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Welcome to the BATLab’s Weekly Lit Review, where every week we post peer-reviewed papers relevant to our research projects.

This week, take a look at this interesting paper published in the American Journal of Preventive Medicine in 2013:

Abstract

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous
ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the
results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

 

This article was written by:

  • Santosh Kumar, PhD,
  • Wendy J. Nilsen, PhD,
  • Amy Abernethy, MD,
  • Audie Atienza, PhD,
  • Kevin Patrick, MD, MS,
  • Misha Pavel, PhD,
  • William T. Riley, PhD,
  • Albert Shar, PhD,
  • Bonnie Spring, PhD
  • Donna Spruijt-Metz, PhD,
  • Donald Hedeker, PhD,
  • Vasant Honavar, PhD,
  • Richard Kravitz, MD,
  • R. Craig Lefebvre, PhD,
  • David C. Mohr, PhD,
  • Susan A. Murphy, PhD,
  • Charlene Quinn, PhD,
  • Vladimir Shusterman, MD, PhD,
  • Dallas Swendeman, PhD, MPH
  •  

     

    Contributors are from:

    1. From the Department of Computer Science (Kumar), University of
      Memphis, Memphis, Tennessee;
    2. The Office of Behavioral and Social Sciences Research (Nilsen), the National Cancer Institute (Atienza), the National Heart, Lung and Blood Institute (Riley), NIH, Bethesda, the Department of Epidemiology and Public Health (Quinn), University of Maryland School of Medicine, Baltimore, Maryland;
    3. The Department of Medicine (Abernethy), Duke University Medical Center, Durham, the Health Communication and Marketing (Lefebvre), RTI International, Research Triangle Park, North Carolina;
    4. The Department of Preventive Medicine (Patrick), University of California, San Diego, La Jolla,
    5. The Department of Preventive Medicine (Spruijt-Metz), University of Southern California
    6. The Department of Psychiatry and Biobehavioral Sciences (Swendeman), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles,
    7. The Department of Internal Medicine (Kravitz), University of California, Davis, Sacramento, California;
    8. The Directorate for Computer and Information Science and Engineering (Pavel, Honavar), National Science Foundation, Arlington, Virginia
    9. The Pioneer Portfolio (Shar), Robert Wood Johnson Foundation, Princeton, New Jersey
    10. The Department of Preventive Medicine (Spring, Mohr), Northwestern University, Evanston, the Division of Epidemiology and Biostatistics (Hedeker), University of Illinois at Chicago, Chicago, Illinois
    11. The Department of Statistics (Murphy), University of Michigan, Ann Arbor, Michigan;
    12. The Noninvasive Cardiac Electrophysiology Laboratories (Shusterman), University of Pittsburgh Medical Center & PinMed, Pittsburgh, Pennsylvania

    Read more here: http://dx.doi.org/10.1016/j.amepre.2013.03.017

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