Embedding Cardiovascular Research Into Practice

Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina2Duke Translational Medicine Institute, Duke University, Durham, North Carolina.
JAMA The Journal of the American Medical Association (Impact Factor: 35.29). 11/2013; 310(19):2037-8. DOI: 10.1001/jama.2013.282771
Source: PubMed
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    • "The era of so-called “pilotitis” of mHealth projects and unfettered proliferation of mHealth strategies is well recognized [31,32] and there is dire need to evaluate for health impact alongside implementation. For NCDs, the need for high-quality evidence to inform many important decisions regarding diagnosis, prevention and treatment is too great for research and implementation to operate independently [33]. This highlights the need to incorporate implementation research, monitoring and evaluation as part of mHealth deployments in this field and region in a systematic manner (Figure 2). "
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    ABSTRACT: Background Mobile health (mHealth) approaches for non-communicable disease (NCD) care seem particularly applicable to sub-Saharan Africa given the penetration of mobile phones in the region. The evidence to support its implementation has not been critically reviewed. Methods We systematically searched PubMed, Embase, Web of Science, Cochrane Central Register of Clinical Trials, a number of other databases, and grey literature for studies reported between 1992 and 2012 published in English or with an English abstract available. We extracted data using a standard form in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results Our search yielded 475 citations of which eleven were reviewed in full after applying exclusion criteria. Five of those studies met the inclusion criteria of using a mobile phone for non-communicable disease care in sub-Saharan Africa. Most studies lacked comparator arms, clinical endpoints, or were of short duration. mHealth for NCDs in sub-Saharan Africa appears feasible for follow-up and retention of patients, can support peer support networks, and uses a variety of mHealth modalities. Whether mHealth is associated with any adverse effect has not been systematically studied. Only a small number of mHealth strategies for NCDs have been studied in sub-Saharan Africa. Conclusions There is insufficient evidence to support the effectiveness of mHealth for NCD care in sub-Saharan Africa. We present a framework for cataloging evidence on mHealth strategies that incorporates health system challenges and stages of NCD care. This framework can guide approaches to fill evidence gaps in this area. Systematic review registration: PROSPERO CRD42014007527.
    Globalization and Health 06/2014; 10(1):49. DOI:10.1186/1744-8603-10-49 · 2.25 Impact Factor

  • JAMA The Journal of the American Medical Association 11/2013; 310(19):2048-9. DOI:10.1001/jama.2013.282990 · 35.29 Impact Factor
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    ABSTRACT: Spontaneous reporting (SR) adverse event system databases, large observational databases, large clinical trials, and large health records databases comprise repositories of information that may be useful for early detection of potential harms associated with drugs, devices, and vaccines. All of the data sources include many different adverse events and many medical products, so that any approach designed to detect “important” signals of potential harm must have adequate specificity to protect against false alarms yet provide satisfactory sensitivity for detecting issues that really need further investigation. Algorithms for evaluating potential risks using information from these sources, especially SR databases, have been described in the literature. The algorithms may seek to identify potential product-event associations without any prior specifications, to identify events associated with a particular product or set of products, or to identify products associated with a particular event or set of events. This article provides recommendations for using information from postmarketing spontaneous adverse event reporting databases to provide insight into risks of potential harm expressed by safety signals and offers guidance regarding appropriate methods, both frequentist and Bayesian, to use in various situations as a function of the objective of the analysis.
    Therapeutic Innovation and Regulatory Science 01/2014; 49(1):65-75. DOI:10.1177/2168479014533114 · 0.46 Impact Factor
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