In a novel study, researchers at Noom, a subscription-based app focused on diet and exercise-excercise-based behaviour change and mental wellness, sought to determine if written language on a mobile weight management program could be used to predict program attrition and weight loss.
Using Receptiviti’s Linguistic Inquiry Word Count (LIWC) API, they analyzed transcripts of goal-setting and goal-striving language from over 17,000 participants in a commercial weight management program. The study found that psychologically distanced language in goal-striving was associated with more weight loss and less attrition, while psychologically immediate language was associated with less weight loss and higher attrition. These results demonstrate the potential for language analysis to identify high-risk moments and individuals in real-time, allowing for more targeted and effective interventions.
The findings of this study have important implications for the development of more effective weight loss programs. By understanding how language use is associated with program outcomes, interventions can be developed to encourage the use of psychologically distanced language during goal striving, potentially leading to greater weight loss and lower attrition rates. Furthermore, the use of automated text analysis programs like LIWC has the potential to revolutionize the field of weight loss and other health interventions, allowing for real-time monitoring and targeted interventions based on language use.