The aim was to summarize research on telehealth remote patient monitoring interventions that incorporate key elements of structured self-monitoring of blood glucose (SMBG) identified as essential for improving A1C. A systematic review was conducted using the Medline, Cumulative Index to Nursing and Allied Health Literature, EMBASE, and OVID Medline databases with search terms "Telemedicine" AND "Monitoring, Physiologic" AND "Diabetes Mellitus, Type 2." Study selection criteria included original randomized clinical trials evaluating the impact of telehealth remote patient monitoring on A1C among adults with type 2 diabetes and incorporated 1 or more essential elements of SMBG identified by the International Diabetes Federation (patient education, provider education, structured SMBG profile, SMBG goals, feedback, data used to modify treatment, interactive communication or shared decision making). Fifteen studies were included, with interventions ranging from 3 to 12 months (mean 8 months) with sample sizes from 30 to 1665. Key SMBG elements were grouped into 3 categories: education, SMBG protocols, and feedback. Research incorporating 5 of the 7 elements consistently achieved significant A1C improvements between study groups. Interventions using more SMBG elements are associated with an improvement in A1C. Studies with the largest A1C decrease incorporated 6 of the 7 elements and computer decision support. Two studies with 5 of the 7 elements and active medication management achieved significant A1C decreases. Telehealth remote patient monitoring interventions in type 2 diabetes have not included all structured monitoring elements recommended by the IDF. Incorporating more elements of structured SMBG is associated with improved A1C.

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.

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.