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
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Abstract 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 Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUC Mean  = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.
AbstractList 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 Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUCMean = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.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 Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUCMean = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.
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 Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUC Mean  = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.
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 Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUC  = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.
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 Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUCMean = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.
Abstract 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 Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUCMean = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.
ArticleNumber 12563
Author Dooley, Erin E.
Kiss, Orsolya
Pettee Gabriel, Kelley
Palovics, Robert
Nagata, Jason M.
Baker, Fiona C.
Author_xml – sequence: 1
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  surname: Kiss
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  organization: Center for Health Sciences, SRI International
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  givenname: Fiona C.
  surname: Baker
  fullname: Baker, Fiona C.
  organization: Center for Health Sciences, SRI International, School of Physiology, University of the Witwatersrand
– sequence: 3
  givenname: Robert
  surname: Palovics
  fullname: Palovics, Robert
  organization: Department of Neurology and Neurological Sciences, Stanford University School of Medicine
– sequence: 4
  givenname: Erin E.
  surname: Dooley
  fullname: Dooley, Erin E.
  organization: Department of Epidemiology, University of Alabama at Birmingham
– sequence: 5
  givenname: Kelley
  surname: Pettee Gabriel
  fullname: Pettee Gabriel, Kelley
  organization: Department of Epidemiology, University of Alabama at Birmingham
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  givenname: Jason M.
  surname: Nagata
  fullname: Nagata, Jason M.
  organization: Department of Pediatrics, University of California, San Francisco
BackLink https://www.ncbi.nlm.nih.gov/pubmed/38821981$$D View this record in MEDLINE/PubMed
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Snippet Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period...
Abstract Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical...
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SubjectTerms 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
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Title Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity
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