Clustering-Based Accelerometer Measures of Physical Activity Patterns in Children With Overweight or Obesity: Baseline Cross-Sectional Methodological Analysis.

BACKGROUND

Accelerometers produce high-resolution physical activity data, but commonly used summary measures often reduce these data to total volume, intensity, or variability and may not retain interpretable temporal structure across the day. Cluster-based summaries may provide a way to characterize daily physical activity profiles while preserving information about when activity occurs.

OBJECTIVE

This study evaluated whether cluster-derived accelerometer summary measures could represent daily physical activity patterns and explain variation in pediatric cardiometabolic outcomes comparably to traditional accelerometer summary metrics.

METHODS

This baseline cross-sectional methodological analysis used data from 268 Latino children with overweight or obesity from low-income families participating in the Stanford GOALS trial. Participants were aged 7 to 11 years old and wore accelerometers for a minimum of 1 week according to study protocol. Valid daily activity profiles were summarized within a 7:00 AM-11:00 PM analytic window using consecutive, nonoverlapping 10-minute intervals. Daily profiles were clustered using unsupervised learning, and participant-level cluster-derived measures were created from the distribution of valid days assigned to each cluster. We compared these measures with traditional accelerometer summaries, including time spent in activity intensity states, Time Active Mean, Time Active Variability, Activity Intensity Mean, and Activity Intensity Variability. Linear regression models were used to evaluate associations with waist circumference, fasting insulin, and fasting triglycerides, adjusting for age and sex. Model performance was compared using R2 and the Akaike information criterion.

RESULTS

Cluster-derived measures explained a comparable proportion of variation in the 3 cardiometabolic outcomes to traditional accelerometer summary metrics. For example, the highest R² values among the cluster-derived measures were 25%, 11%, and 6% for waist circumference, fasting insulin, and fasting triglycerides, respectively, compared with 25%, 10%, and 6% for Time Active Mean. No single summary-measure approach consistently yielded the highest R² across all 3 outcomes.

CONCLUSIONS

Cluster-derived accelerometer measures provide a regression-ready approach for summarizing daily physical activity patterns while preserving interpretable clock-time structure. In this baseline analysis, these measures performed comparably to traditional accelerometer summaries while capturing temporal features not represented by conventional volume- or intensity-based metrics. Future work should evaluate external validation across diverse populations and settings and assess the use of these measures in longitudinal and intervention analyses.