2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause

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Human Factors and Usability in Mobile Health Design – Factors for Sustained Patient Engagement in Diabetes Care Jason C. Goldwater, MA, MPA Clinovations Government Solutions Washington, DC The degree of health care resource consumption within the United States is significant, even with the passage of the Patient Protection and Affordable Care Act (PPACA), with annual expenditures expected to reach 14% of the US gross domestic product by 2016. This suggests the need for population-level solutions that cover the spectrum of both clinical and non-clinical care for the primary prevention of chronic disease, in particular diabetes, which affects over eight percent of the population and costs over $100 billion annually. Over the past decade, there have been an abundance of technological solutions that provide the potential of mitigating the risk issues associated with diabetes and improving self-management practices. One such technology in particular is emerging that may become very important to the delivery of health care: mobile phones. In 2012, a study undertook a comprehensive assessment of the use of mobile health (mHealth) in the management and treatment of diabetes. This study encompassed the review of over 514 articles, as well as series of key informant interviews and site visits, to determine the effectiveness and utility of mHealth in managing and treating diabetes. The research suggested that the usability of mHealth applications could be understood and assessed according to four major factors: user-friendliness, user design, user satisfaction and user confidence. The first two primarily deal with the type of technology and the design of the interface. The last two concern user perception and are crucial in appropriately evaluating how well the application meets a patient’s expectations, which is a critical component of both quality of care and patient outcomes.

Not subject to U.S. copyright restrictions. DOI 10.1177/2327857914031009

INTRODUCTION Affecting over eight percent of the United States population, diabetes costs the nation almost $100 billion annually and can cause severe complications in individuals, including cardiovascular disease, neuropathy and retinopathy. (Mainous, et al., 2007) The risk of morbidity from diabetes is higher among patients of a lower socioeconomic status, as these groups are disproportionally affected by Type 2 diabetes. (American Diabetes Association, 2011) Each of the primary risk factors, including obesity, poor self-management and a sedentary lifestyle, interact multiplicatively in the development of the disease and therefore need to be managed comprehensively. In fact, a number of published studies indicate that interventions aimed at changing an at-risk person’s lifestyle can reduce the overall incidence rate. (Chin, et al., 2001) The Diabetes Prevention Program, which consisted of a structured diet and increased physical activity, demonstrated a 58% reduction in the incidence of Type 2 diabetes for participants. (Mainous, et al., 2007) Treating diabetes requires a number of coordinated care processes and resources involving both the provider and the patient. Successful management of the disease relies on educating the patient about their condition and providing them the tools to practice self-management. (Chin, et al., 2001) Glucose levels must be measured at home by the patient and treated with a combination of diet, exercise and medication. Additionally, patients must undergo routine foot and eye examination as well as screenings for other risk factors, including hypertension and hyperlipidemia. As part of the treatment process, patients and providers need to communicate frequently about the patient’s status and care plan. Diabetes

care uniquely blends patient and provider responsibilities, as much of the care takes place outside of the physician’s office. (Demris, et al., 2007) THE RISE OF MOBILE HEALTH DEVICES AND APPLICATIONS Mobile communication devices and applications (mHealth), in conjunction with the Internet, present opportunities to enhance disease prevention and management by extending health interventions beyond the reach of traditional care involving a singular patient and provider. (Patrick, Griswold, Raab, & Intille, 2008) These technologies represent an evolution of telemedicine from the desktop to wearable technologies, which may improve the accessibility of treatment for diabetes as well as the ability of patients to actively engage their providers. (Logan, McIssac, & Tisler, 2007) Additionally, the innovations and functionality of mHealth, such as text messaging, smartphone applications and wireless sensor technology, can improve the speed, accuracy and convenience of diagnostic tests; improve medication adherence and test result delivery; improve interactive, two-way communication; and provide a simple methods for data collection, remote diagnosis, emergency tracking and access to health records. (Chomutare, Luque-Fernandez, Arsand, & Hartvigsen, 2011) The use of mHealth devices and applications for chronic disease care has been one of the most significant health IT developments of the past five years. (Cafazzo, 2012) According to data published in a Nielsen Report analyzing smartphone penetration by ethnicity within the United States in the first quarter of 2012, each ethnic population had a larger increase in smartphone adoption as compared to non-Hispanic

2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause

whites. Furthermore, the Nielsen research also indicates that the adoption of smartphones is highest among individuals’ ages 18-45, with potential increases occurring over time, even among poorer and elderly populations. Likewise, the number of mHealth applications available for smartphones is accelerating. As of 2012, there are over 40,000 health-related applications and this number is expected to double as the number of smartphone users increases and the sophistication of the technology improves. (Research2guidance, 2012) Additionally, the number of mHealth application users – defined as those who downloaded an mHealth application at least once – will reach 247 million by the end of 2012, a significant increase from the 124 million users identified in 2011. (Adaji, Schattner, & Jones, 2008) DEVELOPING A USABILITY FRAMEWORK The International Organization of Standardization broadly defines usability as the “extent to which a product can be used by specified users to achieved specified goals with effectiveness, efficiency and satisfaction in a specified context of use” (Kaufman, et al., 2003). Usability is an important factor to consider when weighing the efficacy of mHealth technology because if it is not designed to be accessible and easy to use, then the potential benefits and functionalities of these technologies are meaningless. Just as patient-centered care models have brought the patient back into the equation of health care delivery, usability encourages user-centered design to achieve meaningful and quality use of technology, and is often the lynchpin to ensuring sustainable patient engagement. (Jordan, Briggs, Brand, & Osborne, 2008) Because of the incorporation of disciplines such as human-computer interaction, engineering, ergonomics, design and psychology, there are a wide range of constructs and models that define usability in different ways to measure how technology is used and perceived by its users. However, universal to them all is the recognition that there is a strong need to understand mHealth devices and applications and the set of core factors and knowledge that are required to properly use this technology. For the purposes of this paper, usability can be assessed according to four major factors: user-friendliness, user design, user satisfaction and user confidence. The first two factors primarily deal with the type of technology and the design of the interface, while the last two are related to user perception. User –friendliness and user-design are shaped by a multidisciplinary cognitive engineering approach to humancomputer interaction. Incorporating principles, methods and tools and guides the analysis and design of computer-based systems have been developed to illustrate how humans interact with technology, such as Paul Norman’s theory of action. This model represents a continuous process of interaction with a system, beginning with a user’s intention (e.g., opening the application), leading to an action specification (clicking on an icon) and results in a change to the state of the system, which is reflected in the interface (the application opens a new document). The expectation is that the user realizes the

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change in the system and interprets it correctly in order to achieve their intended goal. If there is a problem with the system responding correctly to the user’s needs, or if the user is unable to comprehend the system change, then it indicates a usability flaw in the way the system is designed (“gulf of execution”) or the way it is interpreted by the user (“gulf of evaluation”). User acceptance, on the other hand, can be evaluated against two main constructs: ease of use and confidence with the technology. Poor execution in the design of an mHealth device or application is problematic in effectively utilizing its functionality to self-manage diabetes care. (Cafazzo, 2012) For example, tasks that require the user to execute lengthy sequences of actions or require movement between different screens increase the demands on a user’s working memory. (Cafazzo, 2012) Similarly, a graphical representation or display that is dense with objects and text requires a high degree of perpetual processing. These systems place a strain on a user’s attention and render them ineffective. (Patrick, Griswold, Raab, & Intille, 2008) The approach to properly assess user-friendliness and user-design for mHealth contains the following elements: 1. 2. 3. 4.

Characterizing how easy a user can carry out tasks using a mHealth device or application Assessing how users attain mastery in using tools and/or applications of this kind Assessing the effects of these tools and/or applications on individual self-management practices Identifying problems users have in their interaction with mHealth tools and/or applications.

User-Design Appropriately designing a device or application that simplifies tasks for end-users requires the development of an interface that incorporate users’ characteristics, tasks and workflow, independent of the available technology. (Kaufman, et al., 2003) By encompassing human-design principles that enhance the abilities of a users and allows them to overcome their own limitations, the probability of overall acceptance by the user increases. (Kaufman, et al., 2003) Our research identified a couple of studies in which participants were recruited to evaluate the usability and functionality of mHealth applications for diabetes, particularly focusing on features such as the self-monitoring of blood glucose, weight, physical activity, diet and medication. Participants were asked to think aloud as they were completing tasks and were asked about usability problems they encountered afterwards. Aggregating the data across these studies demonstrated that most users agreed the applications were usable in terms of clarity of information; consistency of messaging regarding good self-management practices; learnability; and information organizations. Drawbacks in usability were identified as difficulty in navigating to the appropriate screen; data entry problems; and lack of understanding of medical terminology

2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause

that was included in the application. (Goldwater, Dimsdale, Kontur, & Bordenick, 2012) Most patients have not managed their health through the use of a mobile device or application, and little is known about the characteristics of users to determine how to design a device or application that simplifies tasks and makes them easy to master. (Franklin & Pagliari, 2006) The European Union developed smartphone applications within the Enhanced Complete Ambient Assisted Living Experiment (eCAALYX) for adults with multiple chronic conditions, including diabetes. (Kushniruk & Patel, 2005) In order to ensure that users could master the applications, the mobile platform needed to be transparent to the user, and the interface functionality needed to be as accessible as possible. The following solutions were applied: the use of a mobile phone without buttons and with large-touch screens; all maintenance actions performed either remotely and transparently to the user; navigation of the application was reduced to two accessible screens; the phone runs autonomously without any mandatory interaction from the user from the time it is turned on. The conclusions from this study indicated that user mastery is obtainable through adherence to design principles that are applicable to the targeted demographic. (Kushniruk & Patel, 2005) However, the mastery of these devices and applications only took into account core functionality within the technology itself. It did not address either health or technology illiteracy within mHealth as there is little data indicating its effect on the sustained use of these technologies. Yet, health illiteracy can present users with difficulty in comprehending and applying information they receive within a device or application, and technology illiteracy can be a limiting factor in the adoption and use of these technologies, particularly among older adults or individuals with cognitive disabilities. (Kaufman, et al., 2003) While a preponderance of the research demonstrates that the usability can be enhanced through the application of appropriate design principles, and that the mastery of tasks associated with diabetes self-management becomes easier when the users’ perspective is taken into account, there is still a noticeable gap in the research regarding the most appropriate methods to incorporate health and technology literacy into an application, as well as analyzing its effect on continued and persistent usage of self-management applications for chronic disease. (Demris, et al., 2007) User-Friendliness mHealth technologies are popular because they are portable and contain several functions that can be used to send and receive information. For a device or application to affect an individual’s self-management behavior, it must allow temporal synchronization of the intervention delivery and allow the intervention to claim an individual’s attention when it is most relevant. (Free, et al., 2013) For example, a patient can be sent messages reminding them to check their blood glucose or blood pressure, enter the foods or liquids they have consumed throughout the day, or send a link to an interface to assist with

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smoking cessation. This type of temporal synchronization of the interface delivery also allows intervention to be accessed or delivered within the relevant context. (Free, et al., 2013) Individuals who smoke can send text messages requesting extra support while they are experiencing cravings due to withdrawal from nicotine, or those with asthma can access support or educational materials on how to increase the use of inhalers during an exacerbation of asthma. Additionally, there have been a number of studies on other forms of mHealth devices and applications that have shown its effect on an individual’s practice on the self-management of their diabetes, as shown in Table 1. Table 1: Types of mHealth Applications Identified Application Studied Smartphone application; text (SMS) messaging; real-time transfer of information (Chomutare, LuqueFernandez, Arsand, & Hartvigsen, 2011) Two-way pagers (Adaji, Schattner, & Jones, 2008)

Wireless, portable diabetes management system (Logan, McIssac, & Tisler, 2007)

Cell phones; text messaging; email; smartphone monitoring via text (SMS) messaging; real-time transfer of information (Logan, McIssac, & Tisler, 2007) Cardiac rehabilitation via 3G mobile phone with built-in accelerometer sensor, camera, and video teleconferencing features (Patrick, Griswold, Raab, & Intille, 2008)

Effect on Diabetes Care • Positive changes in HbA1C of 1.2% • Positive changes in systolic blood pressure from -6 to +10 • Positive changes in LDL Cholesterol of -29 to 0 • 79% of the participants in this study enjoyed using the pager and felt their care was improved at the end of the study. • Lower median carbohydrate intake • Higher rate of transmitted HgbA1C levels • Improved knowledge scores of diabetes • Increased intention to exercise • Reduction in body mass index (BMI) • Reduction in systolic/diastolic blood pressure. • Improved adherence (92% compared to 70% in control) • Improved physical activity and emotional state • Reduction in weight and triglyceride levels

User-Satisfaction and User-Confidence A 2012 ethnographic study used data from interviews and focus groups to identify four common usability design themes among mHealth technologies for diabetes care that successful applications would share that aligned with user-friendliness and user-confidence. (Cafazzo, 2012) Researchers combined the information from the interviews with knowledge of usercentered design approaches employed by other consumeroriented products to identify the following four themes that are central to the effective use of mHealth devices or applications for chronic care: Theme #1: Fast, Discrete Transactions. A substantial number of the telemedicine and mobile applications assessed for this study take blood glucose readings directly from a glucometer or through user input. The information is transferred either directly to a central server or

2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause

to an Internet cloud. The data is collected and transferred wirelessly though Bluetooth technology within a matter of seconds. Additional weight, nutrition and exercise information are transmitted directly to a central web server upon input, where they can be viewed by the patient and/or family member within moments after the data is entered. Theme #2: Data Collecting To Facilitate Decision Making Many mobile applications used for diabetes management utilize visual charts and graphs to illustrate a patient’s health information, including daily glycemic levels, calorie consumption, weight, blood pressure and physical activity. Further, many of these applications include decision-support prompts and alerts to notify a user when their levels fall or rise dangerously. These prompts also can inform an individual know when they have consumed more calories than needed, when their carbohydrate intake is too high, or if they need to increase their activity level for the day. Theme #3: Behavior Modification A number of pilot studies have demonstrated the utility of eHealth Tools in capturing and reporting glycemic levels and other health information. In many of these studies, patients were even able to achieve better control of their blood glucose, blood pressure, and weight in controlled environments, suggesting the potential for tools like telemedicine and mobile applications to affect behavior change. Unfortunately, we could find little research indicating that these behavioral effects are sustainable in the long term, as many of the articles we reviewed did not include follow-up studies using the same cohort of patients. Thus, it is difficult to ascertain whether mHealth devices and applications have a long-term impact on diabetes control or on mitigating the risk factors associated with Type 2 diabetes. Theme #4: Information Sharing Mobile applications, such as Vree or iBGStar, integrate with either a web-based application, such as a patient web portal, or with a personal health record. User data from these applications can be stored and shared with family members, informal caregivers or providers based on the preferences of the user. Additionally, patients are also seeking to meet and interact with a community of patients with similar problems, both to share clinical information and to provide and receive support. The use of social networks, such as Facebook, provides a source of information, support and engagement for patients suffering from diabetes. (Greene, Choudhy, Kiabuk, & Shrank, 2010) The ability to share information from individuals using these mHealth devices and applications to receive support or validation on specific issues, such as possible adverse events on medications, or what type of weight loss strategies were most effective, was viewed as crucial by patients as means of sustaining engagement in their care. (Greene, Choudhy, Kiabuk, & Shrank, 2010) TYPES OF MOBILE HEALTH DEVICES AND APPLICATIONS

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Results from studies of the use of mHealth devices and applications in heart disease and diabetes care strongly suggest mHealth applications can help patients reduce LDL cholesterol, blood pressure, and blood glucose; monitor vital signs; and reduce sedentary behavior by encouraging a healthy, active lifestyle through diet and physical activity in patients. Described in greater detail below are examples of mHealth devices and smartphone applications that are used for the management and treatment of diabetes. 1.

WellDoc Diabetes Manager System is a mobile health application that provides weekly automated clinical coaching through behavioral algorithms driven by real-time patient data, such as blood glucose values, carbohydrate intake, medications, and weight.. A cluster-randomized clinical trial was conducted over one year in 2010 to evaluate the use of WellDoc in conjunction with a One Touch Ultra 2 blood glucose meter. Over 150 patients were divided into four clusters, with one cluster (n=23) using only the WellDoc system with the blood glucose meter. The average decrease in HbA1C of 1.6% for patients in this intervention group was higher than the 0.7% change observed in the control group (which used no technology).

2.

DiaBetNet, developed by the MIT Media Lab, uses a wireless personal digital assistant (PDA) with diabetes management software and an integrated motivational game to assist youths between 8 and 18 years manage their Type 2 diabetes. Patients enter their vital signs for transmission to a physician, and are encouraged to play the interactive game to educate themselves about blood glucose levels, blood pressure, diet and exercise. Over 70 patients improved their overall knowledge of diabetes and maintenance of HgbA1C levels, and lowered their overall carbohydrate intake. (MIT Media Laboratory, 2012)

3.

Diabetes Pilot10 contains the essential logging features in addition to a comprehensive database that includes nutritional information on thousands of food items. It also contains information about carbohydrate, fat, protein, fiber, sodium, cholesterol, and other nutrients. There is a logbook to record and monitor medication intake, food, weight, and blood glucose averages for 7, 30, 60, and 90 days. It also offers an insulin calculator that takes into account the fiber, protein, and carbohydrate content of foods entered for a meal and calculates the number of insulin units required to reach a targeted blood glucose value.

4.

WaveSense Diabetes Manager: This is a free application that can track a diabetic user’s glucose results, carbohydrate intake, and insulin doses. The WaveSense Diabetes Manager helps the user enter

2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause

his/her information, review the data with color-coded charts and graphs and educate them on their diabetes management. The user is also able to watch educational videos from within the application to learn about healthy eating, lifestyle choices, and hear from others who are living with diabetes. The application offers an option to e-mail results to the user’s healthcare team 5.

Glucose Buddy, which was created by TuDiabetes.Com (an online community for diabetics), allows patients to enter information about their diet, exercise regimen and medications. Users can access a variety of graphs and reports to trend their diabetes and health status, and access an interactive forum for diabetes education and support

6.

Vree is an application that enables users to selfmanage their diabetes by providing an interface to enter data on blood glucose, diet, exercise and medication. The application also contains a large food database that provides nutritional information to help manage diet, access to articles and advice on diabetes management, and the ability to email a provider with the information recorded by the application.

7.

iBGStar Diabetes Manager App & Glucose Meter includes a device that is plugged into the smartphone to view, store and track blood glucose levels. Additionally, the application matches blood sugars to a meal that an individual has just finished; stores nutritional information about the meal; and communicates that information to a provider.

There are a number of other mobile health devices and applications that have been developed over the past several years, but have not been scientifically evaluated for their overall effectiveness in managing Type 2 diabetes. Many of these are electronic glucometers that capture data on a patient’s blood glucose level and transfer it to a provider through a centralized server or through an Internet cloud. None of these technologies have been independently evaluated, however, this functionality combined with other features likely plays a key role in helping patients manage many of the risk factors associated with Type 2 diabetes. Examples include: 1.

PositiveID Corporation has created the iglucose mobile health solution, which collects and transmits stored data from a number of compatible electronic blood glucose meters. The data is sent to a diabetes management portal via wireless cellular technology, where glucose readings can be shared with family members, primary care providers, or other specialists. This tool utilizes a variety of reports to inform patients about their health status, and uses a number of methods to communicate with the patient

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including online access, email, fax or SMS text. (Positive ID Corporation, 2011) 2.

Telcare BGM is another wireless-capable blood glucose meter that captures patient data on HgbA1C levels and sends it directly to a centralized server. Data is then sent to a cloud-based web application where health information can be viewed by patients, family members and health providers on a dashboard that is available through a computer, tablet or smartphone application. The dashboard features functionality that alerts the patient when their glucose levels are trending too high or low, and suggests appropriate corrective actions. (Allison, 2012)

3.

As part of a new initiative, the American Diabetes Association, the Centers for Disease Control and Prevention (CDC), the Health Resources and Services Administration (HRSA), two Beacon Communities and Voxia have created the Text4Diabetes campaign. The campaign utilizes SMS messaging to encourage individuals to engage with and manage their health, help them assess their diabetes risk levels, and better connect them with diabetes care and wellness educational materials. The program uses text message questions to assess an individual’s risk for diabetes and determine which resources are most appropriate for the user. (GrahamJones, 2012) Resources may include an online social forum; a check-up at a local pharmacy, or contact information for local health providers.

4.

Diabetes QOL allowed patients to transfer their weekly self-managed blood glucose levels to their provider. The application interacted directly with a glucometer, allowing patients to seamlessly send the information via SMS on their smartphone. Every three months, the patient was asked to take the Diabetes Quality of Life Survey. Responses to the survey, along with the patient’s glycemic values, were sent to health care providers. Patients received weekly SMS treatment advice based on their glucose values and follow-up calls were made based on the results of the survey. Using a randomized controlled trial design, evaluation of the application indicated a decrease in glucose levels of 0.14% among the intervention group as opposed to an increase of 0.12% within the control group. The evaluation also demonstrated a statistically significant reduction in the number of hypoglycemic episodes and improvements in the overall quality of life of the patient. (Harris, Haneuse, Martin, & Ralston, 2009)

CONCLUSION Based on the information gathered during this study, and incorporating the principle of user-friendliness, userconfidence, user-satisfaction and user design, an appropriate

2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause

model to assess usability for mHealth applications should include the following: •

•

•

•

Determining how effectively a user can carry out tasks of self-management using an mHealth device or application. Any device or application that creates an intuitive interface that provides easy-to-access data points and educational resources on diabetes, will provide a means for the individual to access the application over a sustained period of time Identifying the appropriate features to allow a user to eventually obtain mastery over the application in a short period of time. The device or application must integrate into an individual’s personal workflow and become a vital part of their diabetes care. Evaluate whether the interface creates ease-of-use for data entry; processes the information quickly; and creates visuals or other forms of information dissemination that are easy to interpret and facilitate decision-making and decision-sharing. The individual must be able to comprehend the information they are receiving and use it is a manner to facilitate better decision-making to control the care and risk factors associated with their diabetes. They must also be able to share the information with their care team, their family and with support groups that may develop through social media platforms. Use forms of randomized controlled trials to determine if the effects of the applications modify behavior and lead to better self-management practices.

STUDY METHODOLOGY We began this study with a comprehensive literature review utilizing the following databases: the Medical Literature Analysis and Retrieval System Online (Medline); PubMed; and the Cumulative Index to Nursing and Allied Health Literature (CINAHL). A search was also conducted through Google Scholar. Relevant references from extracted articles were identified to increase the literature search yield. Search terms comprised of “diabetes & medically underserved,” “diabetes & telemedicine,” “diabetes & mobile health, and “social media & diabetes.” Only original studies that evaluated the use of mobile health devices applications for diabetes management in medical practice and were published after 2005 were reviewed. These included studies using randomized controlled trials or observational (non-randomized controlled trials, pre-post studies, and post-intervention studies) or qualitative methods. Studies evaluating the use of health IT for other chronic diseases, review papers, which described other studies and opinion pieces, were excluded. Titles and abstracts of selected articles were independently reviewed by two authors and, if found eligible, the full article was then obtained for additional review. When there was disagreement between the two authors about the eligibility of

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an article, the third author adjudicated the conflict. A total of 514 articles were identified using the above search strategies, with 107 satisfying the inclusion/exclusion criteria. For this report, the studies identified and abstracted were classified based on methodology used, as shown in Table 2: Table 2: Number and Types of Studies Identified Study Methodology Randomized Controlled Trial Quasi-Experimental Design Pre-Post Test Design Post-Intervention Studies Case Studies Systematic Reviews

Number of Studies 68 4 12 3 5 15

Each of the articles was abstracted through a disciplined process to identify the technologies being studied; the results of the utilization of those technologies on diabetic patient outcomes; the relationship between those outcomes and risk factors associated with certain populations; and specific characteristics of each technology, including usability. Additionally, a non-traditional literature review was conducted through Google to identify specific products that employ the features and functionalities of the eHealth Tools identified in the literature review. Information about the development and proliferation of these Tools, in addition to projections about their use in the future, were abstracted from online news sources, such as Healthcare Data Management and others. Key informant interviews were conducted to fill in the identified gaps within the literature. The informants were chosen based on the recommendation of a Technical Advisory Group formed for this project, in addition to specific individuals who were selected based on a review of their articles. A semi-structured interview protocol was designed for this purpose. STUDY LIMITATIONS A limitation to this study is the inability to identify research that demonstrates the utility and effectiveness of mHealth devices and application tools on the non-clinical factors associated with Type 2 diabetes. Particularly amongst underserved and rural populations, the need for comprehensive lifestyle changes associated with diet and increased physical activity are paramount in effective management of the disease. However, the vast majority of the applications found in the research that underwent pilot studies focused specifically on clinical outcomes, with an emphasis on blood glucose, lipids and blood pressure. Many of the mHealth applications that provided data screens for input on diet and exercise were not evaluated to determine their effectiveness within socially disadvantaged populations. Given the amount of evidence indicating that lifestyle changes are essential for control of Type 2 diabetes and that poor nutrition and sedentary lifestyle are causal risk factors for the study population, additional research is needed to determine the effectiveness of these devices and applications.

2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause

Additionally, the demographic characteristics of socially disadvantaged populations indicate a wide array of cultures and ethnicities. Each group has its own distinct culture, beliefs and language when communicating with providers. A significant limitation within the studies found for this brief was the lack of a robust and comprehensive framework to assess usability. While some research indicated the functionality needed for the acceptance and use of patientcentered applications; very little demonstrated how various cultures could use these applications successfully. ACKNOWLEDGEMENTS This research was conducted by the eHealth Initiative in 2012 under a grant from the California Healthcare Foundation. The author would like to acknowledge Glen Moy, the Program Officer, for his guidance, as well as Jennifer Covich Bordenick, Jon Dimsdale and Alex Kontur for their invaluable contributions. For more information on the eHealth Initiative, please visit: www.ehidc.org For more information regarding mHealth and usability, please contact Jason Goldwater, the Principal Investigator at [email protected] or go to www.GovHealth.com REFERENCES Adaji, A., Schattner, P., & Jones, K. (2008). The Use of Information Technology to Enhance Diabetes Management in Primary Care: A Literature Review. Informatics in Primary Care , 16, 229-37. Allison, B. (2012). SmartMobile Glucose Meter Update: Telecare & OneTouch Verio IQ. New York: Diabetic Connect. American Diabetes Association. (2011, January 26). Diabetes Statistics. Retrieved June 8, 2012, from American Diabetes Association: http://www.diabetes.org Cafazzo, J. (2012). Design of an mHealth App for the SelfManagement of Adolescent Type I Diabetes. Journal of Medical Internet Research , 14 (3), E70. Chin, M., Cook, S., Jin, L., Drum, M., Harrison, F., Koppert, F., et al. (2001). Barriers to Providing Diabetes Care in Community Health Centers. Diabetes Care , 24 (2), 268-74. Chomutare, T., Luque-Fernandez, L., Arsand, E., & Hartvigsen, G. (2011). Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current applications Compared Against Evidence-Based Guidelines. Journal of Medical Internet Research , 13 (3), e65. Demris, G., Afrin, L., Speedle, S., Courtney, K., Sondhi, M., Vimarlund, V., et al. (2007). Patient-Centered Application: Use of Information Technology to Promote Disease Management and Wellness: A White Paper by the AMIA

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Knowledge in Motion Working Group. Journal of the American Medical Informatics Association , 15 (1), 8-13. Franklin, V. W., & Pagliari, C. G. (2006). A randomized controlled trial of Sweet Talk: a text-messaging system to support young people with diabetes. Diabetic Medicine , 23 (12), 1332-1338. Free, C., Phillips, G., Galli, L., Watson, L., Felix, L., Edwards, P., et al. (2013). The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic Review. PLOS Medicine , 10 (1), e1001362. Goldwater, J., Dimsdale, J., Kontur, A., & Bordenick, J. (2012). A Study and Report on the Use of eHealth Tools for Chronic Disease Care Among Socially Disadvantaged Populations. eHealth Initiative. California Healthcare Foundation. Graham-Jones, P. (2012, June 2). New Mobile App Will Use Texting for Diabetes Management. Retrieved June 8, 2012, from Health IT Gov: http://www.healthit.gov Greene, J., Choudhy, N., Kiabuk, E., & Shrank, W. (2010). Online Social Networking by Patients with Diabetes: A Qualitative Evaluation of Communication with Facebook. Journal of General Internal Medicine , 26 (3), 287-92. Harris, L., Haneuse, S., Martin, D., & Ralston, J. (2009). Diabetes Quality of Care and Outpatient Utilization Associated with Electronic Patient-Provider Messaging: A Cross-Sectional Analysis. Diabetes Care , 32 (7), 1182-1187. Jordan, J., Briggs, A., Brand, C., & Osborne, R. (2008). Enhancing patient engagement in chronic disease selfmanagement support initiatives in Austrailia: the need for an integrated approach. Medical Journal of Austrailia , 189 (10), S9-S13. Kaufman, D., Patel, V., Hilliman, C., Morin, P., Pevzner, J., Weinstock, R., et al. (2003). Usability in the real world: assessing medical information technologies in patients' homes. Journal of Biomedical Informatics , 36, 45-60. Kushniruk, A., & Patel, V. (2005). Cognitive And Usability Engineering Methods for the Evaluation of Clinical Information Systems. Journal of Biomedical Informatics , 37, 56-76. Logan, A., McIssac, W., & Tisler, A. (2007). Mobile phonebased remote patient monitoring system for management of hypertenstion in diabeteic patients. American Journal of Hypertension , 20 (9), 942-948. Mainous, A., Baker, R., Koopman, J., Saxena, S., Diaz, C., Everett, J., et al. (2007). Impact of the Population at Risk of Diabetes on Projections of Diabetes Burden in the United

2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause

States: An Epidemic on the Way. Diabetologia , 50 (5), 93440. MIT Media Laboratory. (2012, June 20). DiaBetNet. Retrieved June 26, 2012, from SLIE: http://slie.dyndns.org Patrick, K., Griswold, W., Raab, F., & Intille, S. (2008). Health and the Mobile Phone. American Journal of Preventative Medicine , 35 (2), 177-181. Positive ID Corporation. (2011, September 9). PositiveID Corporations' iGlucose Mobile Health System for Diabetes Management Selected to Be Featured at Rogers Wireless Technology Showcase. Retrieved June 8, 2012, from Seeking Alpha: http://www.seekingalpha.com Research2guidance. (2012, February 7). The Booming Market for Mobile Health Apps. Retrieved June 26, 2012, from Research2guidance: http://www.research2guidance.com

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