Browsing by Subject "Lifestyle"

Now showing 1 - 3 of 3
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    Effects of a Community-based Lifestyle Intervention on Change in Physical Activity Among Economically Disadvantaged Adults With Prediabetes
    (Taylor and Francis, 2016) Hays, Laura M.; Hoen, Helena M.; Slaven, James E.; Finch, Emily A.; Marrero, David G.; Saha, Chandan; Ackermann, Ronald T.; School of Nursing
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    Hepatic Fat in Participants With and Without Incident Diabetes in the Diabetes Prevention Program Outcome Study
    (The Endocrine Society, 2021) Goldberg, Ronald B.; Tripputi, Mark T.; Boyko, Edward J.; Budoff, Matthew; Chen, Zsu-Zsu; Clark, Jeanne M.; Dabelea, Dana M.; Edelstein, Sharon L.; Gerszten, Robert E.; Horton, Edward; Mather, Kieren J.; Perreault, Leigh; Temprosa, Marinella; Wallia, Amisha; Watson, Karol; Irfan, Zeb; Medicine, School of Medicine
    Context: There is little information about fatty liver in prediabetes as it transitions to early diabetes. Objective: This study is aimed at evaluating the prevalence and determinants of fatty liver in the Diabetes Prevention Program (DPP). Methods: We measured liver fat as liver attenuation (LA) in Hounsfield units (HU) in 1876 participants at ~14 years following randomization into the DPP, which tested the effects of lifestyle or metformin interventions versus standard care to prevent diabetes. LA was compared among intervention groups and in those with versus without diabetes, and associations with baseline and follow-up measurements of anthropometric and metabolic covariates were assessed. Results: There were no differences in liver fat between treatment groups at 14 years of follow-up. Participants with diabetes had lower LA (mean ± SD: 46 ± 16 vs 51 ± 14 HU; P < 0.001) and a greater prevalence of fatty liver (LA < 40 HU) (34% vs 17%; P < 0.001). Severity of metabolic abnormalities at the time of LA evaluation was associated with lower LA categories in a graded manner and more strongly in those with diabetes. Averaged annual fasting insulin (an index of insulin resistance [OR, 95% CI 1.76, 1.41-2.20]) waist circumference (1.63, 1.17-2.26), and triglyceride (1.42, 1.13-1.78), but not glucose, were independently associated with LA < 40 HU prevalence. Conclusion: Fatty liver is common in the early phases of diabetes development. The association of LA with insulin resistance, waist circumference, and triglyceride levels emphasizes the importance of these markers for hepatic steatosis in this population and that assessment of hepatic fat in early diabetes development is warranted.
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    Predicting Participant Engagement in a Social Media–Delivered Lifestyle Intervention Using Microlevel Conversational Data: Secondary Analysis of Data From a Pilot Randomized Controlled Trial
    (JMIR, 2022-07-28) Xu, Ran; Divito, Joseph; Bannor, Richard; Schroeder, Matthew; Pagoto, Sherry; Medicine, School of Medicine
    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.