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Background: Hunger and food cravings predict poor outcomes in lifestyle interventions for weight management. For this reason, flexible weight management programs, as opposed to restrictive weight management programs, are needed. WW (formerly Weight Watchers)--a widely available, commercial weight management and wellness program--includes an approach that allows participants to obtain a personalized zero-point food (ZPF) list, which includes foods that do not need to be weighed, measured, or tracked. With over 300 potential options, ZPFs can include fruits, vegetables, legumes, whole grains, nonfat dairy, and lean sources of protein. Participants are assigned an individualized daily and weekly point target and can use ZPFs to help budget their points throughout the day, which can nudge participants toward a healthier overall dietary pattern. Objective: In a 6-month, single-arm trial, we examined the efficacy of WW when delivered via multimodal digital tools, including a mobile app for assisting with point tracking, weekly virtual workshops, weekly 5-minute wellness check-ins, and a Facebook group in which participants could socialize and support each other. Methods: The outcomes included weight change from baseline, as measured by the Bluetooth scales provided to each participant; hunger (visual analogue scale); food cravings (Food Craving Inventory); the intake of fruits and vegetables (The Five Factor Screener); physical activity (Global Physical Activity Questionnaire); and overall well-being (WHO-5 Well-Being Index). Results: Of the 153 participants, 70% were female, and 66% were White. Participants' mean age was 41.09 (SD 13.78) years, and they had a mean BMI of 31.8 (SD 5.0) kg/m2. Retention was high, as 91.5% provided 6-month follow-up data. Participants lost an average of 5.1% of body weight from baseline to 6 months (mean -4.4, SD 4.87 kg; P<.01), with 51% losing clinically significant weight (=5%). Hunger significantly declined over 6 months (mean percent change -14.74%, SD 64.28%; P<.01), as did food cravings (mean percent change -16.99%, SD 19.98%). The intake of fruits (mean percent change 65.95%, SD 188.78%; P<.01), vegetables (mean percent change 68.29%, SD 172.61%; P<.05), and salad (mean percent change 127.43%, SD 250.82%; P<.001) significantly increased. Engagement in moderate physical activity increased by an average of 32 (SD 133) minutes per day (P<.01), and sedentary time decreased by 90 (SD 24.5) minutes per day (P<.001). Finally, well-being significantly increased (mean change 17.77%, SD 46.21%; P<.01). Conclusions: This program, which used a less restrictive method of food tracking and provided personalized ZPFs, resulted in significant weight loss and an increase in fruits, vegetables, and exercise, while also reducing hunger and food cravings. Future research should compare the effectiveness of these approaches to traditional programs that require the self-monitoring of all foods and beverages. [ABSTRACT FROM AUTHOR]

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 in these interventions, but engagement generally declines over time and is highly variable within and across studies. Research on factors that influence participant engagement remains scant in the context of social media-delivered lifestyle interventions. Objective: The objective of this study was to identify predictors of participant engagement in a social media-delivered lifestyle intervention, including the characteristics of the participants, the posts, and the conversations that followed the posts. 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 (defined as replies/comments) for each of the 761 posts generated over the 16-week intervention period and linked them to predictors, including characteristics of the posts they engaged on, conversation sentiment, and participant characteristics, using a mixed-effects model. We also performed machine learning-based classification to explore how well the aforementioned measures can predict whether participants will engage with a specific post. Results: We found that the probability of participants' engagement with each post decreased by 0.28% (95% CI 0.16%-0.4%; P<.001) each week, and the probability of participants engaging with posts generated by interventionists was 6.3% (95% CI 5.1%-7.5%; P<.001) higher than their probability of engaging with posts generated by other participants. Participants also had a 6.5% (95% CI 4.9%-8.1%; P<.001) and 6.1% (95% CI 4.1%-8.1%; P<.001) higher probability of engaging with posts that directly mentioned weight and goals, respectively, rather than other types of posts. Participants were 44.8% (95% CI 42.8%-46.9%; P<.001) and 46% (95% CI 44.1%-48%; P<.001) more likely to engage with posts 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% (95% CI 4.9%-5.9%; P<.001) increase in the probability of participants'subsequent engagement with the post. Participants' engagement on previous posts was also a predictor of engagement in subsequent posts (95% CI 0.74%-0.79%; P<.001). In addition, an ensemble of gradient boosting machine and deep learning-based classification algorithms 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: Our findings revealed several predictors of engagement derived from the content generated by interventionists and other participants. Our 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. [ABSTRACT FROM AUTHOR]

Background: Generally, adherence to diet and physical activity in behavioral weight management programs predicts weight loss. Moreover, meeting attendance and platform or app use predict weight loss. Many weight-loss-prediction studies primarily use regression. However, little is known about the levels of adherence to specific web-based weight management program tools as well as behavioral and psychosocial variables that predict significant weight loss using machine-learning approaches. Objective: In this paper, we aimed to examine variables that can predict clinically significant weight loss (=5%) in a multimodal commercial digital weight management program, including levels of adherence to intervention tools and changes in behavioral and psychosocial states using machine-learning approaches. Methods: We performed secondary analyses using data from a one-arm trial online WW (formerly Weight Watchers) weight-loss intervention program, lasting 6 months, that recruited US adults with a BMI range of 25-45 kg/m². We used a WW Bluetooth scale and digital intervention tools such as mobile app for point tracking, weekly virtual workshops, weekly wellness check-ins and a Facebook group, and changes in psychosocial and behavioral variables (ie, food craving, Pittsburgh sleep quality index, diet, hunger, and physical activity). Using the receiver operating characteristics (ROC) curve, we identified the predictors of significant weight loss, as well as the associated cut points (CP) and area-under-curve (AUC) values for each variable. We further used a classification tree to confirm the importance of these predictors and assessed the out-of-sample prediction accuracies using 5-fold cross-validation. Results: Participants (N=153) were 70% female and 66% White, with a mean age of 41.09 (SD 13.78) years, and had a mean BMI of 31.8 kg/m² (SD 5.0). Approximately 51% of participants lost ≥5% weight. Using ROC curve, food tracking (CP≥9.4%; AUC=0.744; P<.001), increase in self-weighing (CP≥0.5; AUC=0.733; P<.001), wellness check-in attendance (CP≥31.3%; AUC=0.723; P<.001), and workshop attendance (CP≥35.4%; AUC=0.699; P<.001) were identified as significant predictors of achieving ≥5% weight loss. [ABSTRACT FROM AUTHOR]