Taridzo Chomutare

Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current Applications Compared Against Evidence-Based Guidelines

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) data export and communication, 16 (62%). Interestingly, although clinical guidelines widely refer to the importance of education, this is missing from the top functionalities in both cases. Conclusions While a wide selection of mobile applications seems to be available for people with diabetes, this study shows there are obvious gaps between the evidence-based recommendations and the functionality used in study interventions or found in online markets. Current results confirm personalized education as an underrepresented feature in diabetes mobile applications. We found no studies evaluating social media concepts in diabetes self-management on mobile devices, and its potential remains largely unexplored.

Mobile Health Applications to Assist Patients with Diabetes: Lessons Learned and Design Implications

Self-management is critical to achieving diabetes treatment goals. Mobile phones and Bluetooth® can support self-management and lifestyle changes for chronic diseases such as diabetes. A mobile health (mHealth) research platform—the Few Touch Application (FTA)—is a tool designed to support the self-management of diabetes. The FTA consists of a mobile phone-based diabetes diary, which can be updated both manually from user input and automatically by wireless data transfer, and which provides personalized decision support for the achievement of personal health goals. Studies and applications (apps) based on FTAs have included: (1) automatic transfer of blood glucose (BG) data; (2) short message service (SMS)-based education for type 1 diabetes (T1DM); (3) a diabetes diary for type 2 diabetes (T2DM); (4) integrating a patient diabetes diary with health care (HC) providers; (5) a diabetes diary for T1DM; (6) a food picture diary for T1DM; (7) physical activity monitoring for T2DM; (8) nutrition information for T2DM; (9) context sensitivity in mobile self-help tools; and (10) modeling of BG using mobile phones. We have analyzed the performance of these 10 FTA-based apps to identify lessons for designing the most effective mHealth apps. From each of the 10 apps of FTA, respectively, we conclude: (1) automatic BG data transfer is easy to use and provides reassurance; (2) SMS-based education facilitates parent-child communication in T1DM; (3) the T2DM mobile phone diary encourages reflection; (4) the mobile phone diary enhances discussion between patients and HC professionals; (5) the T1DM mobile phone diary is useful and motivational; (6) the T1DM mobile phone picture diary is useful in identifying treatment obstacles; (7) the step counter with automatic data transfer promotes motivation and increases physical activity in T2DM; (8) food information on a phone for T2DM should not be at a detailed level; (9) context sensitivity has good prospects and is possible to implement on todays phones; and (10) BG modeling on mobile phones is promising for motivated T1DM users. We expect that the following elements will be important in future FTA designs: (A) automatic data transfer when possible; (B) motivational and visual user interfaces; (C) apps with considerable health benefits in relation to the effort required; (D) dynamic usage, e.g., both personal and together with HC personnel, long-/short-term perspective; and (E) inclusion of context sensitivity in apps. We conclude that mHealth apps will empower patients to take a more active role in managing their own health.

Surgical Telementoring in Knowledge Translation—Clinical Outcomes and Educational Benefits A Comprehensive Review

Background. Surgical telementoring has been reported for decades. However, there exists limited evidence of clinical outcome and educational benefits. Objective. To perform a comprehensive review of surgical telementoring surveys published in the past 2 decades. Results. Of 624 primary identified articles, 34 articles were reviewed. A total of 433 surgical procedures were performed by 180 surgeons. Most common telementored procedures were laparoscopic cholecystectomy (57 cases, 13%), endovascular treatment of aortic aneurysm (48 cases, 11%), laparoscopic colectomy (32 cases, 7%), and nefrectomies (41 cases, 9%). In all, 167 (38%) cases had a laparoscopic approach, and 8 cases (5%) were converted to open surgery. Overall, 20 complications (5%) were reported (liver bleeding, trocar port bleeding, bile collection, postoperative ileus, wound infection, serosa tears, iliac artery rupture, conversion open surgery). Eight surveys (23%) have structured assessment of educational outcomes. Telementoring was combined with simulators (n = 2) and robotics (n = 3). Twelve surveys (35%) were intercontinental. Technology satisfaction was high among 83% of surgeons. Conclusion. Few surveys have a structured assessment of educational outcome. Telementoring has improved impact on surgical education. Reported complication rate was 5%.

Inferring community structure in healthcare forums. An empirical study.

BACKGROUND Detecting community structures in complex networks is a problem interesting to several domains. In healthcare, discovering communities may enhance the quality of web offerings for people with chronic diseases. Understanding the social dynamics and community attachments is key to predicting and influencing interaction and information flow to the right patients. OBJECTIVES The goal of the study is to empirically assess the extent to which we can infer meaningful community structures from implicit networks of peer interaction in online healthcare forums. METHODS We used datasets from five online diabetes forums to design networks based on peer-interactions. A quality function based on user interaction similarity was used to assess the quality of the discovered communities to complement existing homophily measures. RESULTS Results show that we can infer meaningful communities by observing forum interactions. Closely similar users tended to co-appear in the top communities, suggesting the discovered communities are intuitive. The number of years since diagnosis was a significant factor for cohesiveness in some diabetes communities. CONCLUSION Network analysis is a tool that can be useful in studying implicit networks that form in healthcare forums. Current analysis informs further work on predicting and influencing interaction, information flow and user interests that could be useful for personalizing medical social media.

Designing a diabetes mobile application with social network support.

Although mobile applications and social media have emerged as important facets of the Internet, their role in healthcare is still not well-understood. We present design artefacts, inspired by persuasive technology concepts, from a study of social media as part of a diabetes mHealth application. We used the design science approach for mobile application design, and real-life user testing and focus group meetings to test the application over a 12-week period with 7 participants. Based on the System Usability Score (SUS), the mobile application scored an average of 84.6 (SD=13.2), which represents a fairly high usability score compared to the literature. Regression analysis on the daily blood glucose levels showed significant decreases for some patients, and although the study is not powered, the HbA1c showed a promising trend, and self-efficacy marginally increased. Incorporating persuasive elements such as blood glucose tracking and visualisation, and social media access directly from the mobile application produced promising results that warrant a larger study of behaviour change for people with diabetes.

Characterizing development patterns of health-care social networks

The growing amount of data in health-care social media requires innovative new analysis methods, which are elementary to exploration of relationship dynamics, in a bid to understand the new roles social media plays in health care. In this work, we use network analysis to explore the temporal nature of two large diabetes social networks, with a view to enhancing our knowledge of the development of community structures and cohesion factors. We compare our findings with analysis of two other nonhealth-care social networks. Current results reveal how diabetes online communities are very dynamic, suggesting diabetes patients are usually actively engaged for periods of less than a year, typically immediately following diagnosis. Additionally, we observe shrinking of both diameter and density, as well as disassortative mixing. The presented empirical study informs future online intervention strategies for promoting health behavior and lifestyle changes among people with diabetes.

Clinical and Educational Benefits of Surgical Telementoring

Background: Videoconference technology has substantially improved making surgical telementoring more feasible. However, evidence of potential benefits is missing.

Systematic Review of Behavioral Obesity Interventions and Their Persuasive Qualities

In this systematic review of weight loss interventions, we reviewed interventions aimed at maintaining weight loss, and identify persuasive elements that drive weight maintenance. Methods: We searched the Medline database for long-term obesity interventions, and targeted randomized control trials that aimed to reduce weight among adults for over 12 months, and extracted outcomes related to body weight change. Results: Seventeen publications were in the final review. Tailoring, or group counseling led by a health care professional, was shown to have a significant effect on long-term weight loss. Positive effects were also obtained by personalization one-on-one counseling, competition competing against other people trying to lose weight, and reminders. Conclusion: Maintaining weight loss long-term as so far eluded researchers, but results suggest that that some elements of the interventions are more greatly associated with weight maintenance than others. Future interventions might be more effective if they were based on persuasive technology.

Social Network Analysis to Delineate Interaction Patterns That Predict Weight Loss Performance

Social media is an interesting, relatively new topic in health and self-management, which is generating enormous amounts of data, but little is yet known about its effect on the health of participants. The goal of this study is to determine online interaction behaviours that predict weight loss performance. The problem is modelled as a binomial classification task for predicting whether a patient would lose significant weight, based on analysis of two obesity online communities. An expansion-reduction method was developed for the patient feature vector, where the expansion is based on concatenating network structure features and the reduction is based on feature subset selection. Further, empirical evaluation of classifiers was done on the datasets, before and after the expansion. Based on feature subset selection, centrality measures such as degree and betweenness were more predictive than basic demographic features. Top performers, compared with bottom performers, were significantly more active online and connected to more than one sub-community (at 95% CI and p<;.05). In terms of classification, we found naive Bayes and decision tree methods had superior performance on the datasets, drastically reducing the false positive (FP) rate in some instances, and reaching a maximum F-score of 0.977, precision of 0.978 and AUC of 0.996. Current findings are consistent with previous reports that amount of online engagement correlates with weight loss, but our findings speak further to the types of engagement that yield best results.

Text Classification to Automatically Identify Online Patients Vulnerable to Depression

Online communities are emerging as important sources of support for people with chronic illnesses such as diabetes and obesity, both of which have been associated with depression. The goal of this study was to assess the performance of text classification in identifying at-risk patients. We manually created a corpus of chat messages based on the ICD-10 depression diagnostic criteria, and trained multiple classifiers on the corpus. After selecting informative features and significant bigrams, a precision of 0.92, recall of 0.88, f-score of 0.92 was reached. Current findings demonstrate the feasibility of automatically identifying patients at risk of developing severe depression in online communities.