ArticleScientific reports2024
Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Weight gain and brain development in the ABCD study cohort: A decade of insight and the road ahead.Developmental cognitive neuroscience · 2026Review
- Multidimensional association of sleep health with dietary habits and physical activity in adolescents.Sleep health · 2026Article
- Detection and Analysis of Circadian Biomarkers for Metabolic Syndrome Using Wearable Data: Cross-Sectional Study.JMIR medical informatics · 2025Article
- Differences between Type 2 Diabetes Mellitus and Obesity Management: Medical, Social, and Public Health Perspectives.Diabetes & metabolism journal · 2025Review
- Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary.Sleep · 2025Article
- Machine learning applied to wearable fitness tracker data and the risk of hospitalizations and cardiovascular events.American journal of preventive cardiology · 2025Article
- Body Weight Perception and Other Factors Associated with Overweight and Obesity in U.S. Adolescents.Children (Basel, Switzerland) · 2025Article
- A lifestyle-based prediction model for obesity in Chinese adolescent students.Frontiers in sports and active living · 2025Article
- Race, Ethnicity, and Sleep in US Children.JAMA network open · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.