JMIR Publications

JMIR Diabetes

Emerging Technologies, Medical Devices, Apps, Sensors, and Informatics to Help People with Diabetes.


Journal Description

JMIR Diabetes (JD) is a new sister journal of JMIR (the leading open-access journal in health informatics with a 2015 impact factor of 4.532) 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. 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, crowdsourcing and quantified self-based research data, new sensors and actuators to be applied to diabetes.


Recent Articles:

  • image for table of contents.

    Evaluating the Accuracy of Google Translate for Diabetes Education Material


    Background: Approximately 21% of the US population speaks a language other than English at home; many of these individuals cannot effectively communicate in English. Hispanic and Chinese Americans, in particular, are the two largest minority groups having low health literacy in the United States. Fortunately, machine-generated translations represent a novel tool that non-English speakers can use to receive and relay health education information when human interpreters are not available. Objective: The purpose of this study was to evaluate the accuracy of the Google Translate website when translating health information from English to Spanish and English to Chinese. Methods: The pamphlet, “You are the heart of your family…take care of it,” is a health education sheet for diabetes patients that outlines six tips for behavior change. Two professional translators translated the original English sentences into Spanish and Chinese. We recruited 6 certified translators (3 Spanish and 3 Chinese) to conduct blinded evaluations of the following versions: (1) sentences translated by Google Translate, and (2) sentences translated by a professional human translator. Evaluators rated the sentences on four scales: fluency, adequacy, meaning, and severity. We performed descriptive analysis to examine differences between these two versions. Results: Cronbach's alpha values exhibited high degrees of agreement on the rating outcome of both evaluator groups: .919 for the Spanish evaluators and .972 for the Chinese evaluators. The readability of the sentences in this study ranged from 2.8 to 9.0 (mean 5.4, SD 2.7). The correlation coefficients between the grade level and translation accuracy for all sentences translated by Google were negative (eg, rMeaning=-.660), which indicates that Google provided accurate translation for simple sentences. However, the likelihood of incorrect translation increased when the original English sentences required higher grade levels to comprehend. The Chinese human translator provided more accurate translation compared to Google. The Spanish human translator, on the other hand, did not provide a significantly better translation compared to Google. Conclusion: Google produced a more accurate translation from English to Spanish than English to Chinese. Some sentences translated by Google from English to Chinese exhibit the potential to result in delayed patient care. We recommend continuous training and credential practice standards for professional medical translators to enhance patient safety as well as providing health education information in multiple languages.

  • Blood glucose testing.
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    Information and Communication Technology-Powered Diabetes Self-Management Systems in China: A Study Evaluating the Features and Requirements of Apps and Patents


    Background: For patients with diabetes, the self-monitoring of blood glucose (SMBG) is a recommended way of controlling the blood glucose level. By leveraging the modern information and communication technology (ICT) and the corresponding infrastructure, engineers nowadays are able to merge the SMBG activities into daily life and to dramatically reduce patient’s burden. Such type of ICT-powered SMBG had already been marketed in the United States and the European Union for a decade, but was introduced into the Chinese market only in recent years. Although there is no doubt about the general need for such type of SMBG in the Chinese market, how it could be adapted to the local technical and operational environment is still an open question. Objective: Our overall goal is to understand the local requirements and the current status of deploying ICT-powered SMBG to the Chinese market. In particular, we aim to analyze existing domestic SMBG mobile apps and relevant domestic patents to identify their various aspects, including the common functionalities, innovative feature, defects, conformance to standards, prospects, etc. In the long run, we hope the outcome of this study could help the decision making on how to properly adapt ICT-powered SMBG to the Chinese market. Methods: We identified 289 apps. After exclusion of irrelevant apps, 78 apps remained. These were downloaded and analyzed. A total of 8070 patents related to glucose were identified from patent database. Irrelevant materials and duplicates were excluded, following which 39 patents were parsed to extract the important features. These apps and patents were further compared with the corresponding requirements derived from relevant clinical guidelines and data standards. Results: The most common features of studied apps were blood health data recording, notification, and decision supporting. The most common features of studied patents included mobile terminal, server, and decision supporting. The main difference between patents and apps is that the patents had 2 specific features, namely, interface to the hospital information system and recording personal information, which were not mentioned in the app. The other major finding is that, in general, in terms of the components of the features, although the features identified in both apps and patents conform to the requirements of the relevant clinical guidelines and data standards, upon looking into the details, gaps exist between the features of the identified apps and patents and the relevant clinical guidelines and data standards. In addition, the social media feature that the apps and patents have is not included in the standard requirements list. Conclusions: The development of Chinese SMBG mobile apps and relevant patents is still in the primitive stage. Although the functionalities of most apps and patents can meet the basic requirements of SMBG, gaps have been identified when comparing the functionalities provided by apps and patents with the requirements necessitated by the standards. One of the most important gaps is that only a small portion of the studied apps provides the automatic data transmission and exchange feature, which may hamper the overall performance. The clinical guidelines can thus be further developed to leverage new features provided by ICT-powered SMBG apps (eg, the social media feature, which may help to improve the social intervention of patients with diabetes).

  • Food tracker.

    Data Mining of a Remote Behavioral Tracking System for Type 2 Diabetes Patients: A Prospective Cohort Study


    Background: Complications from type 2 diabetes mellitus can be prevented when patients perform health behaviors such as vigorous exercise and glucose-regulated diet. The use of smartphones for tracking such behaviors has demonstrated success in type 2 diabetes management while generating repositories of analyzable digital data, which, when better understood, may help improve care. Data mining methods were used in this study to better understand self-monitoring patterns using smartphone tracking software. Objective: Associations were evaluated between the smartphone monitoring of health behaviors and HbA1c reductions in a patient subsample with type 2 diabetes who demonstrated clinically significant benefits after participation in a randomized controlled trial. Methods: A priori association-rule algorithms, implemented in the C language, were applied to app-discretized use data involving three primary health behavior trackers (exercise, diet, and glucose monitoring) from 29 participants who achieved clinically significant HbA1c reductions. Use was evaluated in relation to improved HbA1c outcomes. Results: Analyses indicated that nearly a third (9/29, 31%) of participants used a single tracker, half (14/29, 48%) used two primary trackers, and the remainder (6/29, 21%) of the participants used three primary trackers. Decreases in HbA1c were observed across all groups (0.97-1.95%), but clinically significant reductions were more likely with use of one or two trackers rather than use of three trackers (OR 0.18, P=.04). Conclusions: Data mining techniques can reveal relevant coherent behavior patterns useful in guiding future intervention structure. It appears that focusing on using one or two trackers, in a symbolic function, was more effective (in this sample) than regular use of all three trackers.

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  • Statin and fibrate for Diabetic Retinopathy-Systematic Review

    Date Submitted: Sep 14, 2016

    Open Peer Review Period: Sep 15, 2016 - Nov 10, 2016

    Background: Diabetes mellitus is a metabolic chronic disease characterized by increased rates of blood glucose. High blood sugar can promote changes of acute and chronic form. When blood glucose is ch...

    Background: Diabetes mellitus is a metabolic chronic disease characterized by increased rates of blood glucose. High blood sugar can promote changes of acute and chronic form. When blood glucose is chronically increased, it triggers a cascade of reactions culminating in the final products of non- enzymatic glycosylation called AGE (Advanced Glycosylation End Product). [1.2] Currently the oxidative stress and dyslipidemias are also considered as metabolic conditions for the increase of the AGE. [3.4] Diabetic retinopathy is characterized by wall vascular proteins glycosylation that leads to vessels permeability increase and consequently exudation. They may also lead to vascular occlusion leading to retinal ischemia. Ischemic areas producing angiogenic factors proliferating vessels culminating in the disease proliferative phase. When the protein AGE joins any of its receptors in the vascular endothelium occurs the release of tissue factor that triggers the extrinsic pathway of coagulation, protein C inhibition and increased production of Endothelin 1 that together lead to diminishing light of the vessel and increase the permeability of the vascular wall by decreasing fibrinolytic activity and platelet aggregation leading to the formation of microaneurysms , oozing, tissue ischemia, calling angiogenic factors culminating in angiogenesis. Data from World Health Organization( WHO) claim 33000 new cases of diabetic macular edema, 86000 new cases of proliferative diabetic retinopathy and 12000 a 14000 new cases of blindness by diabetic retinopathy per year in United States. [5] According to the CDC the number of diabetics who reported decreased vision increased from 1.7 to 4,000,000 of 1997 until 2011 [6] so it is considered a public health problem. Although the photocoagulation persists as a treatment of choice she makes sequels of visual field loss and impairment of night vision so irreversible by ablar retinal tissue. In this way, to find a systemic medication to provide efficiency and effectiveness preventing the onset and progression of diabetic retinopathy would be of great value because it would prevent the manipulation of the eye and with the chance to act in a preventive manner across vascular endothelium. Objective: To find a systemic medication to provide efficiency and effectiveness preventing the onset and progression of diabetic retinopathy would be of great value because it would prevent the manipulation of the eye and with the chance to act in a preventive manner across vascular. In this way, the objective of this study is to evaluate the efficacy and effectiveness of statins and/or fibrates in the diabetic retinopathy incidence and progression. . Methods: Methods/design This protocol is registered in PROSPERO (Inter-national prospective register of systematic reviews) at the National Institute for Health Research and the Centre for Reviews and Dissemination (CRD) at the University of York. It was developed according to the Cochrane Handbook of Interventions Reviews [7] and was reported according to the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P).[8] Type of studies For this Systematic Review, only randomised clinical trials will be included. Given the progressive nature of the clinical situation, cross-over designs will not be considered. Type of patients Patients with type 1 or 2 diabetes, with or without non proliferative retinopathy (for treatment and prevention, respectively) will be considered. Patients with proliferative retinopathy wil be excluded. Type of interventions Any type of statins and/or fibric acids given in isolation or in association, at any dose or duration course will be considered. Type of outcome measures Primary outcomes 1. Incidence of diabetic retinopathy will be considered for prevention proposal at any time point and progression of Diabetic retinopathy, that will be defined as mild non-proliferative, or more severe diabetic retinopathy, from the Early Treatment Diabetic Retinopathy Study (ETDRS) final scale of 35 grade or greater, based on evaluation of stereoscopic color fundus photographs of the eyes of participants who did not have retinopathy at baseline on the ETDRS scale. However, other criteria may be considered on a case by case basis.[9] 2. Progression of diabetic retinopathy at any time point. This will be defined as progression of two steps or greater from baseline on the ETDRS scale based on the development of stereoscopic color fundus photographs of participants' eyes who had diabetic retinopathy at baseline. However, other criteria may be considered on a case by case basis. Secondary outcomes Decrease of visual acuity (any decrease) in both eyes by Snellem charts technique or Logmar; Adverse events, including death, myopathy, liver enzymes alterations, hepatitis induced by drugs; Rate of patients with necessity of laser therapy; Quality of life measured by any tecnique. Methods for search Electronic search We will systematically search the databases: MEDLINE (via Pubmed), EMBASE (via Elsevier), LILACS (via Biblioteca Virtual em Saúde - BVS) and CENTRAL (via Wiley) using search strategy including MESH terms and free-text terms related to “diabetic retinopathy”, “hypolipidemic agents”, “statin”, “fibrates” and drugs within these class and “hypolipidemic agents”. No limit for data, language and status of the publication (conference abstracts, full-text, ongoing studies) will be used. The aditional search will be conducted in Handsearch We will assess reference lists of all included studies and review articles for additional references. We will contact authors of identified trials and ask them about other published and unpublished studies. We will also contact manufacturers and specialists in the area. Selection of studies Two authors (VM and CGF) independently will select the texts and qualify according to the criteria for inclusion and exclusion exposing the reasons for deletion. A third reviewer (RR) will solve any disagreement. We will exclude duplicates and collate multiple reports of the same study so that each study rather than each report will be the unit of interest in the review. We will record the selection process in sufficient detail to fulfill a PRISMA flow diagram and a characteristics of excluded studies table.[10] Data extraction and management [10] We will use a standard data collection form for extracting study characteristics and outcome data. Two reviewers (VM and CGF) will extract the following study characteristics: • Methods: study design, total duration study and run in, number of study centres and location, study setting, withdrawals, date of study; • Participants: N, mean age, age range, gender, severity of condition, diagnostic criteria, inclusion criteria, exclusion criteria; • Interventions: intervention, comparison, concomitant medications, excluded medications; • Outcomes: primary and secondary outcomes specified and collected, time points reported; • Notes: funding for trial, notable conflicts of interest of trial authors; One reviewer [VM] will copy the data from the data collection form into the Review Manager (VerMan 5.3) file [10]. We will double check that the data is entered correctly by comparing the study reports with how the data is presented in the systematic review. A second reviewer will double-check study characteristics for accuracy against the trial report. The same two reviewers (VM and CGF) will independently judge the risk of bias for each study using the criteria outlined. Assesment of risk of bias in included studies According to the following domains: (a) random sequence generation; (b) allocation concealment; (c) blinding of participants and personnel; (d) blinding of outcome assessment; (e) incomplete outcome data; (f) selective outcome reporting; and other bias. Each domain will be judge as: high risk, low risk or unclear risk of bias. We will summarise the risk of bias judgements across different studies for each of the domains listed. We will consider blinding separately for different key outcomes where necessary e.g. for unblinded outcome assessment, risk of bias for all-cause mortality may be very different than for a patient- reported quality of life scale. [10] When considering treatment effects, we will take into account the risk of bias for the studies that contribute to that outcome. Data synthesis We will carry all as is possible to statistical meta-analysis Review Manager version5-3 [11], if we are not able to analyze due to a lack of data or high heterogeneity, we will report the result narratively. Measures of treatment effect Will be analyzed dichotomous data as risk ratio (RR) and continuous data as mean difference (MD) or standardized mean difference (SMD). Will be undertaken meta-analyses only where this is meaningful i.e. if the treatments participants and the underlying clinical question are similar enough for pooling to make sense. If multiple trial arms are reported in a single trial, it will be included only the relevant arms. If two comparisons (e.g. drug X versus placebo and drug Y versus placebo) must be included into the same meta-analysis, we will have the control group to avoid double counting.[10] Dealing with missing data We will contact authors or study sponsors in order to verify key study characteristics and obtain missing numerical outcome data where possible (e.g. when a study is identified as abstract only). If outcome data are missing in both intervention groups, but reasons for these are both reported and balanced across group, then important bias would not be expected unless the reasons have different implications in the compared groups. In dichotomus study, the potential impact of missing depends on the frequency or risk of outcomes. In continuous outcomes, the potential impact increses with the proportion of participants with missing data. [12] Assessment of heterogeneity We will assess heterogeneity by using chi-squared and I-squared statistics. Will be considered heterogeneous if chi-squared value is lower than 0.10 and I-squared value is greater than 50 %. An I-squared value greater than 50% will be considered as substantial heterogeneity and, in this case, random effect model will be use rather than fixed effect model. The reason of heterogeneity will be investigated trought subgroup and sensitivity analysis. [12] Assessment of reporting bias If there are 10 or more studies in the meta-analysis, we will assess reporting biases using funnel plots and visually interpret for the funnel plot asymmetry. [12] Subgroup analysis and investigation of heterogeneity Subgroup analysis for the primary outcomes considering the following group will be conducted: the different types of diabetes and the different kinds of hypolipemic drugs (statins, fibrates) and doses. [12] Sensitivity analysis Sensitivity analyses will be conducted to determine the impact of exclusion of studies with overall lower methodological quality (high risk of bias). We will consider as low methodological quality those studies judged as low quality for at least one of main domains of Risk of Bias Table (generation of randomization sequence, allocation concealment and blinding).[12] Results: We will generate two summary of findings table using all primary outcomes for each key-question of this review: development and progression of diabetic retinopathy. We will use the five GRADE criteria (study limitations, consistency of effect, imprecision, indirectness and publication bias) to assess the quality of a body of evidence as it relates to the studies which contribute data to the meta-analyses for the pre-specified outcomes. We will use methods and recommendations described in Section 8.5 and Chapter 12 of the Cochrane Handbook (Higgins 2011) [13] and using GRADEpro software[14].We will justify all decisions to down- or up-grade the quality of studies using footnotes and make comments to aid reader's understanding of the review where necessary. We will consider whether there is any additional outcome information that was not able to be incorporated into meta-analyses and note this in the comments and state if it supports or contradicts the information from the meta-analyses. Conclusions: Dyslipidemia is one well- known risk factor for the development of vascular disease in diabetics. However, the effects of statins and/or fibrates use have not been addressed by a systematic review yet. The findings of this review will provide an evidence-based recommendation for patients and health care professionals that deal with this severe and prevalent complication of diabetes. Clinical Trial: PROSPERO: CRD42016029746

  • Smartphone Application Use for Diabetes Management: Evaluating Patient Perspectives

    Date Submitted: Sep 12, 2016

    Open Peer Review Period: Sep 13, 2016 - Nov 8, 2016

    Background: Finding novel ways to engage patients in chronic disease management has led to an increased in interest in the potential of mobile health technologies in the management of diabetes. There...

    Background: Finding novel ways to engage patients in chronic disease management has led to an increased in interest in the potential of mobile health technologies in the management of diabetes. There are currently a wealth of smartphone applications (apps) available for free download or purchase. However, the usability and desirability of these apps has not been extensively studied. These are important considerations as, at a very practical level these apps must be accepted by the patient population if they are to be utilized. Objective: The purpose of this study was to gain insight into patient experiences with use of smartphone applications (“apps”) for management of type 1 diabetes. Methods: Adults with type 1 diabetes who previously or currently used apps to manage their diabetes were eligible to participate. Participants (n=12) completed a questionnaire in which they were required to list the names of preferred apps and indicate which app functions they had used. They were given opportunity to comment on app functions they perceived to be missing from the current technology. Participants were also asked whether they had previously paid for an app and whether they would be willing to do so. Results: The apps most commonly listed by participants as the best apps they had encountered included IBG star and MyFitnessPal. Blood glucose tracking, carbohydrate counting, and activity tracking were the most commonly use features. 100% of participants indicated that they had not encountered any one app that included all of the functions that they used. Ability to synchronize with a glucometer or insulin pump was the most common function participants stated was missing from app technology. 10% of participants had previously paid for a diabetes related app and 90% of participants indicated they would be willing to pay. Conclusions: In spite of dissatisfaction with the currently available apps, there is interest in using these tools for diabetes management. Adapting existing technology to better meet the needs of this patient population may allow these apps to become more widely utilized.