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Journal Description

JMIR Diabetes (JD, Editor-in-Chief: Caroline Richardson) is a Pubmed journal of JMIR the leading open-access journal in health informatics. JMIR Diabetes focuses 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 JD is 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 prevention and 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:

  • Source: Freepik; Copyright: Freepik; URL: https://www.freepik.com/free-photo/woman-eating-salad-looking-phone_5200459.htm#page=4&query=phone+eating&position=48; License: Licensed by JMIR.
    Diabetes Self-Management

    Jul 24, 2020

    Changes in Patient-Reported Outcome Measures With a Technology-Supported Behavioral Lifestyle Intervention Among Patients With Type 2 Diabetes: Pilot...

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    Background: In the United States, more than one-third of the adult population is obese, and approximately 25.2% of those aged ≥65 years have type 2 diabetes (T2D), which is the seventh leading cause of death. It is important to measure patient-reported outcomes and monitor progress or challenges over time when managing T2D to understand patients’ perception of health and quantify the impact of disease processes or intervention effects. The evaluation of patient-reported outcome measures (PROMs) is especially important among patients with multiple chronic conditions in which clinical measures do not provide a complete picture of health. Objective: This study examined the feasibility of collecting Patient-Reported Outcome Measurement Information System (PROMIS) measures, and preliminarily evaluated changes in PROMIS scores and compared the scores with standard scores of the general US population. The parent study is a pilot randomized controlled clinical trial testing three different modes (mobile health [mHealth], paper diary, and control) of self-monitoring in a behavioral lifestyle intervention among overweight or obese patients with T2D. Methods: Patients with comorbid overweight or obesity and a diagnosis of T2D for at least 6 months were recruited from a diabetes education program. Participants were randomized to the following three groups: mHealth, paper diary, and control (standard of care) groups. Paper diary and mHealth experimental groups received additional behavioral lifestyle intervention education sessions, as well as tools to self-monitor weight, physical activity, diet, and blood glucose. All participants completed PROMIS-57 and PROMIS-Global Health (GH) version 1.0 questionnaires during visits at baseline, 3 months, and 6 months. The PROMIS-57 includes the following seven domains: anxiety, depression, fatigue, pain interference, physical function, satisfaction with participation in social roles, and sleep disturbance. The PROMIS-GH is composed of the following two domains: global mental health and global physical health. Results: A total of 26 patients (mHealth, 11; paper diary, 9; control, 6) were included in our analysis. The study sample was predominantly African American (68%) and female (57%), with a mean age of 54.7 years and a mean BMI of 37.5 kg/m2. All patients completed the PROMIS-57 and PROMIS-GH questionnaires, and we compared the mean scores of the three groups to investigate potential differences. No relevant differences were noted across the groups. However, positive trends were noted in both intervention (mHealth and paper diary) groups in the middle (month 3) and end (month 6) of the study. Conclusions: Our pilot study provides evidence for the feasibility of using PROMIS questionnaires to record important components of T2D-related symptoms among overweight or obese individuals. The results from our study support the use of PROMIS questionnaires to provide clinicians and researchers with a benchmark for assessing the overall need for symptom management and determining the success or challenges of an intervention. Clinical Trial: ClinicalTrials.gov NCT02858648; https://clinicaltrials.gov/ct2/show/NCT02858648

  • Source: Freepik; Copyright: Dragana_Gordic; URL: https://www.freepik.com/free-photo/close-up-portrait-happy-smiling-forty-years-old-caucasian-man-talking-mobile-phone-outdoor_1190307.htm#page=1&query=middle%20aged%20man%20with%20phone&position=42; License: Licensed by the authors.
    Apps, Mobile, Wearables for Diabetes

    Jul 17, 2020

    User Experiences With a Type 2 Diabetes Coaching App: Qualitative Study

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    Background: Diabetes self-management apps have the potential to improve self-management in people with type 2 diabetes (T2D). Although efficacy trials provide evidence of health benefits, premature disengagement from apps is common. Therefore, it is important to understand the factors that influence engagement in real-world settings. Objective: This study aims to explore users’ real-world experiences with the My Diabetes Coach (MDC) self-management app. Methods: We conducted telephone-based interviews with participants who had accessed the MDC self-management app via their smartphone for up to 12 months. Interviews focused on user characteristics; the context within which the app was used; barriers and facilitators of app use; and the design, content, and delivery of support within the app. Results: A total of 19 adults with T2D (8/19, 42% women; mean age 60, SD 14 years) were interviewed. Of the 19 interviewees, 8 (42%) had T2D for <5 years, 42% (n=8) had T2D for 5-10 years, and 16% (n=3) had T2D for >10 years. In total, 2 themes were constructed from interview data: (1) the moderating effect of diabetes self-management styles on needs, preferences, and expectations and (2) factors influencing users’ engagement with the app: one size does not fit all. Conclusions: User characteristics, the context of use, and features of the app interact and influence engagement. Promoting engagement is vital if diabetes self-management apps are to become a useful complement to clinical care in supporting optimal self-management. Trial Registration: Australia New Zealand Clinical Trials Registry CTRN126140012296; URL https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366925&isReview=true

  • Source: Freepik; Copyright: pressfoto; URL: https://www.freepik.com/free-photo/mature-man-using-laptop_6799924.htm; License: Licensed by JMIR.
    Diabetes Self-Management

    Jul 9, 2020

    Web-Based and mHealth Technologies to Support Self-Management in People Living With Type 2 Diabetes: Validation of the Diabetes Self-Management and...

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    Background: A growing number of web-based and mobile health (mHealth) technologies have been developed to support type 2 diabetes self-management. Little is known about individuals’ experiences with these technologies and how they support self-management. Appropriate tools are needed to understand how web-based and mHealth interventions may impact self-management. Objective: This study aimed to develop an instrument, the Diabetes Self-Management and Technology Questionnaire (DSMT-Q), to assess self-management among people living with type 2 diabetes who use web-based and mHealth technologies. Methods: A total of 36 candidate questionnaire items, drafted previously, were refined using cognitive debriefing interviews (n=8), expert consultation, and public patient involvement feedback. Item reduction steps were performed on survey data (n=250), and tests of validity and reliability were subsequently performed. Results: Following amendments, patients and experts found 21 items relevant and acceptable for inclusion in the instrument. Survey participants included 104 (41.6%) women and 146 (58.4%) men. Two subscales with high construct validity, internal consistency, and test-retest reliability were identified: “Understanding individual health and making informed decisions” and “Confidence to reach and sustain goals.” Conclusions: Analyses confirmed good psychometric properties in the DSMT-Q scales. This tool will facilitate the measurement of self-management in people living with type 2 diabetes who use web-based or mHealth technologies.

  • Source: Shutterstock; Copyright: Andrey_Popov; URL: https://www.shutterstock.com/image-photo/man-holding-smartphone-hand-bad-level-1510586687; License: Licensed by the authors.
    Patient Experiences with Diabetes...

    Jun 17, 2020

    Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study

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    Background: Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. Objective: This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. Methods: A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. Results: Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P<.001), and age, with the older population reporting lower noticeability than the young and middle age groups (P<.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices. Conclusions: Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.

  • Source: Unsplash; Copyright: Adam Nieścioruk; URL: https://unsplash.com/photos/9ChfwZzv4ys; License: Licensed by JMIR.
    Exercise and Diet Tracking for...

    Jun 4, 2020

    Relationship Between Age and Weight Loss in Noom: Quasi-Experimental Study

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    Background: The prevalence of obesity and diabetes among middle-aged and older adults is on the rise, and with an increase in the world population of adults aged 60 years and older, the demand for health interventions across age groups is growing. Noom is an mHealth behavior change lifestyle intervention that provides users with tracking features for food and exercise logging and weighing-in as well as access to a virtual 1:1 behavior change coach, support group, and daily curriculum that includes diet-, exercise-, and psychology-based content. Limited research has observed the effect of age on a mobile health (mHealth) lifestyle intervention. Objective: The goal of the research was to analyze engagement of middle-aged and older adults using a mobile lifestyle or diabetes prevention intervention. Methods: A total of 14,767 adults (aged 35 to 85 years) received one of two curricula via an mHealth intervention in a quasi-experimental study: the Healthy Weight program (HW) by Noom (84%) or the Noom-developed Diabetes Prevention Program (DPP), recognized by the US Centers for Disease Control and Prevention (CDC). The main outcome measure was weight over time, observed at baseline and weeks 16 and 52. Results: Linear mixed modeling found age to be a significant predictor of weight at week 16 (F2,1398.4=9.20; P<.001; baseline vs week 16: β=–.12, 95% CI –0.18 to –0.07), suggesting that as age increases by 1 year, weight decreased by 0.12 kg. An interaction between engagement and age was also found at week 52 (F1,14680.51=6.70; P=.01) such that engagement was more strongly associated with weight for younger versus older adults (age × engagement: β=.02, 95% CI 0.01 to 0.04). HW users lost 6.24 (SD 6.73) kg or 5.2% of their body weight and DPP users lost 5.66 (SD 7.16) kg or 8.1% of their body weight at week 52, meeting the CDC standards for weight loss effects on health. Conclusions: Age and engagement are significant predictors of weight. Older adults lost more weight using an mHealth evidence-based lifestyle intervention compared with younger adults, despite their engagement. These preliminary findings suggest further clinical implications for adapting the program to older adults’ needs.

  • Source: Freepik; Copyright: Tongcom; URL: https://www.freepik.com/premium-photo/woman-hands-using-mobile-smart-phone_3678796.htm; License: Licensed by the authors.
    Apps, Mobile, Wearables for Diabetes

    May 20, 2020

    Health App Use and Its Correlates Among Individuals With and Without Type 2 Diabetes: Nationwide Population-Based Survey

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    Background: Evidence suggests that mobile health app use is beneficial for the prevention and management of type 2 diabetes (T2D) and its associated complications; however, population-based research on specific determinants of health app use in people with and without T2D is scarce. Objective: This cross-sectional study aimed to provide population-based evidence on rates and determinants of health app use among adults with and without T2D, thereby covering a prevention perspective and a diabetes management perspective, respectively. Methods: The study population included 2327 adults without a known diabetes diagnosis and 1149 adults with known T2D from a nationwide telephone survey in Germany conducted in 2017. Rates of smartphone ownership and health app use were estimated based on weighted sample proportions. Among smartphone owners, determinants of health app use were identified for both groups separately in multivariable logistic regression models. Sociodemographic factors, diabetes-related factors or indicators, psychological and health-related factors, and physician-provided information were selected as potential determinants. Results: Among participants without known diabetes, 74.72% (1690/2327) were smartphone owners. Of those, 49.27% (717/1690) used health apps, most often to improve regular physical activity. Among participants with T2D, 42.26% (481/1149) were smartphone owners. Of those, 41.1% (171/481) used health apps, most commonly to target a healthy diet. Among people without known diabetes, determinants significantly (all P values <.05) associated with an increased likelihood of health app use compared with their reference group were as follows: younger and middle age of 18 to 44 or 45 to 64 years (odds ratios [ORs] 3.89; P<.001 and 1.76; P=.004, respectively), overweight or obesity (ORs 1.58; P<.001 and 2.07; P<.001, respectively), hypertension diagnosis (OR 1.31; P=.045), former or current smoking (ORs 1.51; P=.002 and 1.58; P<.001, respectively), perceiving health as very good (OR 2.21; P<.001), other chronic diseases (OR 1.48; P=.002), and having received health advice from a physician (OR 1.48; P<.001). A slight or high perceived diabetes risk (ORs 0.78; P=.04 and 0.23; P<.001, respectively) was significantly associated with a decreased likelihood of health app use. Among people with T2D, younger and middle age (18-64 years; OR 1.84; P=.007), female gender (OR 1.61; P=.02), and using a glucose sensor in addition or instead of a glucose meter (OR 2.74; P=.04) were significantly positively associated with health app use. Conclusions: In terms of T2D prevention, age, diabetes-related risk factors, psychological and health-related factors, and medical health advice may inform app development for specific target groups. In addition, health professionals may encourage health app use when giving advice on health behaviors. Concerning T2D management, only a few determinants seem relevant for explaining health app use among people with T2D, indicating a need for more future research on which people with T2D use health apps and why.

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://diabetes.jmir.org/2020/2/e19581/; License: Creative Commons Attribution (CC-BY).
    Viewpoints on Diabetes Technology and...

    May 15, 2020

    The Challenges of COVID-19 for People Living With Diabetes: Considerations for Digital Health

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    The coronavirus disease (COVID-19) is a global pandemic that significantly impacts people living with diabetes. Diabetes-related factors of glycemic control, medication pharmacodynamics, and insulin access can impact the severity of a COVID-19 infection. In this commentary, we explore how digital health can support the diabetes community through the pandemic. For those living with diabetes, digital health presents the opportunity to access care with greater convenience while not having to expose themselves to infection in an in-person clinic. Digital diabetes apps can increase agency in self-care and produce clinically significant improvement in glycemic control through facilitating the capture of diabetes device data. However, the ability to share these data back to the clinic to inform virtual care and enhance diabetes coaching and guidance remains a challenge. In the end, it requires an unnecessarily high level of technical sophistication on the clinic’s part and on those living with diabetes to routinely use their diabetes device data in clinic visits, virtual or otherwise. As the world comes together to fight the COVID-19 pandemic, close collaboration among the global diabetes community is critical to understand and manage the sustained impact of the pandemic on people living with diabetes.

  • Reader at telescreening platform. Source: The Authors; Copyright: The Authors; URL: https://diabetes.jmir.org/2020/2/e17309; License: Creative Commons Attribution (CC-BY).
    Telemedicine for Diabetes

    Apr 7, 2020

    Assessment of Training Outcomes of Nurse Readers for Diabetic Retinopathy Telescreening: Validation Study

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    Background: With the high prevalence of diabetic retinopathy and its significant visual consequences if untreated, timely identification and management of diabetic retinopathy is essential. Teleophthalmology programs have assisted in screening a large number of individuals at risk for vision loss from diabetic retinopathy. Training nonophthalmological readers to assess remote fundus images for diabetic retinopathy may further improve the efficiency of such programs. Objective: This study aimed to evaluate the performance, safety implications, and progress of 2 ophthalmology nurses trained to read and assess diabetic retinopathy fundus images within a hospital diabetic retinopathy telescreening program. Methods: In this retrospective interobserver study, 2 ophthalmology nurses followed a specific training program within a hospital diabetic retinopathy telescreening program and were trained to assess diabetic retinopathy images at 2 levels of intervention: detection of diabetic retinopathy (level 1) and identification of referable disease (level 2). The reliability of the assessment by level 1−trained readers in 266 patients and of the identification of patients at risk of vision loss from diabetic retinopathy by level 2−trained readers in 559 more patients were measured. The learning curve, sensitivity, and specificity of the readings were evaluated using a group consensus gold standard. Results: An almost perfect agreement was measured in identifying the presence of diabetic retinopathy in both level 1 readers (κ=0.86 and 0.80) and in identifying referable diabetic retinopathy by level 2 readers (κ=0.80 and 0.83). At least substantial agreement was measured in the level 2 readers for macular edema (κ=0.79 and 0.88) for all eyes. Good screening threshold sensitivities and specificities were obtained for all level readers, with sensitivities of 90.6% and 96.9% and specificities of 95.1% and 85.1% for level 1 readers (readers A and B) and with sensitivities of 86.8% and 91.2% and specificities of 91.7% and 97.0% for level 2 readers (readers A and B). This performance was achieved immediately after training and remained stable throughout the study. Conclusions: Notwithstanding the small number of trained readers, this study validates the screening performance of level 1 and level 2 diabetic retinopathy readers within this training program, emphasizing practical experience, and allows the establishment of an ongoing assessment clinic. This highlights the importance of supervised, hands-on experience and may help set parameters to further calibrate the training of diabetic retinopathy readers for safe screening programs.

  • Low Carb Program. Source: https://www.lowcarbprogram.com; Copyright: Low Carb Program; License: Licensed by the authors.
    Health Coaching for Diabetes Patients

    Mar 4, 2020

    Novel Digital Architecture of a “Low Carb Program” for Initiating and Maintaining Long-Term Sustainable Health-Promoting Behavior Change in Patients with...

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    Globally, the burden of noncommunicable diseases such as type 2 diabetes is crippling health care systems. Type 2 diabetes, a disease linked with obesity, affects 1 in every 30 people today and is expected to affect 1 in 10 people by 2030. Current provisions are struggling to manage the trajectory of type 2 diabetes prevalence. Offline, face-to-face education for patients with type 2 diabetes has shown to lack long-term impact or the capacity for widespread democratized adoption. Digitally delivered interventions have been developed for patients with type 2 diabetes, and the evidence shows that some interventions provide the capacity to support hyperpersonalization and real-time continuous support to patients, which can result in significant engagement and health outcomes. However, digital health app engagement is notoriously difficult to achieve. This paper reviews the digital behavior change architecture of the Low Carb Program and the application of health behavioral theory underpinning its development and use in scaling novel methods of engaging the population with type 2 diabetes and supporting long-term behavior change.

  • Source: freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/hands-holding-phone-with-twitter-laptop_1208839.htm#page=3&query=twitter&position=3; License: Licensed by JMIR.
    Diabetes Surveillance and Epidemiology

    Feb 11, 2020

    Using Social Media to Track Geographic Variability in Language About Diabetes: Infodemiology Analysis

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    Background: Social media posts about diabetes could reveal patients’ knowledge, attitudes, and beliefs as well as approaches for better targeting of public health messages and care management. Objective: This study aimed to characterize the language of Twitter users’ posts regarding diabetes and describe the correlation of themes with the county-level prevalence of diabetes. Methods: A retrospective study of diabetes-related tweets identified from a random sample of approximately 37 billion tweets from the United States from 2009 to 2015 was conducted. We extracted diabetes-specific tweets and used machine learning to identify statistically significant topics of related terms. Topics were combined into themes and compared with the prevalence of diabetes by US counties and further compared with geography (US Census Divisions). Pearson correlation coefficients are reported for each topic and relationship with prevalence. Results: A total of 239,989 tweets from 121,494 unique users included the term diabetes. The themes emerging from the topics included unhealthy food and drink, treatment, symptoms/diagnoses, risk factors, research, recipes, news, health care, management, fundraising, diet, communication, and supplements/remedies. The theme of unhealthy foods most positively correlated with geographic areas with high prevalence of diabetes (r=0.088), whereas tweets related to research most negatively correlated (r=−0.162) with disease prevalence. Themes and topics about diabetes differed in overall frequency across the US geographical divisions, with the East South Central and South Atlantic states having a higher frequency of topics referencing unhealthy food (r range=0.073-0.146; P<.001). Conclusions: Diabetes-related tweets originating from counties with high prevalence of diabetes have different themes than tweets originating from counties with low prevalence of diabetes. Interventions could be informed from this variation to promote healthy behaviors.

  • Source: The Authors / Placeit; Copyright: The Authors; URL: http://diabetes.jmir.org/2020/1/e15189/; License: Licensed by JMIR.
    Health Coaching for Diabetes Patients

    Jan 20, 2020

    Effectiveness of a Digital Lifestyle Change Program in Obese and Type 2 Diabetes Populations: Service Evaluation of Real-World Data

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    Background: The prevalence of type 2 diabetes mellitus (T2DM) and obesity is increasing, and the way people interact with health care is evolving. People traditionally access advice and support to improve their lifestyle and learn more about the self-management of T2DM in a face-to-face setting. Although these services have a strong evidence base, they have limitations for reaching specific groups of people. Digital programs could provide a new delivery model to help more people access health education and behavior change support, but long-term data supporting these programs are limited. Objective: The purpose of this service evaluation was to analyze the weight change of people who participated in OurPath (also known as Second Nature), a UK-based digital lifestyle change program, for either weight management or diabetes-related weight management and structured education at 6 and 12 months. Methods: Participants either paid to access the program privately (self-funded clients) or were referred by their general practitioner to participate in the program free of charge (funded by the National Health Service). Additional follow-up support was provided to help people to maintain lifestyle changes. To retrospectively assess potential weight loss, the analysis included data from participants who submitted weight readings at baseline and 6 and 12 months after starting the program. Changes in weight after 6 and 12 months were primary outcome measures. Results: For the 896 participants who submitted baseline and 6- and 12-month data, a significant change in mean weight of −7.12 kg (−7.50%; SD 6.37; P<.001) was observed at 6 months. Data from the same participants at 12 months showed a change in mean weight when compared with a baseline of −6.14 kg (−6.48%; SD 6.97; P<.001). Conclusions: The data presented here had several limitations, and there were too many uncertainties to make any reliable conclusions. However, these results suggest that digital lifestyle change programs could provide a new way to help people to access nutritional advice and support to achieve weight loss. Further research into digital education and coaching platforms is needed to establish their effectiveness.

  • Source: The Authors / Placeit; Copyright: JMIR Publications; URL: http://diabetes.jmir.org/2020/1/e15744/; License: Creative Commons Attribution (CC-BY).
    Diabetes Education and Elearning for...

    Jan 6, 2020

    Feasibility, Acceptability, and Impact of a Web-Based Structured Education Program for Type 2 Diabetes: Real-World Study

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    Background: Structured education for people with type 2 diabetes improves outcomes, but uptake is low globally. In the United Kingdom in 2016, only 8.3% of people who were referred to education programs attended the program. We have developed a Web-based structured education program named Healthy Living for People with type 2 Diabetes (HeLP-Diabetes): Starting Out (HDSO), as an alternative to face-to-face courses. A Web-based program gives people more options for accessing structured education and may help improve overall uptake. Objective: The aim was to explore the feasibility and acceptability of delivering a Web-based structured education program (named HeLP-Diabetes: Starting Out) in routine primary health care and its potential impact on self-efficacy and diabetes-related distress. Methods: HDSO was delivered as part of routine diabetes services in primary health care in the United Kingdom, having been commissioned by local Clinical Commissioning Groups. Quantitative data were collected on uptake, use of the program, demographic characteristics, self-reported self-efficacy, and diabetes-related distress. A subsample of people with type 2 diabetes and health care professionals were interviewed about acceptability of the program. Results: It was feasible to deliver the program, but completion rates were low: of 791 people with type 2 diabetes registered, only 74 (9.0%) completed it. Completers improved their self-efficacy (change in median score 2.5, P=.001) and diabetes-related distress (change in median score 6.0, P=.001). Interview data suggested that the course was acceptable, and that uptake and completion may be related to nonprioritization of structured education. Conclusions: The study provides evidence of the feasibility and acceptability of a Web-based structured education. However, uptake and completion rates were low, limiting potential population impact. Further research is needed to improve completion rates, and to determine the relative effectiveness of Web-based versus face-to-face education.

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