TY  - JOUR
AU  - Woo, Sarah
AU  - Jung, Sunho
AU  - Lim, Hyunjung
AU  - Kim, YoonMyung
AU  - Park, Kyung Hee
PY  - 2023
DA  - 2023/8/17
TI  - Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study
JO  - J Med Internet Res
SP  - e45407
VL  - 25
KW  - behavioral dynamics
KW  - behavioral phenotype
KW  - functional data analysis
KW  - FDA
KW  - machine learning analysis
KW  - mobile health
KW  - mHealth
KW  - obesity intervention
KW  - pediatric obesity
KW  - mobile phone
AB  - 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 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e45407
UR  - https://doi.org/10.2196/45407
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37590040
DO  - 10.2196/45407
ID  - info:doi/10.2196/45407
ER  -