Background: Electronic health records (EHRs) facilitate the accessibility and sharing of patient data among various health care providers, contributing to more coordinated and efficient care. Objective: This study aimed to summarize the evolution of secondary use of EHRs and their interoperability in medical research over the past 25 years. Methods: We conducted an extensive literature search in the PubMed, Scopus, and Web of Science databases using the keywords Electronic health record and Electronic medical record in the title or abstract and Medical research in all fields from 2000 to 2024. Specific terms were applied to different time periods. Results: The review yielded 2212 studies, all of which were then screened and processed in a structured manner. Of these 2212 studies, 2102 (93.03%) were included in the review analysis, of which 1079 (51.33%) studies were from 2000 to 2009, 582 (27.69%) were from 2010 to 2019, 251 (11.94%) were from 2020 to 2023, and 190 (9.04%) were from 2024. Conclusions: The evolution of EHRs marks an important milestone in health care’s journey toward integrating technology and medicine. From early documentation practices to the sophisticated use of artificial intelligence and big data analytics today, EHRs have become central to improving patient care, enhancing public health surveillance, and advancing medical research.

Background: In recent years, the rapid development of mobile medical technology has provided multiple ways for the long-term management of chronic diseases, especially diabetes. As a new type of management model, smartphone apps are global, convenient, cheap, and interactive. Although apps were proved to be more effective at glycemic control, compared with traditional computer- and Web-based telemedicine technologies, how to gain a further and sustained improvement is still being explored. Objective: The objective of this study was to investigate the effectiveness of an app-based interactive management model by a professional health care team on glycemic control in Chinese patients with poorly controlled diabetes. Methods: This study was a 6-month long, single-center, prospective randomized controlled trial. A total of 276 type 1 or type 2 diabetes patients were enrolled and randomized to the control group (group A), app self-management group (group B), and app interactive management group (group C) in a 1:1:1 ratio. The primary outcome was the change in glycated hemoglobin (HbA1c) level. Missing data were handled by multiple imputation. Results: At months 3 and 6, all 3 groups showed significant decreases in HbA1c levels (all P<.05). Patients in the app interactive management group had a significantly lower HbA1clevel than those in the app self-management group at 6 months (P=.04). The average HbA1c reduction in the app interactive management group was larger than that in the app self-management and control groups at both months 3 and 6 (all P<.05). However, no differences in HbA1c reduction were observed between the app self-management and control groups at both months 3 and 6 (both P>.05). Multivariate line regression analyses also showed that the app interactive management group was associated with the larger reduction of HbA1c compared with groups A and B at both months 3 and 6 (all P>.05). In addition, the app interactive management group had better control of triglyceride and high-density lipoprotein cholesterol levels at both months 3 and 6 compared with baseline (both P<.05). Conclusions: In Chinese patients with poorly controlled diabetes, it was difficult to achieve long-term effective glucose improvement by using app self-management alone, but combining it with interactive management can help achieve rapid and sustained glycemic control. Trial Registration: ClinicalTrials.gov NCT02589730; https://clinicaltrials.gov/ct2/show/NCT02589730.

Background: Low muscle mass likely results in reduced capacity for glucose disposal, leading to a significant but under-appreciated contribution to increasing the risk of diabetes. But few prospective studies have investigated the association between the loss of muscle mass and the occurrence of diabetes. We aimed to investigate whether short-term changes in muscle mass affect the incidence of diabetes in a Chinese population. Methods: This study included 1275 individuals without evident diabetes at baseline. In the baseline and re-examination, individuals completed the risk factors survey and underwent body composition measurement. Muscle mass index was defined as the percentage skeletal muscle mass, which was measured by an automatic bioelectric analyzer. Results: After a median follow-up of 2.1 years, 142 individuals developed diabetes (11.1%). There was an inverse association between basal skeletal muscle mass index and the risk of diabetes in participants with impaired glucose regulation but not in those with normal glucose tolerance. Multivariate-adjusted hazard ratios for the risk of developing diabetes were 0.85 (95% CI: 0.74–0.98) and 1.15 (95% CI: 0.98–1.34), respectively. Furthermore, Cox regression analysis revealed that a two-year change in skeletal muscle mass was also inversely associated with the incidence of diabetes in both participants with normal glucose tolerance and with impaired glucose regulation (HR: 0.76, 95% CI: 0.65–0.89; HR: 0.81, 95% CI: 0.71–0.91). Conclusions: These findings emphasized the importance of early detection and control of muscle mass loss for the prevention of diabetes.

Background and Purpose The association between hemoglobin A1c (HbA1c) and stroke risk along with its subtypes is rarely reported. We aimed to investigate the association between HbA1c and the risk of incident stroke in patients with type 2 diabetes based on real world data from three healthcare systems.Methods We performed a retrospective cohort study of 27,113 African Americans and 40,431 whites with type 2 diabetes. Demographic, anthropometric, laboratory, and medication information were abstracted from the National Patient-Centered Clinical Research Network common data model. Incident stroke events including both ischemic and hemorrhagic stroke were defined.Results During a mean follow-up period of 3.79±1.68 years, 7,735 patients developed stroke (6,862 ischemic and 873 hemorrhagic). Multivariable-adjusted hazard ratios across levels of HbA1c at baseline (<6.0%, 6.0% to 6.9% [reference group], 7.0% to 7.9%, 8.0% to 8.9%, 9.0% to 9.9%, and ≥10%) were 1.07, 1.00, 1.13, 1.23, 1.27, and 1.37 (Ptrend <0.001) for total stroke, 1.02, 1.00, 1.13, 1.20, 1.24, and 1.35 (Ptrend <0.001) for ischemic stroke, and 1.40, 1.00, 1.14, 1.47, 1.47, and 1.51 (Ptrend=0.002) for hemorrhagic stroke. When we used an updated mean value of HbA1c, the U-shaped association of HbA1c with stroke risk did not change. This U-shaped association was consistent among patients of different subgroups. The U-shaped association was more pronounced among patients taking antidiabetic, lipid-lowering, and antihypertensive medications compared with those without these medications.Conclusions These data suggest that diabetes management may have to be individualized according to the guideline recommendations rather than intensively attempting to lower HbA1c.

Background: The definition of clinical obesity was newly announced. The aim of our study was to investigate the association of preclinical obesity and clinical obesity either at baseline or determined during follow‐ups with the risk of all‐cause mortality. Methods: Data were collected from 232,721 participants in the UK Biobank. Dysfunctions caused by obesity, in combination with an excess of anthropometric parameters, were used to diagnose clinical obesity. Participants were categorised into six clusters according to their baseline and follow‐up dysfunction status. Time‐dependent Cox proportional hazards regression was used to compare hazard ratios (HRs) for mortality across six clusters. Results: In a total of 19,704 deaths over a mean follow‐up of 13.4 years, participants in Cluster 6 (clinical obesity at baseline; HR = 2.30, 95% CI: 2.16–2.44) and Cluster 3 (non‐obesity with baseline dysfunctions; HR = 2.02, 95% CI: 1.85–2.20) exhibited the highest multivariable‐adjusted mortality risk compared with participants without obesity and dysfunction at baseline and during follow‐up (Cluster 1). This was followed by participants in Cluster 2 (non‐obesity without baseline but with follow‐up dysfunctions; HR = 1.99, 95% CI: 1.88–2.10), Cluster 5 (preclinical obesity with follow‐up dysfunctions; HR = 1.97, 95% CI: 1.87–2.07), and Cluster 4 (preclinical obesity without follow‐up dysfunctions; HR = 1.15, 95% CI: 1.09–1.22). Subgroup analyses showed consistently higher mortality risks in Cluster 6 across various demographics, notably among individuals with higher educational qualifications. Conclusions: Clinical obesity was significantly associated with elevated all‐cause mortality risk. These findings underscore the importance of early screening and intervention for dysfunctions in patients with obesity.