TY  - JOUR
AU  - Kim, Ho Heon
AU  - Kim, Youngin
AU  - Michaelides, Andreas
AU  - Park, Yu Rang
PY  - 2022
DA  - 2022/4/15
TI  - Weight Loss Trajectories and Related Factors in a 16-Week Mobile Obesity Intervention Program: Retrospective Observational Study
JO  - J Med Internet Res
SP  - e29380
VL  - 24
IS  - 4
KW  - clustering
KW  - mobile health
KW  - weight loss
KW  - weight management
KW  - behavior management
KW  - time series analysis
KW  - mHealth
KW  - obesity
KW  - outcomes
KW  - machine learning
KW  - mobile app
KW  - adherence
KW  - prediction
KW  - mobile phone
AB  - Background: In obesity management, whether patients lose ≥5% of their initial weight is a critical factor in clinical outcomes. However, evaluations that take only this approach are unable to identify and distinguish between individuals whose weight changes vary and those who steadily lose weight. Evaluation of weight loss considering the volatility of weight changes through a mobile-based intervention for obesity can facilitate understanding of an individual’s behavior and weight changes from a longitudinal perspective. Objective: The aim of this study is to use a machine learning approach to examine weight loss trajectories and explore factors related to behavioral and app use characteristics that induce weight loss. Methods: We used the lifelog data of 13,140 individuals enrolled in a 16-week obesity management program on the health care app Noom in the United States from August 8, 2013, to August 8, 2019. We performed k-means clustering with dynamic time warping to cluster the weight loss time series and inspected the quality of clusters with the total sum of distance within the clusters. To identify use factors determining clustering assignment, we longitudinally compared weekly use statistics with effect size on a weekly basis. Results: The initial average BMI value for the participants was 33.6 (SD 5.9) kg/m2, and it ultimately reached 31.6 (SD 5.7) kg/m2. Using the weight log data, we identified five clusters: cluster 1 (sharp decrease) showed the highest proportion of participants who reduced their weight by >5% (7296/11,295, 64.59%), followed by cluster 2 (moderate decrease). In each comparison between clusters 1 and 3 (yo-yo) and clusters 2 and 3, although the effect size of the difference in average meal record adherence and average weight record adherence was not significant in the first week, it peaked within the initial 8 weeks (Cohen d>0.35) and decreased after that. Conclusions: Using a machine learning approach and clustering shape-based time series similarities, we identified 5 weight loss trajectories in a mobile weight management app. Overall adherence and early adherence related to self-monitoring emerged as potential predictors of these trajectories. 
SN  - 1438-8871
UR  - https://www.jmir.org/2022/4/e29380
UR  - https://doi.org/10.2196/29380
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35436211
DO  - 10.2196/29380
ID  - info:doi/10.2196/29380
ER  -