University of Rochester Medical Center, Rochester, NY, United States of America.
Kaiser Permanente Southern California, Pasadena, CA, United States of America.
PLoS One. 2022 May 12;17(5):e0268376. doi: 10.1371/journal.pone.0268376. eCollection 2022.
We examined multi-level factors related to the longitudinal physical activity trajectories of adolescent girls to determine the important predictors for physical activity. The Trial of Activity in Adolescent Girls (TAAG) Maryland site recruited participants at age 14 (n = 566) and followed up with these girls at age 17 (n = 553) and age 23 (n = 442). Individual, social factors and perceived environmental factors were assessed by questionnaire; body mass index was measured at age 14 and age 17, and self-reported at age 23. Neighborhood factors were assessed by geographic information systems. The outcome, moderate-to-vigorous physical activity (MVPA) minutes in a day, was assessed from accelerometers. A mixture of linear mixed-effects models with double penalization on fixed effects and random effects was used to identify the intrinsic grouping of participants with similar physical activity trajectory patterns and the most relevant predictors within the groups simultaneously. Three clusters of participants were identified. Two hundred and forty participants were clustered as "maintainers" and had consistently low MVPA over time; 289 participants were clustered as "decreasers" who had decreasing MVPA over time; 39 participants were grouped as "increasers" and had increasing MVPA over time. Each of the three clusters has its own cluster-specific factors identified using the clustering method, indicating that each cluster has unique characteristics.
我们研究了与青春期女孩纵向体力活动轨迹相关的多层次因素,以确定体力活动的重要预测因素。青少年女孩活动试验(TAAG)马里兰州的研究地点在 14 岁时招募了参与者(n=566),并在 17 岁(n=553)和 23 岁(n=442)时对这些女孩进行了随访。个体、社会因素和感知环境因素通过问卷进行评估;体重指数在 14 岁和 17 岁时进行测量,并在 23 岁时自我报告。邻里因素通过地理信息系统进行评估。结果,每天中等到剧烈的体力活动(MVPA)分钟数,通过加速度计进行评估。采用固定效应和随机效应双重惩罚的线性混合效应模型,同时识别参与者具有相似体力活动轨迹模式的内在分组和组内最相关的预测因素。确定了三组参与者。240 名参与者被归类为“维持者”,他们的 MVPA 始终保持较低水平;289 名参与者被归类为“减少者”,他们的 MVPA 随时间减少;39 名参与者被归类为“增加者”,他们的 MVPA 随时间增加。使用聚类方法确定了每组特有的因素,表明每组都有其独特的特征。