Development of a machine learning model to detect toddlers' physical activity and sedentary time using...

Area of Science:

• Pediatric exercise science
• Biomedical engineering
• Machine learning applications in health

Background:

• Accelerometer-based physical activity (PA) and sedentary time (SED) detection methods face challenges with toddler movement patterns.
• Accurate measurement of PA and SED in toddlers is crucial for understanding health outcomes and developing interventions.

Purpose of the Study:

• To develop a novel machine learning (ML) model for detecting physical activity (PA) and sedentary time (SED) in toddlers.
• To compare the performance of the developed ML model against existing cut-point methods for analyzing toddler PA.

Main Methods:

• 111 toddlers (21±7 months) wore waist-worn accelerometers during two 1-hour visits.
• Video recordings provided ground truth for activity classification (SED, total PA (TPA), light PA (LPA), moderate-to-vigorous PA (MVPA), non-volitional movement (NVM)).
• Four gradient boosted tree ML models were trained using extracted accelerometer features and validated against existing methods.

Main Results:

• ML models achieved 82% (NVM/SED/TPA) and 74% (NVM/SED/LPA/MVPA) accuracy, with low mean absolute differences (3.0-3.2 min/h).
• Existing methods showed significantly lower accuracies (33-74%) and higher mean absolute differences (7.6-25.8 min/h) for TPA and MVPA estimation.
• The developed ML models demonstrated superior performance in classifying toddler movement patterns compared to traditional methods.

Conclusions:

• The novel ML models (NVM/SED/TPA and NVM/SED/LPA/MVPA) offer a significant improvement for measuring toddlers' physical activity and sedentary time.
• These models align with PA guidelines and the health links of MVPA, providing a more accurate assessment tool.
• An open-access, user-friendly interface for these ML models is provided, facilitating wider adoption and advancing research in pediatric PA measurement.