Adolescent health behavior patterns and weight status: a cross-sectional analysis

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Background: Adolescent weight status is shaped by co-occurring behaviors, but variable-centered analyses may obscure heterogeneous patterns. Person-centered approaches can clarify how these patterns relate to BMI. Objective: To identify adolescent health behavior patterns and assess their associations with BMI categories. Methods: We conducted a cross-sectional survey of 1,212 students in grades 7-8 from Lin'an District, Hangzhou, China. Six behavior indicators (diet, sugary drinks, outdoor activity, anxiety control, weight-management awareness, and management needs) informed a latent class analysis. Logistic regression, adjusting for demographic and psychosocial factors, estimated associations between class membership and BMI status. Results: Three health behavior patterns emerged: passive health maintenance (50.9%), self-disciplined health type (32.7%), and high-risk lifestyle (16.5%). Compared to the self-disciplined group, the passive group showed significantly increased risks of overweight (OR = 1.62, 95% CI: 1.02-2.57) and obesity (OR = 1.68, 95% CI: 1.13-2.50), while the high-risk group showed a trend toward increased obesity risk (OR = 1.57, 95% CI: 0.96-2.57, P = 0.072). Female students exhibited lower risks of overweight (OR = 0.56, 95% CI: 0.36-0.87) and obesity (OR = 0.41, 95% CI: 0.28-0.59) compared to males; eighth-grade students had a lower risk of obesity than seventh-grade students (OR = 0.59, 95% CI: 0.40-0.87). Additionally, good sleep quality reduced the likelihood of belonging to the high-risk group (OR = 0.30, 95% CI: 0.17-0.53), and emotional eating increased the risk of being in the passive group (OR = 1.74, 95% CI: 1.31-2.32). Conclusions: Early adolescents show distinct health behavior patterns with differential weight outcomes. The large passive group, though not overtly high-risk, carries significant overweight risk, highlighting a "moderate-risk blind spot" in weight management. Identifying behavior clusters and tailoring interventions by behavioral profile and sociodemographic context may improve adolescent obesity prevention.

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