|Year : 2017 | Volume
| Issue : 2 | Page : 334-340
Mobile health technology in the prevention and management of Type 2 diabetes
Shruti Muralidharan1, Harish Ranjani2, Ranjit Mohan Anjana2, Steven Allender3, Viswanathan Mohan2
1 Global Obesity Centre (GLOBE), World Health Organisation Collaborating Centre for Obesity Prevention, Deakin University, Geelong Waterfront campus, Geelong, Victoria, Australia; Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
2 Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
3 Global Obesity Centre (GLOBE), World Health Organisation Collaborating Centre for Obesity Prevention, Deakin University, Geelong Waterfront campus, Geelong, Victoria, Australia
|Date of Web Publication||14-Mar-2017|
Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, No: 6, Conran Smith Road, Gopalapuram, Chennai - 600 086, Tamil Nadu
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Essential steps in diabetes prevention and management include translating research into the real world, improving access to health care, empowering the community, collaborative efforts involving physicians, diabetes educators, nurses, and public health scientists, and access to diabetes prevention and management efforts. Mobile phone technology has shown wide acceptance across various ages and socioeconomic groups and offers several opportunities in health care including self-management as well as prevention of Type 2 diabetes mellitus (T2DM). The future seems to lie in mobile health (mHealth) applications that can use embedded technology to showcase advanced uses of a smartphone to help with prevention and management of chronic disorders such as T2DM. This article presents a narrative review of the mHealth technologies used for the prevention and management of T2DM. Majority (48%) of the studies used short message service (SMS) technology as their intervention while some studies (29%) incorporated applications for medication reminders and insulin optimization for T2DM management. Few studies (23%) showed that, along with mHealth technology, health-care professionals' support resulted in added positive outcomes for the patients. This review highlights the fact that an mHealth intervention need not be restricted to SMS alone.
Keywords: Management, mobile health, mobile health technology, prevention, Type 2 diabetes mellitus
|How to cite this article:|
Muralidharan S, Ranjani H, Anjana RM, Allender S, Mohan V. Mobile health technology in the prevention and management of Type 2 diabetes. Indian J Endocr Metab 2017;21:334-40
|How to cite this URL:|
Muralidharan S, Ranjani H, Anjana RM, Allender S, Mohan V. Mobile health technology in the prevention and management of Type 2 diabetes. Indian J Endocr Metab [serial online] 2017 [cited 2018 Aug 20];21:334-40. Available from: http://www.ijem.in/text.asp?2017/21/2/334/202038
| Introduction|| |
Globally, Type 2 diabetes mellitus (T2DM) prevalence has increased to about 415 million in 2015, and it is estimated to rise to 642 million by 2040, equivalent to around one in ten adults. According to the International Diabetes Federation, a person with diabetes spends only about 10 h with their physician in a whole year. Although mobile health (mHealth) may not be a substitute for professional health care, it could play a supportive role in diabetes prevention and management by empowering individuals with information related to the primary, secondary, and tertiary prevention so that they might be able to make knowledgeable decisions about their lifestyle and behaviors. Given that T2DM prevention and management is largely dependent on individual behaviors, extending support beyond the clinic may help in self-management. There have been many interventions looking at the effect of face-to-face interventions to support T2DM prevention and management such as the Da Qing study, Finnish Diabetes Prevention study, US Diabetes Prevention Program (DPP), and Indian Diabetes Prevention Program. A less study population are those made recently accessible through the numbers of mobile phone users having increased to over 3 billion (and rapidly rising). Furthermore, mobile phones, which are now referred to as smartphones, bring wireless technology providing the capacity to provide instant access to information and aid in instant communication. This sets the platform for health care to use this ubiquitous advantage in the prevention and management of chronic diseases.
The aim of this narrative review is to present an overview of the mHealth technologies used for prevention and management of T2DM.
We conducted a review of the literature using common online library databases including MEDLINE, PsycINFO, Embase, and many more. The databases were searched between February 2015 and October 2016. The search terms were a combination of: “type 2 diabetes mellitus AND mobile phone/cell phone,” “type 2 diabetes prevention AND mobile phone/cell phone,” “type 2 diabetes control AND mobile phone/cell phone,” “type 2 diabetes management AND mobile phone/cell phone,” “type 2 diabetes AND mobile health AND mobile phone/cell phone,” “Mobile phone* AND type 2 diabetes prevention”, “Mobile phone* AND type 2 diabetes mellitus NOT type 1 diabetes,” “Cell phone*
AND type 2 diabetes mellitus NOT type 1 diabetes,” “text messag* AND type 2 diabetes mellitus NOT type 1 diabetes mellitus,” “mobile application* AND type 2 diabetes mellitus NOT type 1 diabetes mellitus,” “mobile technolog* AND type 2 diabetes mellitus NOT type 1 diabetes mellitus,” mobile health/mHealth/m-health AND type 2 diabetes mellitus NOT type 1 diabetes mellitus, and eHealth/e-health/website/web/web-based/website-based/online/internet AND type 2 diabetes mellitus NOT type 1 diabetes mellitus. Inclusion criteria were publications in English, Type 2 diabetes interventions (including only those focused on prevention and/or management) on humans, and specifically, interventions offered using mHealth technologies. Method papers that did not report study outcomes were excluded from the study. Other exclusion criteria included those interventions that used wired technologies, involved face-to-face interventions, focused on multiple diseases, other types of diabetes, or a primary outcome related to those other than prevention or control of T2DM. Of the final 21 studies included, one study was a cluster randomized study, two were quasi-experimental studies (one was pre- and post-test), four were feasibility tests, one was a pre- and post-controlled study, and the rest were randomized control studies. The flowchart of studies reviewed is now added as [Figure 1].
| Mobile Health Technology|| |
Mobile communication technology is the fastest growing sector of the communications industry with a high geographical coverage with many advances in the mobile technologies., The World Health Organization (WHO) considers mHealth as a component of electronic health (eHealth). The definition of eHealth according to the WHO is “the use of information and communication technologies for health” while mHealth is defined as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices.” Free et al. have defined mobile technologies to include a range of devices such as mobile phones, PDAs and PDA phones, smartphones, enterprise digital assistants, portable media players, handheld media players, handheld video-game consoles, handheld and ultraportable computers, and smartbooks that include various functions including mobile cellular communications such as short message service (SMS), phone calls, multimedia messaging service, access to the internet, and software applications. This transformative potential of mHealth technology owing to the extraordinary growth of mobile phones, fast growth of mobile networks, and technology , could render mHealth as a powerful media for providing individual level support beyond traditional/usual care paving the way forward for mHealth opportunities.,,
| Interventions Using Short Message Service Technology|| |
A majority of the studies published seem to have used SMS technology in T2DM prevention or management. SMS is a telecommunications technology available on all wireless devices to enable quick communication between individuals. A large number of interventions are focused on SMS technology. Most of the interventions that used SMS are focused on T2DM management more than prevention. [Table 1] gives a detail on the interventions done using SMS technology.
Studies of these technologies applied both theory-based SMSs as well as general diabetes care SMSs, and in each case, they demonstrated an effect on glycemic control. Three studies reported the use of theories in their intervention build. A large text messaging intervention carried out in one million individuals in India tested whether exposure to text messages without any cost-based activity such as in-person visits or counseling could result in behavioral changes and lower diabetes risk by helping participants engage in healthy habits such as increasing their physical activity, avoiding foods high in fat, and increasing fruit and vegetable intake. The text messages were tested by a behavior change task force before being disseminated. This intervention emphasizes the potential for large-scale translational trials in low- and middle-income countries where reaching the majority of the population may be a challenge. Wong et al. developed an SMS intervention based on the theory of planned behavior and social cognitive theory. The individuals in the intervention group received the SMS thrice weekly for the first 3 months, once a week in the next 3 months, and once a month for the subsequent 12 months. The study conducted in India by Ramachandran et al. used SMS for the intervention and was based on the transtheoretical model (TTM) of behavioral change. The SMS timing and frequency was tailored to the individual's preferences, receiving two to four messages per week. Both the SMS interventions indicated a decrease in diabetes incidence. Wong et al. showed that the overall incidence of T2DM decreased in the intervention group (11%) compared to the controls (18%) at 24 months while Ramachandran et al. showed that the cumulative incidence of T2DM was lower in the intervention group (18%) compared to the controls (27%). Haddad et al. developed SMS based on diabetes education and categorized it into five themes relating to diet, treatment, complication, awareness, blood glucose monitoring, and enhancement of clinic attendance based on T2DM education while Bin Abbas et al. developed SMS based on general diabetes care with an aim to induce glycemic control; both interventions resulted in reductions in glycated hemoglobin (HbA1c) and improvement in patient knowledge.
Another alternate intervention was bidirectional, allowing continuous, and individualized support to patients with T2DM through SMSs allowing individuals with T2DM to interact with clinicians and diabetes care educators to improve on their diabetes knowledge and self-management skills. Hussein et al. reported that this form of an approach could decrease HbA1c significantly by 1.16%. While the interventions mentioned so far were higher in delivery frequency, Goodarzi et al. sent weekly SMSs based on knowledge, attitude, practice, and self-efficacy and showed significant differences between the intervention (0.89%) and control (0.35%) groups in HbA1c. A unidirectional SMS intervention by Arora et al. focused on motivating patients to engage in self-care activities and demonstrated a significant improvement in medication adherence although there was no significant decrease in HbA1c.
| Interventions Using Mobile Health Technology as Decision Support Systems|| |
mHealth technology may be used as embedded decision support system or clinical decision support system. While systems that support clinical decision-making may help clinicians in understanding patient data, embedded decision support systems can help health-care professionals provide recommendations that can analyze patient feedback. Interventions provided patients with tailored, patient-specific data either in the form of messages ,, or letters. Some interventions created algorithms to provide automated recommendations which were built based on theories such as health behavior change theory.
| Interventions Using Mobile Application Technology|| |
Mobile application refers to software application designed to run on mobile devices such as smartphones and tablet computers which extend the simple text send and response approach described above. Fukuoka et al. developed a 6 weeks' curriculum by modifying the original 16-week DPP curriculum. This was delivered to the intervention group in person and through a mobile phone application. The mobile application was used to reinforce the in-person sessions, and this was done using daily messages, video clips, and quizzes. In this case, positive reinforcement was used to increase the probability of the desired outcomes. Wayne and Ritvo  identified one gap in usual care that the approach could overcome was where participants were unable to attend health-care appointments due to familial or work-related responsibilities. In this case, the use of a mHealth application served as a support system. The Few Touch Application, a mobile phone application, was used to help self-manage blood glucose, food habits, physical activity, and personal goal setting along with provision of diabetes information. The trial showed that use of the application along with counseling showed a significant improvement in T2DM self-management. Quinn et al. also demonstrated that mobile phone diabetes coaching along with analyzed data given to physicians using evidence-based guidelines had greater effect on glycemic control. While the pace of development has been rapid, we must provide more scope for more mHealth apps to be developed and tested as there remains a lack of evidence basis and evaluation which makes it a challenge for sustainability.
| Mobile Health for Insulin Users|| |
Self-management of T2DM pertains to daily activities such as administering insulin, exercising, and resting that go hand in hand with one's treatment to improve one's quality of life. mHealth technology of three steps: adhering, adapting, and performing routinely. Larsen et al. developed an insulin optimization intervention for individuals with poorly controlled diabetes with an aim to promote blood glucose control. The participants were given a mobile phone with preloaded software that enables individuals to enter blood glucose readings and obtain quantity of insulin to use, a glucometer with a Bluetooth cradle to easily transfer data into the phone. These data were reviewed by a diabetes nurse who could contact participants in case of any abnormal value, provide medical nutrition therapy, and refer to the physician to help optimize medical therapy, if need be. The study resulted in a mean HbA1c decrease of 0.69% over 6 months. In a similar study by Turner et al., the authors reported a decrease in HbA1c of 0.91% over 3 months.
| The Place of Theory in Interventions|| |
In general, interventions using mHealth technologies usually target or reinforce healthy behavior. Fukuoka et al. used a mobile application to reinforce the face-to-face learning. Social cognitive theory and theory of planned behavior are commonly used in eHealth interventions., Wong et al. developed SMSs based on the theory of planned behavior and social cognitive theory and grouped the SMSs into 4 broad themes including information on diabetes and prediabetes, lifestyle modification, social influence on lifestyle change, and self-efficacy to prevent T2DM in prediabetes patients. This randomized controlled trial showed that the incidence of T2DM was lower in the intervention group (6%) when compared to the control group (16%). Ramachandran et al. conducted a randomized controlled trial using SMSs based on the TTM as a foundation for lifestyle modification on working Indian men and tested it against standard lifestyle advice. The SMSs in the study were modeled according to the five stages of behavioral change as in TTM, namely, precontemplation, contemplation, preparation, action, and maintenance, and the participants in both the groups were tested at each stage. Pfammatter et al. indicated an improvement in at least two diabetes risk behaviors in a large cohort of high-risk population (20% of intervention group vs. 11% in the control group) using unidirectional SMSs developed by a behavioral change task force. Riley et al. urge that theory-driven intervention to improve mHealth interventions alongside strengthening health behavioral theories. Although the use of health behavior theories is rapidly increasing, there is a lack in data that shows evaluation of the theoretical components. As a result, reproducibility of such interventions may be questionable. The use of SMS seemed to be low-cost and less burdensome in terms of delivery. Although it is still unclear as to whether participant-specific messages, theory-based messages or general messages have impact on T2DM prevention or management; as from this review, all three types of messages seem to have an impact.
| Barriers in Mobile Health Interventions|| |
Various technological barriers were reported across studies including network issues, individuals changing numbers, and interference from telecommunications providers. In the SMS intervention by Faridi et al. which used the NICHE technology providing participants with phone, glucometer, and pedometer, technological barriers reported were that there were too many menus to navigate through, with small buttons, making it difficult to follow when the process altered too often. The pedometers, on the other hand, were reported to be uncomfortable to wear around and with the batteries draining out too often resulting in frequent reprogramming to have affected the ease of use of the intervention at large. In another study by Cho et al., the participants were provided with a “diabetes phone” – a mobile phone with a glucometer inbuilt into the battery pack. When it came to clinical implementation, “diabetes phone” was not a cost-effective approach.
Although mHealth applications may be able to transmit and store large amounts of health-care data, there is a growing concern about the privacy of the data accessibility, confidentiality, and storage. To address this concern, the US Food and Drug Administration under the Food, Drug, and Cosmetic Act ensures that all mHealth apps including devices are regulated, based on their functionality and risk of the data collected, and disseminated before it can be used. A recent review on the feasibility and efficacy of telemedicine for diabetes in India  highlights the lack of strict regulatory protocols in delivering interventions for the prevention or management of health conditions through telemedicine. Standardized protocols need to be adapted to all eHealth interventions (thus including mHealth) which can help minimize communication errors between the provider and the user. Many physicians are skeptical about transitioning to mHealth from traditional ways mainly because of the effort it will take to change from an old system to a new one. In addition, although many physicians believed that technology had the ability to improve quality of care, in the long run, they found adaptability a challenge.,
Another critical aspect of mHealth platforms is the user experience. User experience has a major role in improving their engagement with the app, thereby gaining motivation to use and interpret the content better. Using adept user interfaces with real-time data interpretation and smart algorithms may be useful in developing improved user experience, thereby improving engagement.
| Conclusions|| |
Noncommunicable diseases such as diabetes, obesity, cancers, and others are among the main causes of increasing morbidity and mortality worldwide. The field of mHealth although a new field, with its ubiquitous nature and wide adaptability, shows great potential to counter the tremendous burden on health-care costs resulting from noncommunicable diseases.,, A successful mHealth platform for T2DM should have a user interface that is visually appeasing, the ability to transfer data real time, involve both the healthcare provider and user, and finally have mechanisms that will motivate the user to engage constantly with the app. This review shows that mHealth technology with added support from the health-care professionals can result in improved outcomes in patients with T2DM. It also highlights the fact that a mHealth intervention need not be restricted to SMS alone.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
International Diabetes Federation. IDF Diabetes. 7th
ed. Brussels, Belgium: International Diabetes Federation; 2015. Available from: http://www.diabetesatlas.org
. [Last cited on 2016 Apr 05].
International Diabetes Federation. IDF Diabetes. 4th
ed. Montreal: International Diabetes Federation; 2009. Available from: https://www.idf.org/sites/default/files/IDF-Diabetes-Atlas-4th
-edition.pdf. [Last cited on 2016 Apr 02].
How mobile health can help tackle the diabetes epidemic and strengthen health systems: International Diabetes Federation (IDF) response to the public consultation on the European Commission's Green Paper on mobile health. Diabetes Res Clin Pract 2014;105:271-2.
Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, et al.
Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care 1997;20:537-44.
Lindström J, Eriksson JG, Valle TT, Aunola S, Cepaitis Z, Hakumäki M, et al.
Prevention of diabetes mellitus in subjects with impaired glucose tolerance in the Finnish diabetes prevention study: Results from a randomized clinical trial. J Am Soc Nephrol 2003;14 7 Suppl 2:S108-13.
Diabetes Prevention Program (DPP) Research Group. The Diabetes Prevention Program (DPP): Description of lifestyle intervention. Diabetes Care 2002;25:2165-71.
Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V, et al
. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia 2006;49:289-97.
Klonoff DC. The current status of mHealth for diabetes: Will it be the next big thing? J Diabetes Sci Technol 2013;7:749-58.
Donner J. Research approaches to mobile use in the developing world: A review of the literature. Inf Soc 2008;24:140-59.
Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, et al.
The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: A systematic review. PLoS Med 2013;10:e1001362.
Labrique AB, Vasudevan L, Kochi E, Fabricant R, Mehl G. mHealth innovations as health system strengthening tools: 12 common applications and a visual framework. Glob Health Sci Pract 2013;1:160-71.
International Telecommunications Union. Key 2005-2015 ICT Data for the World, by Geographic Regions and by Level of Development. Available from: https://wwwgoo.gl/pwG7Bw. [Last cited on 2016 Feb 25].
Donker T, Petrie K, Proudfoot J, Clarke J, Birch MR, Christensen H. Smartphones for smarter delivery of mental health programs: A systematic review. J Med Internet Res 2013;15:e247.
Pfammatter A, Spring B, Saligram N, Davé R, Gowda A, Blais L, et al.
mHealth Intervention to improve diabetes risk behaviors in India: A prospective, parallel group cohort study. J Med Internet Res 2016;18:e207.
Wong CK, Fung CS, Siu SC, Lo YY, Wong KW, Fong DY, et al.
A short message service (SMS) intervention to prevent diabetes in Chinese professional drivers with pre-diabetes: A pilot single-blinded randomized controlled trial. Diabetes Res Clin Pract 2013;102:158-66.
Ramachandran A, Snehalatha C, Ram J, Selvam S, Simon M, Nanditha A, et al.
Effectiveness of mobile phone messaging in prevention of type 2 diabetes by lifestyle modification in men in India: A prospective, parallel-group, randomised controlled trial. Lancet Diabetes Endocrinol 2013;1:191-8.
Haddad NS, Istepanian R, Philip N, Khazaal FA, Hamdan TA, Pickles T, et al.
A feasibility study of mobile phone text messaging to support education and management of type 2 diabetes in Iraq. Diabetes Technol Ther 2014;16:454-9.
Bin Abbas B, Al Fares A, Jabbari M, El Dali A, Al Orifi F. Effect of mobile phone short text messages on glycemic control in type 2 diabetes. Int J Endocrinol Metab 2015;13:e18791.
Hussein WI, Hasan K, Jaradat AA. Effectiveness of mobile phone short message service on diabetes mellitus management; the SMS-DM study. Diabetes Res Clin Pract 2011;94:e24-6.
Goodarzi M, Ebrahimzadeh I, Rabi A, Saedipoor B, Jafarabadi MA. Impact of distance education via mobile phone text messaging on knowledge, attitude, practice and self efficacy of patients with type 2 diabetes mellitus in Iran. J Diabetes Metab Disord 2012;11:10.
Arora S, Peters AL, Burner E, Lam CN, Menchine M. Trial to examine text message-based mHealth in emergency department patients with diabetes (TExT-MED): A randomized controlled trial. Ann Emerg Med 2014;63:745-54.e6.
Faridi Z, Liberti L, Shuval K, Northrup V, Ali A, Katz DL. Evaluating the impact of mobile telephone technology on type 2 diabetic patients' self-management: The NICHE pilot study. J Eval Clin Pract 2008;14:465-9.
Kim SI, Kim HS. Effectiveness of mobile and internet intervention in patients with obese type 2 diabetes. Int J Med Inform 2008;77:399-404.
Yoon KH, Kim HS. A short message service by cellular phone in type 2 diabetic patients for 12 months. Diabetes Res Clin Pract 2008;79:256-61.
Istepanian RS, Zitouni K, Harry D, Moutosammy N, Sungoor A, Tang B, et al.
Evaluation of a mobile phone telemonitoring system for glycaemic control in patients with diabetes. J Telemed Telecare 2009;15:125-8.
Orsama AL, Lähteenmäki J, Harno K, Kulju M, Wintergerst E, Schachner H, et al.
Active assistance technology reduces glycosylated hemoglobin and weight in individuals with type 2 diabetes: Results of a theory-based randomized trial. Diabetes Technol Ther 2013;15:662-9.
Fukuoka Y, Gay CL, Joiner KL, Vittinghoff E. A novel diabetes prevention intervention using a mobile app: A randomized controlled trial with overweight adults at risk. Am J Prev Med 2015;49:223-37.
Wayne N, Ritvo P. Smartphone-enabled health coach intervention for people with diabetes from a modest socioeconomic strata community: Single-arm longitudinal feasibility study. J Med Internet Res 2014;16:e149.
Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control. Diabetes Care 2011;34:1934-42.
Lee J. Hype or hope for diabetes mobile health applications? Diabetes Res Clin Pract 2014;106:390-2.
Larsen ME, Turner J, Farmer A, Neil A, Tarassenko L. Telemedicine-supported insulin optimisation in primary care. J Telemed Telecare 2010;16:433-40.
Turner J, Larsen M, Tarassenko L, Neil A, Farmer A. Implementation of telehealth support for patients with type 2 diabetes using insulin treatment: An exploratory study. Inform Prim Care 2009;17:47-53.
Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: Are our theories up to the task? Transl Behav Med 2011;1:53-71.
Cho JH, Lee HC, Lim DJ, Kwon HS, Yoon KH. Mobile communication using a mobile phone with a glucometer for glucose control in type 2 patients with diabetes: As effective as an Internet-based glucose monitoring system. J Telemed Telecare 2009;15:77-82.
Holmen H, Torbjørnsen A, Wahl AK, Jenum AK, Småstuen MC, Arsand E, et al.
A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: One-year results from the Norwegian randomized controlled trial renewing health. JMIR Mhealth Uhealth 2014;2:e57.
Mitka M. FDA lays out rules for regulating mobile medical apps. JAMA 2013;310:1783-4.
Kesavadev J, Saboo B, Shankar A, Krishnan G, Jothydev S. Telemedicine for diabetes care: An Indian perspective – Feasibility and efficacy. Indian J Endocrinol Metab 2015;19:764-9.
Asimakopoulos S, Asimakopoulos G, Spillers F. Motivation and user engagement in fitness tracking: Heuristics for mobile healthcare wearables. Informatics 2017;4:5.
Gryfe CI. Physician resistance to new information technology. CMAJ 2007;176:659.
Viswanathan M, Golin CE, Jones CD, Ashok M, Blalock SJ, Wines RC, et al.
Interventions to improve adherence to self-administered medications for chronic diseases in the United States: A systematic review. Ann Intern Med 2012;157:785-95.
Shah VN, Garg SK. Managing diabetes in the digital age. Clin Diabetes Endocrinol 2015;1:16.
|This article has been cited by|
||Applying Precision Medicine to Healthy Living for the Prevention and Treatment of Cardiovascular Disease
| ||Ross Arena,Cemal Ozemek,Deepika Laddu-Patel,Tavis Campbell,Codie R. Rouleau,Robert Standley,Samantha Bond,Euląlia P. Abril,Andrew P Hills,Carl Lavie |
| ||Current Problems in Cardiology. 2018; |
|[Pubmed] | [DOI]|
||Remote Patient Monitoring in IBD: Current State and Future Directions
| ||Ashish Atreja,Emamuzo Otobo,Karthik Ramireddy,Allyssa Deorocki |
| ||Current Gastroenterology Reports. 2018; 20(2) |
|[Pubmed] | [DOI]|
||Health Care Professionals Attitudes Toward, and Experiences of Using, a Culture-Sensitive Smartphone App for Women with Gestational Diabetes Mellitus: Qualitative Study
| ||Lisa Garnweidner-Holme,Therese Hoel Andersen,Mari Wastvedt Sando,Josef Noll,Mirjam Lukasse |
| ||JMIR mHealth and uHealth. 2018; 6(5): e123 |
|[Pubmed] | [DOI]|
||Design and content validation of a set of SMS to promote seeking of specialized mental health care within the Allillanchu Project
| ||M. Toyama,F. Diez-Canseco,P. Busse,I. Del Mastro,J. J. Miranda |
| ||Global Health, Epidemiology and Genomics. 2018; 3 |
|[Pubmed] | [DOI]|
||Womenęs experiences with using a smartphone app (the Pregnant+ app) to manage gestational diabetes mellitus in a randomised controlled trial
| ||Jeanette B. Skar,Lisa M. Garnweidner-Holme,Mirjam Lukasse,Laura Terragni |
| ||Midwifery. 2018; 58: 102 |
|[Pubmed] | [DOI]|