Self-care barriers and facilitators in older adults with T1D during a time of sudden isolation.

Older adults with type 1 diabetes (T1D) have unique challenges and needs. In this mixed-methods study, we explored the impact of isolation during a pandemic on diabetes management and overall quality of life in this population. Older adults (age ≥ 65 years) with T1D receiving care at a tertiary care diabetes center participated in semi-structured interviews during COVID-19 pandemic isolation between June and August 2020. A multi-disciplinary team coded transcripts and conducted thematic analysis. Thirty-four older adults (age 71 ± 5 years, 97% non-Hispanic white, diabetes duration of 38 ± 7 years, A1C of 7.4 ± 0.9% (57.3 ± 10.1 mmol/mol) were recruited. Three themes related to diabetes self-care emerged regarding impact of isolation on: (1) diabetes management and self-care behaviors (how isolation prompted changes in physical activity and dietary habits); (2) emotional stress and anxiety (related to isolation and lack of support system, economic concerns); and (3) concerns regarding the COVID-19 pandemic (impact on timely medical care and access to information). Our findings identify modifiable barriers and challenges faced by older adults with T1D during isolation. As this population has a higher risk of decline in physical and psychosocial support even during non-pandemic times, clinicians will benefit from understanding these issues to improve care of this population.

Use of Telemedicine in Adults with Type 1 Diabetes: Do Age and Use of Diabetes-Related Technology Matter?

Objective: Older adults are generally less proficient in technology use compared with younger adults. Data on telemedicine use during the COVID-19 pandemic in older persons with type 1 diabetes (T1D) and the association of telemedicine with the use of diabetes-related technology are limited. We evaluated care delivery to older adults compared with younger adults with T1D in a prepandemic and pandemic period. Methods: Data from electronic health records were evaluated for visit types (in-person, phone, and video) from two sequential 12-month intervals: prepandemic (April 2019-March 2020) and pandemic (April 2020-March 2021). Results: Data from 2,832 unique adults with T1D were evaluated in two age cohorts: younger (40-64 years) and older (≥65 years). Half of each group used continuous glucose monitoring (CGM), whereas 54% of the younger and 37% of the older cohort used pump therapy (p < 0.001). During the pandemic compared with the prepandemic period, visit frequency increased in both the younger (0.65 vs. 0.76 visits/patient/quarter; p < 0.01) and older (0.72 vs. 0.80 visits/patient/quarter; p < 0.01) cohorts. During the pandemic, older adults used more phone visits compared with younger adults (48% vs. 32%; p = 0.001). Patients using either pump therapy or CGM were more likely to use video visits compared with phone visits in both younger (41% vs. 24%; p < 0.001) and older cohorts (53% vs. 42%; p < 0.001). Conclusions: Adults using diabetes-related technologies, independent of age, accessed more video visits than those not using devices. Telemedicine visits appeared to maintain continuity of care for younger and older adults with T1D, supporting the future of a hybrid-care model.

Use of Telecommunication and Diabetes-Related Technologies in Older Adults With Type 1 Diabetes During a Time of Sudden Isolation: Mixed Methods Study.

BACKGROUND: The COVID-19 lockdown imposed a sudden change in lifestyle with self-isolation and a rapid shift to the use of technology to maintain clinical care and social connections. OBJECTIVE: In this mixed methods study, we explored the impact of isolation during the lockdown on the use of technology in older adults with type 1 diabetes (T1D). METHODS: Older adults (aged ≥65 years) with T1D using continuous glucose monitoring (CGM) participated in semistructured interviews during the COVID-19 lockdown. A multidisciplinary team coded the interviews. In addition, CGM metrics from a subgroup of participants were collected before and during the lockdown. RESULTS: We evaluated 34 participants (mean age 71, SD 5 years). Three themes related to technology use emerged from the thematic analysis regarding the impact of isolation on (1) insulin pump and CGM use to manage diabetes, including timely access to supplies, and changing Medicare eligibility regulations; (2) technology use for social interaction; and (3) telehealth use to maintain medical care. The CGM data from a subgroup (19/34, 56%; mean age 74, SD 5 years) showed an increase in time in range (mean 57%, SD 17% vs mean 63%, SD 15%; P=.001), a decrease in hyperglycemia (>180 mg/dL; mean 41%, SD 19% vs mean 35%, SD 17%; P<.001), and no change in hypoglycemia (<70 mg/dL; median 0.7%, IQR 0%-2% vs median 1.1%, IQR 0%-4%; P=.40) during the lockdown compared to before the lockdown. CONCLUSIONS: These findings show that our cohort of older adults successfully used technology during isolation. Participants provided the positive and negative perceptions of technology use. Clinicians can benefit from our findings by identifying barriers to technology use during times of isolation and developing strategies to overcome these barriers.

Impact of Diabetes Duration on Functional and Clinical Status in Older Adults With Type 1 Diabetes.

OBJECTIVE: Adults with type 1 diabetes (T1D) are aging successfully. The impact of diabetes duration on clinical and functional status as people age with T1D is not well known. RESEARCH DESIGN AND METHODS: We performed a cross-sectional study of older adults (age ≥65 years) with T1D. RESULTS: We evaluated 165 older adults, mean age 70 ± 10 years. After adjustment for age, sex, and A1C, longer duration of T1D, ≥50 years, was associated with a higher likelihood of depression (odds ratio [OR] 2.8; P = 0.008), hypoglycemia unawareness (OR 2.6; P = 0.01), lower scores on 6-Minute Walk Test (OR 0.99; P = 0.01) and the Physical Component Summary (PCS) of Short Form-36 (SF-36) (OR 0.96; P = 0.02), and greater daily medication use (OR 1.1; P = 0.004) compared with those with duration <50 years. CONCLUSIONS: In older adults with T1D, duration of diabetes impacts clinical and functional status, independent of age and glycemic control, and should be considered in development of management strategies for safety and success.

Closed-Loop Insulin Therapy in Older Adults with Type 1 Diabetes: Real-World Data.

Objective: To assess the impact of initiation of closed-loop control (CLC) on glycemic metrics in older adults with type 1 diabetes (T1D) in the real world. Methods: Retrospective analysis of electronic health records from a single tertiary diabetes center of older adults prescribed CLC between January and December 2020. Results: Forty-eight patients (mean age 70 ± 4 years, T1D duration 42 ± 14 years) were prescribed CLC and 39/48 started on the CLC. Among the CLC starters, 97.5% and 95% were prior pump and continuous glucose monitoring (CGM) users, respectively. CGM metrics showed an increase in time-in-range (62% ± 13% to 76% ± 9%; P < 0.001), a reduction in both time spent <70 mg/dL [2% (1%-3%) to 1% (1%-2%); P = 0.03] and >180 mg/dL (30% ± 11% to 20% ± 9%; P < 0.001) at 3 months. Conclusion: In this real-world data most of the older patients with T1D initiating CLC were prior pump and CGM users. Initiation of CLC improved glycemic control and reduced time spent in hypoglycemia compared with prior therapy.

Use of Technology in Older Adults with Type 1 Diabetes: Clinical Characteristics and Glycemic Metrics.

Background: The use of diabetes-related technology, both for insulin administration and glucose monitoring, has shown benefits in older adults with type 1 diabetes (T1D). However, the characteristics of older adults with T1D and their use of technology in real-world situations are not well documented. Methods: Older adults (age ≥65 years) with T1D, using insulin pump or multiple daily injections (MDI) for insulin administration, and continuous glucose monitoring (CGM) or glucometer (blood glucose monitoring [BGM]) for glucose monitoring were evaluated. Participants wore CGM for 2 weeks, completed surveys, and underwent laboratory evaluation. Results: We evaluated 165 older adults with T1D; mean age 70 ± 10 years, diabetes duration 40 ± 17 years, and A1C 7.4% ± 0.9% (57 ± 10 mmol/mol). For insulin administration, 63 (38%) were using MDI, while 102 (62%) were using pump. Compared to MDI, pump users were less likely to have cognitive dysfunction (49% vs. 65%, P = 0.04) and had lower scores on the hypoglycemia fear survey (P = 0.03). For glucose monitoring, 95 (58%) used CGM, while 70 (42%) used BGM. Compared to BGM, CGM users were more likely to report impaired awareness of hypoglycemia (IAH) (P = 0.01), and had lower A1C (P = 0.02). Participants who used any technology (pump or CGM) had lower A1C (P = 0.04, 0.006), less hypoglycemia ≤54 mg/dL (P = 0.0006, <0.0001) and <70 mg/dL (P = 0.0002, 0.0001), and fewer glycemic excursions (coefficient of variation %) (P = 0.0001, <0.0001), while reporting more IAH (P = 0.04, P = 0.006) and diabetes distress (P = 0.02, 0.004). Conclusion: Older adults with T1D who use newer diabetes-related technology had better glycemic control, lower hypoglycemia risk, and fewer glycemic excursions. However, they were more likely to report IAH and diabetes-related distress. Clinical trials.gov NCT03078491.

Usefulness of CGM-Derived Metric, the Glucose Management Indicator, to Assess Glycemic Control in Non-White Individuals With Diabetes.

OBJECTIVE: To assess the relationship between the glucose management indicator (GMI) and HbA1c in non-White individuals with diabetes. RESEARCH DESIGN AND METHODS: We performed a retrospective analysis of continuous glucose monitoring metrics in individuals with diabetes divided by race into non-White and White cohorts. RESULTS: We evaluated 316 individuals (non-White n = 68; White n = 248). Although GMI was not different (7.6 vs. 7.7; P = not significant) between the cohorts, HbA1c was higher in the non-White cohort (8.7% vs. 8.1%; P = 0.004). HbA1c higher than GMI by ≥0.5% was more frequently observed in the non-White cohort (90% vs. 75%; P = 0.02). In the non-White cohort only, duration of hypoglycemia was longer among those with HbA1c higher than GMI by ≥0.5% compared with those with HbA1c and GMI within 0.5%. CONCLUSIONS: A differential relationship between HbA1c and GMI in non-White versus White individuals with diabetes was observed. In non-White individuals, a greater difference between HbA1c and GMI was associated with higher risk of hypoglycemia.

The Relationship Between CGM-Derived Metrics, A1C, and Risk of Hypoglycemia in Older Adults With Type 1 Diabetes.

OBJECTIVE: Continuous glucose monitoring (CGM) is now commonly used in the management of type 1 diabetes (T1D). The CGM-derived coefficient of variation (CV) measures glucose variability, and the glucose management indicator (GMI) measures mean glycemia (previously called estimated A1C). However, their relationship with laboratory-measured A1C and the risk of hypoglycemia in older adults with T1D is not well studied. RESEARCH DESIGN AND METHODS: In a single-center study, older adults (age ≥65 years) with T1D wore a CGM device for 14 days. The CV (%) and GMI were calculated, and A1C and clinical and demographic information were collected. RESULTS: We evaluated 130 older adults (age 71 ± 5 years), of whom 55% were women, 97% were White, diabetes duration was 39 ± 17 years, and A1C was 7.3 ± 0.6% (56 ± 15 mmol/mol). Participants were stratified by high CV (>36%; n = 77) and low CV (≤36%; n = 53). Although there was no difference in A1C levels between the groups with high and low CV (7.3% [56 mmol/mol] vs. 7.3% [53 mmol/mol], P = 0.4), the high CV group spent more time in hypoglycemia (<70 mg/dL and ≤54 mg/dL) compared with the group with low CV (median 31 vs. 84 min/day, P < 0.0001; 8 vs. 46 min/day, P < 0.001, respectively). An absolute difference between A1C and GMI of ≥0.5% was observed in 46% of the cohort. When the A1C was higher than the GMI by ≥0.5%, a higher duration of hypoglycemia was observed (P = 0.02). CONCLUSIONS: In older adults with T1D, the use of CGM-derived CV and GMI can better identify individuals at higher risk for hypoglycemia compared with A1C alone. These measures should be combined with A1C for better diabetes management in older adults with T1D.

Examining the Relationship Between Pre- and Postprandial Glucose Levels and Insulin Bolus Timing Using Bluetooth-Enabled Insulin Pen Cap Technology and Continuous Glucose Monitoring.

Background: Timing of insulin administration in persons using multiple daily injections (MDI) is self-reported. New technology enabling tracking and logging of insulin doses, combined with continuous glucose monitoring (CGM), may provide insight into the relationship between insulin administration and glucose levels. Methods: We performed a prospective observational study using Bluetooth-enabled pen caps, along with CGM, in participants with type 1 diabetes (T1D) on MDI to objectively assess the relationship between the timing of insulin administration and its impact on pre- and postprandial glucose levels for a 2-week period. Results: We evaluated 50 participants (age 40.3 ± 19 years; A1c 8.2% ± 1.5%, duration of T1D 20 ± 15 years). Thirty-seven percent of total boluses resulted in persistent hyperglycemia (glucose >180 mg/dL 3 h postprandially), while 10% resulted in clinically significant hypoglycemia (glucose <55 mg/dL 3 h postprandially) on CGM. Preprandial glucose levels at the time of the bolus did not correlate with postprandial glucose levels. Late boluses, defined as a rise in glucose of ≥50 mg/dL before a bolus, were seen two times/patient/week. Missed boluses, defined as a rise in glucose of ≥50 mg/dL without a bolus within 2 h, occurred 17 times/patient/week. Late and missed boluses were associated with worse glycemic control (A1c; R2 = 0.1, P = 0.02; R2 = 0.1, P = 0.02). Conclusions: The use of Bluetooth-enabled pen caps, with CGM, in persons with T1D on MDI can illustrate the relationship between insulin bolus timing and postprandial glucose. These data may help clinicians and patients understand the impact of timing of insulin doses on glucose levels and glycemic control.

Nonadherence to Insulin Therapy Detected by Bluetooth-Enabled Pen Cap Is Associated With Poor Glycemic Control.

OBJECTIVE: To objectively evaluate adherence to timing and dosing of insulin by using Bluetooth pen caps and examine factors related to adherence. RESEARCH DESIGN AND METHODS: Bluetooth-enabled insulin pen caps were used in younger (ages 18-35 years) and older (ages ≥65 years) adults on two or more insulin injections per day. RESULTS: We evaluated 75 participants with diabetes, 42 younger (29 ± 4 years) and 33 older (73 ± 7 years). Nonadherence was found in 24% of bolus (Apidra) doses and 36% of basal (Lantus) doses. We divided participants into tertiles on the basis of overall adherence, with the most adherent tertile having 85% dose adherence compared with 49% in the least adherent tertile (P < 0.001). Participants in the most adherent tertile had better glycemic control than those in the least adherent tertile (7.7 ± 1.1% [61 ± 12 mmol/mol] vs. 8.6 ± 1.5% [70 ± 16.4 mmol/mol], P < 0.03). CONCLUSIONS: Nonadherence to insulin dosing and timing can be objectively assessed by Bluetooth pen caps and is associated with poor glycemic control.

Liberating A1C goals in older adults may not protect against the risk of hypoglycemia.

AIMS: Hemoglobin A1C is universally used as a marker for glycemic control and to establish glycemic goals in patients with diabetes. In the older population, experts recommend liberating A1C goals to decrease the risk of hypoglycemia. However, it's not clear which A1C level is optimal for this purpose. This study's aim was to understand the relationship between A1C levels and risk of hypoglycemia. METHODS: In a prospective study, we performed continuous glucose monitoring (CGM) on older adults on insulin. Hypoglycemia duration and A1C were measured at baseline while patients were on multiple insulin injections, and again after de-intensification to once-a-day basal insulin with non-insulin agents. RESULTS: We assessed 65 patients; mean age76±6years with on average 3.7±1.3 insulin injections/day. At baseline, 26% of the patients had A1C<7% (53mmol/mol), 42% between 7.1% and 8% (54-64mmol/mol), 21% between 8.1% and 9% (65-75mmol/mol), and 11% >9% (76mmol/mol). The duration of hypoglycemia (<70mg/dl, <60mg/dl, <50mg/dl) was not different between the A1c groups, regardless of treatment intensity (multiple insulin injections or once-a-day-basal insulin with non-insulin agents). CONCLUSIONS: A1C levels are not associated with hypoglycemia risk in older population with type-2 diabetes on insulin therapy. Higher A1C goals do not protect against hypoglycemia.