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

Abstract

Background: 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.

Objective: 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.

Methods: 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.

Results: The probability of participants' engagement with each post decreased by 0.28% each week (P<.001; 95% CI 0.16%-0.4%). The probability of participants engaging with posts generated by interventionists was 6.3% (P<.001; 95% CI 5.1%-7.5%) higher than posts generated by other participants. Participants also had a 6.5% (P<.001; 95% CI 4.9%-8.1%) and 6.1% (P<.001; 95% CI 4.1%-8.1%) higher probability of engaging with posts that directly mentioned weight and goals, respectively, than other types of posts. Participants were 44.8% (P<.001; 95% CI 42.8%-46.9%) and 46% (P<.001; 95% CI 44.1%-48.0%) more likely to engage with a post when they were replied to by other participants and by interventionists, respectively. A 1 SD decrease in the sentiment of the conversation on a specific post was associated with a 5.4% (P<.001; 95% CI 4.9%-5.9%) increase in the probability of participants' subsequent engagement with the post. Participants' engagement in previous posts was also a predictor of engagement in subsequent posts (P<.001; 95% CI 0.74%-0.79%). Moreover, using a machine learning approach, we confirmed the importance of the predictors previously identified and achieved an accuracy of 90.9% in terms of predicting participants' engagement using a balanced testing sample with 1600 observations.

Conclusions: Findings revealed several predictors of engagement derived from the content generated by interventionists and other participants. Results have implications for increasing engagement in asynchronous, remotely delivered lifestyle interventions, which could improve outcomes. Our results also point to the potential of data science and natural language processing to analyze microlevel conversational data and identify factors influencing participant engagement. Future studies should validate these results in larger trials.

Trial registration: ClinicalTrials.gov NCT02656680; https://ichgcp.net/clinical-trials-registry/NCT02656680.

Keywords: NLP; data science; engagement; lifestyle; machine learning; mobile phone; natural language processing; social media; social media intervention; weight loss.

Conflict of interest statement

Conflicts of Interest: SP has been a paid advisor for WW (formerly Weight Watchers) and FitBit.

©Ran Xu, Joseph Divito, Richard Bannor, Matthew Schroeder, Sherry Pagoto. Originally published in JMIR Formative Research (https://formative.jmir.org), 28.07.2022.

Figures

Figure 1

Analysis framework to identify important predictors of participant engagement. Left panel: an example of the intervention post and the comments or replies following it. Right panel: flow chart of the analysis. NLP: natural language processing.

Figure 2

Variable importance of predicting participant engagement across 20 machine learning models. The x-axis shows different model names, and variables from top to bottom on the y-axis are baseline weight, number of people in the household, baseline BMI, age, post topic weight, post topic goal or plan, education, treatment assignment, post topic diet, post topic MyFitnessPal app, post topic exercise, employment status, marital status, post topic drink, post topic sleep, gender, race, post topic expressing emotion, post sentiment, whether the post is created by interventionists, day of the intervention when the post is created, word count of the post, replies or comments sentiment, day of the week when the post is created, percentage of previous posts engaged, and whether replied by other participants or by interventionists.