Scaling Up mHealth: Where Is the Evidence?

Centre for Public Mental Health, Department of Psychology, Stellenbosch University, Stellenbosch, South Africa.
PLoS Medicine (Impact Factor: 14.43). 02/2013; 10(2):e1001382. DOI: 10.1371/journal.pmed.1001382
Source: PubMed


Mark Tomlinson and colleagues question whether there is sufficient evidence on implementation and effectiveness to match the wide enthusiasm for mHealth interventions, and propose a global strategy to determine needed evidence to support mHealth scale-up.

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Available from: Leslie Swartz, Oct 04, 2015
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    • "This research concluded Smartphone applications for pregnant women will be an effective educational tool compared to other existing mediators even though frequency or scope of using it would be varied according to the user's age. Frequently mentioned challenges of research on APE using Smartphone are methodological issues and lack of strong evidence for the use of mHealth [26], [27]. Accordingly, Educational use of Smartphones in the real world has to be studied closely to seek advanced ways of antenatal parent education using ICTs. "
    01/2016; 6(5):404-409. DOI:10.7763/IJIET.2016.V6.722
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    • "Figure 1 presents a schematic of the four-term unit. These components are relevant to health behavior interventions that assess and introduce antecedents and consequences to change unhealthy behavior , which constitute the majority of technology-based interventions (Kaplan and Stone 2013; Riley et al. 2011; Tomlinson et al. 2013). Motivating operations (MOs) can increase or decrease the efficacy of consequences as reinforcers or punishers (Laraway et al. 2003; Michael 2000; Sundberg 2013; for an MO account of eating behavior, see Tapper 2005; for an MO account of side effects of medications, see Valdovinos and Kennedy 2004). "
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    ABSTRACT: Modifiable behavioral risk factors such as cigarette smoking, physical inactivity, and obesity contribute to over 40 % of premature deaths in the USA. Advances in digital and information technology are creating unprecedented opportunities for behavior analysts to assess and modify these risk factors. Technological advances include mobile devices, wearable sensors, biomarker detectors, and real-time access to therapeutic support via information technology. Integrating these advances with behavioral technology in the form of conceptually systematic principles and procedures could usher in a new generation of effective and scalable behavioral interventions targeting health behavior. In this selective review of the literature, we discuss how technological tools can assess and modify a range of antecedents and consequences of healthy and unhealthy behavior. We also describe practical, methodological, and conceptual advantages for behavior analysts that stem from the use of technology to assess and treat health behavior.
    The Behavior analyst / MABA 05/2015; 38(1). DOI:10.1007/s40614-014-0017-y · 1.08 Impact Factor
    • "Furthermore, greater emphasis should be placed by funding bodies on translational research, encouraging additional T3 research (implementation of research into practice) and T4 research (evaluation of the effectiveness of the implementation on health system outcomes). The rapid and relevant research paradigm (Glasgow et al., 2014; Tomlinson et al., 2013) incorporates multiple small-scale experiments among diverse users and settings and studies to rapidly test and refine various components of e-health interventions and new technologies. Such approaches to research may progress translation more effectively than traditional large-scale trials. "
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    ABSTRACT: e-Mental health services have been shown to be effective and cost-effective for the treatment of depression. However, to have optimal impact in reducing the burden of depression, strategies for wider reach and uptake are needed. A review was conducted to assess the evidence supporting use of e-mental health programmes for treating depression. From the review, models of dissemination and gaps in translation were identified, with a specific focus on characterising barriers and facilitators to uptake within the Australian healthcare context. Finally, recommendations for promoting the translation of e-mental health services in Australia were developed. There are a number of effective and cost-effective e-health applications available for treating depression in community and clinical settings. Four primary models of dissemination were identified: unguided, health service-supported, private ownership and clinically guided. Barriers to translation include clinician reluctance, consumer awareness, structural barriers such as funding and gaps in the translational evidence base. Key strategies for increasing use of e-mental health programmes include endorsement of e-mental health services by government entities, education for clinicians and consumers, adequate funding of e-mental health services, development of an accreditation system, development of translation-focused activities and support for further translational research. The impact of these implementation strategies is likely to include economic gains, reductions in disease burden and greater availability of more interventions for prevention and treatment of mental ill-health complementary to existing health and efficient evidence-based mental health services. © The Royal Australian and New Zealand College of Psychiatrists 2015.
    Australian and New Zealand Journal of Psychiatry 04/2015; 49(9). DOI:10.1177/0004867415582054 · 3.41 Impact Factor
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