Department of Medical Sciences, College of Medicine, Hallym University, Chuncheon-si, Republic of Korea.
School of Management, Kyung Hee University, Seoul, Republic of Korea.
J Med Internet Res. 2023 Aug 17;25:e45407. doi: 10.2196/45407.
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.
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.
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.
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).
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.
Clinical Research Information Science KCT0004137; https://tinyurl.com/ytxr83ay.
移动健康技术和机器学习方法的进步扩展了肥胖治疗中的行为表型框架，以探索时间变化的动态。
本研究旨在使用混合机器学习方法探讨肥胖干预期间行为变化的动态，并确定与体重变化相关的行为表型。
共有 88 名年龄在 8-16 岁（62/88，71%为男性）、年龄和性别特异性 BMI 大于等于第 85 百分位的儿童和青少年参与了研究。使用基于干预期间对 5 项行为目标的依从性的时间动态的混合 2 阶段程序来识别行为表型。功能主成分分析用于通过从每个参与者的功能数据中提取主成分因素来确定行为表型。弹性网回归用于研究行为表型与体重变化之间的关联。
功能主成分分析确定了 2 种不同的行为表型，分别命名为高或低依从水平和晚或早行为变化。第一种表型解释了每个因子的 47%至 69%，而第二种表型解释了总行为动态的 11%至 17%。高或低依从水平与屏幕时间（β=-.0766，95%CI -.1245 至 -.0312）、水果和蔬菜摄入（β=.1770，95%CI.0642-.2561）、运动（β=-.0711，95%CI -.0892 至 -.0363）、饮水（β=-.0203，95%CI -.0218 至 -.0123）和睡眠持续时间的依从变化相关。晚或早行为变化与屏幕时间（β=.0440，95%CI.0186-.0550）、水果和蔬菜摄入（β=-.1177，95%CI -.1441 至 -.0680）和睡眠持续时间（β=-.0991，95%CI -.1254 至 -.0597）的变化与体重减轻显著相关。
整体依从水平，或高或低的依从水平，以及健康相关行为的逐渐改善或恶化，或晚或早的行为变化，与不同的肥胖相关生活方式行为的体重减轻有关。在整个干预过程中，很大一部分健康相关行为保持稳定，这表明医疗保健专业人员应密切监测干预早期阶段的变化。
临床研究信息科学 KCT0004137；https://tinyurl.com/ytxr83ay。
