BackgroundMobile 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.MethodsSeven 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.ResultsSearches 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.ConclusionsFindings 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.

Objective. To assess a text messaging intervention to promote tuberculosis (TB) treatment adherence. Methods. A mixed-methods pilot study was conducted within a public pulmonary-specialized hospital in Argentina. Patients newly diagnosed with TB who were 18 or older, and had mobile phone access were recruited and randomized to usual care plus either medication calendar (n = 19) or text messaging intervention (n = 18) for the first two months of treatment. Primary outcomes were feasibility and acceptability; secondary outcomes explored initial efficacy. Results. Feasibility was evidenced by high access to mobile phones, familiarity with texting, most phones limited to basic features, a low rate of participant refusal, and many describing suboptimal TB understanding. Acceptability was evidenced by participants indicating feeling cared for, supported, responsible for their treatment, and many self-reporting adherence without a reminder. Participants in the texting group self-reported adherence on average 77% of the days whereas only 53% in calendar group returned diaries. Exploring initial efficacy, microscopy testing was low and treatment outcomes were similar in both groups. Conclusion. The texting intervention was well accepted and feasible with greater reporting of adherence using text messaging than the diary. Further evaluation of the texting intervention is warranted.

Background: Precision health calls for collecting and analyzing large amounts of data to capture an individual's unique behavior, lifestyle, genetics, and environmental context. The diffusion of digital tools has led to a significant growth of patient generated health data (PGHD), defined as health-related data created, gathered or inferred by or from patients and for which the patient controls data collection and data sharing. Purpose:We assessed the current evidence of the impact of PGHD use in clinical practice and provide recommendations for the formal integration of PGHD in clinical care. Methods:We searched PubMed, Ovid, Embase, CINAHL, Web of Science, and Scopus up to May 2018. Inclusion criteria were applied and four reviewers screened titles and abstracts and consequently full articles.Findings: Our systematic literature review identified 21 studies that examined the use of PGHD in clinical settings. Integration of PGHD into electronic records was extremely limited, and decision support capabilities were for the most part basic.Discussion: PGHD and other types of patient-reported data will be part of the health care system narrative and we must continue efforts to understand its impact on health outcomes, costs, and patient satisfaction. Nursing scientists need to lead the process of defining the role of PGHD in the era of precision health.

Study Objectives: Mobile health (mHealth) tools such as smartphone applications (apps) have potential to support sleep self-management. The objective of this review was to identify the status of available consumer mHealth apps targeted toward supporting sleep self-management and assess their functionalities. Methods: We searched four mobile app stores (iTunes Appstore, Android Google Play, Amazon Appstore, and Microsoft Appstore) using the terms "sleep", "sleep management," "sleep monitoring," and "sleep tracking." Apps were evaluated using the Mobile Application Rating Scale (MARS) and the IMS Institute for Healthcare Informatics functionality scores. Results: We identified 2,431 potentially relevant apps, of which 73 met inclusion criteria. Most apps were excluded because they were unrelated to sleep selfmanagement, simply provided alarm service, or solely played relaxation sounds in an attempt to improve sleep. The median overall MARS score was 3.1 out of 5, and more than half of apps (42/73, 58%) had a minimum acceptability score of 3.0. The apps had on average 7 functions based on the IMS functionality criteria (range 2 to 11). A record function was present in all apps but only eight had the function to intervene. About half of the apps (33/73, 45%) collected data automatically using embedded sensors, 27 apps allowed the user to manually enter sleep data, and 14 apps supported both types of data recording. Conclusions:The findings suggest that few apps meet prespecified criteria for quality, content, and functionality for sleep self-management. Despite the rapid evolution of sleep self-management apps, lack of validation studies is a significant concern that limits the clinical value of these apps.

Men who have sex with men and transgender women are hard-to-reach populations for research. Social media-based tools may overcome certain barriers in accessing these groups and are being tested in an ongoing study exploring HIV home-test kit use to reduce risk behavior. We analyzed pre-screening responses about how volunteers learned about the study (n = 896) and demographic data from eligible participants who came for an initial study visit (n = 216) to determine the strengths and weaknesses of recruitment strategies. Social media-based strategies resulted in the highest number of individuals screened (n = 444, 26% eligible). Dating sites/apps reached large numbers of eligible participants. White-Hispanics and African-Americans were more likely to be recruited through personal contacts; community events successfully reached Hispanic volunteers. Incorporating recruitment queries into pre-screening forms can help modify recruitment strategies for greater efficacy and efficiency. Findings suggest that recruitment strategies need to be tailored to reach specific target populations.