Sensors (Basel, Switzerland) · 2026
Quantifying Physical Activity in Toddlers with Down Syndrome with Wearable Sensors During a Home-Based Mobility Intervention.
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Athreya Varsha, Mishra Simoni, Collimore-Doherty Ashley N, Sutter Ellen N, Iverson Jana M
Аннотация
BACKGROUND: Quantifying movement during early mobility interventions is important for evaluating intervention participation and progression in toddlers with Down syndrome (DS), but behavioral video coding is time and labor-intensive. This study established and evaluated an activity count cutpoint for distinguishing moderate-to-vigorous physical activity (MVPA) and sedentary-to-light physical activity (SLPA) in toddlers with DS during a home-based mobility intervention using a wearable accelerometer integrated into a body-weight support harness system.
METHODS: Video recordings behaviorally coded for MVPA and SLPA were synchronized with accelerometer activity counts binned in 5 s epochs. Receiver operating characteristic curve analysis was used to determine both group-derived and participant-specific activity count cutpoints.
RESULTS: The optimal group-derived cutpoint was 141 activity counts per 5 s, demonstrating strong classification performance (AUC = 0.903), with 94.4% sensitivity, 70.2% specificity, and an overall classification accuracy of 79.2%. Participant-specific cutpoints resulted in only minimal improvements in classification performance.
CONCLUSIONS: These findings suggest that using individualized cutpoints may not be necessary for classifying MVPA in toddlers with DS during this intervention, though replication in a larger sample is warranted. A standardized activity count cutpoint may provide a practical approach for objectively monitoring activity during home-based mobility interventions.
METHODS: Video recordings behaviorally coded for MVPA and SLPA were synchronized with accelerometer activity counts binned in 5 s epochs. Receiver operating characteristic curve analysis was used to determine both group-derived and participant-specific activity count cutpoints.
RESULTS: The optimal group-derived cutpoint was 141 activity counts per 5 s, demonstrating strong classification performance (AUC = 0.903), with 94.4% sensitivity, 70.2% specificity, and an overall classification accuracy of 79.2%. Participant-specific cutpoints resulted in only minimal improvements in classification performance.
CONCLUSIONS: These findings suggest that using individualized cutpoints may not be necessary for classifying MVPA in toddlers with DS during this intervention, though replication in a larger sample is warranted. A standardized activity count cutpoint may provide a practical approach for objectively monitoring activity during home-based mobility interventions.