@文章{信息:doi/10.2196/38068，作者=“Xu, Ran and Divito, Joseph and Bannor, Richard and Schroeder, Matthew and Pagoto, Sherry”，标题=“预测社交媒体中的参与者参与度——使用微观层面的对话数据进行生活方式干预:来自试验随机控制试验的数据的二次分析”，期刊=“JMIR Form Res”，年=“2022”，月=“7月”，日=“28”，量=“6”，数=“7”，页=“e38068”，关键词=“减肥;社交媒体干预;参与;数据科学;自然语言处理;NLP;社交媒体;生活方式;机器学习;背景:社交媒体提供的生活方式干预已经显示出有希望的结果，通常能产生适度但显著的减肥效果。 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://clinicaltrials.gov/ct2/show/NCT02656680 ", issn="2561-326X", doi="10.2196/38068", url="https://formative.www.mybigtv.com/2022/7/e38068", url="https://doi.org/10.2196/38068", url="http://www.ncbi.nlm.nih.gov/pubmed/35900824" }