%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 26
%N 
%P e45469
%T App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World Data
%A Lehmann,Marco
%A Jones,Lucy
%A Schirmann,Felix
%+ Oviva AG, Dortustraße 48, Potsdam, 14467, Germany, 49 3055572034, marco.lehmann@oviva.com
%K obesity
%K weight loss
%K blended-care
%K digital health
%K real-world data
%K app engagement
%K mHealth
%K mobile health
%K technology engagement
%K weight management
%K mobile phone
%D 2024
%7 7.6.2024
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Early weight loss is an established predictor for treatment outcomes in weight management interventions for people with obesity. However, there is a paucity of additional, reliable, and clinically actionable early predictors in weight management interventions. Novel blended-care weight management interventions combine coach and app support and afford new means of structured, continuous data collection, informing research on treatment adherence and outcome prediction. Objective: Against this backdrop, this study analyzes app engagement as a predictor for weight loss in large-scale, real-world, blended-care interventions. We hypothesize that patients who engage more frequently in app usage in blended-care treatment (eg, higher logging activity) lose more weight than patients who engage comparably less frequently at 3 and 6 months of intervention. Methods: Real-world data from 19,211 patients in obesity treatment were analyzed retrospectively. Patients were treated with 3 different blended-care weight management interventions, offered in Switzerland, the United Kingdom, and Germany by a digital behavior change provider. The principal component analysis identified an overarching metric for app engagement based on app usage. A median split informed a distinction in higher and lower engagers among the patients. Both groups were matched through optimal propensity score matching for relevant characteristics (eg, gender, age, and start weight). A linear regression model, combining patient characteristics and app-derived data, was applied to identify predictors for weight loss outcomes. Results: For the entire sample (N=19,211), mean weight loss was –3.24% (SD 4.58%) at 3 months and –5.22% (SD 6.29%) at 6 months. Across countries, higher app engagement yielded more weight loss than lower engagement after 3 but not after 6 months of intervention (P3 months<.001 and P6 months=.59). Early app engagement within the first 3 months predicted percentage weight loss in Switzerland and Germany, but not in the United Kingdom (PSwitzerland<.001, PUnited Kingdom=.12, and PGermany=.005). Higher age was associated with stronger weight loss in the 3-month period (PSwitzerland=.001, PUnited Kingdom=.002, and PGermany<.001) and, for Germany, also in the 6-month period (PSwitzerland=.09, PUnited Kingdom=.46, and PGermany=.03). In Switzerland, higher numbers of patients’ messages to coaches were associated with higher weight loss (P3 months<.001 and P6 months<.001). Messages from coaches were not significantly associated with weight loss (all P>.05). Conclusions: Early app engagement is a predictor of weight loss, with higher engagement yielding more weight loss than lower engagement in this analysis. This new predictor lends itself to automated monitoring and as a digital indicator for needed or adapted clinical action. Further research needs to establish the reliability of early app engagement as a predictor for treatment adherence and outcomes. In general, the obtained results testify to the potential of app-derived data to inform clinical monitoring practices and intervention design. 
%M 38848556
%R 10.2196/45469
%U https://www.jmir.org/2024/1/e45469
%U https://doi.org/10.2196/45469
%U http://www.ncbi.nlm.nih.gov/pubmed/38848556