The mobile health (mHealth) space, especially as it relates to health behavior change and chronic and infectious disease management, has been a long running theme of mine for increasing reach, effectiveness, efficiency and equity of interventions. (See Integrating cellphones and mobile technologies into public health practice -pdf). The allure of mHealth, often promoted via stories, case studies and visions of the mobile future, has raced ahead of most objective analyses. For the best mHealth papers of 2012, I selected ones that address many important - but skimmed over - issues. The first paper examines how to assess health systems, their readiness and capacity to scale up mHealth interventions. This paper focuses on South Africa, but the framework and conclusions could fit most developed and LMIC contexts. The article by Whittaker and colleagues reflects on their experiences and discusses the important steps for developing quality mHealth apps and evaluating their effectiveness. It is a paper that most app developers and mHealth designers should put at the top of their reading list for 2013.
The next four articles are systematic reviews and meta-analyses of mHealth interventions for, respectively, behavior change programs in developing countries, increasing levels of physical activity, the management of diabetes, and self-management of chronic illnesses. Each of these areas generates a lot of excitement and buzz at mobile health conferences, but as every one of these authors note, the evidence-base is both structurally weak (poor research designs and data reporting) and inconsistent with respect to outcomes.
The two papers that follow describe intervention trials. The first utilizes an RCT to demonstrate the value of integrating mobile technologies into standard care approaches to weight loss management. The second study, a diabetes management program for low-income urban women, is significant not so much for its findings but for its penetrating review of its failures – the nearly 50% drop-out rate. All program designers should give this one a careful read for the lessons it offers.
Finally, the last two articles look at the current and future state of mHealth. Whittaker reports her findings from interviews with key US mHealth stakeholders and Dolan wraps up the first year in which FDA regulatory approval popped up on many developers’ radar; he notes that at least 75 mHealth apps were approved by the agency.
Before shifting to the list, a quote from the Whittaker summary of stakeholder interviews captures my appraisal and mood about mHealth at the end of 2012:
“Several informants raised the concern that many mHealth applications available in practice may not be effective, engaging, usable, or meeting the needs of users. Few applications have been evaluated, and those that have often involve complex interventions where the components or mechanisms have not been examined. Many felt that not a lot is known as yet about what aspects of mHealth work, for whom, and why. Few published health interventions delivered via mobile technologies discuss a theoretical basis or evaluate theoretical components hypothesized to be important in the intervention. It was stated that there is much hype and lots of players all “doing their own thing.” Some informants felt that some mHealth developers may have a bias toward developing programs for people like themselves using the technologies they like, rather than starting with the problem and working with end users to develop the most useful and usable solution. Some pointed to statistics in the media showing that many smartphone applications are downloaded but not used. More recently, reviews have found poor quality in terms of accuracy, usability, consistency with national practice guidelines, and effective practices.”
I suspect that if you read and apply the insights and lessons from this top ten list, you might make significant contributions to the solutions for these and other issues that perplex the field.
Leon N, Schneider H, Daviaud E. Applying a framework for assessing the health system challenges to scaling up mHealth in South Africa. BMC Medical Informatics and Decision Making, 2012; 12:123 doi:10.1186/1472-6947-12-123 Full Text - pdf
Applies a framework of government stewardship and the organizational, technological and financial systems of a health care system to assess the opportunities and challenges to effectively implement mHealth interventions at scale. Stewardship includes strategic leadership and creating a learning environment; organizational issues include having a culture for use of health information for management, and a capacity for implementation; technological components include usability, interoperability and privacy and security safeguards; and financial system variables are demonstrating the cost-effectiveness of mHealth approaches and having in place sustainable funding sources.
Whittaker R, Merry S, Dorey E, Maddison R. A development and evaluation process for mHealth interventions: Examples from New Zealand. Journal of Health Communication, 2012;17: 11-21. Abstract
Using examples of the development of a video messaging smoking cessation intervention and a mobile phone multimedia messaging depression prevention intervention, the authors describe a series of steps for developing mHealth interventions: conceptualization, formative research to inform the development of the intervention, pretesting content, pilot study, pragmatic randomized controlled trial, and further qualitative research to inform improvement or implementation. Several themes underlie the entire process, including the integrity of the underlying behavior change theory, allowing for improvements on the basis of participant feedback, and a focus on implementation from the start. The strengths of this process are the involvement of the target audience in the development stages and the use of rigorous research methods to determine effectiveness.
Gurman TA, Rubin SE, Roess AA. Effectiveness of mHealth behavior change communication interventions in developing countries: A systematic review of the literature. Journal of Health Communication, 2012; 17 (sup1): 82-104. Full Text
The authors reviewed 44 articles; 16 (36%) reported evaluation data from mHealth interventions, mostly in Africa and Asia. HIV/AIDS and family planning/pregnancy were the health topics most frequently addressed by interventions. Studies did not consistently demonstrate significant effects of exposure to mHealth interventions on the intended audience. The majority of publications (n = 12) described interventions that used two-way communication in their message delivery design. Although most publications described interventions that conducted formative research about the intended audience (n = 10), less than half (n = 6) described targeting or tailoring the content.
Fanning J, Mullen SP, McAuley E. Increasing physical activity with mobile devices: A meta-analysis. Journal of Medical Internet Research, 2012; 14(6):e161 Full Text
The aims of this review were to: (1) examine the efficacy of mobile devices in the physical activity setting, (2) explore and discuss implementation of device features across studies, and (3) make recommendations for future intervention development. After searching electronic databases, four research studies were considered to be of “good” quality and seven of “fair” quality, involving a total of 1,351 participants. Their meta-analysis of outcomes, duration of moderate to vigorous physical activity and/or pedometer steps, provide some preliminary support for mobile technology (SMS) interventions to increase physical activity behavior. They also note that that much can be added to current theoretical models of behavior change so that they are better suited to design mobile interventions and interpret results.
Baron J, McBain H, Newman S. The impact of mobile monitoring technologies on glycosylated hemoglobin in diabetes: A systematic review. Journal of Diabetes Science and Technology, 2012; 6:1185-1196. Abstract
The investigators used database searches to identify studies that investigated the clinical effectiveness of mobile-based applications that allowed patients to record and send their blood glucose readings to a central server; 24 papers were reviewed. Results for patients with type 1 and type 2 diabetes were examined separately. Study variability and poor reporting made comparison difficult, and most studies had important methodological weaknesses. Evidence for the effectiveness of mHealth interventions for diabetes was inconsistent for both types of diabetes and remains weak.
de Jongh T, Gurol-Urganci I, Vodopivec-Jamsek V, Car J, Atun R. Mobile phone messaging for facilitating self-management of long-term illnesses. Cochrane Database Systematic Review, 2012;12:CD007459 Abstract
The authors conducted a review of experimental studies that assessed the effects of mobile phone messaging applications on health outcomes and patients' capacity to self-manage their condition. Four RCTs with a total of 182 participants were the focus of their analysis. They concluded that because of the small number of trials included, and the low overall number of participants, the quality of the evidence for positive impacts of SMS on health outcomes or self-management capabilities can at best be considered moderate. Furthermore, the usefulness and potential negative consequences of mobile phone messaging over extended periods of use for self-managing long-term conditions are not yet known.
Spring B, Duncan JM, Janke, A et al. Integrating technology into standard weight loss treatment: A randomized controlled trial. Archives of Internal Medicine, Published online 10 December 2012. doi:10.1001/jamainternmed.2013.1221 Full Text
Notable for being an RCT of a mHealth intervention, the current study demonstrates the feasibility of using mobile connective technology to interface with a hospital based, standard-of-care weight loss treatment. Adding technology and coaching to the standard-of-care group obesity treatment significantly enhanced weight loss outcomes at 3, 6, 9, and 12 months. More than 36% of participants using the mobile technology system and coaching, compared with 0% in the standard-of-care condition, lost at least 5% of their initial body weight at 3 months and this effect was significant, though less pronounced at 12 months (29.6% vs 14.8%).
Katz R, Mesfin T, Barr K. Lessons from a community-based mHealth diabetes self-management program: “It’s not just about the cell phone.” Journal of Health Communication, 2012; 17: 67-72. DOI: 10.1080/10810730.2012.650613 Abstract
An unflinching look at the many lessons learned from a pilot study of a cell-phone assisted diabetes self-management program carried out in an urban community clinic. There were high no-show rates for the baseline visit (49%) and the drop-out rate for participants was 50%. Six measures of diabetes standard-of-care improved; hospitalizations and emergency department visits were reduced during the study period in comparison with baseline.
They identified challenges to a mobile health chronic care program as including: (a) cell phones, (b) disease management software (in this case, Diabetes Manager), (c) support staff, (d) patients, (e) case managers, and (f) primary care providers (PCPs).
Cell phone factors include (a) type of handset and service, (b) size of screen, (c) type of keyboard, (d) length of message, (e) ease of access to software system, and (f) passwords. Disease management software factors include (a) literacy; (b) frequency of messaging, reminders, tips, questionnaires; (c) ease of access of the disease management software application to case managers and PCPs; and (d) interface with the medical record. Support staff factors include (a) assisting with cell phone arrangements, (b) registration and cell phone set up of the Diabetes Manager, (c) training and retraining patients in the use of the diabetes cell phone system, (d) monitoring patient system usage and confirmation of cell phone payments, (e) arranging patient incentives, (f) updating patient medical information into Diabetes Manager, and (g) providing Diabetes Manager monthly reports to PCPs. Patient factors include (a) literacy, (b) belief in medications/medication adherence, (c) affordability/incentives of cell phone service, (d) access to working glucometer/ medications/testing supplies, (e) creating and maintaining strong relationships with case managers and PCPs, (f) encouraging patient responses to Diabetes Manager messages, and (g) use of advanced Diabetes Manager features. Case manager factors include (a) staff turnover, (b) diabetes knowledge, (c) WellDoc Diabetes Manager training, (d) relationship with patients, (e) relationship with PCPs and a defined role and responsibility in patient care, (f) access to patient chart/labs, (g) and integration of the cell phone activities into case manager work flow. PCP factors include (a) identifying a PCP champion for program, (b) ease of access to WellDoc information, (c) integration into the PCP usual workflow, (d) communicating Diabetes Manager information to the consulting diabetologist, (e) improving PCP diabetes knowledge, (f) overcoming PCP inertia, and (g) fostering a relationship with patients and case managers.
Whittaker R. Issues in mHealth: Findings from key informant interviews. Journal of Medical Internet Research, 2012; 14(5):e129 doi:10.2196/jmir.1989 Full Text
The author conducted semi-structured interviews with 27 key informants from across the health sector and mHealth sector in the US to determine the important issues facing the implementation of mHealth. The most common issues included privacy and data security, the regulation of mHealth tools and programs as medical devices, the fragmentation and large number of wireless providers that makes comprehensive implementation difficult, the costs of mHealth services to the public, the proliferation of proprietary systems and multiple platforms, funding, a lack of good examples of the efficacy and cost-effectiveness of mHealth in practice, and the need for more high quality research.
Dolan B. Analysis: 75 FDA-cleared mobile medical apps. MobiHealthNews, 20 December 2012. Full Text
The emergence of FDA regulatory guidance this past year has led more mHealth developers to submit their apps for review and approval. At least 75 510(k) clearances included a mention or description of a mobile software component. Most FDA cleared apps are focused on chronic condition management — often taking the form of a digital logbook that receives data from a companion medical device. This group includes diabetes, asthma, and blood pressure management apps. The next largest contingent of FDA-cleared apps are related to electrocardiographs (ECGs), and typically take the form of a remote, mobile viewer for ECG data. Vital sign monitoring apps, imaging apps and medication adherence apps round out the group with a few apps that don’t fit any of those categories. These findings are compiled in the MobiHealthNews Research report: 75 FDA Regulated Mobile Medical Apps.