Low levels of physical activity remain a major challenge in managing prediabetes and type 2 diabetes in primary care. While brief advice from general practitioners (GPs), including self-monitoring, can be effective, its impact is often limited by time constraints and lack of follow-up. Digital tools, including mobile health (mHealth) adaptive messaging, may extend behavioural support into daily life.To examine whether adding adaptive digital support to wearable-based self-monitoring leads to meaningful long-term changes in physical activity and related health outcomes in adults with prediabetes or type 2 diabetes in primary care.ENERGISED was a 12-month pragmatic multicentre randomised trial in 28 general practices in the Czech Republic. Adults with prediabetes or type 2 diabetes (n = 343) were allocated to an intervention combining wearable self-monitoring, context-sensitive text messages and six-month phone counselling, or to an active control with wearable self-monitoring and brief GP advice. The primary outcome was change in daily steps at 12 months; secondary outcomes included activity patterns, mental health, quality of life, functional performance and cardiometabolic markers. Mixed models for repeated measures were used.After 12 months, changes in daily step count were comparable between groups, indicating no additional effect of adaptive digital support on overall activity volume (between-group difference 309 steps/day; 95% CI −299 to 917). Short-term benefits were observed at 6 months, when the intervention group showed a more favourable activity intensity distribution and better mental health scores than the control group. These differences diminished after counselling ended and were not significant at 12 months. No differences were found for cardiometabolic outcomes, and adverse events were rare.These results suggest that adding adaptive digital messaging to self-monitoring provides limited additional benefit in usual primary care. Transient effects during counselling highlight the importance of human support in behaviour change. Further research is needed to determine how digital and human components can be optimally combined for sustainable outcomes.In real-world primary care, supplementing self-monitoring with adaptive messaging and brief counselling had only transient effects on activity intensity and mental health. These findings suggest that sustained behaviour change may require hybrid models integrating digital tools with human support.

Type 2 diabetes is one of the most managed chronic diseases in primary care and requires continuous follow-up. Improving patients' self-management skills, ensuring regular follow-up, and encouraging lifestyle changes directly influence the quality of primary diabetes care. Recently, e-health tools such as telemonitoring, mobile health apps, digital education modules, and message-based reminders have become more prevalent in supporting patients' chronic care. The Chronic Care Model (CCM) provides an evidence-based framework for organizing effective chronic disease management. However, how health tools are integrated into type 2 diabetes care and their relationship to the chronic care model still remain unclear.This scoping review aims to present current evidence on the effectiveness of eHealth interventions in managing type 2 diabetes and their alignment with the core components of the chronic care model.A review was conducted using PubMed, Scopus, Embase, the Web of Science, CINAHL, and the Cochrane Library, including studies published between 2010 and 2025. Clinical trials, reviews, and meta-analyses were searched using terms such as “type 2 diabetes,” “primary care,” “digital health,” “telemonitoring,” “mobile health,” and “Chronic Care Model.” Data were charted and summarized using a scoping review approach, and the findings were mapped to the six components of the Chronic Care Model.eHealth applications used in primary care diabetes management mostly engage with self-management support components and clinical information systems. Conversely, interaction with the decision support component is more limited, and the literature provides little information on integration with community resources and health system organization.E-health interventions are recommended to enhance patient engagement in care and improve information exchange within primary care teams. However, challenges and limitations persist in using these technologies, such as disparities, digital literacy issues, and physical barriers.E-Health applications closely align with the components of the Chronic Care Model, especially self-management support and clinical information systems.

The Diabetic Eye Screening Programme (DESP) in the National Health Service (NHS) in England generates approximately 18 million retinal images per year, which are graded for diabetic retinopathy. Automated retinal image analysis using Artificial Intelligence (AI) can detect diabetic retinopathy as accurately as human graders, but it is not yet licensed in England. Understanding perceptions among People Living with Diabetes (PLD) and Healthcare Professionals (HCP) is essential for successful implementation.This study explored attitudes and concerns towards AI-assisted screening and how these vary across sociodemographic groups.A mixed-methods design combined quantitative and qualitative data from two online surveys co-developed with PLD and HCP focus groups. The surveys were distributed across DESPs, diabetes charities, and professional networks between September and December 2023. Likert-scale responses were analysed using linear regression to identify subgroup differences. Free-text responses were coded thematically using a co-developed framework.1,577 PLD and 262 HCP completed the surveys; 387 PLD (24%) and 98 HCP (37%) provided free-text comments. Both groups generally viewed AI integration as inevitable and potentially beneficial for efficiency and cost-saving, with concerns around data security, accountability for AI errors, and reduced human interaction. Qualitative themes included trust, workforce impact, patient–practitioner relationships, and implementation challenges. Quantitative analysis showed that 58% of PLD believed AI would perform equally well across different ages and ethnicities, compared with 32% of HCP. Most PLD (81%) felt humans should remain responsible for screening outcomes, and 71% of HCP disagreed that AI could wholly replace human grading. Females were less accepting of AI, while PLD of Black and Asian ethnicities expressed greater concern about data security and impact on screening experience. HCP of Asian ethnicity were generally more sceptical of AI’s role.Both PLD and HCP recognise the potential benefits of AI-assisted screening, but express concerns around safety, responsibility, and workforce implications. Limited understanding of AI’s role in the DESP is a key barrier to acceptance.Targeted outreach activities based on socioeconomic factors can build trust in future AI implementation into the DESP. These activities could include co-designed educational materials with transparent communication around the role of AI, data use, and safety.

Pancreatic cancer stands for one of the most fatal malignancies, featuring a subtle onset, aggressive behavior, and a poor prognosis. Among the top 5 causes of cancer-related deaths in the U.S., pancreatic cancer is the only cancer without a clear, systematic approach to early diagnosis. New-onset diabetes (NOD) has emerged as a potential early clinical manifestation of PDAC, offering a possible window for targeted screening. Because most new diabetes diagnoses occur in primary care, this setting represents a pivotal point for initiating risk assessment and early-detection pathways.To evaluate current evidence supporting NOD as a risk group for pancreatic cancer and to outline a primary care–centered screening framework that balances risk, feasibility, and resource use.We conducted a scoping review by searching PubMed for relevant studies published in the past 10 years. Our keywords included “new onset diabetes”, “pancreatic cancer”, “screening”. We included meta-analysis, observational studies and systematic reviews. We identified 14 studies that met our inclusion criteria.: Studies show that adults ≥50 with NOD have an elevated short-term risk of PDAC, with risk peaking in the first 2–3 years of diabetes onset. Clinical indicators such as unintentional weight loss, rapidly worsening glycemia, personal history of pancreatitis, and family history of PDAC significantly improve risk stratification and can be readily assessed in primary care. Incorporating structured risk scores and algorithmic triggers into electronic health records may help identify a small subset of NOD patients whose risk is high enough to warrant further evaluation. Emerging blood-based biomarkers and machine-learning models show promise for future integration into primary-care workflows but require prospective validation.In primary care, where most new diabetes diagnoses originate, clinicians can use routine data—such as weight trends, glycemic trajectories, and relevant medical history—to flag patients who may benefit from further evaluation. Although imaging remains essential for definitive assessment, its selective use guided by risk stratification improves efficiency and reduces unnecessary testing.As tools for identifying high-risk NOD patients evolve, the primary care setting will play a central role in coordinating screening, educating patients, and ensuring timely referral pathways.

Albuminuria is an early marker of diabetic kidney disease, indicating silent renal damage. With rising diabetes-related kidney complications in Saudi Arabia, early detection is crucial. This study assesses the prevalence of albuminuria and examines its association with hemoglobin levels and other risk factors in patients with type 2 diabetes.To assess the prevalence of albuminuria in type 2 diabetes, identify its clinical and biochemical predictors, and examine the association with hemoglobin levels.This retrospective cross-sectional study included adults with type 2 diabetes with measured Urinary Albumin-to-Creatinine Ratio levels , receiving care at XXX in the last 5 years. Analyses used R software (version 4.4.1). Continuous variables are shown as mean ± SD and categorical as frequency (percentage). Results are expressed as odds ratios (OR) with 95% confidence intervals (CI). Statistical significance was set at p < .05 for adjusted odds ratios (aOR).Among 15,822 patients with type 2 diabetes, 19.6% had microalbuminuria and 5.4% had macroalbuminuria. Albuminuria severity was associated with older age, higher systolic blood pressure, longer diabetes duration, elevated HbA1c, worse lipid profile, reduced eGFR, and hypertension. A graded increase in anemia prevalence was observed across albuminuria categories (32.2% normo-, 40.6% micro-, 55.6% macroalbuminuria).Albuminuria severity was independently associated with older age, hypertension, higher systolic blood pressure, longer diabetes duration, poor glycemic control, elevated triglycerides, and former smoking, consistent with regional and international studies. Notably, our study identified a novel graded relationship between low hemoglobin levels and increasing albuminuria severity, suggesting anemia may serve as an early marker of renal vulnerability. Strengths of the study include its large, heterogeneous population and inclusion of all albuminuria stages, while limitations include its cross-sectional design and the lack of other hematological assessments. These findings emphasize the importance of monitoring both traditional risk factors and hemoglobin to prevent progression of diabetic kidney disease.Anemia may serve as an early marker for the severity of albuminuria in patients with type 2 diabetes. Primary care physicians should routinely monitor hemoglobin, along with other biomarkers, to help prevent the progression of diabetic kidney disease.

The escalating global epidemics of obesity and type 2 diabetes (T2DM) pose a severe threat to public health, with lower-middle-income countries like Kyrgyzstan facing a disproportionately high burden. Primary care is recognized as the crucial frontline for prevention, yet significant barriers often hinder its effectiveness in resource-limited settings.This study aimed to comprehensively assess the role, barriers, and opportunities related to obesity and diabetes prevention within the primary care system of Kyrgyzstan.A qualitative, exploratory study based on grounded theory was conducted in 2025. Thirteen family physicians (12 female, 1 male) with a mix of urban and rural practices in Northern Kyrgyzstan were recruited. Data were collected via semi-structured interviews, which were transcribed, translated, and analyzed using axial coding to identify central categories and their relationships.The analysis identified a central issue: the ineffective prevention and management of obesity and diabetes. Several key themes emerged, including patient-related barriers, systemic obstacles, contextual challenges, a lack of institutional support, compensatory strategies, and negative consequences.  This situation was primarily driven by several causal factors, including a critical shortage of specialists, such as endocrinologists and dietitians, financial barriers faced by patients, and low public awareness about these health issues. These challenges were further exacerbated by overwhelming physician workloads and deeply ingrained cultural dietary habits. In response to these pressures, physicians, constrained by a lack of government assistance and a patient mindset that often places health responsibility elsewhere, resorted to compensatory strategies. These included taking on extra duties and focusing health education efforts on younger, more receptive audiences. However, these measures ultimately resulted in late-stage diagnoses, physician burnout, and a continued failure of the preventive health system.Kyrgyzstan's primary care physicians are constrained by systemic and sociocultural barriers, resulting in a reactive care model unlike more integrated systems elsewhere.There is an urgent need for systemic interventions to address specialist shortages, reduce physician workload by transferring competencies and involving other health specialists in primary health care, and implement supportive policies. Strengthening the primary care system is essential to unlock its preventive potential and curb the growing epidemic of obesity and diabetes in Kyrgyzstan.

Care that is concordant with clinical practice guidelines improves the quality and outcomes of chronic disease management. A new ratio model was recently developed to measure patient-level guideline concordance using electronic health records (EHRs) by considering patients’ past clinical activities and their timings (that is, past clinical trajectories). This patient-level approach offers significant clinical advantages over previous broad-based approaches. We applied the ratio model to a cohort of patients with type 2 diabetes mellitus (T2DM) in primary care clinics in XXX.To examine the distribution of patients’ individual concordance scores for T2DM clinical indicators and identify individual-level predictors of concordance for patients at XXX’s primary care clinics.XXX EHRs from 2018–2020 were analysed for 49,621 patients with T2DM. Concordance scores were calculated for eight clinical indicators using the ratio model. Concordance scores from the ratio model ranged from 0 (not concordant at all) to 1 (perfect concordance). Multivariate regressions were used at the patient level to assess associations between concordance and patient characteristics such as age, sex, ethnicity, HbA1c control, and comorbidities.Patients’ mean age was 64.8 years (SD 11, range 23-105). There were 24,628 (49.6%) females. Blood pressure monitoring, serum low-density lipoprotein cholesterol, and glycated haemoglobin had higher mean concordance scores (mean 0.94, SD 0.17 for blood pressure monitoring), while foot and retinal assessments had lower concordance scores (mean 0.37, SD 0.42 for retinal screening). Compared to females, males had lower odds of concordant blood pressure monitoring (OR 0.78, 95% CI 0.72—0.84), and higher odds of urine albumin-creatinine ratio measurements (OR 1.12, 95% CI 1.07–1.16).The ratio model can be used to track patients’ concordance over time and to study associations between concordance and health outcomes. Concordance scores can also be used to support clinical decision by triggering alerts for physicians when the scores exceed a pre-determined threshold. A limitation of the ratio model is the additional data needed for the calculations.The ratio model measures individual patients’ concordance to chronic disease clinical guidelines. It has the potential to assist physicians in making more individualised clinical decisions for patients with chronic diseases.