Identification of Latent Subgroups of Obese Adolescents Enrolled in a Healthy Weight Management Program

In obesity research, it is assumed that the population is homogeneous. While this approach has yielded important insights, testing this supposition might reveal information that could impact our understanding of the phenomena and its treatment. In this study, data from obese teenagers (N = 248, Mean BMI percentile = 99%; Mean age = 13.9, SD = 1.8) who were predominantly minority (n = 182), female (n = 169), and enrolled in a weight loss intervention were analyzed. Latent profile analysis (LPA) was used to segment patients into groups based on their scores on PedsQL 4.0 scales (physical-, emotional-, social-, and school functioning) and the Coopersmith Self-Esteem Scale. A 3-class solution was parsimonious and demonstrated the best statistical fit (Bayesian information criterion = 10596.96; Lo-Mendell-Rubin-adjusted likelihood ratio test = 73.020, p \u3c .05). The 3 groups were ordinal and composed of respondents with high- (HF; n = 72, 29%), medium- (MF; n = 110, 44%), and low functioning (LF; n = 66, 27%). Further analyses (chi squares and linear regressions) showed that the LF group had a significantly higher proportion of Caucasians and males compared to the HF (referent) group. Also, when controlling for demographics and weight, the LF group had significantly higher blood pressure (diastolic and systolic), lower self-reported physical activity (on two different measures), and a higher total score on a scale of depressed mood. Four groups of ordinal regressions (since the pair of self-reported exercise variables and blood pressure variables were correlated, only one from each pair was included in each set) consistently found that self-reported physical activity and blood pressure improved significantly from the LF to HF groups. However, when depressed mood was included, it became the only significant variable. These findings suggest that LF group members are demographically and clinically distinct and that depressed mood may be the critical factor connecting self-report and metabolic dysfunction. Theory suggests depressed mood is both associated with cognitive schemas that affect responses on self-report measures; skewing them negative, and is also manifested metabolically