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.
Keywords: Diet; Feedback; Physical activity; Weight loss; mHealth.
Mobile applications and paired devices now enable users to track their physical activity levels, dietary intake, and weight fluctuations all in one user interface. We know that tracking each of these behaviors generally facilitates weight loss, but it is not clear how people with overweight or obesity may tend to use these multiple functions together when trying to lose weight. In a sample of 225 school district employees with overweight or obesity, we investigated whether there were common patterns in tracking these behaviors over time, and whether patterns were associated with weight loss. We identified groups reflecting four common patterns, which we termed the minimal users (n = 65, 29%), activity trackers (n = 124, 55%), dedicated all-around users (n = 25, 11%), and dedicated all-around users with exceptional food logging (n = 11, 5% of sample). The dedicated all-around users with exceptional food logging was the only group that reliably lost weight and was characterized by high tracking of activity, diet, and weight. Overall, regular use of the weight scale was most strongly associated with weight loss. It may be useful to broadly encourage self-monitoring of weight, and selectively encourage food logging to individuals amenable to this self-monitoring technology.
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