Online Features Of Mobile

Abstract

Background

Interest in mobile health (mHealth) applications for self-management of diabetes is growing. In July 2009, we found 60 diabetes applications on iTunes for iPhone; by February 2011 the number had increased by more than 400% to 260. Other mobile platforms reflect a similar trend. Despite the growth, research on both the design and the use of diabetes mHealth applications is scarce. Furthermore, the potential influence of social media on diabetes mHealth applications is largely unexplored.

Objective

Our objective was to study the salient features of mobile applications for diabetes care, in contrast to clinical guideline recommendations for diabetes self-management. These clinical guidelines are published by health authorities or associations such as the National Institute for Health and Clinical Excellence in the United Kingdom and the American Diabetes Association.

Methods

We searched online vendor markets (online stores for Apple iPhone, Google Android, BlackBerry, and Nokia Symbian), journal databases, and gray literature related to diabetes mobile applications. We included applications that featured a component for self-monitoring of blood glucose and excluded applications without English-language user interfaces, as well as those intended exclusively for health care professionals. We surveyed the following features: (1) self-monitoring: (1.1) blood glucose, (1.2) weight, (1.3) physical activity, (1.4) diet, (1.5) insulin and medication, and (1.6) blood pressure, (2) education, (3) disease-related alerts and reminders, (4) integration of social media functions, (5) disease-related data export and communication, and (6) synchronization with personal health record (PHR) systems or patient portals. We then contrasted the prevalence of these features with guideline recommendations.

Results

The search resulted in 973 matches, of which 137 met the selection criteria. The four most prevalent features of the applications available on the online markets (n = 101) were (1) insulin and medication recording, 63 (62%), (2) data export and communication, 61 (60%), (3) diet recording, 47 (47%), and (4) weight management, 43 (43%). From the literature search (n = 26), the most prevalent features were (1) PHR or Web server synchronization, 18 (69%), (2) insulin and medication recording, 17 (65%), (3) diet recording, 17 (65%), and (4) 

We present an in-depth analysis of the features of diabetes mobile applications. In addition, we contrast the requirements derived from evidence-based recommendations with the functions available in existing interventions. The rationale is to identify gaps and contribute to improving the tools available to the target group. The aim of the analysis is to answer three questions: (1) what functionality is available on the market for diabetes mobile applications?, (2) what gaps exist in relation to the evidence-based recommendations for this target group? and (3) what new use cases from social media could enhance such applications?

Methods

Our goal was to review as many and as diverse diabetes mobile applications as possible, both in the literature and in commercial markets. Many successful applications do not have any grounding in research, hence our decision to include the online markets and gray literature, where people in general showcase their innovation, often based on personal needs. While the literature typically reflects emerging applications and new trends, the market gives a good indication of mature applications and functionality. Comparing and contrasting the current functionality with recommendations in clinical guidelines constitutes a gap analysis.

Selection Criteria

The main inclusion criterion was that the application had a self-monitoring of blood glucose (SMBG) component. This inclusion criterion had the potential to preclude relevant applications, but in reality none of the excluded applications had a clear focus on diabetes. We settled on SMBG as the main inclusion criterion in order to filter out applications intended exclusively for medical professionals rather than patients, as well as other general health and lifestyle applications. We excluded applications without English-language user interfaces and those designed exclusively for health care professionals. We also excluded hardware-based solutions geared toward blood glucose tracking or insulin pumps only. Applications with their latest updates or publications prior to 2006 were excluded.

Search Strategy

The search was based on two main source types. The first source was online journal databases, indexers, and reference lists. We searched for prototypes and work in progress using the search terms “diabetes,” “mobile,” “PDA,” “cell,” “phone,” and “application”. We constructed a search string using both the conjunction “AND” and the disjunction “OR” logical operators (diabetes AND [mobile OR PDA OR cell OR phone OR application]). The search was based on the metadata—that is, title, abstract, and keywords. We targeted both original research papers and review articles indexed by Medline, ScienceDirect, ACM (Association for Computing Machinery) Digital Library, IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, Google Scholar, and DBLP (Digital Bibliography & Library Project) Computer Science Bibliography. The databases reflect the multidisciplinary nature of the research involving both medical and computer science fields. We identified three recent relevant reviews by Årsand et al [18], Tatara et al [7], and Liang et al [4], where we cross-checked descriptions. We also searched the gray literature: