American journal of preventive medicine
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The onset of cigarette smoking typically occurs during childhood or early adolescence. Nicotine dependence symptoms can manifest soon after onset, contributing to sustained, long-term smoking. Previous reviews have not clarified the determinants of onset. ⋯ Predictors of smoking onset for which there is robust evidence should be considered in the design of interventions to prevent first puff in order to optimize their effectiveness. Future research should seek to define onset clearly as the transition from never use to first use (e.g., first few puffs).
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Randomized Controlled Trial Comparative Study
All Nations Breath of Life: A Randomized Trial of Smoking Cessation for American Indians.
American Indians have the highest cigarette smoking prevalence of any racial/ethnic group in the U.S. There is currently no effective empirically based smoking-cessation program for American Indians. The purpose of this study was to determine if a culturally tailored smoking-cessation program, All Nations Breath of Life (ANBL), is more effective than a non-tailored cessation program among American Indian smokers. ⋯ The culturally tailored smoking-cessation program ANBL may or may not be an effective program in promoting cessation at 12 weeks and 6 months. Participants in the culturally tailored ANBL program were approximately twice as likely to quit smoking at 6 months compared with the CBP program, using self-reported abstinence.
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Digital health interventions have enormous potential as scalable tools to improve health and healthcare delivery by improving effectiveness, efficiency, accessibility, safety, and personalization. Achieving these improvements requires a cumulative knowledge base to inform development and deployment of digital health interventions. However, evaluations of digital health interventions present special challenges. ⋯ Relevant research questions include defining the problem and the likely benefit of the digital health intervention, which in turn requires establishing the likely reach and uptake of the intervention, the causal model describing how the intervention will achieve its intended benefit, key components, and how they interact with one another, and estimating overall benefit in terms of effectiveness, cost effectiveness, and harms. Although RCTs are important for evaluation of effectiveness and cost effectiveness, they are best undertaken only when: (1) the intervention and its delivery package are stable; (2) these can be implemented with high fidelity; and (3) there is a reasonable likelihood that the overall benefits will be clinically meaningful (improved outcomes or equivalent outcomes at lower cost). Broadening the portfolio of research questions and evaluation methods will help with developing the necessary knowledge base to inform decisions on policy, practice, and research.
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To be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. ⋯ The "state" is that of the individual based on multiple variables that define the "space" when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions.
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This paper introduces and discusses key issues in the economic evaluation of digital health interventions. The purpose is to stimulate debate so that existing economic techniques may be refined or new methods developed. The paper does not seek to provide definitive guidance on appropriate methods of economic analysis for digital health interventions. ⋯ These characteristics imply that more-complex methods of economic evaluation are likely to be better able to capture fully the impact of the intervention on costs and benefits over the appropriate time horizon. This complexity includes wider measurement of costs and benefits, and a modeling framework that is able to capture dynamic interactions among the intervention, the population of interest, and the environment. The authors recommend that future research should develop and apply more-flexible modeling techniques to allow better prediction of the interdependency between interventions and important environmental influences.