Translational behavioral medicine
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Observational Study
User characteristics of a smartphone app to reduce alcohol consumption.
Digital interventions are available to help people reduce their alcohol consumption, but it is not known who uses these interventions and how this treatment-seeking group compares with the general population of drinkers. The study objective was to compare the socio-demographic and drinking characteristics of users of the 'Drinks Meter' smartphone app with the general population of drinkers in England and website users of the same intervention. Data were used from the Drinks Meter app and website, and a nationally representative cross-sectional survey in England (Alcohol Toolkit Study). ⋯ Drinks Meter app users were younger than website users and reported greater alcohol consumption and related harms. Drinkers using the Drinks Meter app are more likely to be younger and report greater alcohol consumption and related harms compared with the general population of drinkers in England and website users of the same intervention. Apps that provide feedback on drinking appear to be reaching those who report greater alcohol consumption and related harms.
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The use of online communities and websites for health information has proliferated along with the use of mobile apps for managing health behaviors such as diet and exercise. The scarce evidence available to date suggests that users of these websites and apps differ in significant ways from non-users but most data come from US- and UK-based populations. In this study, we recruited users of nutrition, weight management, and fitness-oriented websites in the Czech Republic to better understand who uses mobile apps and who does not, including user sociodemographic and psychological profiles. ⋯ Controlling for sociodemographics and web and phone use, drive for thinness predicted the frequency of use of apps for healthy eating (β = 0.14, p < .05), keeping a diet (β = 0.27, p < .001), and losing weight (β = 0.33, p < .001), whereas excessive exercise predicted the use of apps for keeping a diet (β = 0.18, p < .01), losing weight (β = 0.12, p < .05), and managing sport/exercise (β = 0.28, p < .001). Sensation seeking was negatively associated with the frequency of use of apps for maintaining weight (β = - 0.13, p < .05). These data unveil the user characteristics of mHealth app users from nutrition, weight management, and fitness websites, helping inform subsequent design of mHealth apps and mobile intervention strategies.
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Evidence-based intervention (EBI) de-adoption and its influence on public health organizations are largely unexplored within public health implementation research. However, a recent shift in support for HIV prevention EBIs by the Centers for Disease Control and Prevention provides an opportunity to explore EBI de-adoption. The current mixed-method study examines EBI de-adoption and the subsequent impact on a community-based organization (CBO) dedicated to HIV prevention. ⋯ RESPECT de-adoption had wide-reaching influences on individual staff, interactions between the staff and the community, the agency overall, and for implementation of future EBIs. We propose a revision of the implementation framework, incorporating EBI de-adoption as a phase of the implementation cycle. Furthermore, EBI de-adoption may have important, unintended consequences and can inform future HIV prevention strategies and guide research focusing on EBI de-adoption.