PDF(Pubmed)
Abstract：
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.
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.
摘要：
背景：移动健康技术和机器学习方法的进步已经扩展了肥胖治疗中行为表型的框架，以探索时间变化的动力学。
目的：本研究旨在调查肥胖干预过程中行为变化的动态，并使用混合机器学习方法确定与体重变化相关的行为表型。
方法：总共，88名年龄和性别特定BMI≥85百分位数的儿童和青少年（8-16岁；62/88，男性占71％）参加了研究。根据干预期间遵守5个行为目标的时间动态，使用混合2阶段程序鉴定行为表型。通过从每个参与者的功能数据中提取主成分因子，使用功能主成分分析来确定行为表型。弹性网络回归用于研究行为表型与体重变化之间的关联。
结果：功能主成分分析确定了2种独特的行为表型，这被称为高或低依从性水平和晚期或早期行为改变。第一种表型解释了每个因素的47％至69％，而第二种表型解释了总行为动力学的11%至17%。高或低依从性水平与体重变化有关（β=-.0766，95％CI-.1245至-.0312），水果和蔬菜摄入量（β=.1770，95%CI.0642-.2561），运动（β=-.0711，95%CI-.0892至-.0363），饮用水（β=-.0203，95%CI-.0218至-.0123），和睡眠时间。晚期或早期行为变化与屏幕时间变化的体重减轻显着相关（β=.0440，95％CI.0186-.0550），水果和蔬菜摄入量（β=-.1177，95%CI-.1441至-.0680），和睡眠持续时间（β=-.0991，95%CI-.1254至-.0597）。
结论：总体依从性水平，或高或低坚持水平，与健康相关的行为逐渐改善或恶化，或者后期或早期的行为改变，与肥胖相关的独特生活方式行为与体重减轻的相关性不同。大部分健康相关行为在整个干预过程中保持稳定，这表明卫生保健专业人员应密切监测干预早期阶段的变化。
背景：临床研究信息科学KCT0004137;https://tinyurl.com/ytxr83ay。
目的：本研究旨在调查肥胖干预过程中行为变化的动态，并使用混合机器学习方法确定与体重变化相关的行为表型。
方法：总共，88名年龄和性别特定BMI≥85百分位数的儿童和青少年（8-16岁；62/88，男性占71％）参加了研究。根据干预期间遵守5个行为目标的时间动态，使用混合2阶段程序鉴定行为表型。通过从每个参与者的功能数据中提取主成分因子，使用功能主成分分析来确定行为表型。弹性网络回归用于研究行为表型与体重变化之间的关联。
结果：功能主成分分析确定了2种独特的行为表型，这被称为高或低依从性水平和晚期或早期行为改变。第一种表型解释了每个因素的47％至69％，而第二种表型解释了总行为动力学的11%至17%。高或低依从性水平与体重变化有关（β=-.0766，95％CI-.1245至-.0312），水果和蔬菜摄入量（β=.1770，95%CI.0642-.2561），运动（β=-.0711，95%CI-.0892至-.0363），饮用水（β=-.0203，95%CI-.0218至-.0123），和睡眠时间。晚期或早期行为变化与屏幕时间变化的体重减轻显着相关（β=.0440，95％CI.0186-.0550），水果和蔬菜摄入量（β=-.1177，95%CI-.1441至-.0680），和睡眠持续时间（β=-.0991，95%CI-.1254至-.0597）。
结论：总体依从性水平，或高或低坚持水平，与健康相关的行为逐渐改善或恶化，或者后期或早期的行为改变，与肥胖相关的独特生活方式行为与体重减轻的相关性不同。大部分健康相关行为在整个干预过程中保持稳定，这表明卫生保健专业人员应密切监测干预早期阶段的变化。
背景：临床研究信息科学KCT0004137;https://tinyurl.com/ytxr83ay。
