Abstract
Smartphones allow for prompting users with a short questionnaire about their current subjective experience, a technique often called Ecological Momentary Assessments. One of the biggest challenges for such studies is a lack of adherence, diminishing the benefits for both user and researcher. Being able to predict if a user is going to stop answering the questionnaire prompts would be beneficial for researchers and developers. This would allow for, for example, specifically addressing those users, or for over-sampling populations at higher risk of dropping out of a study. In this work, based on an observational study of the general population, we analyzed data from almost 1,000 users. The data include a large variety of sensor data from the users’ smartphones. We utilized machine learning to predict adherence on a day-to-day level, as well as predict adherence based on participant data after on-boarding. For day-to-day prediction, the best performing model was a model based on metadata features (days since first questionnaire was filled out, days since the last questionnaire was filled out, number of filled-out questionnaires, days since app installation), yielding an area under the precision–recall curve of 0.89. The inclusion of sensor data did not improve the model’s performance, indicating that the high cost of collecting and processing sensor data is not worth the benefits for predicting fill-out behavior. Predicting at sign-up if a user will adhere to a questionnaire prompt at least once was better than chance, but further studies are needed.
Published Version
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