Obesity affects millions of individuals worldwide and is associated with a significantly increased risk for cardiovascular and metabolic diseases. A study publishing June 16th in the open access journal PLOS Digital Health by Annabell Ho at Noom, Inc. New York, United States, suggests that while setting a weight-loss goal, analytical language was associated with greater weight loss success and a lower likelihood of attrition.

Outcomes for behavioral interventions treating obesity vary widely, with some individuals dropping off the program before they receive the full intervention. Yet the factors contributing to attrition or weight loss are poorly understood. To better understand how language may affect weight loss and program attrition, researchers conducted a retrospective study of 1,350 Noom Weight—an app-based weight management program—users who paid to participate in a 16-week program. Each participant set an initial goal and interacted with a coach to provide more detail about their weight loss goals. The researchers then analyzed the language using an automated text analysis program and calculated weight loss as well as weight loss and the dropout rate by analyzing program activity data.

The authors found that in goal striving conversations, such as talking to a coach about efforts to pursue a goal, analytical versus present-focused language was associated with greater weight loss and lower likelihood of attrition. While these findings may be useful, the study did not examine other related variables, for example the effects of education level or English proficiency on goal-setting language. Future studies should focus on the factors mediating the relationship between language and outcomes to confirm exactly why analytical language is helpful.

According to the authors, "Our results are among the first to identify individuals' language, which has not been studied much previously, as relevant and informative for understanding weight loss and dropout. This raises directions for future research to improve intervention development and ascertain whether language is informative in other lifestyle behavior change interventions."

Ho adds, "Using analytical language, for example analyzing what's important and why, predicts more weight loss and less program attrition on a digital weight loss program. On the other hand, using words that are more self-focused or present-focused like 'I' and 'me' predict less weight loss and more attrition."