A machine learning model using objective sensor data predicted siesta patterns with 83% success; longer predicted siesta duration was associated with increased obesity risk (OR 2.081; P=0.002).
Observational (n=889)
Does a machine learning model using objective wrist sensor data accurately predict siesta behavior and its association with obesity-related traits in adults?
Objective wrist sensor data combined with machine learning accurately predicts siesta behavior, revealing significant associations between longer siesta duration and increased obesity risk.
Objectives: To predict siesta behavior using machine learning models trained on self-reported and objective data-temperature (T), activity (A), position (P), and the integrated TAP variable-and to explore its associations with obesity-related traits.Methods: From ONTIME-MT, 889 adults wore wrist sensors for 7 days to continuously record temperature, activity, and position, and self-reported daily siesta.Machine learning models were developed to classify 30-second epoch siesta data, to reconstruct weekly siesta behavior.Anthropometric and metabolic parameters were assessed.Associations were analyzed using linear and logistic regression.Model generalizability was evaluated in an independent Mediterranean cohort (n = 70).Results: The machine learning model allowed to obtain 83% of success in siesta patterns prediction.Among the input variables, activity was the most discriminative by the decision tree (threshold: 27 /min), followed by TAP (0.51 AU) and position (4.7).In an independent external validation cohort, success in prediction reached 77%, indicating strong alignment between algorithm-based and self-reported siesta patterns detection.Predicted siesta-but not self-reported alone-was significantly associated with obesityrelated traits.Later siesta timing was linked to increased waist circumference in women ( = 0.769 cm per hour; P = 0.026).Longer siesta duration was associated with increased obesity risk (OR=2.081;P=0.002), BMI (=0.013kg/m/h; P = 0.034), and systolic blood pressure ( = 3.540 mmHg/h; P = 0.049).Greater siesta frequency was associated with lower corrected insulin response ( = -0.037AU/day; P = 0.012).Conclusion: Objective data from temperature, activity, position, and TAP, combined with ML models, accurately predict siesta behavior and its metabolic relevance.These findings support the use of machine learning approaches based on temperature, activity, position, and the integrated TAP, to assess siesta under free-living conditions.ClinicalTrials.
Martín et al. (Wed,) conducted a observational in Siesta behavior and obesity (n=889). Machine learning model based on temperature, activity, position, and TAP vs. Self-reported siesta was evaluated on Siesta patterns prediction success. A machine learning model using objective sensor data predicted siesta patterns with 83% success; longer predicted siesta duration was associated with increased obesity risk (OR 2.081; P=0.002).
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