The Families and Schools for Health Project: A Longitudinal Cluster Randomized Controlled Trial Targeting Children with Overweight and Obesity.
Glade L TophamIsaac J WashburnLaura Hubbs-TaitTay S KennedyJulie M RutledgeMelanie C PageTaren SwindleLenka H ShriverAmanda W HarristPublished in: International journal of environmental research and public health (2021)
This cluster randomized controlled trial aimed at overweight and obese children compared three treatments. Two psychoeducation interventions for parents and children were conducted: Family Lifestyle (FL) focused on food and physical activity; Family Dynamics (FD) added parenting and healthy emotion management. A third Peer Group (PG) intervention taught social acceptance to children. Crossing interventions yielded four conditions: FL, FL + PG, FL + FD, and FL + FD + PG-compared with the control. Longitudinal BMI data were collected to determine if family- and peer-based psychosocial components enhanced the Family Lifestyle approach. Participants were 1st graders with BMI%ile >75 (n = 538: 278 boys, 260 girls). Schools were randomly assigned to condition after stratifying for community size and percent American Indian. Anthropometric data were collected pre- and post-intervention in 1st grade and annually through 4th grade. Using a two-level random intercept growth model, intervention status predicted differences in growth in BMI or BMI-M% over three years. Children with obesity who received the FL + FD + PG intervention had lower BMI gains compared to controls for both raw BMI (B = -0.05) and BMI-M% (B = -2.36). Interventions to simultaneously improve parent, child, and peer-group behaviors related to physical and socioemotional health offer promise for long-term positive impact on child obesity.
Keyphrases
- physical activity
- body mass index
- mental health
- weight gain
- randomized controlled trial
- young adults
- healthcare
- metabolic syndrome
- weight loss
- type diabetes
- insulin resistance
- cardiovascular disease
- clinical trial
- risk assessment
- health information
- human health
- drug delivery
- adipose tissue
- body composition
- artificial intelligence
- deep learning