Family-based treatment program contributors to child weight loss

被引:9
作者
Boutelle, Kerri N. [1 ,2 ,3 ]
Kang Sim, D. Eastern [1 ]
Rhee, Kyung E. [1 ]
Manzano, Michael [1 ,4 ]
Strong, David R. [2 ]
机构
[1] Univ Calif, Dept Pediat, La Jolla, CA 92093 USA
[2] Univ Calif, Dept Family Med & Publ Hlth, La Jolla, CA 92093 USA
[3] Univ Calif, Dept Psychiat, La Jolla, CA 92093 USA
[4] San Diego State Univ Univ Calif, San Diego Joint Doctoral Program Clin Psychol, San Diego, CA USA
关键词
CONFIRMATORY FACTOR-ANALYSIS; HOME FOOD ENVIRONMENT; INVERSE PROBABILITY; FEEDING QUESTIONNAIRE; OBESITY TREATMENT; ADULT OBESITY; MANAGEMENT; INTERVENTIONS; OVERWEIGHT; MODELS;
D O I
10.1038/s41366-020-0604-9
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background Multicomponent family-based behavioral treatment (FBT) program for pediatric obesity includes nutrition and physical activity education, as well as behavior therapy techniques. Studies suggest that parent weight loss is the best predictor of child weight loss in FBT. However, given the important role that parents play in the implementation of FBT for their child, isolating the effects of specific FBT treatment component requires consideration of parent influences over time. Methods The following treatment components were assessed: stimulus control (high/low-fat food items in home), nutrition knowledge, energy intake, physical activity, and parental monitoring, as well as weekly anthropometric measures. Adjusted models of interest using inverse probability weights were used to evaluate the effect of specific FBT components on time-varying child weight loss rate, adjusting for time-varying influence of parent weight loss. Results One hundred thirty-seven parent-child dyads (CHILD: mean BMI = 26.4 (3.7) and BMIz = 2.0 (0.3); mean age = 10.4 (1.3); 64.1% female; ADULT: mean BMI = 31.9 (6.3); mean age = 42.9 (6.5); 30.1% Hispanic parents; 87.1% female) participated in an FBT program. In traditional model, adult BMI change (b = 0.08;p < 0.01) was the most significant predictor of child weight loss rates and no other treatment components were significant (p's > 0.1). In models that accounted for potential influences from parental weight loss and differential attendance during treatment period, lower availability of high-fat items (b = 1.10,p < 0.02), higher availability of low-fat items (b = 3.73;p < 0.01), and higher scores on parental monitoring practices (b = 1.10,p < 0.01) were associated with greater rates of weight loss, respectively. Conclusion Results suggest that outside of parent weight change, changes in stimulus control strategies at home and improved parental-monitoring practices are important FBT components for child weight loss.
引用
收藏
页码:77 / 83
页数:7
相关论文
共 49 条
[11]   Family-based obesity treatment, then and now: Twenty-five years of pediatric obesity treatment [J].
Epstein, Leonard H. ;
Paluch, Rocco A. ;
Roemmich, James N. ;
Beecher, Meghan D. .
HEALTH PSYCHOLOGY, 2007, 26 (04) :381-391
[12]  
Epstein LH, 2003, EVIDENCE-BASED PSYCHOTHERAPIES FOR CHILDREN AND ADOLESCENTS, P374
[13]   Childhood Obesity, Other Cardiovascular Risk Factors, and Premature Death [J].
Franks, Paul W. ;
Hanson, Robert L. ;
Knowler, William C. ;
Sievers, Maurice L. ;
Bennett, Peter H. ;
Looker, Helen C. .
NEW ENGLAND JOURNAL OF MEDICINE, 2010, 362 (06) :485-493
[14]   Interrelationships between BMI, skinfold thicknesses, percent body fat, and cardiovascular disease risk factors among US children and adolescents [J].
Freedman, David S. ;
Ogden, Cynthia L. ;
Kit, Brian K. .
BMC PEDIATRICS, 2015, 15
[15]   Why item response theory should be used for longitudinal questionnaire data analysis in medical research [J].
Gorter, Rosalie ;
Fox, Jean-Paul ;
Twisk, Jos W. R. .
BMC MEDICAL RESEARCH METHODOLOGY, 2015, 15
[16]   Modeling time-varying exposure using inverse probability of treatment weights [J].
Graffeo, Nathalie ;
Latouche, Aurelien ;
Geskus, Ronald B. ;
Chevret, Sylvie .
BIOMETRICAL JOURNAL, 2018, 60 (02) :323-+
[17]   Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines [J].
Grajeda L.M. ;
Ivanescu A. ;
Saito M. ;
Crainiceanu C. ;
Jaganath D. ;
Gilman R.H. ;
Crabtree J.E. ;
Kelleher D. ;
Cabrera L. ;
Cama V. ;
Checkley W. .
Emerging Themes in Epidemiology, 13 (1)
[18]   Multiple Imputation of Missing Data for Multilevel Models: Simulations and Recommendations [J].
Grund, Simon ;
Luedtke, Oliver ;
Robitzsch, Alexander .
ORGANIZATIONAL RESEARCH METHODS, 2018, 21 (01) :111-149
[19]  
Guo S S, 1999, Am J Clin Nutr, V70, p145S, DOI 10.1093/ajcn/70.1.145s
[20]   A computer-based approach for assessing dietary supplement use in conjunction with dietary recalls [J].
Harnack, Lisa ;
Stevens, Mary ;
Van Heel, Nancy ;
Schakel, Sally ;
Dwyer, Johanna T. ;
Himes, John .
JOURNAL OF FOOD COMPOSITION AND ANALYSIS, 2008, 21 :S78-S82