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Journal Description

JMIR Diabetes (JD) is a new sister journal of JMIR (the leading open-access journal in health informatics (Impact Factor 2017: 4.671), focusing on technologies, medical devices, apps, engineering, informatics and patient education for diabetes prevention, self-management, care, and cure, to help people with diabetes. As open access journal we are read by clinicians and patients alike and have (as all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies, as well as on diabetes epidemiology. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews) covering for example wearable devices and trackers, mobile apps, glucose monitoring (including emerging technologies such as Google contact lens), medical devices for insulin and metabolic peptide delivery, closed loop systems and artificial pancreas, telemedicine, web-based diabetes education and elearning, innovations for patient self-management and "quantified self", diabetes-specific EHR improvements, clinical or consumer-focused software, diabetes epidemiology and surveillance, crowdsourcing and quantified self-based research data, new sensors and actuators to be applied to diabetes.


Recent Articles:

  • Low-Carb Program (montage). Source: The Authors /; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Outcomes of a Digitally Delivered Low-Carbohydrate Type 2 Diabetes Self-Management Program: 1-Year Results of a Single-Arm Longitudinal Study


    Background: Type 2 diabetes mellitus has serious health consequences, including blindness, amputation, stroke, and dementia, and its annual global costs are more than US $800 billion. Although typically considered a progressive, nonreversible disease, some researchers and clinicians now argue that type 2 diabetes may be effectively treated with a carbohydrate-reduced diet. Objective: Our objective was to evaluate the 1-year outcomes of the digitally delivered Low-Carb Program, a nutritionally focused, 10-session educational intervention for glycemic control and weight loss for adults with type 2 diabetes. The program reinforces carbohydrate restriction using behavioral techniques including goal setting, peer support, and behavioral self-monitoring. Methods: The study used a quasi-experimental research design comprised of an open-label, single-arm, pre-post intervention using a sample of convenience. From adults with type 2 diabetes who had joined the program and had a complete baseline dataset, we randomly selected participants to be followed for 1 year (N=1000; mean age 56.1, SD 15.7 years; 59.30% (593/1000) women; mean glycated hemoglobin A1c (HbA1c) 7.8%, SD 2.1%; mean body weight 89.6 kg, SD 23.1 kg; taking mean 1.2, SD 1.01 diabetes medications). Results: Of the 1000 study participants, 708 (70.80%) individuals reported outcomes at 12 months, 672 (67.20%) completed at least 40% of the lessons, and 528 (52.80%) completed all lessons of the program. Of the 743 participants with a starting HbA1c at or above the type 2 diabetes threshold of 6.5%, 195 (26.2%) reduced their HbA1c to below the threshold while taking no glucose-lowering medications or just metformin. Of the participants who were taking at least one hypoglycemic medication at baseline, 40.4% (289/714) reduced one or more of these medications. Almost half (46.40%, 464/1000) of all participants lost at least 5% of their body weight. Overall, glycemic control and weight loss improved, especially for participants who completed all 10 modules of the program. For example, participants with elevated baseline HbA1c (≥7.5%) who engaged with all 10 weekly modules reduced their HbA1c from 9.2% to 7.1% (P<.001) and lost an average of 6.9% of their body weight (P<.001). Conclusions: Especially for participants who fully engage, an online program that teaches a carbohydrate-reduced diet to adults with type 2 diabetes can be effective for glycemic control, weight loss, and reducing hypoglycemic medications.

  • FullFlow + knowledge-based module instance on iPad (montage). Source: The Authors /; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Design and Development of a Context-Aware Knowledge-Based Module for Identifying Relevant Information and Information Gaps in Patients With Type 1 Diabetes...


    Background: Patients with diabetes use an increasing number of self-management tools in their daily life. However, health institutions rarely use the data generated by these services mainly due to (1) the lack of data reliability, and (2) medical workers spending too much time extracting relevant information from the vast amount of data produced. This work is part of the FullFlow project, which focuses on self-collected health data sharing directly between patients’ tools and EHRs. Objective: The main objective is to design and implement a prototype for extracting relevant information and documenting information gaps from self-collected health data by patients with type 1 diabetes using a context-aware approach. The module should permit (1) clinicians to assess the reliability of the data and to identify issues to discuss with their patients, and (2) patients to understand the implication their lifestyle has on their disease. Methods: The identification of context and the design of the system relied on (1) 2 workshops in which the main author participated, 1 patient with type 1 diabetes, and 1 clinician, and (2) a co-design session involving 5 patients with type 1 diabetes and 4 clinicians including 2 endocrinologists and 2 diabetes nurses. The software implementation followed a hybrid agile and waterfall approach. The testing relied on load, and black and white box methods. Results: We created a context-aware knowledge-based module able to (1) detect potential errors, and information gaps from the self-collected health data, (2) pinpoint relevant data and potential causes of noticeable medical events, and (3) recommend actions to follow to improve the reliability of the data issues and medical issues to be discussed with clinicians. The module uses a reasoning engine following a hypothesize-and-test strategy built on a knowledge base and using contextual information. The knowledge base contains hypotheses, rules, and plans we defined with the input of medical experts. We identified a large set of contextual information: emotional state (eg, preferences, mood) of patients and medical workers, their relationship, their metadata (eg, age, medical specialty), the time and location of usage of the system, patient-collected data (eg, blood glucose, basal-bolus insulin), patients’ goals and medical standards (eg, insulin sensitivity factor, in range values). Demonstrating the usage of the system revealed that (1) participants perceived the system as useful and relevant for consultation, and (2) the system uses less than 30 milliseconds to treat new cases. Conclusions: Using a knowledge-based system to identify anomalies concerning the reliability of patients’ self-collected health data to provide information on potential information gaps and to propose relevant medical subjects to discuss or actions to follow could ease the introduction of self-collected health data into consultation. Combining this reasoning engine and the system of the FullFlow project could improve the diagnostic process in health care.

  • Diabetic foot ulcer. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Models Used in Clinical Decision Support Systems Supporting Healthcare Professionals Treating Chronic Wounds: Systematic Literature Review


    Background: Chronic wounds such as diabetic foot ulcers, venous leg ulcers, and pressure ulcers are a massive burden to health care facilities. Many randomized controlled trials on different wound care elements have been conducted and published in the Cochrane Library, all of which have only a low evidential basis. Thus, health care professionals are forced to rely on their own experience when making decisions regarding wound care. To progress from experience-based practice to evidence-based wound care practice, clinical decision support systems (CDSS) that help health care providers with decision-making in a clinical workflow have been developed. These systems have proven useful in many areas of the health care sector, partly because they have increased the quality of care, and partially because they have generated a solid basis for evidence-based practice. However, no systematic reviews focus on CDSS within the field of wound care to chronic wounds. Objective: The aims of this systematic literature review are (1) to identify models used in CDSS that support health care professionals treating chronic wounds, and (2) to classify each clinical decision support model according to selected variables and to create an overview. Methods: A systematic review was conducted using 6 databases. This systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for systematic reviews. The search strategy consisted of three facets, respectively: Facet 1 (Algorithm), Facet 2 (Wound care) and Facet 3 (Clinical decision support system). Studies based on acute wounds or trauma were excluded. Similarly, studies that presented guidelines, protocols and instructions were excluded, since they do not require progression along an active chain of reasoning from the clinicians, just their focus. Finally, studies were excluded if they had not undergone a peer review process. The following aspects were extracted from each article: authors, year, country, the sample size of data and variables describing the type of clinical decision support models. The decision support models were classified in 2 ways: quantitative decision support models, and qualitative decision support models. Results: The final number of studies included in the systematic literature review was 10. These clinical decision support models included 4/10 (40%) quantitative decision support models and 6/10 (60%) qualitative decision support models. The earliest article was published in 2007, and the most recent was from 2015. Conclusions: The clinical decision support models were targeted at a variety of different types of chronic wounds. The degree of accessibility of the inference engines varied. Quantitative models served as the engine and were invisible to the health care professionals, while qualitative models required interaction with the user.

  • NODE mobile health intervention (montage). Source: The Authors /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    New-Onset Diabetes Educator to Educate Children and Their Caregivers About Diabetes at the Time of Diagnosis: Usability Study


    Background: Diabetes self-management education is essential at the time of diagnosis. We developed the New-Onset Diabetes Educator (NODE), an animation-based educational web application for type 1 diabetes mellitus patients. Objective: Our hypothesis is that NODE is a feasible, effective and user-friendly intervention in improving diabetes self-management education delivery to child/caregiver-dyads at the time of diagnosis. Methods: We used a pragmatic parallel randomized trial design. Dyads were recruited within 48 hours of diagnosis and randomized into a NODE-enhanced diabetes self-management education or a standard diabetes self-management education group. Dyads randomized in the NODE group received the intervention on an iPad before receiving the standard diabetes self-management education with a nurse educator. The Diabetes Knowledge Test 2 assessed disease-specific knowledge pre- and postintervention in both groups, and was compared using t tests. Usability of the NODE mobile health intervention was assessed in the NODE group. Results: We recruited 16 dyads (mean child age 10.75, SD 3.44). Mean Diabetes Knowledge Test 2 scores were 14.25 (SD 4.17) and 18.13 (SD 2.17) pre- and postintervention in the NODE group, and 15.50 (SD 2.67) and 17.38 (SD 2.26) in the standard diabetes self-management education group. The effect size was medium (Δ=0.56). Usability ratings of NODE were excellent. Conclusions: NODE is a feasible mobile health strategy for type 1 diabetes education. It has the potential to be an effective and scalable tool to enhance diabetes self-management education at time of diagnosis, and consequently, could lead to improved long-term clinical outcomes for patients living with the disease.

  • Source: Vimeo; Copyright: Katie Verrant; URL:; License: Creative Commons Attribution (CC-BY).

    Experiences of Using Web-Based and Mobile Technologies to Support Self-Management of Type 2 Diabetes: Qualitative Study


    Background: The prevalence of type 2 diabetes is rising, placing increasing strain on health care services. Web-based and mobile technologies can be an important source of information and support for people with type 2 diabetes and may prove beneficial with respect to reducing complications due to mismanagement. To date, little research has been performed to gain an insight into people’s perspectives of using such technologies in their daily management. Objective: The purpose of this study was to understand the impact of using Web-based and mobile technologies to support the management of type 2 diabetes. Methods: In-depth interviews were conducted with 15 people with type 2 diabetes to explore experiences of using Web-based and mobile technologies to manage their diabetes. Transcripts were analyzed using the framework method. Results: Technology supported the users to maintain individualized and tailored goals when managing their health. A total of 7 themes were identified as important to participants when using technology to support self-management: (1) information, (2) understanding individual health and personal data, (3) reaching and sustaining goals, (4) minimizing disruption to daily life, (5) reassurance, (6) communicating with health care professionals, and (7) coordinated care. Conclusions: Patients need to be supported to manage their condition to improve well-being and prevent diabetes-related complications from arising. Technologies enabled the users to get an in-depth sense of how their body reacted to both lifestyle and medication factors—something that was much more difficult with the use of traditional standardized information alone. It is intended that the results of this study will inform a new questionnaire designed to assess self-management in people using Web-based and mobile technology to manage their health.

  • TuDiabetes online community (montage). Source: TuDiabetes /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Effect of Diabetes Online Community Engagement on Health Indicators: Cross-Sectional Study


    Background: Successful diabetes management requires ongoing lifelong self-care and can require that individuals with diabetes become experts in translating care recommendations into real-life day-to-day diabetes self-care strategies. The diabetes online community comprises multiple websites that include social media sites, blogs, and discussion groups for people with diabetes to chat and exchange information. Online communities can provide disease-specific practical advice and emotional support, allow users to share experiences, and encourage self-advocacy and patient empowerment. However, there has been little research about whether diabetes online community use is associated with better diabetes self-care or quality of life. Objective: The aim of this study was to survey adults with diabetes who participated in the diabetes online community to better understand and describe who is using the diabetes online community, how they are using it, and whether the use of the diabetes online community was associated with health indicators. Methods: We recruited adults diagnosed with diabetes who used at least one of 4 different diabetes-related online communities to complete an online survey. Participants’ demographics, reported glycated hemoglobin (HbA1c), health-related quality of life (SF-12v2), level of diabetes self-care (Self-Care Inventory-Revised), and diabetes online community use (level of intensity and engagement) were collected. We examined the relationships between demographics, diabetes online community use, and health indicators (health-related quality of life, self-care, and HbA1c levels). We used binary logistic regression to determine the extent to which diabetes online community use predicted an HbA1c <7% or ≥7% after controlling statistically for other variables in the model. Results: A total of 183 adults participated in this study. Participants were mostly female (71.6%, 131/183), white (95.1%, 174/183), US citizens (82.5%, 151/183), had type 1 diabetes (69.7%, 129/183), with a mean age of 44.7 years (SD 14) and diabetes duration of 18.2 years (SD 14.6). Participants had higher diabetes self-care (P<.001, mean 72.4, SD 12.1) and better health-related quality of life (physical component summary P<.001, mean 64.8, SD 19; mental component summary P<.001, mean 66.6, SD 21.6) when compared with norms for diabetes. Diabetes online community engagement was a strong predictor of A1c, reducing the odds of having an A1c ≥7% by 33.8% for every point increase in diabetes online community engagement (0-5). Our data also indicated that study participants are oftentimes (67.2%, 123/183) not informing their healthcare providers about their diabetes online community use even though most (91.2%, 161/181) are seeing their healthcare provider on a regular basis. Conclusions: Our results suggest that individuals highly engaged with diabetes online community are more likely to have better glycemic levels compared with those with lower engagement. Furthermore, diabetes online community users have high health-related quality of life and diabetes self-care levels. Supplementing usual healthcare activities with diabetes online community use may encourage knowledge and support among a population that needs to optimize its diabetes self-care. Further studies are needed to determine how diabetes online community engagement may affect health outcomes.

  • Woman with different digital health services supporting diabetes self-management. Source: Image created by the Authors; Copyright: Ulrika Öberg; URL:; License: Creative Commons Attribution (CC-BY).

    Perceptions of Persons With Type 2 Diabetes Treated in Swedish Primary Health Care: Qualitative Study on Using eHealth Services for Self-Management Support


    Background: Digital health services are increasing rapidly worldwide. Strategies to involve patients in self-monitoring of type 2 diabetes (T2D) on a daily basis is of crucial importance, and there is a need to optimize the delivery of care such as self-management support. Digitalized solutions have the potential to modify and personalize the way in which people use primary health services, both by increasing access to information and providing other forms of support at a distance. It is a challenge to integrate core values of person-centered care into digitalized health care services. Objective: The objective of this study was to describe perceptions of using electronic health (eHealth) services and related technologies for self-management support among people with T2D treated in Swedish primary health care. Methods: This is a qualitative study based on interviews analyzed using qualitative content analysis conducted among people diagnosed with T2D. Results: Findings suggest that the participants had mixed feelings regarding the use of digital health services for self-management support. They experienced potentials such as increased involvement, empowerment, and security, as well as concerns such as ambivalence and uncertainty. Conclusions: Digital health services for self-management are easily accessible and have the potential to reach a wide population. However, targeted training to increase digital skills is required, and personalized devices must be adapted and become more person-centered to improve patients’ involvement in their own care.

  • GlucoMan supports the patient in managing her diabetes in her home environment. Source: Institute for Medical Informatics, Bern University of Applied Sciences; Copyright: Anna Lena Holm; URL:; License: Licensed by the authors.

    Mobile App for Simplifying Life With Diabetes: Technical Description and Usability Study of GlucoMan


    Background: Patients with diabetes can be affected by several comorbidities that require immediate action when occurring as they may otherwise cause fatal or consequential damage. For this reason, patients must closely monitor their metabolism and inject insulin when necessary. The documentation of glucose values and other relevant measurements is often still on paper in a diabetes diary. Objective: The goal of this work is to develop and implement a novel mobile health system for the secure collection of relevant data referring to a person’s metabolis and to digitize the diabetes diary to enable continuous monitoring for both patients and treating physicians. One specific subgoal is to enable data transmission of health parameters to secure data storage. Methods: The process of implementing the system consists of (1) requirements analysis with patients and physicians to identify patient needs and specify relevant functionalities, (2) design and development of the app and the data transmission, and (3) usability study. Results: We developed and implemented the mobile app GlucoMan to support data collection pertaining to a person’s metabolism. An automated transfer of measured values from a glucometer was implemented. Medication and nutrition data could be entered using product barcodes. Relevant background knowledge such as information on carbohydrates was collected from existing databases. The recorded data was transmitted using international interoperability standards to the storage platform. The usability study revealed some design issues that needs to be solved, but in principle, the study results show that the app is easy to use and provides useful features. Conclusions: Data collection on a patient’s metabolism can be supported with a multifunctional app such as GlucoMan. Besides monitoring, continuous data can be documented and made available to the treating physician. GlucoMan allows patients to monitor disease-relevant parameters and decide who accesses their health data. In this way, patients are empowered not only to manage diabetes but also manage their health data.

  • Source: Wikimedia Commons; Copyright: Mallinaltzin; URL:; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Exploring the Use of Personal Technology in Type 2 Diabetes Management Among Ethnic Minority Patients: Cross-Sectional Analysis of Survey Data from the...


    Background: Minority populations have higher morbidity from chronic diseases and typically experience worse health outcomes. Internet technology may afford a low-cost method of ongoing chronic disease management to promote improved health outcomes among minority populations. Objective: The objective of our study was to assess the feasibility of capitalizing on the pervasive use of technology as a secondary means of delivering diabetic counseling though an investigation of correlates to technology use within the context of an ongoing diabetes intervention study. Methods: The Lifestyle Intervention for the Treatment of Diabetes study (LIFT Diabetes) randomly assigned 260 overweight and obese adults with type 2 diabetes mellitus to 2 intervention arms. At baseline, we administered a survey evaluating access to and use of various technologies and analyzed the responses using descriptive statistics and logistic regression. Results: The sample population had a mean age of 56 (SD 11) years; 67.3% (175/260) were female and 54.6% (n=142) self-identified as being from ethnic minority groups (n=125, 88.0% black; n=6, 4.3% Hispanic; and n=11, 7.7% other). Minority participants had higher baseline mean body mass index (P=.002) and hemoglobin A1c levels (P=.003). Minority participants were less likely to have a home computer (106/142, 74.7% vs 110/118, 93.2%; P<.001) and less likely to have email access at home (P=.03). Ownership of a home computer was correlated to higher income (P<.001), higher educational attainment (P<.001), full-time employment (P=.01), and ownership of a smartphone (P=.001). Willingness to complete questionnaires online was correlated to higher income (P=.001), higher education (P<.001), full-time employment (P=.01), and home access to a computer, internet, and smartphone (P≤.05). Racial disparities in having a home computer persisted after controlling for demographic variables and owning a smartphone (adjusted OR 0.26, 95% CI 0.10-0.67; P=.01). Willingness to complete questionnaires online was driven by ownership of a home computer (adjusted OR 3.87, 95% CI 1.14-13.2; P=.03). Conclusions: Adults who self-identified as being part of a minority group were more likely to report limited access to technology than were white adults. As ownership of a home computer is central to a willingness to use online tools, racial disparities in access may limit the potential of Web-based interventions to reach this population. Trial Registration: NCT01806727; (Archived by WebCite at

  • A person interacting with a digital therapeutic to report biometrics and behaviors related to the treatment of their diabetes (montage). Source: The Authors /; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Change in Glycemic Control With Use of a Digital Therapeutic in Adults With Type 2 Diabetes: Cohort Study


    Background: Intensive lifestyle change can treat and even reverse type 2 diabetes. Digital therapeutics have the potential to deliver lifestyle as medicine for diabetes at scale. Objective: This 12-week study investigates the effects of a novel digital therapeutic, FareWell, on hemoglobin A1c (HbA1c) and diabetes medication use. Methods: Adults with type 2 diabetes and a mobile phone were recruited throughout the United States using Facebook advertisements. The intervention aim was to effect a sustainable shift to a plant-based dietary pattern and regular exercise by advancing culinary literacy and lifestyle skill acquisition. The intervention was delivered by an app paired with specialized human support, also delivered digitally. Health coaching was provided every 2 weeks by telephone, and a clinical team was available for participants requiring additional support. Participants self-reported current medications and HbA1c at the beginning and end of the 12-week program. Self-efficacy related to managing diabetes and maintaining dietary changes was assessed via survey. Engagement was recorded automatically through the app. Results: We enrolled 118 participants with a baseline HbA1c >6.5%. Participants were 81.4% female (96/118) and resided in 38 US states with a mean age of 50.7 (SD 9.4) years, baseline body mass index of 38.1 (SD 8.8) kg/m2, and baseline HbA1c of 8.1% (SD 1.6). At 12 weeks, 86.2% (94/109) of participants were still using the app. Mean change in HbA1c was –0.8% (97/101, SD 1.3, P<.001) for those reporting end-study data. For participants with a baseline HbA1c >7.0% who did not change medications midstudy, HbA1c change was –1.1% (67/69, SD 1.4, P<.001). The proportion of participants with an end-study HbA1c <6.5% was 28% (22/97). After completion of the intervention, 17% (16/97) of participants reported a decrease in diabetic medication while 8% (8/97) reported an increase. A total of 57% (55/97) of participants achieved a composite outcome of reducing HbA1c, reducing diabetic medication use, or both; 92% (90/98) reported greater confidence in their ability to manage their diabetes compared to before the program, and 91% (89/98) reported greater confidence in their ability to maintain a healthy dietary pattern. Participants engaged with the app an average of 4.3 times per day. We observed a significantly greater decrease in HbA1c among participants in the highest tertile of app engagement compared to those in the lowest tertile of app engagement (P=.03). Conclusions: Clinically meaningful reductions in HbA1c were observed with use of the FareWell digital therapeutic. Greater glycemic control was observed with increasing app engagement. Engagement and retention were both high in this widely distributed sample.

  • Use of Glooko (montage). Source: The Authors /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Use of a Mobile App to Facilitate Blood Glucose Monitoring in Adolescents With Type 1 Diabetes: Single-Subject Nonrandomized Clinical Trial


    Background: Cloud-based glucose monitoring programs allow users with diabetes to wirelessly synchronize their glucometers to their mobile phones. They also provide visualization and remote access of their data through its mobile app. There have been very few studies evaluating their effectiveness in managing diabetes among adolescents with type 1 diabetes (T1D). Objective: The purpose of this study was to assess the feasibility of using a mobile app to improve daily average blood glucose (BG) levels and increase BG monitoring frequency. Methods: We used an ABA single-subject prospective study design. We recruited five participants aged 13 to 17 years with uncontrolled T1D, glycated hemoglobin A1c 9.0%-10.7%, self-monitoring behavior of ≤5 checks/day, and on multiple daily insulin injections. The study consisted of 4-week intervals of three phases: (1) phase A: usual glucose monitoring log (fax); (2) phase B: mobile app; and (3) phase A': second phase A. A certified diabetes educator and endocrinologist reviewed logs and provided recommendations weekly. Data were analyzed using a quasi-Poisson model to adjust for overdispersion among individual participants, and a generalized estimating equation model for overall intervention effect in aggregate. Results: For mean daily BG (mg/dL) levels, participant 1 had decreased values on the mobile app (298 to 281, P=.03) and maintained in phase A'. Participant 4 had an increase in mean daily BG in phase A' (175 to 185, P=.01), whereas participant 5 had a decrease in mean daily BG in phase A' (314 to 211, P=.04). For daily monitoring (checks/day), participant 3 increased in phase B (4.6 to 8.3, P=.01) and maintained in phase A'. Participant 5 also had increased daily monitoring at each phase (2.1 to 2.4, P=.01; 2.4 to 3.4, P=.02). For the five participants combined, the overall mean BG and BG checks per day in phase A were mean 254.8 (SD 99.2) and mean 3.6 (SD 2.0), respectively, mean 223.1 (SD 95.7) and mean 4.5 (SD 3.0) in phase B, and mean 197.5 (SD 81.3) and mean 3.7 (SD 2.1) in phase A'. Compared to phase A, mean glucose levels declined during phase B and remained lower during phase A' (P=.002). There was no overall change in BG checks by phase (P=.25). However, mean BG levels negatively correlated with daily BG checks (r=–.47, P<.001). Although all participants had positive opinions about the app, its utilization was highly variable. Conclusions: We demonstrated modest feasibility of adolescents with uncontrolled T1D utilizing a glucose monitoring mobile app. Further study is needed to better determine its effects on BG level and monitoring frequency. Psychosocial factors and motivational barriers likely influence adoption and continuous use of technology for diabetes management.

  • Source:; Copyright: Tsippendale; URL:; License: Public Domain (CC0).

    An Interactive Simulation to Change Outcome Expectancies and Intentions in Adults With Type 2 Diabetes: Within-Subjects Experiment


    Background: Computerized simulations are underutilized to educate or motivate patients with chronic disease. Objective: The aim of this study was to test the efficacy of an interactive, personalized simulation that demonstrates the acute effect of physical activity on blood glucose. Our goal was to test its effects on physical activity-related outcome expectancies and behavioral intentions among adults with type 2 diabetes mellitus (T2DM). Methods: In this within-subjects experiment, potential participants were emailed a link to the study website and directed through 7 tasks: (1) consent; (2) demographics, baseline intentions, and self-reported walking; (3) orientation to the diurnal glucose curve; (4) baseline outcome expectancy, measured by a novel drawing task in which participants use their mouse to draw the expected difference in the diurnal glucose curve if they had walked; (5) interactive simulation; (6) postsimulation outcome expectancy measured by a second drawing task; and (7) final measures of intentions and impressions of the website. To test our primary hypothesis that participants’ outcome expectancies regarding walking would shift toward the outcome presented in the interactive simulation, we used a paired t test to compare the difference of differences between the change in area under the curve in the simulation and participants’ two drawings. To test whether intentions to walk increased, we used paired t tests. To assess the intervention’s usability, we collected both quantitative and qualitative data on participants’ perceptions of the drawing tasks and simulation. Results: A total of 2019 individuals visited the website and 1335 (566 males, 765 females, and 4 others) provided complete data. Participants were largely late middle-aged (mean=59.8 years; standard deviation=10.5), female 56.55% (755/1335), Caucasian 77.45% (1034/1335), lower income 64.04% (855/1335) t1334=3.4, P ≤.001). Our second hypothesis, that participants’ intentions to walk in the coming week would increase, was also supported; general intention (mean difference=0.31/7, t1001=10.8, P<.001) and minutes of walking last week versus planned for coming week (mean difference=33.5 min, t1334=13.2, P<.001) both increased. Finally, an examination of qualitative feedback and drawing task data suggested that some participants had difficulty understanding the website. This led to a post-hoc subset analysis. In this analysis, effects for our hypothesis regarding outcome expectancies were markedly stronger, suggesting that further work is needed to determine moderators of the efficacy of this simulation. Conclusions: A novel interactive simulation is efficacious in changing the outcome expectancies and behavioral intentions of adults with T2DM. We discuss applications of our results to the design of mobile health (mHealth) interventions.

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  • Diabetes, Technology and Care Homes: The Influence of Technology on Practice and Care Delivery in Care Homes: A Systematic Review and Interpretive Synthesis.

    Date Submitted: Jul 9, 2018

    Open Peer Review Period: Jul 11, 2018 - Sep 5, 2018

    Background: Diabetes is increasing in prevalence and complexity in the care home setting, affecting up to a quarter of care home residents. Health outcomes for these residents are impacted by manageme...

    Background: Diabetes is increasing in prevalence and complexity in the care home setting, affecting up to a quarter of care home residents. Health outcomes for these residents are impacted by management of the disease, healthcare professionals’ decision-making skills within the care home setting, and access to specialist services. The use of technology has the potential to recognise opportunities for early intervention enabling efficient responsive care, and taking a fundamental role in linking the care home community to wider multi-disciplinary teams for support. Objective: To identify evidence that explores factors relevant to the use of technology in and around the care home setting for the management of diabetes. Methods: Databases searched using a structured pre-specified approach included: PUBMED, CINAHL, OVID Nursing database, SCOPUS, MEDLINE, Cochrane Library and the KINGS FUND from 2012- 2017, handsearching was undertaken additionally for any grey literature. PRISMA P was used as protocol with ROBIS to assess the risk of bias across studies. Studies had to include interventions that combined technology to or from the care home setting to support residents living with diabetes. Results: The combined search strategy identified a total of 493 electronic records.171 were screened for eligibility, 66 full papers were accessed and 13 have been included in this study. Interpretive synthesis has identified different strands of research evidence in what and how technology is currently being used in and around care homes to enhance diabetes management. New initiatives and implementations of technology and emerging models of care that included the use of technology have also been included. Conclusions: By triangulating the perspectives of healthcare professionals, practitioners, specialists and members of the care home community, the authors anticipate that this review will represent an up to date, evidenced-based overview of the potential for using technology within the care home setting for diabetes management as well as stimulate research in this area. Clinical Trial: N/A