53 research outputs found

    Is Fun For Wellness Engaging? Evaluation of User Experience of an Online Intervention to Promote Well-Being and Physical Activity

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    Online well-being interventions demonstrate great promise in terms of both engagement and outcomes. Fun For Wellness (FFW) is a novel online intervention grounded in self-efficacy theory and intended to improve multidimensional well-being and physical activity through multi-modal methods. These strategies include capability-enhancing opportunities, learning experiences such as games, video vignettes, and self-assessments. RCT studies have suggested that FFW is efficacious in improving subjective and domain-specific well-being, and effective in improving mental health, physical health, physical activity, and self-efficacy in United States. adults who are overweight and in the general population. The present study uses qualitative and quantitative user experience data collected during two RCT trials to understand and evaluate engagement with FFW, its drivers, and its outcomes. Results suggest that FFW is enjoyable, moderately engaging, and easy to use; and contributes to positive outcomes including skill development and enhanced confidence, for both overweight individuals and the general adult population. Drivers of engagement appear to include rewards, gamification, scenario-based learning, visual tracking for self-monitoring, ease of use and simple communications, and the entertaining, interactive nature of program activities. Findings indicate that there are opportunities to streamline and simplify the experience. These results can help improve FFW and contribute to the science of engagement with online interventions designed to improve well-being

    Multilevel models to identify contextual effects on individual group member outcomes: a family example

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    This manuscript illustrates methods for utilizing measurements of individuals to identify group contextual effects on individual outcomes. Contextual effects can be identified by 1 of 3 methods: (1) divergence of the simple within- and between-group regression coefficients, (2) the presence of a cross-level interaction of the within- and between-group predictor variable, or (3) the effect of discrepancies within the group. These methods can be used to incorporate group context into an individual model and can be utilized for any individual process variable that might be affected by a group context. Example data include measures of hassles and coping adequacy of inner city, poor, African American new mothers, and their family members

    Modeling Site Effects in the Design and Analysis of Multi-site Trials

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    Background: Careful consideration of site effects is important in the analysis of multi-site clinical trials for drug abuse treatment. The statistical choices for modeling these effects have implications for both trial planning and interpretation of findings. Objectives: Three broad approaches for modeling site effects are presented: omitting site from the analysis; modeling site as a fixed effect; and modeling site as a random effect. Both the direct effect of site and the interaction of site and treatment are considered. Methods: The statistical model, and consequences, for each approach are presented along with examples from existing clinical trials. Power analysis calculations provide sample size requirements for adequate statistical power for studies utilizing 6, 8, 10, 12, 14, and 16 treatment sites. Results: Results of the power analyses showed that the total sample required falls rapidly as the number of sites increases in the random effect approach. In the fixed effect approach in which the interaction of site and treatment is considered, the required number of participants per site decreases as the number of sites increases. Conclusions: Ignoring site effects is not a viable option in multi-site clinical trials. There are advantages and disadvantages to the fixed effect and random effect approaches to modeling site effects. Scientific Significance: The distinction between efficacy trials and effectiveness trials is rarely sharp. The choice between random effect and fixed effect statistical modeling can provide different benefits depending on the goals of the study

    Screen-based sedentary behaviors and internalizing symptoms across time among U.S. Hispanic adolescents

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    The pervasive use of technology has raised concerns about its association with adolescent mental health, including internalizing symptoms. Existing studies have not always had consistent findings. Longitudinal research with diverse subgroups is needed. This study examines the relationship between screen-based sedentary (SBS) behaviors and internalizing symptoms among 370 Hispanic adolescents living in Miami, Florida- United States, who were followed for 2 ½ years and assessed at baseline, 6, 18 and 30 months post-baseline between the years 2010 and 2014. Approximately 48% were girls, and 44% were foreign-born, most of these youth being from Cuba. Mean age at baseline was 13.4 years, while at the last time-point it was 15.9 years. Findings show that girls had higher internalizing symptoms and different patterns of screen use compared to boys, including higher phone, email, and text use. SBS behaviors and internalizing symptoms cooccurred at each time-point, and their trajectories were significantly related (r = 0.45, p < .001). Cross-lagged panel analyses found that SBS behaviors were not associated with subsequent internalizing symptoms. Among girls, however, internalizing symptoms were associated with subsequent SBS behaviors during later adolescence, with internalizing symptoms at the 18-month assessment (almost 15 years old) associated with subsequent SBS behaviors at the 30-month assessment (almost 16 years old; β = 0.20, p < .01). Continued research and monitoring of internalizing symptoms and screen use among adolescents is important, especially among girls. This includes assessments that capture quantity, context, and content of screen time

    Optimizing the US-AUDIT for Alcohol Screening in U.S. College Students

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    This study examined cutoff scores on the new (2014) US-AUDIT (Alcohol Use Disorders Identification Test), adapted for U.S. standard drinks. No studies have examined optimal cutoff scores on the US-AUDIT for college students. 250 undergraduates (65% men) completed the US-AUDIT. At-risk drinkers reported at least four binge drinking episodes per week. Likely alcohol use disorder was assessed with a selfreport diagnostic measure. Using the Youden method, the ideal cutoff to identify at-risk drinkers for the US-AUDIT was 5 for men (sensitivity = .93, specificity = .96) and 6 for women (sensitivity = .77, specificity = .86); and to identify likely alcohol use disorder was 13 for men (sensitivity = .69, specificity = .81) and 8 for women (sensitivity = .83, specificity = .80). Cutoffs were lower than the original AUDIT. Different US-AUDIT cutoffs for men and women should be used for likely alcohol use disorder, which may reflect differences in drinking quantity and frequency. Empirical guidelines for alcohol screening with the new US-AUDIT may be used to enhance research or identification of at-risk drinkers in college settings, or for college students in primary care or other health care settings.The University of Miami, School of Nursing and Health StudiesUCR::Vicerrectoría de Docencia::Salud::Facultad de Medicina::Escuela de Enfermerí
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