The Methodology Center, The Pennsylvania State University, University Park, Pennsylvania, USA.
Obesity (Silver Spring). 2010 Apr;18(4):833-40. doi: 10.1038/oby.2009.275. Epub 2009 Aug 20.
We apply latent class analysis (LCA) to quantify multidimensional patterns of weight-loss strategies in a sample of 197 women, and explore the degree to which dietary restraint, disinhibition, and other individual characteristics predict membership in latent classes of weight-loss strategies. Latent class models were fit to a set of 14 healthy and unhealthy weight-loss strategies. BMI, weight concern, body satisfaction, depression, dietary disinhibition and restraint, and the interaction of disinhibition and restraint were included as predictors of latent class membership. All analyses were conducted with PROC LCA, a recently developed SAS procedure available for download. Results revealed four subgroups of women based on their history of weight-loss strategies: No Weight Loss Strategy (10.0%), Dietary Guidelines (26.5%), Guidelines+Macronutrients (39.4%), and Guidelines+Macronutrients+Restrictive (24.2%). BMI, weight concerns, the desire to be thinner, disinhibition, and dietary restraint were all significantly related to weight-control strategy latent class. Among women with low dietary restraint, disinhibition increases the odds of engaging in any set of weight-loss strategies vs. none, whereas among medium- and high-restraint women disinhibition increases the odds of use of unhealthy vs. healthy strategies. LCA was an effective tool for organizing multiple weight-loss strategies in order to identify subgroups of individuals who have engaged in particular sets of strategies over time. This person-centered approach provides a measure weight-control status, where the different statuses are characterized by particular combinations of healthy and unhealthy weight-loss strategies.
我们应用潜在类别分析(LCA)来量化 197 名女性样本中减肥策略的多维模式,并探讨饮食抑制、去抑制和其他个体特征在多大程度上预测减肥策略潜在类别中的成员身份。潜在类别模型适用于一组 14 种健康和不健康的减肥策略。BMI、体重关注、身体满意度、抑郁、饮食去抑制和抑制以及去抑制和抑制的相互作用被纳入潜在类别成员身份的预测因素。所有分析均使用最近开发的 SAS 程序 PROC LCA 进行,该程序可下载。结果显示,根据减肥策略的历史,女性分为四个亚组:无减肥策略(10.0%)、饮食指南(26.5%)、指南+宏量营养素(39.4%)和指南+宏量营养素+限制(24.2%)。BMI、体重担忧、变瘦的愿望、去抑制和饮食抑制都与控制体重的策略潜在类别显著相关。在低饮食抑制的女性中,去抑制增加了参与任何一组减肥策略的几率,而在中、高度抑制的女性中,去抑制增加了使用不健康策略的几率,而不是健康策略。潜在类别分析是一种有效的工具,可以组织多种减肥策略,以便识别随着时间的推移参与特定策略组的个体亚组。这种以人为中心的方法提供了一种衡量体重控制状态的方法,其中不同的状态特征是特定的健康和不健康减肥策略的组合。