Obesity is a major public health problem and cause of significant burden across the lifespan. Longitudinal samples, beginning in early childhood offer an advantageous approach to studying obesity, given the potential to observe within-individual changes over time. Yet among the many available longitudinal studies of children, particularly those studying psychological disorders, do not assess for overweight/obesity status or related constructs necessary to compute BMI. We offer a unique thin slice approach for assessing obesity/overweight status using previously collected video data. The current study observationally coded overweight/obesity status in a clinically enriched sample of preschoolers oversampled for depression (N = 299). Preschoolers (ages 3–6 years) completed 1–8 structured observational tasks with an experimenter. Overweight/obesity was coded using a “thin slice” technique with 7820 unique ratings available for analysis. Parent-reported physical health problems were assessed throughout the study and BMI percentiles were available from ages 8–19 years. Thin-slice ratings of overweight/obesity were reliably observed in preschoolers’ ages 3–6 years. Thin-slice ratings of overweight/obesity during preschool significantly predicted adolescent BMI percentiles at six separate assessments spanning ages 8–19 years. Further, preschool overweight/obese thin-slice ratings were associated with more physical health problems over time and less sport/activity participation during preschool. Overweight/obesity can be observationally identified in preschool-age children and offers a reliable estimate of future BMI percentile. Study findings highlight how previously collected data could be utilized to study the developmental trajectories of overweight/obesity to inform this critical public health problem.

Original languageEnglish
Pages (from-to)3991-4000
Number of pages10
JournalCurrent Psychology
Issue number5
StatePublished - Feb 2023


  • Obesity
  • Observational coding
  • Overweight
  • Preschool
  • Secondary data analysis
  • Thin-slice coding


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