The Growing Role of Technology in the Care of Older Adults With Diabetes

Narrative Review: Continuous Glucose Monitoring (CGM) in Older Adults with Diabetes

INTRODUCTION

Continuous glucose monitoring (CGM) has revolutionised diabetes care, with proven effect on glycaemic control, adverse diabetic events (such as hypoglycaemia and diabetic ketoacidosis) and hospitalisations in the general population. However, the evidence for CGM in older people is less robust.

METHOD

We conducted a narrative review of trials reporting data comparing standard blood glucose monitoring (SBGM) and CGM in adults over 65 with type 1 or type 2 diabetes who were treated with insulin published between 1999 and 2024.

RESULTS

Seventeen studies were identified, including eight retrospective cohort studies and five randomised controlled trials (RCTs). Sixteen of the 17 papers were based in Europe or North America. The studies were highly heterogeneous; however, they provided clear evidence supporting the use of CGM in reducing hypoglycemia in older adults, with potential benefits for overall wellbeing and quality of life..

CONCLUSIONS

Current approaches to diabetes care in older adults may over-rely on HbA1c (haemoglobin A1c) as a measurement of control given accuracy may be reduced in older adults and propensity for hypoglycaemia. Although goals should be personalised, avoidance of hypoglycaemia is a key goal for many older people with diabetes. There is good evidence that CGM can improve time-in-range and reduce hypoglycaemia and glucose variability in older adults. CGM should be considered for older adults as a means of reducing hypoglycaemia and associated potential harm.

A Telehealth Program Using Continuous Glucose Monitoring and a Connected Insulin Pen Cap in Nursing Homes for Older Adults with Insulin-Treated Diabetes: The Trescasas Study

Objective: To assess the impact and feasibility of a telehealth program using continuous glucose monitoring (CGM) and a connected insulin pen cap (CIPC) in nursing homes for older adults with insulin-treated diabetes. Research Methods: This multicenter, prospective, sequential, single-arm study consisted of three phases: (1) baseline, blind CGM (Freestyle Libre Pro®); (2) intervention 1, CGM (Freestyle Libre2®) without alarms; and (3) intervention 2, CGM with alarms for hypo and hyperglycemia. Two telehealth visits from reference diabetes units were conducted to adjust antidiabetic treatments. Insulin treatment was tracked using the Insulclock® CIPC. The study's primary objective was to evaluate the reduction of hypoglycemia rate. Results: Of 82 eligible patients at seven nursing homes, 54 completed the study (age: 87.7 ± 7.1, 68-102 years, 56% women, duration of diabetes: 18.7 years, baseline glycated hemoglobin: 6.9% [52 mmol/mol]). The mean number of hypoglycemic events was significantly reduced from baseline (4.4) to intervention 1 (2.8; P = 0.060) and intervention 2 (2.1; P = 0.023). The time below range 70 mg/dL (3.9 mmol/L) significantly decreased from 3.7% at baseline to 1.4% at intervention 2 (P = 0.036). The number of insulin injections significantly decreased from baseline to intervention 1 (1.2 to 0.99; P = 0.027). Nursing home staff expressed a positive view of the program, greater convenience, and potential to reduce hypoglycemia with the Freestyle Libre2® CGM versus the glucometer. Conclusions: A telehealth program using CGM and a CIPC was associated with improved glycemic profiles among institutionalized older individuals with diabetes receiving insulin and was well perceived by professionals.

Continuous Glucose Monitoring With Geriatric Principles in Older Adults With Type 1 Diabetes and Hypoglycemia: A Randomized Controlled Trial

OBJECTIVE

Continuous glucose monitoring (CGM) use in older adults with type 1 diabetes (T1D) has shown benefits. However, the impact of CGM use, coupled with simplified treatment regimens and personalized glycemic goals that are better suited for older patients with multiple comorbidities and hypoglycemia, is not known.

RESEARCH DESIGN AND METHODS

Older adults (≥65 years) with T1D with hypoglycemia (two or more episodes of hypoglycemia [blood glucose <70 mg/dL for ≥20 min over 2 weeks]) who were CGM naive or CGM users were randomized to intervention and control groups. The intervention consisted of the combined use of CGM with geriatric principles (i.e., adjusting goals based on overall health, and simplification of regimens based on CGM patterns and clinical characteristics) over 6 months. The control group received usual care by their endocrinologist. The primary end point was change in time when blood glucose was <70 mg/dL from baseline to 6 months. Cost-effectiveness was calculated using a health care sector perspective.

RESULTS

We randomized 131 participants (aged 71 ± 5 years; 21% ≥75 years old) to the intervention (n = 68; CGM users = 33) or the control (n = 63; CGM users = 40) group. The median change in hypoglycemia from baseline to 6 months was -2.6% in the intervention group and -0.3% in the control group (median difference, -2.3% [95% CI -3.7%, -1.3%]; P < 0.001). This improvement was seen in both CGM naive (-2.8%; 95% CI -5.6%, -0.8%) and CGM users (-1.2%; 95% CI -2.7%, -0.1%). The HbA1c did not differ between the groups (7.5% vs. 7.3%). The intervention was cost-effective (incremental cost-effectiveness ratio $71,623 per quality adjusted life-year).

CONCLUSIONS

In older adults with T1D and high risk of hypoglycemia, CGM use enhanced by geriatric principles can lower hypoglycemia without worsening glycemic control in a cost-effective fashion.

Realigning diabetes regimens in older adults: a 4S Pathway to guide simplification and deprescribing strategies

Treating older people with diabetes is challenging due to multiple medical comorbidities that might interfere with patients' ability to perform self-care. Most diabetes guidelines focus on improving glycaemia through addition of medications, but few address strategies to reduce medication burden for older adults-a concept known as deprescribing. Strategies for deprescribing might include stopping high-risk medications, decreasing the dose, or substituting for less harmful agents. Accordingly, glycaemic management strategies for older adults with type 1 and type 2 diabetes not responding to their current regimen require an understanding of how and when to realign therapy to meet patient's current needs, which represents a major clinical practice gap. With the gap in guidance on how to deprescribe or otherwise adjust therapy in older adults with diabetes in mind, the International Geriatric Diabetes Society, an organisation dedicated to improving care of older individuals with diabetes, convened a Deprescribing Consensus Initiative in May, 2023, to discuss Optimization of diabetes treatment regimens in older adults: the role of de-prescribing, de-intensification and simplification of regimens. The recommendations from this group initiative are discussed and described in this Review.

Excessive Burden of Hyperglycemia Along With Hypoglycemia in Long-Term Care Facilities Identified by Continuous Glucose Monitoring

OBJECTIVES

Recommendations for diabetes care in long-term care facilities (LTC) focus on the avoidance of hypoglycemia and symptomatic hyperglycemia. Using continuous glucose monitoring (CGM), we evaluated the current state of glycemia in LTC residents with multiple comorbidities.

DESIGN

Cross-sectional prospective observational study.

SETTINGS AND PARTICIPANTS

Participants with diabetes on 1 or more glucose-lowering medications residing in 1 of 8 LTC facilities in Ohio and Michigan.

METHODS

A masked Dexcom G6 pro CGM was placed for 10 days on LTC residents. Clinical and demographic information was collected from medical records.

RESULTS

Sixty-five residents [median age 68 years (range 44-84 years), 51% female, 100% with type 2 diabetes] completed the study. Overall, 68% of the cohort used insulin and 64% were on non-insulin agents (11% on sulfonylurea). The mean A1c of the cohort was 7.2% ± 1.5%. CGM data showed 26% of the cohort with ≥1% time spent in hypoglycemia (time <70 mg/dL). A larger burden of severe hyperglycemia (sensor glucose >250 mg/dL) was seen, with 52% of the cohort spending >10% time, 37% spending >25%, and 18% spending >50% time in severe hyperglycemia. The cohort had a median of 13 comorbid conditions, taking 19 medications daily, with 86% having functional disabilities and 63% reporting a recent fall. Fifty-four percent of the cohort had a body mass index (BMI) >30 kg/m2 and 22% had a BMI >40 kg/m2.

CONCLUSIONS AND IMPLICATIONS

In this multimorbid cohort of residents with diabetes living in LTC facilities, we identified a high burden of both hypoglycemia and severe hyperglycemia, despite optimal control of A1c. More consistent use of CGM may help to identify glycemic excursions and actionable glucose patterns to improve therapeutic decision-making by clinicians.

WITHDRAWN: A Machine Learning Model for Real-Time Hypoglycemia Risk Prediction in Hospitalized Diabetic Patients: Development and Validation

The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

Managing Diabetes in Older Adults: Current Approaches in Long-Term Care Facilities

PURPOSE OF REVIEW

This narrative review aims to explore the literature on advancements in diabetes management within long-term care facilities (LTCFs). Managing chronic diseases like diabetes in LTCFs is particularly challenging due to the dynamic nature of these environments and the significant changes they have undergone over the past decade. Various factors, including rising care costs and government regulations, influence the quality-of-care residents receive in these settings.

RECENT FINDINGS

Many diabetes medications have not been extensively studied in LTCFs, and high-risk medications such as sulfonylureas and sliding scale insulin are still in use, potentially increasing the risk of morbidity and mortality among residents. Additionally, the adoption of diabetes technologies, such as continuous glucose monitors (CGMs) and insulin pumps, remains limited in LTCFs. Despite the high prevalence of diabetes, significant research gaps persist. Diabetes technologies have the potential to greatly improve diabetes management and outcomes for residents. However, more research is needed to evaluate their efficacy and safety in long-term care settings. Furthermore, there is a pressing need to address the gap in staff training on the use of these technologies. Closing these research gaps is essential for developing evidence-based guidelines and improving the quality of diabetes care in LTCFs.

Type 1 Diabetes in care homes: A practical guide on management

The primary purpose of the original NAPCHD project was to develop a national Strategic Document of Diabetes Care for Care Homes which has now been completed and well received as a worthwhile, sustainable, and effective guidance for delivering quality diabetes care in the UK. A Working Group of NAPCHD was established to produce a Position Statement on type 1 diabetes in care homes since this area was recommended as a topic to further develop. There are currently limited data on the prevalence and clinical outcomes associated with type 1 diabetes in care homes and management policies have been non-existent in the UK. Communication among all key stakeholders involved in direct care of residents with type 1 diabetes is generally fragmented and lacks coordination. This is compounded by a slowly growing utilisation of diabetes technology and the absence of a standard/agreed community-based model of interdisciplinary collaboration. The Rationale and Objectives were defined prior to commencing the work and a work plan with individual tasks was initially set out. After multiple correspondences and Team calls over a period of 9 months, the Group successfully generated a first draft in October 2023. This draft was then finalised the following month and circulated among stakeholders for feedback. Nine chapters have been provided including minimum standards of diabetes care, insulin regimens, avoiding hospitalisation and discharge planning. A scheme for a community-based model of care for type 1 diabetes has been included. Eight key messages were developed. In addition, an Appendix has been created which includes key assessments such as nutritional assessment, detection of frailty, sick day rules and foot risk stratification (available online).

Digital Medicine Tools and the Work of Being a Patient: A Qualitative Investigation of Digital Treatment Burden in Patients With Diabetes

Objective

To understand the contribution of digital medicine tools (eg, continuous glucose monitoring systems, scheduling, and messaging applications) to treatment burden in patients with diabetes.

Patients and Methods

Between October and November 2023, we invited patients with type 1 or type 2 diabetes to participate in semistructured interviews. The interviewees completed the Treatment Burden Questionnaire as they reflected on how digital medicine tools affect their daily routines. A published taxonomy of treatment burden guided the qualitative content analysis of interview transcripts.

Results

In total, 20 patients agreed to participate and completed interviews (aged 21-77 years, 55% female, 60% living with type 2 diabetes). We found 5 categories of tasks related to the use of digital medicine tools that patients had to complete (eg, calibrating continuous glucose monitors), 3 factors that made these tasks burdensome (eg, cost of device replacements), and 2 categories of consequences of burdensome tasks on patient wellbeing (eg, fatigue from device alarms).

Conclusion

Patients identified how digital medicine tools contribute to their treatment burden. The resulting digital burden taxonomy can be used to inform the design, implementation, and prescription of digital medicine tools including support for patients as they normalize them in their lives.

The Cross-sectional Relationship Between Use of Automatic Insulin Delivery Systems and Eating Styles Among Older Adults With Type 1 Diabetes: An Exploratory Analysis

BACKGROUND

The number of older adults with type 1 diabetes (T1D) is increasing. Use of automated insulin delivery (AID) may influence nutrition and eating behaviors. We explored how three eating styles (restrained, external, emotional) differ between older adults with T1D who use and do not use AID.

METHODS

We administered a one-time electronic survey from September to November 2023 to adults ≥65 years with T1D receiving care through a university-affiliated hospital system. Clinical and demographic information was collected from medical records. Eating styles were characterized with the Dutch Eating Behavior Questionnaire.

RESULTS

Our sample (n = 77, 95% non-Hispanic white) had mean (SD) age: 71.8 (4.1) years, diabetes duration: 33 (18) years, hemoglobin A1c (HbA1c): 6.83 (1.12%), and body mass index (BMI): 27.3 (4.7) kg/m2. Respondents reported variable eating styles, with the highest median scores for external and restrained eating and lower scores for emotional eating. Older adults using AID systems had higher median scores for emotional and external eating, and more varied restrained eating scores compared to those not using AID systems. Weak correlations were found between eating styles and HbA1c (restrained: r = -0.14; external: r = 0.08; emotional: r = 0.15), as well as between restrained (r = 0.09) and external (r = 0.04) eating with BMI, with a small correlation between emotional eating and BMI (r = 0.27).

CONCLUSIONS

Eating styles may vary between older adult AID users and non-users. To our knowledge, this is the first study to characterize eating styles in this population, though generalizability is limited by a non-diverse and small sample with high technology use overall (eg, continuous glucose monitoring, insulin pumps).

Diabetes Technology Use in Special Populations: A Narrative Review of Psychosocial Factors

As diabetes technologies continue to advance, their use is expanding beyond type 1 diabetes to include populations with type 2 diabetes, older adults, pregnant individuals, those with psychiatric conditions, and hospitalized patients. This review examines the psychosocial outcomes of these technologies across these diverse groups, with a focus on treatment satisfaction, quality of life, and self-management behaviors. Despite demonstrated benefits in glycemic outcomes, the adoption and sustained use of these technologies face unique challenges in each population. By highlighting existing research and identifying gaps, this review seeks to emphasize the need for targeted studies and tailored support strategies to understand and optimize psychosocial outcomes and well-being.

Automated insulin delivery systems in elderly patients with brittle type 2 diabetes

PURPOSE

Geriatric diabetes is complicated by the frailty of this population, and hypoglycemia with insulin is not uncommon in these patients. Automated Insulin Delivery (AID) systems may provide better glycemic control in elderly patients with brittle type 2 diabetes.

METHODS

Thirty-four patients (≥ 60 years) including cancer patients with brittle diabetes were switched to an AID system from multiple-dose insulin (MDI) treatment. HbA1c level, weight, total daily insulin requirement, and C-peptide, creatinine, and lipids were followed for at least six months.

RESULTS

There were 34 patients (14 male, 41.2 %) with a median age of 67 (IQR 63.0-75.5). Six patients (17.6 %) were on chemotherapy and/or steroids (Ch/S). The patients' initial median HbA1c % was 9.3 (IQR 7.6-11.0), c-peptide level was 0.9 (IQR 0.5-2.2) ng/mL, and median total daily insulin dose was 41 IU (IQR 32-53). Six months after the patients were switched to an AID system their HbA1c % decreased to 7.1 (IQR 6.5-8.1), p < 0.001 and c-peptide increased to 1.21 (IQR 0.2-1.7) ng/mL, p = 0.878. Total insulin dose decreased with AID systems [32 IU (IQR 23.9-37.8)), p < 0.001]. There was a decrease in median HbA1c % in patients on Ch/S [8.7 (IQR 7.0-11.5) to 6.9 (IQR 6.3-9.2)] however it didn't reach statistical significance p = 0.225. Total insulin dose also decreased without statistical significance [33 IU (IQR 41-28) to 28 (IQR 23-35), p = 0.173]. The mean time in range (TIR) percent of the patients with AID systems were as follows; <54 mg/dL was 0.5 %, 56-70 mg/dL was 1.3 %, 70-180 mg/dL was 64.8 %, >180 mg/dL was 26.7 % and > 250 mg/dL was 6.7 %.

CONCLUSION

Although AID systems are tested mostly in young type 1 patients our results show that elderly patients with brittle type 2 diabetes also benefit from an AID system. Even in very frail patients such as cancer patients, improvement can be seen.

Beyond the insulin pump: Unraveling diabetes tech dependency

The use of technology for Type 1 diabetes (T1D) has significantly developed in the last 20 years leading to several benefits in life-style management but also to potentially overreliance and addiction to such life changing devices. Insulin pumps (CSII) being small, discreet and sophisticated, offer features such as customizable basal rates, bolus calculators and integration with Continuous Glucose Monitoring (CGM) systems becoming a must have for diabetic patients. Indeed CGM, firstly introduced in the late 1990s and now being highly sophisticated provide trends and patterns hence allowing a better management of T1D. In this review we inquire the multifactorial aspects of dependency on diabetes technology, focusing not only on the benefits and the advancements these automations offer, but also the challenges, limits and possible risks associated with overreliance on them. Specifically, the impact that early introduction to technology had on patients, the dependency on CSII and CGM, the importance of learning and self-management skills and strategies for addressing unexpected events.

The use of technology in type 2 diabetes and prediabetes: a narrative review

The increasing incidence of type 2 diabetes, which represents 90% of diabetes cases globally, is a major public health concern. Improved glucose management reduces the risk of vascular complications and mortality; however, only a small proportion of the type 2 diabetes population have blood glucose levels within the recommended treatment targets. In recent years, diabetes technologies have revolutionised the care of people with type 1 diabetes, and it is becoming increasingly evident that people with type 2 diabetes can also benefit from these advances. In this review, we describe the current knowledge regarding the role of technologies for people living with type 2 diabetes and the evidence supporting their use in clinical practice. We conclude that continuous glucose monitoring systems deliver glycaemic benefits for individuals with type 2 diabetes, whether treated with insulin or non-insulin therapy; further data are required to evaluate the role of these systems in those with prediabetes (defined as impaired glucose tolerance and/or impaired fasting glucose and/or HbA1c levels between 39 mmol/mol [5.7%] and 47 mmol/mol [6.4%]). The use of insulin pumps seems to be safe and effective in people with type 2 diabetes, especially in those with an HbA1c significantly above target. Initial results from studies exploring the impact of closed-loop systems in type 2 diabetes are promising. We discuss directions for future research to fully understand the potential benefits of integrating evidence-based technology into care for people living with type 2 diabetes and prediabetes.

Ageing well with diabetes: the role of technology

Over the past two decades there has been a substantial rise in the adoption of diabetes therapeutic technology among children, adolescents and younger adults with type 1 diabetes, and its use is now also advocated for older individuals. Older people with diabetes are more prone to experience hypoglycaemia because of numerous predisposing factors and are at higher risk of hypoglycaemic events requiring third-party assistance as well as other adverse sequelae. Hypoglycaemia may also have long-term consequences, including cognitive impairment, frailty and disability. Diabetes in older people is often characterised by marked glucose variability related to age-associated changes such as variable appetite and levels of physical activity, comorbidities and polypharmacotherapy. Preventing hypoglycaemia and mitigating glucose excursions may have considerable positive impacts on physical and cognitive function and general well-being and may even prevent or improve frailty. Technology for older people includes continuous glucose monitoring systems, insulin pumps, automated insulin delivery systems and smart insulin pens. Clinical trials and real-world studies have shown that older people with diabetes benefit from technology in terms of glucose management, reductions in hypoglycaemic events, emergency department attendance and hospital admissions, and improvement in quality of life. However, ageing may bring physical impairments and other challenges that hinder the use of technology. Healthcare professionals should identify older adults with diabetes who may benefit from therapeutic technology and then adopt an individualised approach to education and follow-up for individuals and their caregivers. Future research should explore the impact of diabetes technology on outcomes relevant to older people with diabetes.

The critical elements of digital health in diabetes and cardiometabolic care

Digital innovations provide novel opportunities to individualize a person's care to best match their lifestyle needs and circumstances and to support them as they live their daily lives with diabetes. These innovations also serve to provide actionable data and insights for the care team giving them a "Webb telescope-like" view into their individual self-management journey, allowing them to see what cannot be seen during infrequent and limited office visits, thereby facilitating collaboration and communication to optimize the care plan on a timely basis. Technology advances are enabling diabetes care to transition from episodic, synchronous, primarily in-person care to include synchronous virtual care options and to continuous, on-demand, data-informed, asynchronous digital care better matching the demands of living with a relentless 24/7 chronic condition. In this paper we will discuss the critical elements and considerations in designing and implementing successful diabetes digital health tools in clinical practice.

Advances in small-molecule insulin secretagogues for diabetes treatment

Diabetes, a metabolic disease caused by abnormally high levels of blood glucose, has a high prevalence rate worldwide and causes a series of complications, including coronary heart disease, stroke, peripheral vascular disease, end-stage renal disease, and retinopathy. Small-molecule compounds have been developed as drugs for the treatment of diabetes because of their oral advantages. Insulin secretagogues are a class of small-molecule drugs used to treat diabetes, and include sulfonylureas, non-sulfonylureas, glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase 4 inhibitors, and other novel small-molecule insulin secretagogues. However, many small-molecule compounds cause different side effects, posing huge challenges to drug monotherapy and drug selection. Therefore, the use of different small-molecule drugs must be improved. This article reviews the mechanism, advantages, limitations, and potential risks of small-molecule insulin secretagogues to provide future research directions on small-molecule drugs for the treatment of diabetes.

Connected Pens or Smart Pens: Technology Needs Context

Subcutaneous insulin administration has come a long way; pens that are connected to smartphones/cloud enable data transfer about insulin dosing. The usage of detailed dosing information in a smart way can support the optimization of insulin therapy in many ways. This review discusses terminology aspects that are relevant to the optimal usage of this novel option for insulin administration. Taking such aspects into account might also be crucial to improving the uptake of these medical products. In contrast to systems for automated insulin delivery, people with diabetes have to administer the insulin dose themselves; the technology can only support them. Combining smart pens with systems for continuous glucose monitoring provides solutions that are close to an automated solution, but are more discrete and associated with lower costs.

Real-World Evidence of Omnipod® 5 Automated Insulin Delivery System Use in 69,902 People with Type 1 Diabetes

Background: The Omnipod® 5 Automated Insulin Delivery System was associated with favorable glycemic outcomes for people with type 1 diabetes (T1D) in two pivotal clinical trials. Real-world evidence is needed to explore effectiveness in nonstudy conditions. Methods: A retrospective analysis of the United States Omnipod 5 System users (aged ≥2 years) with T1D and sufficient data (≥90 days of data; ≥75% of days with ≥220 continuous glucose monitor readings/day) available in Insulet Corporation's device and person-reported datasets as of July 2023 was performed. Target glucose setting usage (i.e., 110-150 mg/dL in 10 mg/dL increments) was summarized and glycemic outcomes were examined. Subgroup analyses of those using the lowest average glucose target (110 mg/dL) and stratification by baseline characteristics (e.g., age, prior therapy, health insurance coverage) were conducted. Results: In total, 69,902 users were included. Multiple and higher glucose targets were more commonly used in younger age groups. Median percentage of time in range (TIR; 70-180 mg/dL) was 68.8%, 61.3%, and 53.6% for users with average glucose targets of 110, 120, and 130-150 mg/dL, respectively, with minimal time <70 mg/dL (all median <1.13%). Among those with an average glucose target of 110 mg/dL (n = 37,640), median TIR was 65.0% in children and adolescents (2-17 years) and 69.9% in adults (≥18 years). Subgroup analyses of users transitioning from Omnipod DASH or multiple daily injections and of Medicaid/Medicare users demonstrated favorable glycemic outcomes among these groups. Conclusion: These glycemic outcomes from a large and diverse sample of nearly 70,000 children and adults demonstrate effective use of the Omnipod 5 System under real-world conditions.

Mixed reality technology for older adults: Evaluating the impact of a novel virtual humanoid coach in a community-based physical exercise program in the Philippines

Physical inactivity among older adults remains a global burden, leading to a variety of health challenges and even mortality. This study evaluated the impact of a novel virtual humanoid coach-driven physical exercise program among older adults. A non-randomized (quasi) experimental research was conducted in two community senior centers. The recruited participants (n = 130) were primarily female older adults with a mean age of 66.40 and agreed to be purposively assigned either experimental or control groups. Trained healthcare providers performed health assessments in three time points using valid and reliable tools. Descriptive statistics, t-tests, and RM-ANOVA were used to quantitatively analyze the data using SPSS version 22. There are significant mean differences between the groups across all functional capacity assessments and Time 2-3 assessment of sleep quality. RM-ANOVA revealed significant differences in physical assessment over time between the two groups. The analyses of time and group interaction revealed significant improvement in health assessments among the members of the mixed reality group compared to the traditional groups. The impact of virtual coaches in community-based enhancing physical activity programs is comparable to the traditional mode and introduces a novel approach to promoting physical activity among older adults.

Continuous Glucose Monitoring in Older Adults: What We Know and What We Have Yet to Learn

OBJECTIVE

To assess the growing use of continuous glucose monitoring (CGM) systems by older adults and explore additional areas integration that could benefit adults with frailty.

BACKGROUND

The use of CGM devices has expanded rapidly in the last decade. This has been supported by substantial data showing significant benefit in glycemic metrics: hemoglobin A1c improvements, less hypoglycemia, and improved quality of life. However, sub-populations, such as older persons, exist where available data are limited. Furthermore, frail older adults represent a heterogeneous population with their own unique challenges to the management of diabetes. This group has some of the poorest outcomes related to the sequela of diabetes. For example, hypoglycemia resulting in significant morbidity and mortality is more frequent in older person with diabetes than in younger persons with diabetes.

METHOD

We present a concise literature review on CGM use in the older adult as well as expand upon glycemic and nonglycemic benefits of CGM for patients, caregivers, and providers. Retrospective analysis of inpatient glycemic data of 16,935 older adults with Type 2 diabetes mellitus at Atrium Health Wake Forest Baptist indicated those with fraility managed with insulin or sulfonylurea had the highest rates of delirium (4.8%), hypoglycemia (3.5%), cardiovascular complications (20.2%) and ED visits/hospitalizatoins (49%). In addition, we address special consideration of specific situations including inpatient, palliative and long term care settings.

CONCLUSION

This review article summarizes the available data for CGM use in older adults, discusses the benefits and obstacles with CGM use in this population, and identifies areas of future research needed for improved delivery of care to older persons with diabetes.

A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study

BACKGROUND

Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes.

METHODS AND FINDINGS

We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation.

CONCLUSIONS

Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

Characteristics Associated With Elevated Time Below Range in Elderly Patients With Type 1 Diabetes Using an Automated Insulin Delivery System

BACKGROUND

This study investigated the characteristics associated with an increased risk of hypoglycemia, in elderly patients with type 1 diabetes mellitus (T1D) using automated insulin delivery (AID) systems.

METHODS

Cross-sectional observational study including patients >60 years, using sensor-augmented insulin pump therapy with predictive low-glucose management (SAPT-PLGM), hybrid closed-loop (HCL), and advanced hybrid closed-loop (AHCL), for more than three months. A geriatric assessment was performed, and body composition was determined to investigate its association with achieving time below range (TBR) <70 mg/dL goals.

RESULTS

The study included 59 patients (47.5% of men, mean age of 67.6 years, glycated hemoglobin [HbA1c] of 7.5 ± 0.6%, time in range (TIR) 77.8 ± 9.9%). Time below range <70 and <54 mg/dL were 2.2 ± 2.3% and 0.4 ± 0.81%, respectively. Patients with elevated TBR <70 mg/dL (>1%) had higher HbA1c levels, lower TIR, elevated time above range (TAR), and high glycemic variability. Regarding body composition, greater muscle mass, grip strength, and visceral fat were associated with a lower TBR <70 mg/dL. These factors were independent of the type of technology used, but TIR was higher when using AHCL systems compared with SAPT-PLGM and HCL systems.

CONCLUSIONS

In elderly patients treated with AID systems with good functional status, lower lean mass, lower grip strength, and lower visceral fat percentage were associated with TBR greater than 1%, regardless of the device used. A similar finding along was found with CGM indicators such as higher HbA1c levels, lower TIR, higher TAR, and higher CV. Geriatric assessment is crucial for personalizing patient management.

Continuous glucose monitoring in older adults with diabetes: Data from the diabetes prospective follow-up (DPV) registry

AIMS

To analyse predictors for continuous glucose monitoring (CGM) use in people with diabetes aged ≥60 years using insulin therapy and to assess the rates of CGM use during recent years (2019-2021).

RESEARCH DESIGN AND METHODS

Prospective study including 6849 individuals with diabetes and insulin therapy (type 2 diabetes: n = 5320; type 1 diabetes: n = 1529) aged ≥60 years. Data from 129 treatment centres were retrieved from the Diabetes Prospective Follow-up Registry (DPV) in March 2023.

RESULTS

Sensor use in individuals aged ≥60 years has increased in type 1 (2019: 28%, 2020: 39%, 2021: 45%) and type 2 diabetes (2019: 10%, 2020: 16%, 2021: 18%). Predictors for sensor use in older individuals with type 1 diabetes are younger age and CSII use (p < 0.001). Predictors in older individuals with type 2 diabetes are younger age, longer diabetes duration, higher BMI and CSII use (p < 0.001).

CONCLUSIONS

CGM has become more common in older adults with diabetes and will presumably increase further. Age is a predictor for sensor use in older adults with diabetes. Age-related physical barriers and insufficient usability of devices, lack of interest in technologies, but possibly also effects of prejudice on the grounds of age may contribute to this finding.

Guideline for the Management of Diabetes Mellitus in the Elderly in China (2024 Edition)

With the deepening of aging in China, the prevalence of diabetes in older people has increased noticeably, and standardized diabetes management is critical for improving clinical outcomes of diabetes in older people. In 2021, the National Center of Gerontology, Chinese Society of Geriatrics, and Diabetes Professional Committee of Chinese Aging Well Association organized experts to write the first guideline for diabetes diagnosis and treatment in older people in China, the Guideline for the Management of Diabetes Mellitus in the Elderly in China (2021 Edition). The guideline emphasizes that older patients with diabetes are a highly heterogeneous group requiring comprehensive assessment and stratified and individualized management strategies. The guideline proposes simple treatments and de-intensified treatment strategies for older patients with diabetes. This edition of the guideline provides clinicians with practical and operable clinical guidance, thus greatly contributing to the comprehensive and full-cycle standardized management of older patients with diabetes in China and promoting the extensive development of clinical and basic research on diabetes in older people and related fields. In the past 3 years, evidence-based medicine for older patients with diabetes and related fields has further advanced, and new treatment concepts, drugs, and technologies have been developed. The guideline editorial committee promptly updated the first edition of the guideline and compiled the Guideline for the Management of Diabetes Mellitus in the Elderly in China (2024 Edition). More precise management paths for older patients with diabetes are proposed, for achieving continued standardization of the management of older Chinese patients with diabetes and improving their clinical outcomes.