Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity

Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wearing Fitbit Charge HR 2 devices in the Adolescent B...

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Veröffentlicht in:	Scientific reports Jg. 14; H. 1; S. 12563 - 12
Hauptverfasser:	Kiss, Orsolya, Baker, Fiona C., Palovics, Robert, Dooley, Erin E., Pettee Gabriel, Kelley, Nagata, Jason M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 31.05.2024 Nature Publishing Group Nature Portfolio
Schlagworte:	631/378/116 631/378/1385 692/700/1720 692/700/2817 Adolescent Adolescents Cardiorespiratory Fitness - physiology Caregivers Child Child development Cognitive development Critical period Demographics Education Ethnicity Exercise Family income Female Females Heart rate Households Humanities and Social Sciences Humans Learning algorithms Low income groups Machine Learning Male multidisciplinary Obesity Pediatric Obesity - epidemiology Physical activity Physical fitness Risk Factors Science Science (multidisciplinary) Secondary schools Sedentary Behavior Sleep Sleep - physiology Sociodemographics Teenagers Wearable computers
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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