Introduction and Objective: Mobile health (mHealth) applications (apps) offer scalable solutions for health promotion and chronic disease prevention in India. However, their effectiveness depends on individuals’ willingness to adopt digital technologies. This study aimed to assess the influence of sociodemographic, anthropometric, and lifestyle factors on the willingness to use mHealth apps. Methods: A cross-sectional analysis of 18,932 adults aged 25-60 years screened for the Global Health Research Unit (GHRU) digital study, comprising a pilot phase (GHRU 1; n = 5,268) and main phase (GHRU 2; n = 13,664). Data were collected using a screening questionnaire that captured information on smartphone ownership, internet access, English literacy, and willingness to use mHealth apps. Multivariate logistic regression models were employed to examine associations between sociodemographic, anthropometric, and lifestyle factors and willingness to use a health app. Results: Overall, 68.2 % participants (n=12921) were willing to use mHealth apps. Males and urban residence were strong predictors of willingness to use mHealth apps in both studies. Increasing age was inversely associated with willingness, with participants aged ≥50 years showing substantially lower odds compared to those aged <35 years. Higher waist circumference was positively associated with willingness among both males and females. Participants engaging in moderate to vigorous physical activity and having a strong family history of diabetes showed significantly higher odds of using the app. Conclusion: Willingness to use mHealth apps was more common among participants who were young, males, lived in urban areas, and were at high risk of metabolic disorders. Those involved in any exercise also showed better uptake to mHealth apps. There is a need for targeted strategies to increase acceptance among females, older adults and rural populations. Disclosure H. Ranjani: None. N. Jagannathan: None. S. Nitika: None. D. Vinothini: None. K. Yuvarani: None. A. Yadav: None. M. Loomba: None. V. Jha: None. P. Avari: Research Support; Current; Dexcom, Inc., Diabetes UK. N. Oliver: Research Support; Current; Dexcom, Inc., Roche Diabetes Care. Speaker's Bureau; Current; Sanofi. Speaker's Bureau; Ended; AstraZeneca. Research Support; Current; Medtronic. Speaker's Bureau; Ended; Dexcom, Inc. J. Valabhji: None. V. Mohan: None. J. Chambers: None. R. Anjana: None.

INTRODUCTION:Diabetes management in end-stage kidney disease (ESKD) presents unique challenges due to perturbations in renal physiology. ESKD influences the pharmacokinetics of insulin and glucose metabolism, while hemodialysis (HD) adds further complexity. There is an unmet need for adaptable therapies to help glucose management in ESKD. Automated insulin delivery (AID) may provide a solution to the dynamic, complex physiology of diabetes in ESKD on HD. AREAS COVERED:We provide a narrative review of the pathophysiology of diabetes in ESKD on HD and the role of AID in this setting. Literature search included PubMed/MEDLINE and Scopus. We included manuscripts published between 2000 and 2025 focussed on the pathophysiology of diabetes in people with ESKD on HD and 2015 to 2025 for AID use in the setting of ESKD on HD. EXPERT OPINION:Our review of the pathophysiology of diabetes in ESKD on HD and the utilization of AID in this setting highlights the need for further research to establish the evidence base for this technology in this high-risk population. The available data support the use of AID systems as they provide greater flexibility in insulin delivery, increase time within target range (3.9-10 mmol/L), and reduce glycemic variability and hypoglycemia, thus emerging as a promising treatment modality in people with insulin-treated diabetes and ESKD on HD.

Introduction Surgery in patients with diabetes mellitus is associated with increased morbidity and mortality compared with those who do not have diabetes mellitus. This is likely multifactorial and could be attributed to organisational issues; dysglycaemia; hospital-acquired diabetic ketoacidosis; errors with insulin prescribing and administration; issues with fluids and electrolytes; and systemic and surgical site infections. There was a need to update guidance for the peri-operative management of diabetes mellitus given improvements in our understanding, introduction of novel drugs and development of wearable technologies. Methods This was a multidisciplinary consensus statement with a diverse authorship group, including diabetologists; anaesthetists; surgeons; pharmacists; surgical diabetes inpatient specialist nurses; and patients with lived experience. We undertook a directed literature search and a three-round Delphi process to develop, refine and agree recommendations. Results Following three rounds, 38 recommendations were included, spanning all phases of the peri-operative pathway. Recommendations were made for organisations and general principles for the management of patients with diabetes, aiming to improve pathways, implement protocols and support training. We prioritise individualised care plans, encourage clinical judgement regarding proceeding with surgery with out-of-range HbA1c concentrations and recommend ensuring appropriate insulin regimens are prescribed and administered. We also provide guidance for capillary blood glucose and ketone monitoring and management; safe handovers of care; and multidisciplinary care plans for the peri-operative use of wearables. Discussion This consensus statement provides principles to be applied throughout the entire peri-operative pathway by healthcare professionals, institutions and patients. It is hoped that the implementation of these key recommendations will improve experience and outcomes for patients with diabetes mellitus having surgery.

Introduction and Objective: A recent study comparing Freestyle Libre (FSL) 3, Dexcom (Dex) G7, and Medtronic (Med) Simplera CGM systems found discordant glycaemic metrics, with Med showing a significantly lower glucose management indicator (GMI) and a higher time-in-range. The purpose of this study was to assess concordance between laboratory HbA1c and GMI from three sensor families (Dex G6/G7; FSL L2/L2+/L3/L3+ and Med Guardian4/Simplera) under real-world conditions. Methods: Retrospective, observational, three-centre study of adults with type 1 diabetes (n=349, age 43±14, duration of diabetes 25±13) treated with a range of automated insulin delivery (AID) systems, comprising FSL (n=50), Dex (n=212), and Med sensors (n=87). We assessed glycaemic metrics for 90 days preceding the date of the lab A1c. Results: All three sensor families showed good overall GMI alignment with Lab A1c [%], with a glucose-variability (CV%) adjusted mean deviation of <0.2%. [GMI-Lab A1c; Mean, 95% CI; Dex +0.18% (+0.11, +0.26); FSL -0.02% (-0.17, +0.13); Med -0.16% (-0.28, -0.05), p < 0.001 with difference between Dex and the other two sensors, and no difference between Med and the FSL family. Bland-Altman plots for the three sensor families (Figure) show broadly similar patterns. Conclusion: We show good alignment between 90-day GMI and Lab A1c across all three sensor families under real-world conditions. We did not detect a clinically significant systematic bias towards lower GMI with Medtronic sensors. Disclosure L. Farouk: None. B. Bashir: None. R. Seese: None. P. Avari: Research Support; Current; Dexcom, Inc., Diabetes UK. M. Reddy: Research Support; Current; Dexcom, Inc., Medtronic, Roche Diabetes Care. Advisory Panel; Ended; Medtronic. N. Oliver: Research Support; Current; Dexcom, Inc., Roche Diabetes Care. Speaker's Bureau; Current; Sanofi. Speaker's Bureau; Ended; AstraZeneca. Research Support; Current; Medtronic. Speaker's Bureau; Ended; Dexcom, Inc. M. Evans: Other - Triallist, speaker, advisory board; Current; Abbott Diabetes. Other - Research support, speaker, advisory board; Current; Novo Nordisk A/S. Other - Triallist, advisory board, speaker; Current; Sanofi. Other - Triallist, speakers fees; Current; Eli Lilly and Company. Other - Triallist, advisory board; Current; Medtronic. Other - Advisory board; Current; Dexcom, Inc. Advisory Panel; Current; Zucara Therapeutics, vTv Therapeutics. Other - Advisory panel, triallist; Current; Pila Pharma. H. Thabit: Speaker's Bureau; Ended; Eli Lilly and Company. Speaker's Bureau; Current; Insulet Corporation. Research Support; Current; Dexcom, Inc. Advisory Panel; Current; Roche Diabetes Care. L. Leelarathna: Research Support; Current; Abbott Diabetes. Speaker's Bureau; Current; Abbott Diabetes, Insulet Corporation. Consultant; Ended; Insulet Corporation. Advisory Panel; Ended; Vertex Pharmaceuticals Incorporated.

Background: No randomised controlled trials have directly compared commercially available hybrid automated insulin delivery (AID) systems, and real-world comparative data remain limited. This study evaluated glycaemic outcomes across three hybrid AID systems in adults with type 1 diabetes (T1D). Methods: This was a retrospective, observational, single-centre study and included adults with T1D who transitioned from multiple daily injections (MDI) or non-automated insulin pump therapy to hybrid AID. Data were collected from routinely used clinical data-sharing platforms and electronic health records. Outcomes compared across systems included time in range (TIR; 3.9-10.0 mmol/L), time below range (TBR; <3.9 and <3.0 mmol/L), time above range (TAR; >10.0 and >13.9 mmol/L), glucose management indicator (GMI, %) and coefficient of variation (CV, %). Results: A total of 213 participants were included (Medtronic 780G n = 38; Tandem Control-IQ n = 81; Omnipod 5 n = 94). After adjustment for baseline TIR, diabetes duration, insulin modality and AID use duration 780G users achieved a higher TIR increase (21.1% [95% CI 18.4-23.7]) compared to Control-IQ (10.1% [3.2-17.3], p = 0.010) and Omnipod 5 (15.2% [12.9-17.5], p = 0.002), with corresponding reductions in TAR. Conclusion: All three hybrid AID systems were associated with improvements in glycaemic outcomes in real-world use, supporting the role of AID systems in the management of T1D. Medtronic 780G use was associated with higher TIR increase compared with the other systems; however, these findings are based on measurements from different continuous glucose monitors between AID groups and cannot be used to infer superiority in glycaemic attainment.

Diabetes management in end-stage kidney disease (ESKD) is complicated by altered insulin pharmacokinetics and glucose metabolism, particularly in the context of hemodialysis (HD). Automated insulin delivery (AID) systems offer dynamic insulin adjustment and may help address these challenges. We present real-world data from nine individuals (five females) with type 1 diabetes and ESKD on HD, median age 38 years (range 33-50). Over a median follow-up of 7 months (range 1.5-25), AID use led to significant improvements in glucose time in range (3.9-10 mmol/l) which increased from 39.7% to 59.8% (P = 0.001), glucose variability decreased from 39.8% to 33.8% (P = 0.01), and HbA1c improved from 78.6 to 56.1 mmol/mol (P = 0.003). Time below range fell from 4.0% to 1.4%, but this was not significant (P = 0.1). AID was well tolerated and implemented with multidisciplinary support. Our findings highlight the potential of AID to improve glycemic control in this high-risk population and the need for further studies in this area.

Neonatal hypoglycemia (NH) is potentially life-threatening and can lead to long-term neurological sequelae. We retrospectively assessed the association between maternal glycemia in women with type 1 diabetes (T1D) and NH. Continuous glucose monitoring data from 60 mothers, alongside routine capillary blood glucose measurements from their neonates, were analyzed. The analyses used two clinically recognized thresholds for NH (<2.2 mmol/L and <2.6 mmol/L). In total, there were 25 neonates (41.7%) with NH <2.6 mmol/L and 19 neonates (31.7%) with NH <2.2 mmol/L. Neonates with NH <2.2 mmol/L were born at a lower gestational age (37.0 [35.9, 37.7] vs. 37.6 [37.0, 38.4] weeks, P = 0.019), a higher proportion was exposed to antenatal corticosteroids (31.6% vs. 7.3%, P = 0.014), and a higher proportion required admission to the neonatal intensive care unit (42.1% vs.12.2%, P = 0.009). Similar associations were observed for NH <2.6 mmol/L, although admission rates to the neonatal intensive care unit did not reach statistical significance. Mixed-effects logistic regression analysis identified percentage time above range (odds ratio [OR] 1.047, 95% confidence interval [CI] 1.007-1.087, P = 0.01) and percentage time in range (OR 0.951, 95% CI 0.914-0.989, P = 0.01) as significantly associated with NH <2.2 mmol/L. Our data suggest that careful optimization of glycemia early in pregnancy, rather than in the final trimester alone, may help minimize the risk of NH in infants born to mothers with T1D.

The accuracy of the latest generation Dexcom G7 sensors in individuals with diabetes undergoing hemodialysis has not previously been investigated. Participants with diabetes undergoing hemodialysis were recruited, with paired sensor glucose from Dexcom G7 recorded with plasma glucose analyzed in the laboratory, as well as the Freestyle Precision Pro glucometer and EKF Biosen C-Line analyzer. Ten adults (median age 64.0 [58.0-74.5] years) were recruited. Overall percentage (%) mean and median absolute relative differences were 10.4% and 8.5% for matched laboratory pairs, respectively (n = 720). Diabetes Technology Society error grid analysis showed 99.7%, 100%, and 99.9% of pairs within zones A and B for lab, glucometer, and EKF methods, respectively. This, the first Dexcom G7 accuracy study conducted in people on hemodialysis, demonstrates accuracy and safety when compared with lab reference readings. These data support the accessibility of continuous glucose monitoring (CGM) and hybrid closed-loop systems for people with diabetes on hemodialysis.

BACKGROUND:Real-time continuous glucose monitoring (rtCGM) is now the standard care for people with type 1 diabetes. However, whilst its impact on glycaemic outcomes is well-documented, its psychosocial effects, particularly in young adults experiencing extreme hyperglycaemia, remain poorly understood. OBJECTIVES:We aimed to explore the psychosocial impact of rtCGM on young adults with extreme hyperglycaemia who thus far have not been studied extensively. RESEARCH DESIGN AND METHODS:A qualitative study employing semi-structured interviews was undertaken. Young adults 18-25 years (HbA1c >75mmol/mol (9.0%)), naïve to rtCGM, were provided with rtCGM for 6-months. Interviews (centred on barriers to self-management and experience of rtCGM use) were conducted within 2-weeks of recruitment and at the end. An inductive, thematic analysis of interviews was undertaken. RESULTS:Eight participants (median age (IQR) 23.0 (22.0-24.5) years, 100% non-white ethnicity) were recruited with median HbA1c 94 (88-107) mmol/mol [DCCT 10.8 (10.2-12.1)%.]. All participants used multiple daily insulin injections. Despite low rtCGM wear-time (32.2 (23.1-59.4)%), significant improvements were observed in time in range, but no change in HbA1c. Thematic analysis indicated that high levels of disease burden were reported, with rtCGM-related themes identified: 1) interaction with rtCGM data, 2) feelings of control and trust from using rtCGM, and 3) frustration of technology and alarms. Although participants reported that knowledge of glucose levels on their smartphone was convenient and led to 'greater control', this was countered by alarm-fatigue, technical difficulties and feeling overwhelmed. Three participants prematurely stopped using rtCGM. CONCLUSIONS:Young adults with high-risk hyperglycaemia have complex relationships with rtCGM. rtCGM may have benefits in this high-risk group, but are likely to require additional support and must be determined on a case-by-case basis as associated effort may contribute to feelings of distress and/or burnout. Implementing structured educational, psychosocial, and technical support, alongside alternative care models such as more frequent check-ins, should be considered in order to enhance self-management practices with rtCGM and address technology-related challenges.

The rising prevalence of diabetes, particularly in low- and middle-income countries, highlights an urgent need for innovative approaches to prevention. India faces a growing burden of type 2 diabetes (T2D), placing considerable strain on affected individuals and healthcare systems. This study aims to evaluate the effectiveness of a digital intervention designed to prevent T2D in individuals with pre-diabetes in India. A total of 3,240 individuals with pre-diabetes will be recruited across urban and rural settings in Tamil Nadu and Delhi. Participants will be cluster randomised to either the digital intervention group or the control group (usual standard of care). Clusters will comprise of wards in urban areas and villages in rural areas. Initial screening will be conducted using the Indian Diabetes Risk Score (IDRS) ≥ 30 and/or a random capillary blood glucose level ≥ 110 mg/dL. Final recruitment into the study will be based on capillary oral glucose tolerance testing (OGTT) and/or HbA1c levels. The primary outcome is incidence of T2D at 24 months, and secondary outcomes are cardiometabolic events, lifestyle and behavioural factors, and cost effectiveness of digital interventions. The study is registered with the Central Trials Registry of India (CTRI/2022/02/040650). This protocol paper details the comprehensive study design aimed at evaluating the effectiveness of a digital intervention to prevent T2D in India. If effective, the study will provide important insights into the use of technology in resource-constrained settings and offer a scalable solution for diabetes prevention. The findings could also inform future interventions and policies aimed at curbing the rising burden of diabetes in low- and middle-income countries. CTRI/2022/02/040650 (registration date: 28.02.2022). https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=NjE2NzE= Enc= userName=

INTRODUCTION:Continuous glucose monitoring (CGM) has become the standard of care for people with type 1 diabetes (T1D) and some people with type 2 diabetes (T2D). However, there is a lack of data regarding CGM use in people with T2D and chronic kidney disease (CKD), who are at increased risk of hypoglycaemia. We assess the use of CGM and glucose outcomes in this cohort. METHODS:Retrospective, observational analysis of adults on CGM attending a tertiary diabetes renal clinic between January 2023 and September 2024. People with T2D on multiple daily insulin injections (defined as 2 or more insulin injections per day) and CKD stage 3-5 (eGFR <60, on renal replacement therapy or post-renal transplant) were included. HbA1c was assessed pre- and post-initiation of CGM. CGM metrics were analysed for percentage times in ranges and other glycaemic metrics. RESULTS:A total of 177 adults (median (interquartile range)) aged 64.0 (56.0-71.0) years were included. Time below range (<3.9 mmol/L) was significantly reduced (median (interquartile range)) from 1.0 (0.0-2.0)% to 0.0 (0.0-1.0)% following CGM initiation (p = 0.007). Hypoglycaemia leading to an insulin dose reduction was revealed by CGM in 91/177 participants (51.4%). Median HbA1c pre- and post-CGM was 64.0 (53.0-79.0) mmol/mol (8.0, 7.0-9.4%) and 62.0 (51.8-73.0) mmol/mol (7.8, 6.9-8.8%), respectively (p < 0.001). HbA1c reduction was observed in 114/177 participants (64.4%). CONCLUSION:CGM use was associated with the identification of hypoglycaemia and improvements in HbA1c in people with T2D and CKD. These real-world data demonstrate the importance of CGM technology to high-risk individuals with diabetes and CKD.

Hybrid closed-loop (HCL) systems have transformed outpatient diabetes management, yet their application in complex inpatient and hospital-based scenarios remains underexplored. This article examines the utilization of HCL systems in three challenging clinical contexts: the perioperative period, dialysis (hemodialysis and peritoneal dialysis), and during glucocorticoid therapy. Our article and case series examples provide an overview of the current available literature, preliminary data, and practical guidance for clinicians on HCL systems in these settings. Further research is urgently needed to establish the evidence base in this high-risk cohort.

INTRODUCTION:mHealth technology has the potential to deliver personalized health care; however, data on cardiometabolic risk factors are limited. This study aims to assess the effectiveness of mobile health applications (apps) on cardiometabolic risk factor reduction in adults aged 25 to 60 years in urban and rural India. METHODS:The study design was a pilot randomized controlled trial conducted in Tamil Nadu, India. Smartphone users (25-60 years) with basic literacy and at high risk of developing diabetes (Indian Diabetes Risk Score ≥30 and/or fasting blood sugar [FBS] 100-125 mg/dL) were recruited. Four mobile apps (two commercially available, two novel) for cardiometabolic risk reduction were evaluated. Primary outcome (weight loss) was analyzed using intention-to-treat analysis with post hoc analysis and logistic regression models adjusted for confounders. RESULTS:A total of 5264 participants were screened, and 610 were recruited into the study. Participants (7%) dropped out largely due to the COVID-19 pandemic. Data from 567 participants were used for the final analysis. In the intention-to-treat analysis, a significant reduction in body weight was observed in the intervention group as compared with control, more so in the urban (-2.40 kg, 95% confidence interval [CI] = [-3.10, -1.69], P < .001) compared with rural population (-1.19 kg, 95% CI = [-1.55, -0.82], P < .001). Intervention group participants showed significant reductions in body mass index, waist circumference, blood pressure, FBS, total serum cholesterol, and a positive effect on dietary and physical activity behaviors compared with controls. CONCLUSIONS:mHealth interventions can reduce diabetes risk, improve cardiometabolic health, and improve lifestyle behaviors in South Asian populations. TRIAL REGISTRATION:The trial is registered with the Central Trials Registry, India (CTRI/2020/03/024327).