SEARCHCOHORTpediatrics, endocrinology

cohort·pediatrics, endocrinology, clinical trial, public health, epidemiology·PMC10472181

Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study

Journal of Medical Internet Research · 5 authors, 5 centres

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TL;DR

Using functional principal component analysis and elastic net regression on 88 children and adolescents in an mHealth obesity intervention, the study identified two behavioral phenotypes—"high or low adherence level" and "late or early behavior change"—that were differentially associated with changes in BMI z score across five lifestyle behaviors.

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FPCA identified two phenotypes—"high or low adherence level" (explaining 47%-69% of variance per factor) and "late or early behavior change" (explaining 11%-17%). The model was adjusted for demographic, psychosocial, and parental confounders.

STUDY TYPE

cohort

SPECIALTY

pediatrics

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AI pipeline

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ORIGINAL PAPER

PMC10472181

Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study

Journal of Medical Internet Research

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ABSTRACT

Abstract Background Advancements in mobile health technologies and machine learning approaches have expanded the framework of behavioral phenotypes in obesity treatment to explore the dynamics of temporal changes. Objective This study aimed to investigate the dynamics of behavioral changes during obesity intervention and identify behavioral phenotypes associated with weight change using a hybrid machine learning approach. Methods In total, 88 children and adolescents (ages 8-16 years; 62/88, 71% male) with age- and sex-specific BMI ≥85th percentile participated in the study. Behavioral phenotypes were identified using a hybrid 2-stage procedure based on the temporal dynamics of adherence to the 5 behavioral goals during the intervention. Functional principal component analysis was used to determine behavioral phenotypes by extracting principal component factors from the functional data of each participant. Elastic net regression was used to investigate the association between behavioral phenotypes and weight change. Results Functional principal component analysis identified 2 distinctive behavioral phenotypes, which were named the high or low adherence level and late or early behavior change . The first phenotype explained 47% to 69% of each factor, whereas the second phenotype explained 11% to 17% of the total behavioral dynamics. High or low adherence level was associated with weight change for adherence to screen time (β=−.0766, 95% CI −.1245 to −.0312), fruit and vegetable intake (β=.1770, 95% CI .0642-.2561), exercise (β=−.0711, 95% CI −.0892 to −.0363), drinking water (β=−.0203, 95% CI −.0218 to −.0123), and sleep duration. Late or early behavioral changes were significantly associated with weight loss for changes in screen time (β=.0440, 95% CI .0186-.0550), fruit and vegetable intake (β=−.1177, 95% CI −.1441 to −.0680), and sleep duration (β=−.0991, 95% CI −.1254 to −.0597). Conclusions Overall level of adherence, or the high or low adherence level , and a gradual improvement or deterioration in health-related behaviors, or the late or early behavior change , were differently associated with weight loss for distinctive obesity-related lifestyle behaviors. A large proportion of health-related behaviors remained stable throughout the intervention, which indicates that health care professionals should closely monitor changes made during the early stages of the intervention. Trial Registration Clinical Research Information Science KCT0004137; https://tinyurl.com/ytxr83ay Keywords: behavioral dynamics, behavioral phenotype, functional data analysis, FDA, machine learning analysis, mobile health, mHealth, obesity intervention, pediatric obesity, mobile phone

CITATION EXPORT

Sarah Woo, Sunho Jung, Hyunjung Lim et al.. Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study. Journal of Medical Internet Research. (2023). https://doi.org/10.2196/45407

AUTHORS

5 · 5 CENTRES

Sarah Woo, Sunho Jung, Hyunjung Lim, YoonMyung Kim, Kyung Hee Park

1Department of Medical Sciences, College of Medicine, Hallym University, Chuncheon-si, Republic of Korea
2School of Management, Kyung Hee University, Seoul, Republic of Korea
3Department of Medical Nutrition, Kyung Hee University, Yongin-si, Republic of Korea
4University College, Yonsei University International Campus, Incheon, Republic of Korea
5Department of Family Medicine, Hallym University Sacred Heart Hospital, Hallym University, Anyang-si, Gyeonggi-do, Republic of Korea