Predicting early user churn in a public digital weight loss intervention

Digital health interventions (DHIs) offer promising solutions to the rising global challenges of noncommunicable diseases by promoting behavior change, improving health outcomes, and reducing healthcare costs. However, high churn rates are a concern with DHIs, with many users disengaging before achieving desired outcomes. Churn prediction can help DHI providers identify and retain at-risk users, enhancing the efficacy of DHIs. We analyzed churn prediction models for a weight loss app using various machine learning algorithms on data from 1,283 users and 310,845 event logs. The best-performing model, a random forest model that only used daily login counts, achieved an F1 score of 0.87 on day 7 and identified an average of 93% of churned users during the week-long trial. Notably, higher-dimensional models performed better at low false positive rate thresholds. Our findings suggest that user churn can be forecasted using engagement data, aiding in timely personalized strategies and better health results

Effects of simple personalized goals on the usage of a physical activity app

Walking is the most ubiquitous physical activity. Natural walking and other physical activity opportunities, however, have been declining in developed societies. This decline has been linked to the rise of obesity. Smartphone health and fitness apps aim to reverse this trend by motivating people to be more physically active. The core philosophy in many of these applications is to either promote user competition or set universal goals and overwhelm the user with information. We present a physical activity app design that is closer to a goal oriented approach but with a twist. This new design is based on minimalism, where simple targets are set in a personalized manner and social comparison takes a secondary role. Specifically, the app gives to the user a daily caloric goal to consume by walking or biking. The formula that computes this goal is based on the user's food intake, Basal Metabolic Rate (BMR), and Body Mass Index (BMI). Our hypothesis is that methods emphasizing simple and precise personalized directions have better chance than pure competition methods to keep users engaged. Results from a pilot comparative study render initial support to this hypothesis