JMIR Diabetes最新文献

Background: The utility of a nurse-led telemonitoring approach (NLTA) is yet to be firmly established in diabetes management.

Objective: This study aims to examine the effect of a 12-month proactive NLTA on metabolic and psychological health indices in individuals with diabetes during the COVID-19 pandemic, and to evaluate it as a new diabetes model of care.

Methods: The telemonitoring study group (TSG; n=91) comprised adults who had attended an Australian tertiary hospital diabetes center between January 2019 and March 2020. Telehealth surveillance contact with a diabetes nurse educator was subsequently maintained at approximately 3-month intervals over 12 months. Prospective surveillance measures included glycated hemoglobin A1c (HbA1c%), weight, adherence to healthy behaviors, and patient-reported outcomes of diabetes distress, anxiety, and depression using validated instruments. Metabolic changes were compared retrospectively with a comparison group who had not received telemonitoring contact during the study period (non-TSG; n=115).

Results: The average participant age was 57.2 (SD 15) years; 63% (129/206) were male, 48% (99/206) had type 1 diabetes, 50% (104/206) had type 2 diabetes, and the mean HbA1c% was 8.1% (SD 1.4%). At the end of the 12-month study, the relative percentage reduction in unadjusted HbA1c% for the TSG cohort was significantly greater than that observed in the non-TSG cohort (4% vs 1%; P=.04). Following adjustment for baseline HbA1c%, a significant improvement in HbA1c% was observed in the TSG (P=.048) but not in the non-TSG (P=.61). TSG participants were 40% less likely (odds ratio 0.6, 95% CI 0.5-0.7) to experience an unfavorable rise in HbA1c% compared to non-TSG participants, after adjusting for sex, age, prepandemic HbA1c%, ethnicity, diabetes type, and diabetes duration. The NLTA facilitated assessments of psychological risk, with elevated depression, anxiety and diabetes distress scores significantly increased in women and youth <30 years of age (P<.001). Increasing anxiety measures were observed in those with high baseline anxiety scores (P<.001).

Conclusions: A proactive diabetes NLTA is feasible with positive effects on glycemia and the potential to identify those at psychological risk for targeted intervention. In the context of increasing demand for diabetes-related resources, further study of an NLTA model of care is warranted.

Background: Pneumonia is the most common respiratory tract infection among patients with diabetes, affecting individuals across all age groups and sexes.

Objective: This study aims to examine demographic trends in mortality among patients diagnosed with both diabetes mellitus (DM) and pneumonia.

Methods: Deidentified death certificate data for DM- and pneumonia-related deaths in adults aged 25 years and older from 1999 to 2022 were obtained from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database. Age-adjusted mortality rates (AAMRs) per 1,000,000 population were calculated. The Joinpoint Regression Program was used to evaluate annual percentage changes (APCs) in mortality trends, with statistical significance set at P<.05. This study adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting.

Results: Between 1999 and 2022, a total of 425,777 deaths were recorded from DM and pneumonia. The overall AAMR declined significantly (P=.001) from 98.73 in 1999 to 49.17 in 2016 (APC -4.68), and then surged to 97.66 by 2022 (APC 23.55). Men consistently experienced higher mortality than women throughout the study period. Male AAMR rose from 62.61 in 2016 to 127.05 in 2022 (APC 24.88), while female AAMR increased from 41.05 in 2017 to 75.25 in 2022 (APC 27.60). Race-based analysis demonstrated that American Indian or Alaska Native populations had the highest mortality rates among racial groups. Non-Hispanic White individuals exhibited a significant decline in AAMR (P=.002) from 89.76 in 1999 to 44.19 in 2017 (APC -4.58), followed by an increase to 83.11 by 2022 (APC 25.25). Adults aged 65 years or older bore the highest mortality burden, with rates declining steadily to 206.9 in 2017 (APC -5.15) before rising sharply to 371.3 in 2022 (APC 20.01). Nonmetropolitan areas consistently exhibited higher mortality than metropolitan areas, with particularly steep increases after 2018 (APC 64.42). Type-specific mortality revealed that type 1 DM AAMRs declined from 9.2 in 1999 to 1.4 in 2015 (APC -11.94) before rising again. By contrast, type 2 DM AAMRs surged drastically after 2017, peaking at 62.2 in 2020 (APC 58.74) before partially declining to 41.6 by 2022.

Conclusions: DM is associated with an increased risk of mortality following pneumonia, particularly among men, older adults, and American Indian populations. Strengthening health care interventions and policies is essential to curb the rising mortality trend in these at-risk groups.

Background: Artificial intelligence (AI) chatbots have shown competency in a range of areas, including clinical note taking, diagnosis, research, and emotional support. An obesity epidemic, alongside a growth in novel injectable pharmacological solutions, has put a strain on limited resources.

Objective: This study aimed to investigate the use of a chatbot integrated with a digital avatar to create a "digital clinician." This was used to provide mandatory patient education for those beginning semaglutide once-weekly self-administered injections for the treatment of overweight and obesity at a national center.

Methods: A "digital clinician" with facial and vocal recognition technology was generated with a bespoke 10- to 15-minute clinician-validated tutorial. A feasibility randomized controlled noninferiority trial compared knowledge test scores, self-efficacy, consultation satisfaction, and trust levels between those using the AI-powered clinician avatar onsite and those receiving conventional semaglutide education from nursing staff. Attitudes were recorded immediately after the intervention and again at 2 weeks after the education session.

Results: A total of 43 participants were recruited, 27 to the intervention group and 16 to the control group. Patients in the "digital clinician" group were significantly more knowledgeable postconsultation (median 10, IQR 10-11 vs median 8, IQR 7-9.3; P<.001). Patients in the control group were more satisfied with their consultation (median 7, IQR 6-7 vs median 7, IQR 7-7; P<.001) and had more trust in their education provider (median 7, IQR 4.8-7 vs median 7, IQR 7-7; P<.001). There was no significant difference in reported levels of self-efficacy (P=.57). 81% (22/27) participants in the intervention group said they would use the resource in their own time.

Conclusions: Bespoke AI chatbots integrated with digital avatars to create a "digital clinician" may perform health care education in a clinical environment. They can ensure higher levels of knowledge transfer yet are not as trusted as their human counterparts. "Digital clinicians" may have the potential to aid the redistribution of resources, alleviating pressure on bariatric services and health care systems, the extent to which remains to be determined in future studies.

Background: Continuous glucose monitoring (CGM) is used to assess glycemic trends and guide therapeutic changes for people with diabetes. We aimed to increase patient access to this tool by equipping primary care physicians (PCPs) to accurately interpret and integrate CGM into their practice via a multidisciplinary team approach.

Objective: The primary objective of this study was to evaluate the feasibility and effectiveness of integrating CGM into primary care clinics using a multidisciplinary approach that included a clinical pharmacist (PharmD) and a certified diabetes care and education specialist (CDCES).

Methods: Eighteen PCPs received a 1-hour video training module from an endocrinologist teaching a systematic stepwise approach to CGM interpretation. Patient inclusion criteria included type 2 diabetes mellitus, ≥18 years old, hemoglobin A1c (HbA1c) ≥8% or concern for hypoglycemia, and no previous CGM use or an endocrinology visit in the past year. Patients saw physician extenders (CDCES or a PharmD) for professional CGM placement and education on nutrition, medication administration, and physical activity goals based on the PCP's recommendations. The CDCES or PharmD reviewed CGM data with patients and collaborated with PCPs to adjust the care plan, informed by the systematic stepwise approach to CGM interpretation. Patients either converted to personal CGM if desired or had a second professional CGM device placed after ≥1 month from the initial professional CGM placement and obtained a postintervention HbA1c measurement at ≥3 months from the initial HbA1c measurement. The primary outcomes were time in range, HbA1c, and average time from referral to the first CGM device placement. Follow-up continued with the CDCES or PharmD until patients met the study discharge criteria of HbA1c level ≤7%. Paired t tests with 1-sided P values were used to assess changes in glucose metrics from the initial to postintervention measurements. The McNemar test was used to determine the significance of change in patients meeting the goal of ≥70% time in the target range of 70-180 mg/dL.

Results: The CGM users (n=46) had a mean (SD) age of 62.39 (14.57) years, and 14/46 participants (30%) were female. The mean (SD) time in range increased by 28.06%, from 43.25% (33.41%) at baseline to 71.31% (25.49%) postintervention (P<.001), due to reduced hyperglycemia. The proportion of CGM users meeting the consensus target of the time in range ≥70% increased from 23.81% to 57.14% (P<.001). Postintervention HbA1c decreased by an average of 2.37%, from 9.68% (1.78%) to 7.31% (1.32%; P<.001).

Conclusions: The integration of CGM into primary care clinics to increase patient access is feasible and effective using a multidisciplinary approach.

Background: Type 2 diabetes mellitus (T2DM) is a metabolic disease that affects over 38 million adults in the United States, who are disproportionately Hispanic.

Objective: This study describes the development and implementation of Salud Paso por Paso, a culturally tailored and linguistically appropriate intervention to increase engagement in physical activity (PA) for Hispanic adults living with T2DM.

Methods: Participants were enrolled in a 6-week pre-post pilot test of a culturally tailored intervention that included sessions covering different aspects of PA and T2DM. Participants were recruited at a local free clinic. Nonparametric paired-sample Wilcoxon signed-rank tests were used to examine differences between pre- and postintervention measures.

Results: Twenty-one participants were recruited, and 19 (90.5%) completed the intervention. Participants significantly increased average hours spent in moderate PA, by 3.16 hours (from 4.73, SD 3.79 minutes to 9.63, SD 6.39 minutes; Z=-3.52; P<.001), average steps per week (from 23,006.38, SD 14,357.13 steps to 43,000.81, SD 30,237.17 steps; Z=-2.79; P=.005), and minutes per week of PA (from 105.94, SD 72.23 minutes to 224.19, SD 167.85 minutes; Z=-3.36; P<.001).

Conclusions: Developing effective culturally tailored interventions that can ameliorate the deleterious effects of T2DM in Hispanic adults is an important strategy to promote health equity. The Salud Paso por Paso intervention is an effective way to promote PA in Hispanic adults living with T2DM.

Background: Digital health solutions (DHS) are technologies with the potential to improve patient outcomes as well as change the way care is delivered. The value of DHS for people with diabetes is not well understood, nor is it clear how to quantify this value.

Objective: We aimed to summarize current literature on the use of patient-reported outcome measures (PROMs) in diabetes as well as in selected guidelines for Health Technology Assessment (HTA) of DHS to highlight gaps, needs, and opportunities for the use of PROMs to evaluate DHS.

Methods: We searched PubMed and ClinicalTrials.gov to establish which PROMs were most used in diabetes clinical trials and research between 1995 and May 2024. HTA guidelines on DHS evaluation from France, Germany, and the United Kingdom were also assessed to identify PROMs for DHS evaluation in general.

Results: A total of 46 diabetes-specific PROMs and 16 nondiabetes-specific PROMs were identified. The most used diabetes-specific PROMs were (1) Diabetes Distress Scale, (2) Problem Areas in Diabetes, (3) Diabetes Empowerment Scale, (4) Diabetes Quality of Life, and (5) Diabetes Treatment Satisfaction Questionnaire. The most used nondiabetes-specific PROMs were Beck Depression Inventory, Sickness Impact Profile, EuroQol 5-Dimension, and Short Form 36-Item Health Survey. In HTA guidelines, the most prominent domain was health-related quality of life, for whose assessment there are well-established measures (Short Form 36-Item Health Survey and EuroQol 5-Dimension).

Conclusions: Of the many PROMs used in diabetes care, few are currently used to evaluate DHS, and certain domains of value in diabetes are not mentioned in HTA guidelines. A common, comprehensive DHS-specific HTA framework could facilitate and accelerate the evaluation of DHS.