Predicting Participant Engagement in a Social Media-Delivered Lifestyle Intervention Using Microlevel Conversational Data: Secondary Analysis of Data From a Pilot Randomized Controlled Trial

Social media-delivered lifestyle interventions have shown promising outcomes, often generating modest but significant weight loss. Participant engagement appears to be an important predictor of weight loss outcomes; however, engagement generally declines over time and is highly variable both within and across studies. Research on factors that influence participant engagement remains scant in the context of social media-delivered lifestyle interventions. This study aimed to identify predictors of participant engagement from the content generated during a social media-delivered lifestyle intervention, including characteristics of the posts, the conversation that followed the post, and participants' previous engagement patterns. We performed secondary analyses using data from a pilot randomized trial that delivered 2 lifestyle interventions via Facebook. We analyzed 80 participants' engagement data over a 16-week intervention period and linked them to predictors, including characteristics of the posts, conversations that followed the post, and participants' previous engagement, using a mixed-effects model. We also performed machine learning-based classification to confirm the importance of the significant predictors previously identified and explore how well these measures can predict whether participants will engage with a specific post. The probability of participants' engagement with each post decreased by 0.28% each week (P