtering on activity bouts varied by age group.
Conclusions:
- This study provides the first data-driven minimum activity threshold in step/stride units for pediatric research.
- Implementing this "production-idle" filtering significantly impacts activity data, suggesting a need to re-evaluate existing studies and guidelines.
- The findings support more accurate data interpretation and application of wearable activity monitor data in 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 StepWatch 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.
