Predictors of weight discussion in primary care consultations: A multilevel modeling approach.

Objective: To understand how primary care weight-related communication processes are influenced by individual differences in primary care practitioner (PCP) and patient characteristics and communication use.

Methods: Two multilevel logistic regression models were calculated to predict the occurrence of 1) weight-related discussion and 2) weight-related consultation outcomes. Coded communication data (Roter Interaction Analysis System) from 218 video-recorded consultations between PCPs and patients with overweight and obesity in Scottish primary care practices were combined with their demographic data to develop the multilevel models.

Results: Weight-related discussions were more likely to occur when a greater proportion of PCP's total communication was partnership building and activating communication. More discrete weight discussions during a consultation predicted weight-related consultation outcomes. Patient BMI positively predicted both weight-related discussion and consultation outcomes.

Conclusion: This work demonstrates that multilevel modeling is a viable approach to investigating coded primary care weight-related communication data and that it can provide insight into the impact that various patient and PCP factors have on these communication processes.

Practice Implications: Through the increased use of partnership building and activating communications, and by engaging in shorter, but more frequent, discussions about patient weight, PCPs may better facilitate weight-related discussion and weight-related consultation outcomes for their patients.