Superwellness Program: a cognitive-behavioral therapy-based group intervention to reduce weight gain in patients treated with antipsychotic drugs

Abstract

Objective:: To assess the effectiveness of a cognitive-behavioral therapy-based intervention (Superwellness Program) on weight gain compared with a treatment-as-usual (TAU) approach in patients treated with antipsychotics, and to evaluate the relationship between body mass index (BMI) variation and clinical variables.

Method:: Eighty-five patients treated with antipsychotics were allocated across two groups, experimental (n=59) and control (n=26). The Superwellness Program (experimental group) consisted of 32 twice-weekly 1-hour sessions, conducted by a psychologist and a nutritionist/nurse, concurrently with moderate food intake and moderate physical activity plans. Sociodemographic, clinical, and biological variables were collected at baseline, at the end of intervention (16 weeks), and after 6 months.

Results:: BMI change from baseline differed significantly between the experimental and control groups, with a larger decrease in the experimental group (F = 5.5, p = 0.021). Duration of illness moderated the effect of treatment on BMI (p = 0.026). No significant (p = 0.499) effect of intervention during the follow-up period was found. Interestingly, the intervention indirectly induced a significant (p = 0.024) reduction in metabolic risk by reducing BMI.

Conclusion:: A cognitive-behavioral therapy-based intervention could be useful in reducing weight in a clinical population taking antipsychotics, with consequent benefit to physical and mental health.

Figure 1. Structural equation model for longitudinal analysis of body mass index (BMI) changes: standardized estimates. Black dotted line depicts the covariance parameters between Global Assessment of Functioning (GAF), duration of illness, and intervention. E1-E5 indicate the measurement errors. The categorical variables gender and intervention refer to the male and Superwellness groups respectively. The number of different parameters to be estimated was 22, including covariance parameters for improving the model fit. *p < 0.05; ^† p < 0.001.