{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:22:12Z","timestamp":1791523332097,"version":"4.3.4"},"reference-count":267,"publisher":"Georg Thieme Verlag KG","issue":"S 01","license":[{"start":{"date-parts":[[2018,1,31]],"date-time":"2018-01-31T00:00:00Z","timestamp":1517356800000},"content-version":"vor","delay-in-days":395,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Methods Inf Med"],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Background: With the continuous and enormous spread of mobile technologies, mHealth has evolved as a new subfield of eHealth. While eHealth is broadly focused on information and communication technologies, mHealth seeks to explore more into mobile devices and wireless communication. Since mobile phone penetration has exceeded other infrastructure in low and middle-income countries (LMICs), mHealth is seen as a promising component to provide pervasive and patient-centered care.<\/jats:p><jats:p>Objectives: The aim of our research work for this paper is to examine the mHealth literature to identify application areas, target diseases, and mHealth service and technology types that are most appropriate for LMICs.<\/jats:p><jats:p>Methods: Based on the 2011 WHO mHealth report, a combination of search terms, all including the word \u201cmHealth\u201d, was identified. A literature review was conducted by searching the PubMed and IEEE Xplore databases. Articles were included if they were published in English, covered an mHealth solution\/intervention, involved the use of a mobile communication device, and included a pilot evaluation study. Articles were excluded if they did not provide sufficient detail on the solution covered or did not focus on clinical efficacy\/effectiveness. Cross-referencing was also performed on included articles.<\/jats:p><jats:p>Results: 842 articles were retrieved and analyzed, 255 of which met the inclusion criteria. North America had the highest number of applications (n=74) followed by Europe (n=50), Asia (n=44), Africa (n=25), and Australia (n=9). The Middle East (n=5) and South America (n=3) had the least number of studies. The majority of solutions addressed diabetes (n=51), obesity (n=25), CVDs (n=24), HIV (n=18), mental health (n=16), health behaviors (n=16), and maternal and child\u2019s health (MCH) (n=11). Fewer solutions addressed asthma (n=7), cancer (n=5), family health planning (n=5), TB (n=3), malaria (n=2), chronic obtrusive pulmonary disease (COPD) (n=2), vision care (n=2), and dermatology (n=2). Other solutions targeted stroke, dental health, hepatitis vaccination, cold and flu, ED prescribed antibiotics, iodine deficiency, and liver transplantation (n=1 each). The remainder of solutions (n=14) did not focus on a certain disease. Most applications fell in the areas of health monitoring and surveillance (n=93) and health promotion and raising awareness (n=88). Fewer solutions addressed the areas of communication and reporting (n=11), data collection (n=6), tele-medicine (n=5), emergency medical care (n=3), point of care support (n=2), and decision support (n=2). The majority of solutions used SMS messaging (n=94) or mobile apps (n=71). Fewer used IVR\/phone calls (n=8), mobile website\/email (n=5), videoconferencing (n=2), MMS (n=2), or video (n=1) or voice messages (n=1). Studies were mostly RCTs, with the majority suffering from small sample sizes and short study durations. Problems addressed by solutions included travel distance for reporting, self-management and disease monitoring, and treatment\/medication adherence.<\/jats:p><jats:p>Conclusions: SMS and app solutions are the most common forms of mHealth applications. SMS solutions are prevalent in both high and LMICs while app solutions are mostly used in high income countries. Common application areas include health promotion and raising awareness using SMS and health monitoring and surveillance using mobile apps. Remaining application areas are rarely addressed. Diabetes is the most commonly targeted medical condition, yet remains deficient in LMICs.<\/jats:p>","DOI":"10.3414\/me17-05-0003","type":"journal-article","created":{"date-parts":[[2017,8,8]],"date-time":"2017-08-08T12:50:58Z","timestamp":1502196658000},"page":"e105-e122","source":"Crossref","is-referenced-by-count":121,"special_numbering":"S","title":["mHealth Application Areas and Technology Combinations"],"prefix":"10.3414","volume":"56","author":[{"given":"Haitham","family":"Abaza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Marschollek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2018,1,31]]},"reference":[{"issue":"2","key":"10.3414\/ME17-05-0003-1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3233\/THC-2010-0576","volume":"18","author":"Mahmud","year":"2010","journal-title":"Technol Health Care"},{"key":"10.3414\/ME17-05-0003-2","doi-asserted-by":"crossref","unstructured":"Vatsalan D, Arunatileka S, Chapman K, Senaviratne G, Sudahar S, Wijetileka D, et al. 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