Usability of Continuous Glucose Monitoring for Older Adults with Type 2 Diabetes: A Systematic Review

Abstract

This systematic review aims to examine the use and usability of continuous glucose monitoring (CGM) among older adults with type 2 diabetes mellitus. The following databases, PubMed, Embase, and CINAHL, were searched for studies published between 2019 and 2024, and results were documented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Eligibility criteria included English-language studies that researched the use and usability of CGM in adults 60 years or older with a minimum wear time of 2 weeks. We extracted usability (efficiency, effectiveness, and satisfaction) outcomes. Study quality was assessed using the Critical Appraisal Skills Program Checklist. Of the 118 identified titles, 58 articles underwent a full-text review, with seven being included in the final analysis. Studies evaluated clinical management of type 2 diabetes with CGM, assessing the following differences: CGM versus usual care, CGM across device types, and CGM across care models. Clinical effectiveness, based on glycated hemoglobin and time-in-range, and satisfaction were higher across CGM types compared with usual care. Based on descriptive measures, satisfaction was higher with real-time CGM than professional-mode CGM. Efficiency findings were not reported in the included studies. There were no findings on the effectiveness, efficiency, and satisfaction of ambulatory glucose profile (AGP) metrics. Although the clinical effectiveness of CGM among adults 60 years or older was demonstrated in the reviewed studies, our usability assessment is inconclusive. There is a gap in evidence related to the essential components that comprise the context of CGM use, which prohibits a complete usability assessment. Future studies are warranted to investigate CGM usability, emphasizing AGP metrics, to inform improvements and personalization for older adults.

Keywords

diabetes continuous glucose monitoring older adults usability ambulatory glucose profile clinical effectiveness

Introduction

Type 2 diabetes mellitus is a significant health burden around the world, affecting over 500 million older adults and 16.5 million only in the United States, with estimates that the prevalence of diabetes will increase from 5.9% in 2021 to 9.5% in 2050, accounting for more than 1.27 billion older adults.^1,2 Older adults (defined in this study as individuals age 60 and above) diagnosed with type 2 diabetes are confronted with unique challenges related to disease progression and an elevated susceptibility to hypoglycemic events,^3–5 which may increase the risk of mechanical falls, greater glycemic variability, and mortality.^6,7 More so, older adults with diabetes and cognitive impairment may respond inadequately to warning symptoms of hypoglycemia due to reduced awareness.^8–10

To manage type 2 diabetes, continuous glucose monitoring (CGM) has emerged as a promising intervention,¹¹ allowing for minimally invasive measurements of real-time glucose patterns via a subcutaneous sensor. Although glycated hemoglobin (HbA1c or A1c) has been the standard for assessing long-term glycemic management, it does not necessarily reflect glucose variability nor replace the need for self‐monitoring.^12–14 For older adults, particularly those with cognitive impairment or dexterity issues, self-monitoring of blood glucose could be challenging.¹⁵ Unlike self-monitoring of blood glucose, which provides snapshots of glucose levels at specific times, CGM continuously measures glucose levels, including fluctuations that infrequent finger pricks might miss. CGM detects a higher number of hypoglycemic events than self-monitoring of blood glucose, especially nocturnal events, which go unnoticed.¹⁵ The ambulatory glucose profile (AGP) that derives from CGM enables patients, caregivers, and health care providers to make personalized interventions in diabetes management. The AGP report has 10 standard metrics, including data completeness; relevant statistics such as incidence of hypoglycemia and hyperglycemia; the depiction of glucose profiles based on a “summary day” (sometimes also called the AGP); a “glucose management indicator”; and daily glucose profiles.^16,17 These CGM data can inform treatment decisions, improve medication regimens, and promote the adoption of lifestyle habits aimed at enhancing glycemic management and reducing hypoglycemic episodes.^15,18,19

Published in 2019, systematic and narrative reviews provided evidence of CGM’s clinical effectiveness, efficacy, and acceptability among the general adult population with type 2 diabetes.^15,20 In younger populations, CGM contributed to improved self-management behaviors, enhanced glycemic management, and effective reductions in A1c levels.^21,22 Moreover, younger populations reported barriers to CGM satisfaction, including alarm fatigue, pain, cost, and technology issues, invoking future studies to identify human factors that influence CGM use.²⁰ Recent studies have demonstrated the importance of focusing on adults 60 years and older because type 2 diabetes management becomes more complex and individualized.²³ The burden of diabetes, multimorbidity, and age-related physiological changes increases with age,²⁴ as do the rates of severe hypoglycemic events.^4,5 Thus, CGM expectations would adjust to aid therapeutic goals for older adults, which include management of hyperglycemia, minimizing hypoglycemia risk, preserving function, and managing polypharmacy.¹⁵ In 2024, a systematic review of older adults with type 2 diabetes using different CGM types reported reductions in HbA1c; however, no impact on hypoglycemia and increased risk of adverse events were also reported.²⁵ As advancements in CGM become more accessible and available to older adults with type 2 diabetes, evaluating the use of CGM is critical for identifying barriers and facilitators for this population.²⁶

To extend the benefits to older adults with type 2 diabetes, the implementation of CGM must consider the unique abilities and limitations for these individuals and their caregivers.²⁷ Usability studies go beyond clinical effectiveness to include efficiency and satisfaction, considering the interactions of elements comprising the context of use and intended objectives (Fig. 1). The International Standard Organization defines usability as “the extent to which specified user can use a product to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use.”²⁸ As illustrated in Figure 1, CGM is the specified product (or intervention) with the specified goal of managing type 2 diabetes. The specified users are older adults with type 2 diabetes and clinicians. The context of use specifies the users, their tasks, the available tools and technology, and the environments in which CGM use occurs. The operationalization of this definition of usability enables the assessment of CGM effectiveness, efficiency, and satisfaction among older adults. Results from similar assessments in health care and other domains have informed new designs and improved health care delivery associated with better patient outcomes.²⁹

FIG. 1.

ISO/DIS 9241–11.2 usability definition adapted to CGM for older adults.³³ CGM, continuous glucose monitoring.

Recent systematic reviews of the literature have illustrated advancements in CGM technology for the general adult population with type 2 diabetes,^24,25,29 specifically the evolution of real-time sensors, applications, and the availability of CGM data, and have highlighted shifts in health practice and policy. However, existing reviews have focused on clinical effectiveness, with a limited focus on usability and even less on older adults.^15,26,30,31 This systematic review aims to characterize published findings of CGM usability among older adults with type 2 diabetes.

Materials and Methods

This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist (see Supplementary Data). A protocol is publicly registered on Open Science Framework.³²

Search strategy

In February 2024, we searched the PubMed, Embase, and CINAHL databases for studies published since January 1, 2019. A lower “limit” of 2019 was selected to identify articles published during a recent period of significant changes in CGM health policy and technology, and to build on existing literature already identified in prior reviews.^24,26,29 Bibliographies from four published reviews were used to find additional eligible studies.^24–26,29 We also searched these databases in May 2024 and December 2024 using two updated search terms, “AGP” and “ambulatory glucose profile.” The final search phrase is the following: (((CGM) OR (FGM) OR (Flash glucose monitoring) OR (Continuous glucose monitoring)) AND ((elderly) OR (older) OR (aged)) AND ((feasibility) OR (acceptability) OR (usability) OR (user engagement) OR (effectiveness)) AND ((ambulatory glucose profile) OR (AGP))). For this review, “older” participants are defined as individuals aged 60 years or older.

Study selection

Two authors independently screened titles and abstracts using Covidence following a systematic search. If the age range or mean age of the study population was not available in the abstract, the full text of the article was reviewed to determine the age. Studies were excluded at the full-text screening stage if at least two reviewers agreed that the eligibility criteria below were not met. A third reviewer resolved any discrepancies, which were discussed in weekly meetings. The reviewers’ interrater reliability was assessed using Cohen’s Kappa, resulting in a value of 0.367, indicating fair agreement. The iteration of multiple consensus meetings may have contributed to stronger agreements. The small sample size of articles served as a limiting factor for Cohen’s Kappa, where a few disagreements disproportionately reduced the value.

Inclusion and exclusion criteria

The following criteria were used to determine the eligibility of studies for the review: available in English in full text; most participants were at least 60 years of age, or the study population had a mean age of at least 60 years; most of the study population had type 2 diabetes; study evaluated the use of CGM in the study population for at least 2 weeks; and study evaluated aspects of usability, such as effectiveness, efficiency, and satisfaction.

Exclusion criteria were the following: systematic reviews, abstracts, conference presentations, and case studies; interventions whose primary outcome was not related to the CGM sensor; and studies of CGM in a nondiabetes-related context (e.g., diet- or exercise-focused interventions with CGM performed as secondary outcomes, assessed in acute care or hospital settings).

Data extraction

An independent reviewer extracted relevant data from eligible articles using the method used in a previously published review of human factors of CGM.²⁴ The following information was included: study population, study design, and reporting features related to CGM usability, CGM type, and quality appraisal. Following initial data extraction, one of the two remaining reviewers independently cross-checked the extracted data, which were tabulated, to ensure accuracy and consistency. Inconsistencies were discussed in weekly meetings until consensus was reached. Extracted data were stored on a secure online shared network accessible only to the three reviewers.

Study quality assessment

The quality and risk of bias in included articles were assessed using the Critical Appraisal Skills Program (CASP) checklist for randomized controlled trials (RCT), cohort studies, and cross-sectional studies, as appropriate.³⁴ A single independent reviewer initially marked relevant areas of the checklist for each included article. Following individual assessment, a more senior researcher reviewed the initial quality appraisal. Over several weeks, consensus meetings were held between the two reviewers to discuss study elements and reach an agreement on the methodological soundness of each article.

Usability metrics

In this review, the three primary measures of usability—effectiveness, efficiency, and satisfaction—were used, with their definitions tailored to specifically apply to type 2 diabetes management.³⁵ Effectiveness was defined as the extent to which individuals with type 2 diabetes or clinicians achieve care goals for diabetes management. Efficiency was defined as the ease with which older adults or clinicians achieved care goals for type 2 diabetes management. Satisfaction was defined as the level of comfort or acceptability older adults and clinicians had with CGM.

Data and resource availability statement

All data generated or analyzed during this study are included in the published article (and its Supplementary Data).

Results

Of the 118 titles and abstracts screened for eligibility criteria, 58 articles were selected for full-text review. After the full-text review, 51 articles were excluded; seven articles were included in this systematic review (Fig. 2).

FIG. 2.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.

Population and study characteristics

Among the seven included studies, the mean age of the older adults was reported as participants ranged from 60 to 69 years (Table 1).^36–42 Conducted across five countries, the study objectives focused on the effectiveness and satisfaction with CGM. Among the reviewed studies, three main comparisons were made: CGM versus usual care,^36,39,42 CGM across device types (professional mode vs. real time),^40,41 and CGM across care models (peer support model, nurse or diabetes educator-led model, and physician-led model).^37,38 Of the seven articles, 86% (six of seven) of studies blinded all or some of the participants from AGP metrics for a portion or full duration of the study period.^36–41 RCTs were reported for 57% (four of seven) of the included studies. Based on the CASP checklist, the included articles were considered methodologically sound (Supplementary Table S1).

Table 1.

Population and Study Characteristics of Included Articles for Older Adults with Type 2 Diabetes

Citation	Study design	Population characteristics	Glucose monitoring	Quality appraisal
Bao (2022)³⁶	Randomized: Yes Satisfaction: Improved via continuous glucose monitoring (CGM) satisfaction score, diabetes distress scale, hypoglycemia fear survey Effectiveness: Improved CGM metrics Industry funded: Yes (USA)	Adults with type 2 diabetes older than 65 (n = 42) n = 27 with real-time CGM (intervention) n = 15 with blood glucose self-monitoring (control) Older adults using CGM: 63% White, 15% Hispanic, 11% Black 55% with bachelor’s degree or higher 78% insured by Medicare; 19% private insurance 16 years (SD = 9) of diabetes on average Self-monitor less than once a day (59%) Mean A1c at baseline: 9.1% (1.0%) Mean basal insulin at baseline: 0.54 U/kg (0.22) Majority of older adults using CGM (48%) on two glucose-lowering medications Providers: CGM data were remotely interpreted by clinicians, discussed with participants at the 2-, 4-, and 6-month follow-up visits, and shared with participants’ primary care providers for changes related to diabetes management.	CGM type: Dexcom (G6) Duration of CGM wear: 10-day period at 3 months and 8 months (control), or 8 months continuously (intervention) Personal access to CGM data: Use of professional-mode CGM for 10 days before randomization, and for 10 days at 3 months and 8 months among the control group. Use of real-time CGM among the intervention group for 8 months Data completeness: 5.5-day wear time on average A1c was collected at baseline, 3 months, and 8 months.	Methodologically sound
Lind (2024)³⁷	Randomized: Yes Satisfaction: Improved via Diabetes Treatment Satisfaction questionnaire, Glucose Monitoring Satisfaction scale Effectiveness: Improved CGM metrics Industry funded: No (Denmark)	60 adults with year-long type 2 diabetes diagnosis, mean age was 61 years n = 40 randomly assigned to standard CGM use n = 15 randomly assigned to CGM use with peer support group intervention 19 years (6.5) of diabetes on average 25% attended industrial school 77% treated with basal insulin Median A1c at baseline: 72 mmol/mol (IQR = 63 to 79) Median daily insulin dose: 63 units (38 to 96) Majority (77%) on 3+ glucose-lowering medications Providers: Participants randomly assigned to bimonthly 3-h peer support groups received educational content facilitated by two experts in diabetes care.	CGM type: Dexcom (G6) Duration of CGM wear: 12 months Personal access to CGM data: Use of professional-mode CGM for 10 days before randomization, then real-time for the remaining study period Data completeness: >94% active wear time A1c was collected at baseline, 6 months, and 12 months.	Methodologically sound
Simonson (2021)³⁸	Randomized: No Satisfaction: Not reported Effectiveness: Improved in most CGM metrics Industry funded: Yes (USA)	68 older adults with year-long type 2 diabetes diagnosis, mean age was 62 years n = 48 in team-based model with registered nurse and diabetes educator n = 20 in traditional care model 15 years of diabetes on average 81% on basal insulin Mean A1c at baseline: 8.8% (1.2%) Average number of glucose-lowering medications was 2.7 Providers: Participants met with clinicians to review A1c and the ambulatory glucose profile (AGP) report at baseline and follow-up at 3–4 weeks and at 3–6 months after wear time.	CGM type: Abbott Diabetes Care (Freestyle Libre Pro) Duration of CGM wear: 14 days Personal access to CGM data: Professional-mode CGM among all participants throughout study period Data completeness: Complete data were available in 13/20 participants in the standard care model and 24/48 participants in the registered nurse model A1c was collected at baseline.	Methodologically sound
Speight (2021)³⁹	Randomized: Yes Satisfaction: Improved via DAWN Impact of Diabetes Profile, Glucose Monitoring Experience Questionnaire Effectiveness: Not reported Industry funded: No (Australia)	299 adults with year-long type 2 diabetes diagnosis, mean age of 60 years n = 159 randomly assigned to 14-day flash glucose monitoring use every three months for one year n = 150 standard care Adults using flash glucose monitoring: 20% with bachelor’s degree or higher 13.5 years of diabetes on average 54% self-monitor at least once or twice a day 50% on basal insulin Mean A1c at baseline: 74 (14) mmol/mol A smaller percentage of participants (33%) on 3+ glucose-lowering medications Providers: General practitioners discussed AGP with participants every 3 months and received a 2-h training on how to interpret and communicate AGP data with participants.	CGM type: Abbott Diabetes Care (Freestyle Libre Pro) Duration of CGM wear: 14-day intervals every 3 months over the course of a year. Personal access to CGM data: Professional-mode CGM among intervention group every 3 months Data completeness: Median number of days with data available was 13.7 to 13.9 days across 12 months of use A1c was collected at baseline.	Methodologically sound
Taylor (2019)⁴⁰	Randomized: Yes Satisfaction: Improved via CGM acceptance and perceived stress Effectiveness: Improved A1c outcomes Industry funded: No (Australia)	20 overweight adults with type 2 diabetes; mean age of 61 years n = 10 randomized to real-time CGM over 12 weeks n = 10 randomized to blinded CGM with no access to real-time data over 12 weeks 11 years (5) of diabetes on average Mean A1c at baseline: 5.8%–6.9% (41.0%–51.9%) mmol/mol Providers: Nurses instructed participants on the use of CGM insertion and maintenance in the week before baseline. Those with real-time CGM were instructed by a clinical trial manager on activation and identification of the metrics. Participants returned to the clinic every 3 weeks to meet with the research nurse.	CGM type: Medtronic (Guardian Connect with Harmony glucose sensor) Duration of CGM wear: 12 weeks Personal access to CGM data: Real-time CGM use compared with professional-mode CGM use Data completeness: All participants achieved 100% wear time A1c and weight were collected at baseline.	Methodologically sound
Volcansek (2019)⁴¹	Randomized: No Satisfaction: Improved via self-report Effectiveness: Significantly improved CGM metrics Industry funded: No (Slovenia)	25 adults with diabetes mellitus treated by daily basal bolus injections, mean age of 68 years 11 participants (44%) had type 2 diabetes 50% with bachelor’s degree or higher 33 years (3) of diabetes on average Self-monitoring on an average of 3.77 (0.3) per day (for participants with type 2 diabetes only) Excluded adults with cognitive decline Mean A1c at baseline: 55 (7) mmol/mol (type 2 diabetes only) Mean basal insulin dose at baseline: 23 (5) IU/day (type 2 diabetes only) Mean daily insulin dose at baseline: 59 (8) IU/day (type 2 diabetes only) Providers: Group and individual coaching related to CGM was completed at baseline, and participants were able to engage in phone consultations with research staff over the study period.	CGM type: Dexcom (G4) Duration of CGM wear: 4 weeks Personal access to CGM data: Professional mode for first week, then real-time for remaining study period Data completeness: 22 of 25 participants completed 4 weeks of use A1c was collected at baseline.	Methodologically sound
Wong (2023)⁴²	Randomized: No Satisfaction: Improved via Glucose Monitoring System User Satisfaction Survey Effectiveness: Improved A1c outcomes Industry funded: No (China)	180 adults with type 1 or type 2 diabetes and using insulin, mean age of 58.5 and 61.49 years in CGM group and CGM+ type 2 diabetes group, respectively n = 90 with CGM use n = 90 allocated to usual care 65 participants (72.2%) had type 2 diabetes 18.35 years (7) of diabetes on average in participants with type 2 diabetes using CGM Mean A1c at baseline: 8.53% (1.04%) (type 2 diabetes with CGM use only) Majority (40%) of participants with type 2 diabetes and using CGM on 2+ insulin injections per day Providers: CGM users met with a diabetes nurse for a brief orientation and evaluation session. Following CGM wear, an endocrinologist reviewed AGP reports to determine whether medications needed to be adjusted.	CGM type: Dexcom (G6) or Freestyle Libre Duration of CGM wear: 2 weeks Personal access to CGM data: Real-time CGM among intervention group throughout study period Data completeness: Not reported A1c was collected at baseline and then between 12- and 20-week intervals.	Methodologically sound

A1c, glycated hemoglobin; AGP, ambulatory glucose profile; CGM, continuous glucose monitoring.

Usability outcomes

CGM effectiveness, efficiency, and satisfaction assessments varied across studies (Table 2). Overall, CGM was reported to be more effective than usual care (77%, 10 of 13 evaluations). Findings also demonstrated higher satisfaction measures for CGM than usual care (86%, 6 of 7 evaluations). Among the reviewed studies, there were no findings related to the efficiency of CGM.

Table 2.

Measures of Effectiveness, Efficiency, and Satisfaction Across Included Articles

Usability outcome	Measure	Number of studies	Results	Discussion of results
Effectiveness	Glycated hemoglobin (HbA1c or A1c) Individual’s average glucose levels, expressed as a percentage, over 90 days	7	Studies with statistically significant decrease: Bao, Simonson, Wong (3/7 studies) Studies with decrease in A1c, not statistically significant: Lind, Taylor (2/7) Baseline measure only: Speight, Volcansek (2/7)	Continuous Glucose Monitoring (CGM) vs. Usual Care: Two studies reported statistically significant decreases in A1c by CGM users (Bao, Wong). One study only measured A1c at baseline (Speight) Care Models: Improvements in A1c were noted but were not statistically significant in the peer-support group model (Lind). Improvements in A1c were statistically significant across the whole cohort, including the clinical care and nurse-led models (Simonson) CGM Types: One study evaluating real-time (rt) CGM compared with professional-mode (prof) CGM reported decreases in A1c, but they were not statistically significant between groups (Taylor). One study only measured A1c at baseline (Volcansek).
	Time-in-range (TIR) Percentage of time blood glucose levels remain in target range (70–180 mg/dL)	4	Studies with statistically significant increase: Bao, Volcansek (2/4) Studies with increase in TIR, not statistically significant: Lind, Simonson (2/4)	CGM vs. Usual Care: One study reported statistically significant increases in TIR by CGM users compared to controls (Bao) Care Models: Improvements in TIR were not statistically significant in the clinical care compared with nurse-led model, although changes were more pronounced in the former (Simonson). In peer-support group model, there were improvements in TIR, but no statistically significant difference compared with control (Lind) CGM Types: Use of rtCGM lead to statistically significant increases in TIR from baseline (Volcansek)
	Time-below-range (TBR) Percentage of time blood glucose levels fall below target lower threshold (<70 mg/dL)	4	Studies with statistically significant decrease: Volcansek (1/4) Studies with increase in TBR, not statistically significant: Simonson (1/4) No changes in TBR, not statistically significant: Bao, Lind (2/4)	CGM vs. Usual Care: One study reported little difference in TBR between CGM and control groups (Bao) Care Models: The peer-support group model reported little difference in TBR compared with control group (Lind). TBR increased in clinical care model and nurse-led model, with a more pronounced increase in the clinical care model (Simonson) CGM Types: One evaluating rtCGM reported a statistically significant decrease in TBR from baseline (Volcansek)
	Time-above-range (TAR) Percentage of time blood glucose levels rise above target upper threshold (>180 mg/dL)	4	Studies with statistically significant decrease: Bao, Volcansek (2/4) Studies with statistically significant increase: Lind (1/4) Studies with decrease in TAR, not statistically significant: Simonson (1/4)	CGM vs. Usual Care: One study reported statistically significant decrease in TAR by CGM users compared with controls (Bao) Care Models: TAR in the clinical care and nurse-led model was improved but not statistically significant (Simonson). Increase in TAR >13.9 mmol/L was statistically significant in the peer-support group model at 6 months (Lind) CGM Types: One study evaluating rtCGM reported statistically significant decrease in TAR from baseline (Volcansek)
	Coefficient of variation(CV) Average fluctuations in blood glucose levels over time	4	Studies with statistically significant decrease: Lind, Volcansek (2/4) Studies with decrease in CV, not statistically significant: Bao, Simonson (1/4)	CGM vs. Usual Care: One study reported a decrease in CV by CGM users compared with controls, but it did not reach statistical significance among users older than 65 years (Bao) Care Models: The improvements in CV were statistically significant in the peer-support group model at 6 months (Lind). Improvements in CV were reported but were not statistically significant in the clinical care model (Simonson) CGM Types: Studies evaluating rtCGM reported statistically significant decrease in CV from baseline (Volcansek)
	Diabetes management/health behavior Participant-reported diabetes self-care activities in daily life	4	Studies with statistically significant increase: Taylor (1/4) Studies with improvement, statistical significance not evaluated: Volcansek (1/4) No changes in health behavior, not statistically significant: Lind, Speight (2/4)	CGM vs. Usual Care: One study reported improvements in diet and physical activity that were not statistically significant (Speight) Care Models: One study reported little difference in health behaviors among those in the peer-support group model (Lind) CGM Types: There was a statistically significant difference in diabetes management behaviors between rtCGM and profCGM users, but not in diet or exercise behaviors (Taylor). One study documented self-reported improvements in exercise and diet behaviors, but no statistically significant analysis was used for this descriptive measurement (Volcansek)
	Changes in diabetes medication Change in medication dosage or type during the study time frame	4	Studies with statistically significant decrease: Bao, Lind (2/4) Studies with increase in medication, not statistically significant: Simonson (1/4) Studies with improvement, statistical significance not evaluated: Wong (1/4)	CGM vs. Usual Care: One study reported statistically significant differences in adding at least one diabetes medication between CGM users and controls (Bao). Another study shared descriptively an increase in diabetes medication adjustment among the majority of CGM users compared with baseline (Wong) Care Models: The clinical care model was more likely to increase insulin or medication adjustments compared with the nurse-led model, although the difference between groups was not statistically significant (Simonson). Statistically significant decrease in insulin total daily dose was reported in the peer-support group at 6 months, but there was little between-groups difference in number of diabetes medications (Lind) CGM Types: Not evaluated
	Mean glucose (MG) Average blood glucose level on a daily basis or short-term time frame	2	Studies with statistically significant increase: Bao (1/2) Studies with improvement, not statistically significant: Lind (1/2)	CGM vs. Usual Care: One study reported statistically significant decrease in MG by CGM users compared with controls (Bao) Care Models: Peer-support group model found improvements in MG but not statistically significant (Lind) CGM Types: Not evaluated
	Adverse events Unintended negative outcomes or harmful effects during CGM intervention	2	Studies with increase in adverse events, statistical significance not evaluated: Bao (1/2) No changes in adverse events, statistical significance not evaluated: Lind (1/2)	CGM vs. Usual Care: One study reported an increase in adverse events by CGM users (Bao) Care Models: There were no changes in the number of adverse events in the peer-support group model (Lind) CGM Types: Not evaluated
	Involvement in clinical care Qualitative or quantitative measures of clinician relations and shared decision-making, as identified by the participant/provider	1	No changes in perceived involvement, not statistically significant: Speight (1/1)	CGM vs. Usual Care: One study reported no statistically significant changes in CGM users compared with controls (Speight) Care Models: Not evaluatedCGM Types: Not evaluated
Efficiency	No reported measures	0	Not evaluated	Not evaluated
Satisfaction	Glucose monitoring satisfaction Participant-identified level of satisfaction with wearing the CGM tool or with their current method of glucose monitoring	5	Studies with statistically significant increase: Speight (1/5) Studies with improved satisfaction, not statistically significant: Lind, Wong (2/5) Studies with improved satisfaction, statistical significance not evaluated: Bao, Volcansek (2/5)	CGM vs. Usual Care: One study reported statistically significant improvements, and an additional study reported nonstatistically significant improvements in glucose monitoring satisfaction in CGM users (Speight, Wong). One study reported descriptive improvements in CGM satisfaction via self-report (Bao) Care Models: There were nonstatistically significant improvements in glucose monitoring satisfaction in the peer-support group model (Lind) CGM types: One study evaluating rtCGM found high satisfaction using descriptive measures (Volcansek)
	Diabetes-related distress Level of emotional and psychological burden related to living with diabetes or experiencing diabetes complications	5	Studies with decrease in distress, not statistically significant: Lind, Taylor, Volcansek (3/5) Studies with decrease in distress, statistical significance not evaluated: Bao (1/5) Baseline measure only: Speight (1/5)	CGM vs Usual Care: One study reported nonstatistically significant decreases in diabetes-related distress in CGM users compared with controls (Bao). One study only measured distress at baseline (Speight) Care Models: No statistically significant changes in diabetes-related distress were reported in the peer-support group model (Lind) CGM Types: Studies evaluating rtCGM reported nonstatistically significant reductions in diabetes-related distress (Taylor, Volcansek)
	Diabetes-related quality of life(QoL) The extent to which diabetes has affected daily life as identified by the participant	3	Studies with statistically significant increase: Speight (1/3) Studies with improved QoL, statistical significance not evaluated: Bao, Volcansek (2/3)	CGM vs. Usual Care: One study reported statistically significant increase in the diabetes-related QoL in CGM users (Speight). Another showed reductions in burden among CGM users compared with controls, but statistically significant differences were not evaluated (Bao) Care Models: Not evaluated CGM Types: One study evaluating rtCGM found improved QoL using descriptive measures (Volcansek)
Acceptability	Acceptability Participant-reported perceptions of acceptance or satisfaction regarding the CGM device or relevant intervention	3	Studies with statistically significant increase: Taylor (1/2) Studies with increase in acceptability, statistical significance not evaluated: Lind, Volcansek (2/3)	CGM vs Usual Care: Not evaluated Care Models: The peer-support group model was found to have little attendance or acceptance, but qualitative measures indicate high acceptability of the CGM device (Lind) CGM Types: One study showed statistically significant improvements in acceptability at 6 weeks of rtCGM use and nonstatistically significant improvements in device tolerance compared with blind CGM users (Taylor). Another study showed acceptance of CGM among rtCGM users, although this was only measured descriptively (Volcansek)
Other	Emotional well-being Overall emotional well-being (not specifically related to living with diabetes) as identified by the participant	2	Studies with decrease in well-being, not statistically significant: Speight (1/2)Studies with increase in well-being, statistical significance not evaluated: Lind (1/2)	CGM vs Usual Care: One study reported reduced emotional well-being in CGM users and controls at 12 months (Speight) Care Models: An improvement in emotional well-being was reported in the CGM-only model, not the peer-support group model, with no statistically significant difference (Lind) CGM Types: Not evaluated
Other	Hypoglycemia fear Short-form assessment of anticipatory anxiety or fear of low blood sugar	2	Studies with decreases in fear, not statistically significant: Lind (1/2) Studies with decrease in fear, statistical significance not evaluated: Bao (1/2)	CGM vs Usual Care: One study reported a decrease in hypoglycemia fear in CGM users compared with controls, but statistical significance was not reported (Bao) Care Models: No statistically significant changes in hypoglycemia fear were found in the peer-support group model, although scores were reduced (Lind) CGM types: Not evaluated

GV, glucose variability; MG, mean glucose; prof, professional-mode; QoL, quality of life; rt, real time; TAR, time-above-range; TBR, time-below-range; TIR, time-in-range.

Within the included studies, there were 36 evaluations of effectiveness measures, of which 42% (15 of 36) showed statistically significant results. Although three of 20 evaluations demonstrated statistically significant results for satisfaction, 80% (16 of 20) also described improved satisfaction with CGM use. There were no evaluations of efficiency measures. For a complete description of the effectiveness and satisfaction per included article, see Supplementary Table S2.

Effectiveness

Ten measures of effectiveness were reported across the studies, including A1c, time-in-range, time-below-range, time-above-range, and glycemic variability.^36–38,41 In three studies, type 2 diabetes management with CGM resulted in statistically significant reductions in A1c.^36,38,42 Comparing CGM types, eight effectiveness measures were reported.^40,41 A1c and time-in-range were the most commonly measured parameters. No significant differences in A1c values were reported between real-time and professional-mode CGM use (only clinicians could see the data).^40,41 However, one study found significant increases in time-in-range with real-time CGM (users could see the data).⁴¹ Comparing CGM effectiveness across care models, there were improvements in A1c and time-in-range; however, the differences were not significant between clinician-supported models or peer-supported models.^37,38 In addition, six studies reported changes in diabetes management behaviors and adjustments in insulin or diabetes medication among their study population.^36–40,42

Efficiency

The impact of CGM on efficiency in type 2 diabetes management is not consistently addressed in the studies reviewed. Although efficiency measures were not explicitly described, the lack of integration of AGP metrics into the electronic health record was reported as a clinical workflow barrier.³⁸ In the included studies, clinicians mainly uploaded the sensor output sent by older adults in person or via mail,^38,39 or received CGM data via wireless Bluetooth transmission.³⁶ In these cases, clinicians reviewed and discussed the sensor data and AGP report with participants at follow-up visits, occurring at variable time intervals after sensor placement. Researchers suggested that therapeutic inertia and degradation of glycemic impact over time could be prevented with workflow optimization.³⁸ Furthermore, Speight et al. noted a reduced uptake of AGP training by clinicians who were unfamiliar with AGP or lacked experience in interpretation.³⁹

Satisfaction

Six of seven articles reported older adults’ satisfaction with using CGM.^{36,37,39–42} Satisfaction measures were predominantly descriptive. None of the reviewed studies evaluated clinicians’ satisfaction with the CGM. Only differences in satisfaction and quality-of-life scores among flash-CGM intervention participants in an RCT,³⁹ and acceptability scores after 6 weeks of real-time CGM use,⁴⁰ were significant compared with control groups. Studies reported trends of reduced diabetes-related distress,^36,37,40,41 reduced CGM-related general chronic stress for older adults,⁴⁰ and increased diabetes-related quality of life.³⁹ There were few differences in quality of life or perceived stress scores between comparison groups.^39,40 Older adults randomized to a peer support intervention in addition to CGM (n = 15) were more likely to desire additional education from clinicians on the use of CGM data; all desired to continue using CGM, despite no interest in continuing with a peer support group.³⁷ Nine of 11 participants with type 2 diabetes using real-time CGM for 3 weeks indicated their desire to continue to use the device after the study.⁴¹

Discussion

This is one of the first systematic reviews of research evaluating the use of CGM and its usability for managing type 2 diabetes in older adults. Similar to existing reviews, our findings support the use of CGM for people 60 years and older based on the overall clinical effectiveness and satisfaction. Yet, this review expounds findings by identifying a critical gap in the literature: measures of efficiency, effectiveness, and satisfaction were not reported for essential elements of the CGM context of use, including the AGP.^36,39,42 Early studies compared CGM with usual care.^36,39,42 In later studies, we noted a shift in the primary objectives to comparisons of different types of CGM and care models.^37,38,40,41 Among later comparisons, few significant differences in outcomes were reported. Based on our review, critical aspects of AGP metrics were understudied.

More than time-in-range: AGP metrics for effectiveness

Although A1c is the established measurement of glycemic management, publications have denoted its limited ability to convey glucose variability. Through CGM, time-in-range and other measures provide additional insight. Various AGP metrics contribute to a comprehensive and useful understanding of type 2 diabetes. Similar to other studies, in this review, the most reported clinical effectiveness measures were changes in A1c and time-in-range.^31,43 Time-above-range and time-below-range were evaluated inconsistently. Although AGP metrics vary across manufacturers’ reporting formats, 10 standard metrics are consistently reported in the AGP.¹⁶ For example, the coefficient of variation describes how much glucose levels vary daily.⁴⁴ The glucose management indicator estimates an individual’s A1c.⁴⁵ Previous studies have demonstrated the value of these AGP metrics,⁴⁶ but were rarely reported in the articles included. Three CGM studies did not report any AGP metrics other than the A1c.^37,39,42 Future studies are warranted to assess clinicians’ knowledge, interpretation, and use of AGP metrics beyond time-in-range. Although clinician satisfaction with CGM in the management of type 2 diabetes was not reported in the included articles, CGM-integrated workflows could affect clinician-reported CGM satisfaction, efficiency, and effectiveness. A better understanding of the clinicians’ decision-making process may guide advancements in AGP data and improve its ability to support type 2 diabetes management.

Considering CGM’s ability to detect hypoglycemia, few of the included articles examined this feature among adults aged 60 years and older.^36,38,41 While time-below-range was mentioned, the AGP reports and other CGM data provide more information on hypoglycemic events, including the number of events, the average duration of these events, and the days or times of events. With this information, clinicians’ awareness of the individual’s hypoglycemic risk can increase, which is important with an older population prone to hypoglycemic unawareness.⁴⁷ Future research should investigate the usability of time-below-range metrics for hypoglycemia detection in this target population and clinician response.

Personal access to AGP data: Benefit or burden?

For adults 60 years and older, the introduction of CGM raised concerns of increased stress or burden, such as data overload and alarm fatigue.⁴⁸ Consistent with existing studies among younger populations, evidence from the reviewed studies demonstrated that the use of CGM either improved or had no detrimental impact on participants’ general or diabetes-related levels of stress, thus reinforcing the positive value that CGM has on self-reported well-being and quality of life.^32,49 While comparisons among care models were similar,³⁷ comparisons between CGM types demonstrated differences in satisfaction and long-term adoption.⁴¹ In reviewed studies, the number of participants who completed the full intervention protocol with professional-mode CGM, which does not allow personal access to AGP data, reduced progressively after 9 months of use.³⁹ Access to AGP data is perceived as having value, and the visual display of real-time blood glucose data provides positive reinforcement for participants’ behavior.¹⁴ Future research should investigate whether engagement in self-management induces or leads to CGM satisfaction.

CGM-integrated workflows: Efficient clinician access to data

Although efficiency was not reported as an outcome in the included articles, efficient access to AGP data related to the implementation of CGM in clinical workflows was discussed.³⁸ The efficiency of AGP interpretation depends on data access, quality, and reliability of collected information, and implementation of a standardized process.⁵⁰ One study described barriers to data access and an intervention to overcome them;³⁸ no study addressed data standardization. Efficient CGM-driven type 2 diabetes management relies on the integration of AGP into clinical workflows; a lack of integration can be challenging due to data overload, increased clinic staff time, and delayed interventions.⁵¹ Efficient CGM-based management of type 2 diabetes relies on clinicians’ adoption of technology education about data access and interpretation and the use of standardized AGP report-based interventions. Evaluating workflows associated with AGP-based self-management in adults 60 years and older could be valuable in enhancing the efficiency of care. Future research is needed to investigate approaches to integrating AGP metrics with electronic health record systems.

Limitations

There are limitations to consider regarding this review. The search strategy may have missed relevant articles. The 2-week minimum CGM requirement may have omitted studies relevant to the study’s topic. Articles selected for review might not reflect all the important findings. Variability in study quality challenges our ability to compare findings meaningfully and limits the validity of our conclusions. Some included studies had small sample sizes or lacked consistent measurement of key outcomes such as AGP and usability, highlighting a gap in CGM usability research for older adults.

Conclusions

Although CGM is effective among adults 60 years of age or older with type 2 diabetes, the determination of CGM’s usability is inconclusive. Little evidence was reported regarding the effectiveness, efficiency, and satisfaction of CGM tools and technology, including those required to use AGP metrics. From both clinician and older adults’ perspectives, these gaps in evidence prohibit assessment of AGP metrics and insights on its use, interpretation, and communication. AGP metrics inform type 2 diabetes management and decision-making. Therefore, a lack of emphasis on AGP metrics beyond time-in-range limits knowledge of how to improve CGM for older adults across various settings, CGM types, and care models. Existing studies do not provide insights into specific data metrics (beyond time-in-range) or visualizations that are helpful for clinicians or older adults. Future research is warranted to investigate the usability of AGP metrics for clinicians and older adults and to assess the potential benefits of enhanced awareness, shared decision-making, and self-management with CGM.

Authors’ Contributions

A.S., C.B., and M.W. were involved in the conception and design of the systematic review. A.S., C.B., and M.R.T. were involved in the conduct of the review and analysis and interpretation of the results. A.S., C.B., and M.R.T. wrote the first draft of the article, and all authors edited, reviewed, and approved the final version of the article.

Footnotes

Author Disclosure Statement

M.W. reports stock holdings in 3M, AbbVie, Amgen, Bristol Myers Squibb, Centene, CONMED, Crispr Therapeutics, Dexcom, DXC Technology, Embecta, GE Healthcare Technologies, Globus Med, GSK, Haemonetics, Integer, Integra Lifesciences, Johnson & Johnson, Medtronic, Novo Nordisk, Orthofix, Pfizer, Revvity, Roche, Stryker, Teva Pharmaceutical Industries, Veradigm, and Zimmer Biomet. The other authors have no competing interests to declare.

Funding Information

A.S. is supported by a Career Development Award from the National Institute on Aging (Award Number K01AG076971).

Disclaimer

The views expressed in this article are those of the authors and do not necessarily represent the views of the U.S. Department of Veterans Affairs or the U.S. government.

Supplemental Material

References

Centers for Disease Control and Prevention. National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States. Centers for Disease Control and Prevention; 2023.

Ong

, Stafford

, McLaughlin

, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: A systematic analysis for the Global Burden of Disease Study 2021. The Lancet, 2023; 402(10397):203–234; doi: 10.1016/s0140-6736(23)01301-6

Abdelhafiz

, Rodríguez-Mañas

, Morley

, et al. Hypoglycemia in older people-A less well recognized risk factor for frailty. Aging Dis, 2015; 6(2):156–167; doi: 10.14336/AD.2014.0330

Ishikawa

, Koshizaka

, Maezawa

, et al. Continuous glucose monitoring reveals hypoglycemia risk in elderly patients with type 2 diabetes mellitus. J Diabetes Investig, 2018; 9(1):69–74; doi: 10.1111/jdi.12676

Kesavadev

, Ramachandran

, Krishnan

. Glucose monitoring technologies—complementary or competitive? Role of continuous glucose monitoring versus flash glucose monitoring versus self-monitoring of blood glucose. J Diabetol, 2017; 8(3):61; doi: 10.4103/jod.jod_14_17

Lassmann-Vague

. Hypoglycaemia in elderly diabetic patients. Diabetes Metab, 2005; 31 Spec No 2:5S53–5S57; doi: 10.1016/S1262-3636(05)73651-X

Böhm

A-K

, Jensen

, Sørensen

, et al. Real-World evidence of user engagement with mobile health for diabetes management: Longitudinal observational study. JMIR Mhealth Uhealth, 2020; 8(11):e22212; doi: 10.2196/22212

Bremer

, Jauch-Chara

, Hallschmid

, et al. Hypoglycemia unawareness in older compared with middle-aged patients with type 2 diabetes. Diabetes Care, 2009; 32(8):1513–1517; doi: 10.2337/dc09-0114

Agarwal

, Cappola

. Continuous glucose monitoring in adolescent, young adult, and older patients with type 1 diabetes. JAMA, 2020; 323(23):2384–2385; doi: 10.1001/jama.2020.7058

10.

Thomson

, Masson

, Leeming

, et al. Lack of knowledge of symptoms of hypoglycaemia by elderly diabetic patients. Age Ageing, 1991; 20(6):404–406; doi: 10.1093/ageing/20.6.404

11.

American Diabetes Association Professional Practice Committee. 17. Diabetes Advocacy: Standards of Care in Diabetes-2025. Diabetes Care, 2025; 48(Suppl 1):S335–S336; doi: 10.2337/dc25-S017

12.

Adolfsson

, Parkin

, Thomas

, et al. Selecting the appropriate continuous glucose monitoring system – a practical approach. Eur Endocrinol, 2018; 14(1):24–29; doi: 10.17925/EE.2018.14.1.24

13.

Siegelaar

, Holleman

, Hoekstra

JBL

, et al. Glucose variability; does it matter? Endocr Rev, 2010; 31(2):171–182; doi: 10.1210/er.2009-0021

14.

Reddy

, Verma

, Dungan

. Monitoring Technologies-Continuous Glucose Monitoring, Mobile Technology, Biomarkers of Glycemic Control. In: Endotext. ( Feingold

, Anawalt

, Blackman

, et al. eds) MDText.com, Inc.: South Dartmouth (MA); 2000.

15.

Janapala

, Jayaraj

, Fathima

, et al. Continuous glucose monitoring versus self-monitoring of blood glucose in type 2 diabetes mellitus: A systematic review with meta-analysis. Cureus, 2019; 11(9):e5634; doi: 10.7759/cureus.5634

16.

ElSayed

, Aleppo

, Aroda

, et al. 6. Glycemic targets: Standards of care in diabetes-2023. Diabetes Care, 2023; 46(Suppl 1):S97–S110; doi: 10.2337/dc23-S006

17.

Czupryniak

, Dzida

, Fichna

, et al. Ambulatory Glucose Profile (AGP) report in daily care of patients with diabetes: Practical tips and recommendations. Diabetes Ther, 2022; 13(4):811–821; doi: 10.1007/s13300-022-01229-9

18.

ElSayed

, Aleppo

, et al.; American Diabetes Association Professional Practice Committee. 5. Facilitating positive health behaviors and well-being to improve health outcomes: Standards of care in diabetes—2024. Diabetes Care, 2024; 47(Suppl 1):S77–S110; doi: 10.2337/dc24-S005

19.

Gandhi

, Kovalaske

, Kudva

, et al. Efficacy of continuous glucose monitoring in improving glycemic control and reducing hypoglycemia: A systematic review and meta-analysis of randomized trials. J Diabetes Sci Technol, 2011; 5(4):952–965; doi: 10.1177/193229681100500419

20.

Kushniruk

, Borycki

, Kuwata

, et al. Emerging Approaches to Usability Evaluation of Health Information Systems: Towards In-Situ Analysis of Complex Healthcare Systems and Environments. In: User Centred Networked Health Care. Studies in Health Technology and Informatics IOS Press; 2011; pp. 915–919.

21.

Allen

, Fain

, Braun

, et al. Continuous glucose monitoring in non-insulin-using individuals with type 2 diabetes: Acceptability, feasibility, and teaching opportunities. Diabetes Technol Ther, 2009; 11(3):151–158; doi: 10.1089/dia.2008.0053

22.

Rivera-Ávila

, Esquivel-Lu

, Salazar-Lozano

, et al. The effects of professional continuous glucose monitoring as an adjuvant educational tool for improving glycemic control in patients with type 2 diabetes. BMC Endocr Disord, 2021; 21(1):79; doi: 10.1186/s12902-021-00742-5

23.

Centers for Disease Control and Prevention. National Diabetes Statistics Report. 2024.

24.

Smith

, Albanese-O’Neill

, Macieira

TGR

, et al. Human factors associated with continuous glucose monitor use in patients with diabetes: A systematic review. Diabetes Technol Ther, 2019; 21(10):589–601; doi: 10.1089/dia.2019.0136

25.

Seidu

, Kunutsor

, Ajjan

, et al. Efficacy and safety of continuous glucose monitoring and intermittently scanned continuous glucose monitoring in patients with type 2 diabetes: A systematic review and meta-analysis of interventional evidence. Diabetes Care, 2024; 47(7):e52–e53; doi: 10.2337/dc23-1520

26.

Taylor

, Thompson

, Brinkworth

. Effectiveness and acceptability of continuous glucose monitoring for type 2 diabetes management: A narrative review. J Diabetes Investig, 2018; 9(4):713–725; doi: 10.1111/jdi.12807

27.

Savoy

, Holden

, De Groot

, et al. Improving care for people living with dementia and diabetes: Applying the human-centered design process to continuous glucose monitoring. J Diabetes Sci Technol, 2024; 18(1):201–206; doi: 10.1177/19322968221137907

28.

ISO. ISO 9241-11: Ergonomics of Human-System Interaction—Part 11. Usability: Definitions and Concepts, 2018.

29.

Nelson

, Coston

, Cherrington

, et al. Patterns of user engagement with mobile- and web-delivered self-care interventions for adults with T2DM: A review of the literature. Curr Diab Rep, 2016; 16(7):66; doi: 10.1007/s11892-016-0755-1

30.

Uhl

, Choure

, Rouse

, et al. Effectiveness of continuous glucose monitoring on metrics of glycemic control in type 2 diabetes mellitus: A systematic review and meta-analysis of randomized controlled trials. J Clin Endocrinol Metab, 2024; 109(4):1119–1131; doi: 10.1210/clinem/dgad652

31.

Kong

S-Y

, Cho

M-K

. Effects of continuous glucose monitoring on glycemic control in type 2 diabetes: A systematic review and meta-analysis. Healthcare (Basel), 2024; 12(5):571; doi: 10.3390/healthcare12050571

32.

Savoy

, Barboi

, Thomas

. The use and usability of continuous glucose monitoring for older adults with type 2 diabetes. A Systematic Review, 2025; doi: 10.17605/OSF.IO/EUD4S

33.

ISO. ISO 9241: Ergonomic Requirements for Office Work with Visual Display Terminals (VDTs) – Part 11: Guidance on Usability. 1998.

34.

Critical Appraisal Skills Programme. Critical Appraisal Checklists. 2024.

35.

Bevan

, Carter

, Earthy

, et al. New ISO standards for usability, usability reports and usability measures. In: Human-Computer Interaction. Theory, Design, Development and Practice. ( Kurosu

ed). Lecture Notes in Computer Science Springer International Publishing: Cham; 2016; pp. 268–278; doi: 10.1007/978-3-319-39510-4_25

36.

Bao

, Bailey

, Calhoun

, et al. Effectiveness of continuous glucose monitoring in older adults with type 2 diabetes treated with basal insulin. Diabetes Technol Ther, 2022; 24(5):299–306; doi: 10.1089/dia.2021.0494

37.

Lind

, Christensen

, Hansen

, et al. Peer support for adults with type 2 diabetes starting continuous glucose monitoring—An exploratory randomised controlled trial. Diabet Med, 2024; 41(6):e15321; doi: 10.1111/dme.15321

38.

Simonson

, Bergenstal

, Johnson

, et al. Effect of Professional CGM (pCGM) on glucose management in type 2 diabetes patients in primary care. J Diabetes Sci Technol, 2021; 15(3):539–545; doi: 10.1177/1932296821998724

39.

Speight

, Holmes-Truscott

, Thuraisingam

, et al.; GP-OSMOTIC Study Group. Impact of quarterly professional-mode flash glucose monitoring in adults with type 2 diabetes in general practice (GP-OSMOTIC): Secondary psychological and self-care outcomes of a pragmatic, open-label, 12-month, randomised controlled trial. Diabetes Res Clin Pract, 2021; 179:108994; doi: 10.1016/j.diabres.2021.108994

40.

Taylor

, Thompson

, Luscombe-Marsh

, et al. Tolerability and acceptability of real-time continuous glucose monitoring and its impact on diabetes management behaviours in individuals with Type 2 Diabetes – A pilot study. Diabetes Res Clin Pract, 2019; 155:107814; doi: 10.1016/j.diabres.2019.107814

41.

Volčanšek

, Lunder

, Janež

. Acceptability of continuous glucose monitoring in elderly diabetes patients using multiple daily insulin injections. Diabetes Technol Ther, 2019; 21(10):566–574; doi: 10.1089/dia.2019.0131

42.

Wong

T-W

. Use of personal Continuous Glucose Monitoring (CGM) with support in people with type 1 and 2 diabetes treated with insulin in the outpatient clinic: A single-center retrospective cohort study. CD, 2023; 12(2):95–104; doi: 10.5603/DK.a2023.0008

43.

Manov

, Chauhan

, Dhillon

, et al. The effectiveness of continuous glucose monitoring devices in managing uncontrolled diabetes mellitus: A retrospective study. Cureus, 2023; 15(7):e42545; doi: 10.7759/cureus.42545

44.

Lin

, Brown

, Ekinci

. The ambulatory glucose profile and its interpretation. Med J Aust, 2022; 217(6):295–298; doi: 10.5694/mja2.51666

45.

Bergenstal

, Beck

, Close

, et al. Glucose Management Indicator (GMI): A new term for estimating A1C from continuous glucose monitoring. Diabetes Care, 2018; 41(11):2275–2280; doi: 10.2337/dc18-1581

46.

Simonson

, Holt

, Grady

, et al. Unleashing the potential of blood glucose monitoring data with the ambulatory glucose profile report. Clin Diabetes, 2024; 42(4):550–560; doi: 10.2337/cd23-0092

47.

Rickels

, Peleckis

, Dalton-Bakes

, et al. Continuous glucose monitoring for hypoglycemia avoidance and glucose counterregulation in long-standing type 1 diabetes. J Clin Endocrinol Metab, 2018; 103(1):105–114; doi: 10.1210/jc.2017-01516

48.

Huang

, Sinclair

, Conlin

, et al. The growing role of technology in the care of older adults with diabetes. Diabetes Care, 2023; 46(8):1455–1463; doi: 10.2337/dci23-0021

49.

Hirose

, Beverly

, Weinger

. Quality of life and technology: Impact on children and families with diabetes. Curr Diab Rep, 2012; 12(6):711–720; doi: 10.1007/s11892-012-0313-4

50.

Johnson

, Martens

, Criego

, et al. Utilizing the ambulatory glucose profile to standardize and implement continuous glucose monitoring in clinical practice. Diabetes Technol Ther, 2019; 21(S2):S217–S225; doi: 10.1089/dia.2019.0034

51.

Miller

. Using continuous glucose monitoring in clinical practice. Clin Diabetes, 2020; 38(5):429–438; doi: 10.2337/cd20-0043

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.53 MB