Patterns of self-monitoring technology use and weight loss in people with overweight or obesity

Mobile applications and paired devices allow individuals to self-monitor physical activity, dietary intake, and weight fluctuation concurrently. However, little is known regarding patterns of use of these self-monitoring technologies over time and their implications for weight loss. The objectives of this study were to identify distinct patterns of self-monitoring technology use and to investigate the associations between these patterns and weight change. We analyzed data from a 6-month weight loss intervention for school district employees with overweight or obesity (N = 225). We performed repeated measures latent profile analysis (RMLPA) to identify common patterns of self-monitoring technology use and used multiple linear regression to evaluate the relationship between self-monitoring technology use and weight change. RMLPA revealed four distinct profiles: minimal users (n = 65, 29% of sample), activity trackers (n = 124, 55%), dedicated all-Around users (n = 25, 11%), and dedicated all-Around users with exceptional food logging (n = 11, 5%). The dedicated all-Around users with exceptional food logging lost the most weight (X2[1,225] = 5.27, p =. 0217). Multiple linear regression revealed that, adjusting for covariates, only percentage of days of wireless weight scale use (B =-0.05, t(212) =-3.79, p <. 001) was independently associated with weight loss. We identified distinct patterns in mHealth self-monitoring technology use for tracking weight loss behaviors. Self-monitoring of weight was most consistently linked to weight loss, while exceptional food logging characterized the group with the greatest weight loss. Weight loss interventions should promote self-monitoring of weight and consider encouraging food logging to individuals who have demonstrated consistent use of self-monitoring technologies.