What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions.
BACKGROUND
Mobile health (mHealth) is often reputed to be cost-effective or cost-saving. Despite optimism, the strength of the evidence supporting this assertion has been limited. In this systematic review the body of evidence related to economic evaluations of mHealth interventions is assessed and summarized.


METHODS
Seven electronic bibliographic databases, grey literature, and relevant references were searched. Eligibility criteria included original articles, comparison of costs and consequences of interventions (one categorized as a primary mHealth intervention or mHealth intervention as a component of other interventions), health and economic outcomes and published in English. Full economic evaluations were appraised using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist and The PRISMA guidelines were followed.


RESULTS
Searches identified 5902 results, of which 318 were examined at full text, and 39 were included in this review. The 39 studies spanned 19 countries, most of which were conducted in upper and upper-middle income countries (34, 87.2%). Primary mHealth interventions (35, 89.7%), behavior change communication type interventions (e.g., improve attendance rates, medication adherence) (27, 69.2%), and short messaging system (SMS) as the mHealth function (e.g., used to send reminders, information, provide support, conduct surveys or collect data) (22, 56.4%) were most frequent; the most frequent disease or condition focuses were outpatient clinic attendance, cardiovascular disease, and diabetes. The average percent of CHEERS checklist items reported was 79.6% (range 47.62-100, STD 14.18) and the top quartile reported 91.3-100%. In 29 studies (74.3%), researchers reported that the mHealth intervention was cost-effective, economically beneficial, or cost saving at base case.


CONCLUSIONS
Findings highlight a growing body of economic evidence for mHealth interventions. Although all studies included a comparison of intervention effectiveness of a health-related outcome and reported economic data, many did not report all recommended economic outcome items and were lacking in comprehensive analysis. The identified economic evaluations varied by disease or condition focus, economic outcome measurements, perspectives, and were distributed unevenly geographically, limiting formal meta-analysis. Further research is needed in low and low-middle income countries and to understand the impact of different mHealth types. Following established economic reporting guidelines will improve this body of research.
University of Washington, Department of Biobehavioral Nursing and Health Informatics, School of Nursing, Seattle, Washington, United States of America.
Sarah J
http://orcid.org/0000-0003-2980-0717
SJ
Iribarren
Columbia University, School of Nursing, New York, New York, United States of America.|Office of Nursing Research, EBP and Innovation, New York-Presbyterian Hospital, New York, New York, United States of America.
Kenrick
K
Cato
Center for Behavioral Cardiovascular Health, Department of Medicine, Columbia University Medical Center, New York-Presbyterian Hospital, New York, New York, United States of America.
Louise
L
Falzon
Columbia University, School of Nursing, New York, New York, United States of America.|Columbia University, School of Nursing, Center for Health Policy, New York, New York, United States of America.
Patricia W
PW
Stone
D040421:Cell Phone / Q000706:statistics & numerical data; D003362:Cost-Benefit Analysis / Q000379:methods*; D004470:Economics, Medical / Q000706:statistics & numerical data*; D006801:Humans; D017216:Telemedicine / Q000191:economics* / Q000706:statistics & numerical data*