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Diabetes technologies-including continuous glucose monitoring (CGM), insulin pumps, and hybrid closed-loop systems-have profoundly transformed self-management in type 1 diabetes (T1D). While these technologies offer improved glycemic control and safety, their use in ultraendurance sports introduces specific cognitive, material, and organizational challenges that remain underexplored in digital health research. This study aimed to explore how adults living with T1D experience and use diabetes technologies in ultraendurance sports, with particular attention to tensions between autonomy, mental load, and vulnerability. We conducted semistructured interviews with 13 French-speaking adults with T1D who had completed at least one marathon or ultra-endurance event within the last 5 years and used ≥1 diabetes technology (CGM, pump, or hybrid closed loop). We adopted constructivist grounded theory (Charmaz), using iterative cycles of line-by-line and focused coding, constant comparison, and memo-writing to build and refine analytic categories. Sampling combined purposive strategies through associations and online communities with theoretical orientation (additional participants sought to elaborate emergent categories). Data collection ceased upon theoretical sufficiency, when further interviews no longer yielded substantively new insights for core categories. Two patient partners contributed to question framing, interim sense-checking, and manuscript review. Reporting followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist. Five interrelated categories described how athletes negotiated technology in practice: (1) From episodic control to continuous anticipation (reframing glucose management through real-time visibility); (2) Gains in safety and performance (perceived benefits and expanded possibilities); (3) Redistributed mental work (hyper-vigilance, logistics, device management); (4) Keeping things working when they break (fragility in extreme conditions, redundancy, improvisation, and experiential expertise); and (5) Making diabetes visible (technologies mediating identity, solidarity, and stigma). Across categories, participants articulated a tension between optimization-oriented performance and a user-constructed robustness-the capacity to maintain function under uncertainty through redundancy and adaptive know-how. In ultraendurance contexts, diabetes technologies act as both enablers and obligations: they open participation while shifting and sometimes intensifying cognitive and organizational work. A grounded account centered on robustness-in-use highlights practical implications for clinicians (pre-event routines, redundancy planning), designers (context-aware algorithms; improved physical durability), and policy makers (equitable access and exercise-specific education). These findings underscore the value of constructivist, practice-oriented inquiry to inform digital health tool design and support for people living with chronic illness.

Gestational diabetes mellitus (GDM) is associated with substantial risks of adverse maternal and neonatal outcomes. Contemporary management approaches for GDM exhibit insufficient implementation, resulting in suboptimal glycemic control and preventable perinatal complications. The rapid evolution of mobile health technologies offers potential to enhance GDM care, yet evidence from large real-world studies remains limited. This study aimed to evaluate the impact of a telemedicine-enhanced integrated management system on pregnancy outcomes and glycemic control in women with GDM and to explore the dose-response relationship between telemedicine engagement intensity and clinical outcomes. In this real-world, prospective cohort study conducted at a provincial-level medical center in China, women with GDM were categorized into a standard care group and a telemedicine-enhanced group receiving the TangMama smartphone app in addition to standard care. We compared pregnancy outcomes and glycemic parameters between the 2 groups in an inverse probability of treatment weighting population based on propensity scores. Mediation analyses and dose-response analyses were additionally conducted to explore potential mechanisms and engagement effects. A total of 4621 women with GDM were included, with 1711 in the telemedicine-enhanced group and 2910 in the standard care group. Upon inverse probability of treatment weighting analysis, the telemedicine-enhanced group demonstrated significantly lower gestational weight gain (adjusted mean difference -1.49 kg, 95% CI -1.81 to -1.17), reduced rates of excessive gestational weight gain (adjusted odds ratio [aOR] 0.61, 95% CI 0.54-0.69), cesarean section (aOR 0.80, 95% CI 0.71-0.91), hypertensive disorders in pregnancy (aOR 0.76, 95% CI 0.64-0.90), and pre-eclampsia (aOR 0.64, 95% CI 0.49-0.83). Glycemic control in the third trimester was significantly improved, with lower glycated hemoglobin A1c (HbA1c) levels (adjusted mean difference -0.05%, 95% CI -0.08 to -0.03) and higher HbA1c on-target rates. For neonatal outcomes, telemedicine-enhanced management was associated with lower rates of preterm birth (aOR 0.47, 95% CI 0.38-0.59), large-for-gestational age (aOR 0.81, 95% CI 0.69-0.96), neonatal unit admission (aOR 0.80, 95% CI 0.71-0.91), neonatal hypoglycemia (aOR 0.64, 95% CI 0.45-0.93), and neonatal hyperbilirubinemia (aOR 0.69, 95% CI 0.58-0.82). Mediation analyses identified gestational weight gain and third-trimester fasting plasma glucose as significant mediators. Higher telemedicine engagement was associated with improved glycemic control and reduced adverse outcomes in a dose-response manner. Telemedicine-enhanced integrated management is associated with improved maternal glycemic control and substantial reductions of adverse pregnancy outcomes among women with GDM. The observed dose-response relationship between engagement intensity and outcomes underscores the importance of promoting active patient participation. These findings support the broader integration of telemedicine into routine GDM care pathways to optimize maternal and neonatal health.

Gestational diabetes mellitus (GDM) is associated with an elevated risk of adverse maternal and neonatal outcomes. Dietary management is a cornerstone of GDM treatment due to its beneficial effects on metabolic control. However, suboptimal adherence to dietary recommendations has diminished its potential benefits in achieving optimal glycemic outcomes. Cognitive behavioral therapy (CBT)-based interventions have emerged as a promising approach to enhance dietary compliance and glycemic control in patients with GDM. This study aims to investigate the effects of a CBT-based digital dietary intervention on glycemic control and pregnancy outcomes in patients with GDM. The intervention group received standard care plus a digital dietary intervention based on CBT principles, delivered via a customized WeChat (Tencent Inc) mini program. This intervention included structured dietary education and behavioral strategies focused on appropriate food selection and meal sequencing. The control group received standard care alone. The primary outcome was the glycemic qualification rate, and secondary outcomes included fasting blood glucose, postprandial blood glucose (PBG), General Self-Efficacy Scale scores, and incidence of macrosomia. Self-monitored blood glucose data were collected and analyzed at biweekly follow-up visits from enrollment until delivery. Of the 200 participants, 171 completed the study. The average age was 31.2 (SD 4) years, and the average gestational age at enrollment was 26.3 (SD 1.6) weeks. Baseline HbA1c levels were similar between groups (5.2% vs 5.1%; P=.97). The glycemic qualification rate was significantly higher in the intervention group than in the control group at follow-up 3 (mean 87.9%, SD 14.9% vs 81.9%, SD 17.8%; P=.02), follow-up 4 (mean 91.0%, SD 9.9% vs 87.2%, SD 14.4 %; P=.04), follow-up 5 (mean 94.0%, SD 7.4% vs 91.5%, SD 9.5%; P=.04), and follow-up 6 (mean 94.3%, SD 6.7% vs 91.8%, SD 8.9%). PBG levels were significantly lower in the intervention group after lunch (1 h: mean 5.9, SD 0.7 vs 6.0, SD 0.7 mmol/L; P=.0 2 h2h: 5.1, SD 0.7 vs 5.3, SD 0.8 mmol/L; P=.03) and dinner (1 h: mean 6.0, SD 0.5 vs 6.2, SD 0.6; 2 h: 5.5, SD 0.7 vs 5.7, SD 0.8 mmol/L). However, no significant differences were observed in fasting blood glucose or PBG after breakfast between the groups. The intervention group showed significantly higher General Self-Efficacy Scale scores than the control group (mean 195.4, SD 6.9 vs 192.9, SD 5.8). The incidence of macrosomia was significantly lower in the intervention group than in the control group (5% vs 15%; P=.04). The findings of this randomized controlled trial suggest that a CBT-based digital dietary intervention can significantly enhance glycemic control, particularly PBG levels, and may contribute to improved pregnancy outcomes with a reduced incidence of macrosomia in women with GDM.

The global rise in the prevalence of diabetes significantly impacts the quality of life of both patients and their families. Despite advances in diabetes care, numerous challenges remain in its management. In recent years, digital tools have been increasingly integrated into diabetes care, demonstrating some positive outcomes. However, the long-term effectiveness and associated challenges of these tools in diabetes management remain areas for future research. This study aims to assess the types and current usage of digital tools in diabetes management, analyze their benefits and limitations, and provide recommendations for optimizing their future application in diabetes care. This scoping review followed the 5-stage framework proposed by Arksey and O'Malley. A comprehensive literature search was conducted across 9 electronic databases (CNKI, Wanfang Database, VIP, Sinomed, PubMed, Embase, Web of Science, CINAHL, and Cochrane Library) from their inception to July 31, 2024. Study selection was independently performed by 2 reviewers, descriptive analysis was conducted, and findings were presented narratively, key study characteristics-including first author, publication date, country or region, study type, sample size, type of digital tools, intervention methods, intervention duration, limitations, and outcome indicators-were independently extracted and cross-checked by 2 investigators. A total of 6263 articles were initially retrieved. After deduplication and a 2-stage screening process (initial screening based on titles and abstracts followed by full-text assessment), 45 studies meeting the predefined inclusion criteria were ultimately included for analysis, originating from 7 countries. The included studies demonstrated marked heterogeneity in research designs: randomized controlled trials (RCTs; n=26, 57.8%), non-RCTs (n=15, 33.3%), quasi-RCTs (n=1, 2.2%), observational studies (n=1, 2.2%), mixed-methods studies (n=1, 2.2%), and qualitative studies (n=1, 2.2%). The digital tools primarily included mobile health apps, integrated management information platforms, Diabetes Online Community (DOC), specialized monitoring and analytics tools, blood glucose information management systems, and remote monitoring and follow-up systems, among others. These tools were applied across home, hospital, and community settings. Outcome measures were primarily focused on evaluating glycemic control efficacy (eg, fasting blood glucose, postprandial blood glucose, and glycated hemoglobin), blood lipid levels, BMI, self-management capacity, quality of life, patient satisfaction, and diabetes knowledge. This review highlights the diversity and potential value of digital tools in diabetes care, particularly in supporting patient self-management and extending care across multiple settings. From a nursing perspective, digital interventions offer opportunities for individualized care, patient engagement, and continuity of services. However, challenges such as technology acceptance remain. Future studies should address gaps related to long-term effectiveness, economic evaluation, and tool adaptation for aging populations, while incorporating interdisciplinary approaches and real-world evidence to inform sustainable digital health strategies.

The Data-Driven Fasting (DDF) app implements glucose-guided eating (GGE), an innovative dietary intervention that encourages individuals to eat when their glucose level, measured via glucometer or continuous glucose monitor, falls below a personalized threshold to improve metabolic health. Clinical trials using GGE, facilitated by paper logging of glucose and hunger symptoms, have shown promising results. This study aimed to describe user demographics, app engagement, adherence to glucose monitoring, and the resulting impact on weight and glucose levels. Data from 6197 users who logged at least 2 days of preprandial glucose readings were analyzed over their first 30 days of app use. App engagement and changes in body weight and fasting glucose levels by baseline weight and diabetes status were examined. Users rated their preprandial hunger on a 5-point scale. Participants used the app for a median of 19 (IQR 9-28) days, with a median of 7 (IQR 3-13) weight entries and 52 (IQR 25-82) glucose entries. On days when the app was used, it was used a median of 1.8 (IQR 1.4-2.1) times. A significant inverse association was observed between perceived hunger and preprandial glucose concentrations, with hunger decreasing by 0.22 units for every 1 mmol/L increase in glucose (95% CI -0.23 to -0.21; P<.001). Last observation carried forward analysis resulted in weight loss of 0.7 (95% CI -0.8 to -0.6) kg in the normal weight category, 1 (95% CI -1.1 to -0.9) kg in the overweight category, and 1.2 (95% CI -1.3 to -1.1) kg in the obese category. All weight changes nearly doubled when analyzed using a per-protocol (completers) analysis. Fasting glucose levels increased by 0.11 (95% CI 0.09-0.12) mmol/L in the normal range and decreased by 0.14 (95% CI -0.16 to -0.12) mmol/L in the prediabetes range and by 0.5 (95% CI -0.58 to -0.42) mmol/L in the diabetes range. Per-protocol analysis showed fasting glucose reductions of 0.26 (SD 4.7) mg/dL in the prediabetes range and 0.94 (16.9) mg/dL in the diabetes range. The implementation of GGE through the DDF app in a real-world setting led to consistent weight loss across all weight categories and significant improvements in fasting glucose levels for users with prediabetes and diabetes. This study underscores the potential of the GGE to facilitate improved metabolic health.

The use of smart technology in the management of all forms of diabetes mellitus has grown significantly in the past 10 years. Technologies such as the smartwatch have been proposed as a method of assisting in the monitoring of blood glucose levels as well as other alert prompts such as medication adherence and daily physical activity targets. These important outcomes reach across all forms of diabetes and have the potential to increase compliance of self-monitoring with the aim of improving long-term outcomes such as hemoglobin A1c (HbA1c). This systematic review aims to explore the literature for evidence of smartwatch technology in type 1, 2, and gestational diabetes. A systematic review was undertaken by searching Ovid MEDLINE and CINAHL databases. A second search using all identified keywords and index terms was performed on Ovid MEDLINE (January 1966 to August 2023), Embase (January 1980 to August 2023), Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, latest issue), CINAHL (from 1982), IEEE Xplore, ACM Digital Libraries, and Web of Science databases. Type 1, type 2, and gestational diabetes were eligible for inclusion. Quantitative studies such as prospective cohort or randomized clinical trials that explored the feasibility, usability, or effect of smartwatch technology in people with diabetes were eligible. Outcomes of interest were changes in blood glucose or HbA1c, physical activity levels, medication adherence, and feasibility or usability scores. Of the 8558 titles and abstracts screened, 5 studies were included for qualitative synthesis in this review. A total of 322 participants with either type 1 or type 2 diabetes mellitus were included in the review. A total of 4 studies focused on the feasibility and usability of smartwatch technology in diabetes management. One study conducted a proof-of-concept randomized clinical trial including smartwatch technology for exercise time prescriptions for participants with type 2 diabetes mellitus. Adherence of participants to smartwatch technology varied between included studies, with one reporting input submissions of 58% and another reporting that participants logged 50% more entries than they were required to. One study reported significantly improved glycemic control with integrated smartwatch technology, with increased exercise prescriptions; however, this study was not powered and required a longer observational period. This systematic review has highlighted the lack of robust randomized clinical trials that explore the efficacy of smartwatch technology in the management of patients with type 1, type 2, and gestational diabetes. Further research is required to establish the role of integrated smartwatch technology in important outcomes such as glycemic control, exercise participation, drug adherence, and diet monitoring in people with all forms of diabetes mellitus.

Predicting hypoglycemia while maintaining a low false alarm rate is a challenge for the wide adoption of continuous glucose monitoring (CGM) devices in diabetes management. One small study suggested that a deep learning model based on the long short-term memory (LSTM) network had better performance in hypoglycemia prediction than traditional machine learning algorithms in European patients with type 1 diabetes. However, given that many well-recognized deep learning models perform poorly outside the training setting, it remains unclear whether the LSTM model could be generalized to different populations or patients with other diabetes subtypes. The aim of this study was to validate LSTM hypoglycemia prediction models in more diverse populations and across a wide spectrum of patients with different subtypes of diabetes. We assembled two large data sets of patients with type 1 and type 2 diabetes. The primary data set including CGM data from 192 Chinese patients with diabetes was used to develop the LSTM, support vector machine (SVM), and random forest (RF) models for hypoglycemia prediction with a prediction horizon of 30 minutes. Hypoglycemia was categorized into mild (glucose=54-70 mg/dL) and severe (glucose<54 mg/dL) levels. The validation data set of 427 patients of European-American ancestry in the United States was used to validate the models and examine their generalizations. The predictive performance of the models was evaluated according to the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). For the difficult-to-predict mild hypoglycemia events, the LSTM model consistently achieved AUC values greater than 97% in the primary data set, with a less than 3% AUC reduction in the validation data set, indicating that the model was robust and generalizable across populations. AUC values above 93% were also achieved when the LSTM model was applied to both type 1 and type 2 diabetes in the validation data set, further strengthening the generalizability of the model. Under different satisfactory levels of sensitivity for mild and severe hypoglycemia prediction, the LSTM model achieved higher specificity than the SVM and RF models, thereby reducing false alarms. Our results demonstrate that the LSTM model is robust for hypoglycemia prediction and is generalizable across populations or diabetes subtypes. Given its additional advantage of false-alarm reduction, the LSTM model is a strong candidate to be widely implemented in future CGM devices for hypoglycemia prediction.

This study assessed primary care providers' comfort with prescribing and using continuous glucose monitoring technology for type 2 diabetes management, identifying key barriers and educational needs.UnlabelledThis study assessed primary care providers' comfort with prescribing and using continuous glucose monitoring technology for type 2 diabetes management, identifying key barriers and educational needs.

Continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusions (CSIIs) are the current standard treatment devices for type 1 diabetes (T1D) management. With a high prevalence of T1D beginning in pediatrics and carrying into adulthood, insufficient glycemic control leads to poor patient outcomes. Dermatologic complications such as contact dermatitis, lipodystrophies, and inflammatory lesions are among those associated with CGM and CSII, which reduce glycemic control and patient compliance. This systematic review aims to explore the current literature surrounding dermatologic complications of CGM and CSII as well as the impact on patient outcomes. A systematic review of the literature was carried out using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines using 5 online databases. Included articles were those containing primary data relevant to human participants and adverse reactions to CGM and CSII devices in pediatric populations, of which greater than 50% of the sample size were aged 0-21 years. Qualitative analysis was chosen due to the heterogeneity of outcomes. Following the application of exclusion criteria, 25 studies were analyzed and discussed. An additional 5 studies were identified after the initial search and inclusion. The most common complication covered is contact dermatitis, with 13 identified studies. Further, 7 studies concerned lipodystrophies, 5 covered nonspecific cutaneous changes, 3 covered unique cutaneous findings such as granulomatous reactions and panniculitis, and 2 discussed user acceptability. The dermatologic complications of CGM and CSII pose a potential risk to long-term glycemic control in T1D, especially in young patients where skin lesions can lead to discontinuation. Increased manufacturer transparency is critical and further studies are needed to expand upon the current preventative measures such as device site rotation and steroid creams, which lack consistent effectiveness.

Conventional clinical markers guide cardiovascular risk stratification; however, continuous glucose monitoring (CGM) data remain absent from prediction models. A synthesis of the current literature is needed to clarify the prognostic relevance of CGM data for cardiovascular outcomes in people with diabetes. This scoping review aimed to identify published studies examining (1) the associations between glycemic control and cardiovascular outcomes and (2) the predictive value of CGM-derived metrics in cardiovascular risk assessment. MEDLINE and Embase were searched from inception to March 11, 2025, for peer-reviewed, original research that included CGM-derived metrics and cardiovascular disease (CVD) outcomes. Two reviewers screened the records independently. A total of 53 studies were identified. These studies focused on type 1 diabetes, type 2 diabetes, both diabetes types, or prediabetes. Clinical outcomes were examined in 16 studies, while subclinical outcomes were assessed in 40 studies. Of the 53 studies, 47 were cross-sectional studies and 6 were longitudinal studies. All studies were association studies, and 3 included secondary analyses of predictive performance. However, none applied machine learning-based methods. A wide range of CGM-derived metrics and CVD outcomes, both clinical and subclinical, were studied in the literature. Overall, the findings were inconsistent across studies, and this was likely due to methodological weaknesses such as underpowered analyses. Time-in-range was both the most studied metric and associated with cardiovascular risk in the largest single study. Only the mean amplitude of glycemic excursions was consistently associated with CVD in most studies investigating this metric, when using statistical significance as a pragmatic indicator of consistency across heterogeneous studies. The prognostic value of CGM-derived metrics for CVD outcomes is currently underexplored. Longitudinal prediction studies on clinical CVD outcomes, leveraging the potential of routinely collected CGM data, are needed.