Abstract
Background:
Older adults with type 1 diabetes (≥65 years) are often under-represented in clinical trials of automated insulin delivery (AID) systems. We sought to test the efficacy of a recently FDA-approved AID system in this population.
Methods:
Participants with type 1 diabetes used sensor-augmented pump (SAP) therapy for four weeks and then used an AID system (Control-IQ) for four weeks. In addition to glucose control variables, patient-reported outcomes (PRO) were assessed with questionnaires and sleep parameters were assessed by actigraphy.
Results:
Fifteen older adults (mean age 68.7 ± 3.3, HbA1c of 7.0 ± 0.8) completed the pilot trial. Glycemic outcomes improved during AID compared to SAP. During AID use, mean glucose was 146.0 mg/dL; mean percent time in range (TIR, 70-180 mg/dL) was 79.6%; median time below 70 mg/dL was 1.1%. The AID system was in use 92.6% ± 7.0% of the time. Compared to SAP, while participants were on AID the TIR increased significantly (+10%, P = .002) accompanied by a reduction in both time above 180 mg/dL (−6.9%, P = .005) and below 70 mg/dl (−0.4%, P = .053). Diabetes-related distress decreased significantly while using AID (P = .028), but sleep parameters remained unchanged.
Conclusions:
Use of this AID system in older adults improved glycemic control with high scores in ease of use, trust, and usability. Participants reported an improvement in diabetes distress with AID use. There were no significant changes in sleep.
Keywords: artificial pancreas, automated insulin delivery, closed-loop control, continuous glucose monitoring, continuous subcutaneous insulin infusion, sensor-augmented pump therapy
Introduction
Studies have shown that younger adults use a greater breadth of technological devices and are more eager to try new ones than older adults.1-3 Older adults, on the other hand, appear to be more frequent users of technologies belonging to the healthcare domain (blood-pressure devices, monitoring systems, etc.). 4 Data from the Type 1 Exchange registry in US diabetes treatment centers demonstrate a high proportion of insulin pump use in adults 50 years and older (63%) comparable to ages 18-25 (65%) with increasing use of continuous glucose monitoring (CGM) over time overall. 5 In fact, research in adults over the age of 65 shows high rates of satisfaction with CGM use. 3 In recent years, automated insulin delivery (AID) systems have become commercially available to people with type 1 diabetes (T1D), with two AID systems currently approved by the US Food and Drug Administration (FDA): Medtronic 670G 5 and Tandem Control-IQ. 6 Despite this growing availability and the popularity of diabetes technology among older adults with T1D, clinical trials on the use and efficacy of these AID systems have not focused on this population.
Older adults (>65 years of age) are especially vulnerable to episodes of severe hypoglycemia in addition to increased glucose variability, and therefore may benefit substantially from AID technology.7-11 In order to test whether or not older adults show glycemic improvements such as those demonstrated in younger populations, we compared glucose control during sensor-augmented insulin pump (SAP) therapy and AID in individuals ≥65 years of age. In addition to glycemic variables, this study also compared several quality of life outcomes including depression and diabetes distress during SAP and AID. Older adults are also more susceptible to sleep disruption, and there is growing evidence of the significant influence that sleep disorders have on T1D.12,13 Acute complications of insulin intensive treatment such as hypoglycemia14-16 and hyperglycemia17,18 can disrupt sleep, and inversely, sleep disruption and deprivation can cause the secretion of counter-regulatory hormones like cortisol, which may increase insulin resistance. 19 Technology itself can be a source of sleep disruptions, since insulin pumps and CGMs expose users to nocturnal safety alarms. 20 For these reasons, this study also examined sleep quantity and quality.
Materials and Methods
The study was approved by the University of Virginia (UVA) Institutional Review Board and, since the trial was conducted prior to the commercial availability of the system, an investigational device exemption (IDE) was obtained from the FDA (IDE #G180174). The trial was registered at ClinicalTrials.gov, registration number NCT03674281. Participants were recruited from diabetes clinics as well as a web-based recruiting database at UVA. Major eligibility criteria included clinical diagnosis of T1D, insulin treatment for at least one year, insulin pump use for at least six months with or without CGM but no current AID use; aged 65 years or older; HbA1c <10%; no recent diabetic ketoacidosis or severe hypoglycemia; and total daily insulin dose of at least 10 units/day (due to the constraints of the system at the time of study).
The study design was a single arm study with two treatment periods and is illustrated in Figure 1.
Figure 1.
Study Design.
Participants signed informed consent and were screened for eligibility. The screening visit was followed by a two-week run-in phase for CGM-naïve participants in which the study CGM (Dexcom G6, Dexcom Inc. San Diego CA) was used along with their personal pump. Participants then completed two consecutive four-week study phases: the first phase consisted of SAP use in which the study CGM was used along with their personal pump for four weeks. The second phase, following a system training visit, consisted of AID use for four weeks. The AID system consists of an insulin pump running an automated insulin delivery algorithm (t:slim X2 insulin pump with Control-IQ Technology, Tandem Diabetes Care, San Diego, CA), which receives inputs from the Dexcom G6 CGM. This system modulates basal insulin delivery, delivers periodic automatic correction boluses, and has a dedicated hypoglycemia safety system. Glycemic control is intensified overnight with target levels of 112.5 to 120 mg/dL upon awakening. Hemoglobin A1c (HbA1c) was measured at screening and at the final study visit. Participants completed patient-reported outcome (PRO) questionnaires and technology acceptance surveys at baseline and at the end of each study phase. All questionnaires were administered through a secure web-based platform (Qualtrics). The questionnaires included:
1- Hypoglycemia Fear Survey (HFS-II) 21 : this 33-item survey assesses fear of hypoglycemia. Items are rated on a five-point Likert scale ranging from 0 (not at all) to 4 (almost always). It generates a total score and two subscale scores (Behavior and Worry), with higher scores indicating greater fear of hypoglycemia.
2- Diabetes Distress Survey (DDS) 22 : this 17-item scale captures distress related to diabetes as well as four main domains of diabetes related distress. Each item is scored on a six-point Likert scale ranging from 1 (not a problem) to 6 (a very serious problem). Higher scores indicate higher levels of diabetes-related distress.
3- Center for Epidemiologic Studies Depression Scale—Revised (CESD—R) 23 : this 20-item survey is a well-known and widely used screening test for depression and depressive disorder. Each item is scored on a five-point Likert scale with higher scores indicating higher levels of depressive symptoms.
4- Pittsburgh Sleep Quality Index (PSQI) 24 : this ten-item questionnaire assesses sleep quality and disturbances over the last month. Items are rated on a four-point Likert scale ranging from 0 to 3 and higher scores reflect worse sleep quality.
Participants also completed a questionnaire assessing the benefits and burdens they experienced while using the technology in the SAP and AID phases. This 37-item questionnaire is rated on a five-point Likert scale; it has been adapted for this study from versions used in previous AID research (bionic pancreas). 25 It yields to two subscales scores (Burdens and Benefits) with higher scores indicating greater perceived burdens or benefits related to technology.
During the last ten days of each study phase, participants were asked to wear an actigraphy watch beginning at least one hour prior to going to bed until fully awake in the morning (Actiwatch Spectrum Plus, manufactured by Philips, Koninklijke Philips N.V.). Sleep variables collected by the Actiwatch included sleep onset latency, awakenings after sleep onset (number and duration), sleep efficiency, and total sleep time. Prior to analysis, all sleep-related data were validated by a sleep specialist (Pearl Yu). The National Sleep Foundation (NSF) guidelines were used to assess whether participants met the appropriate sleep quality and quantity recommendations.26,27
Parametric (paired t-tests, repeated measures ANOVA) or non-parametric (Wilcoxon Signed Rank, Friedman) tests were used to analyze the data based on the distribution of values. Bonferroni corrections for alpha adjustment due to multiple comparisons were used when appropriate. All data were analyzed for participants who completed the trial, using IBM SPSS statistics Version 26.
Results
Participant Characteristics
Participants were recruited and completed the study between September 2018 and November 2019. A total of 18 adults over the age of 65 were enrolled. Three participants did not complete the study (two screen failures due to recent severe hypoglycemic event and a cardiac event, and one withdrew due to lack of interest in participating). The final sample was composed of 15 older adults (60% male) with mean age 68.7 ± 3.3 (range 65-75), mean diabetes duration 35.2 ± 12.6 years (data not available for two participants), mean HbA1c of 7.0 ± 0.8, and mean daily insulin dose of 0.6 units per kilogram.
Glycemic Outcomes
Table 1 (part A) presents the glycemic outcomes for each study phase computed from CGM data as recommended by the recent International Consensus on use of CGM.28,29 Time in range (TIR) of 70-180 mg/dL improved significantly from SAP to AID (mean 69.6 ± 14.2% vs 79.6 ± 7.8%, P = .002). This was accompanied by a reduction in hyperglycemia (median time >180 mg/dL, 26.6% vs 19.3%, P = .005, and median time >250 mg/dL, 4.18% vs 2.56%, P = .001), a trend toward reduction in mean glucose (154.8 ± 24.5 vs 146.0 ± 13.8 mg/dL, P = .061), and hypoglycemia (median time <70 mg/dL, 1.2% vs 0.8%, P = .053). There was no significant difference in time <54 mg/dL (0.1% vs 0.1%, P = NS). These changes in TIR, hypoglycemia, and hyperglycemia were apparent within one week of AID use: TIR during the last week of SAP was 71.4%, while TIR during the first week of AID was 80.1%. AID use resulted in lower glycemic variability as measured by coefficient of variation (median 33.7% vs 30.3%, P = .006). HbA1c did not differ from baseline to the final study visit after both study phases were completed (mean 7.0 ± 0.8% vs 6.7 ± 0.5%, P = .122).
Table 1.
Glycemic and User Experience Outcomes.
| Part A: Glycemic outcomes | |||
|---|---|---|---|
| Group | Older adults |
P value | |
| N = 15 | |||
| Study phase | SAP | AID | |
| Mean glucose (mg/dl)* | 154.8 [24.5] | 146.0 [13.8] | .061 |
| Coefficient of variation (median)** | 33.7% [4.9%] | 30.3% [4.8%] | .006 |
| % below 54 mg/dL (median)** | 0.1% [0.6%] | 0.1% [0.1%] | NS |
| % below 70 mg/dL (median)** | 1.2% [2.5%] | 0.8% [0.7%] | .053 |
| Percent in range 70-180 mg/dL (mean)* | 69.6% [14.2%] | 79.6% [7.8%] | .002 |
| % above 180 mg/dL (median)** | 26.6% [15.5%] | 19.3% [8.1%] | .005 |
| % above 250 mg/dL (median)** | 4.18% [7.0%] | 2.56% [3.6%] | .001 |
| Part B: User experience outcomes | |||
| Question (1 = lowest rating to 5 = highest rating) | Average score | ||
| How easy to use was the device? | 4.0 | ||
| How useful in managing your diabetes was the device? | 4.7 | ||
| How much did you trust the device? | 4.4 | ||
| I had greater peace of mind while wearing the device | 3.4 | ||
| It will be hard to give up the device once the study is over | 3.1 | ||
Abbreviations: AID: automated insulin delivery; SAP: sensor-augmented pump.
Note. *Variable normally distributed, analyzed with paired t-test.
Non-parametric variable, analyzed with Wilcoxon Signed Rank test.
During the AID phase, the TIR of 70-180 mg/dL was very high reaching 77.9% on average during the day (6 AM-12 AM) and 84.3% overnight (12 AM-6 AM) for older adults, with minimal time spent in the hypoglycemic range below 70 mg/dL at night (0.3% median time). These results are consistent with the algorithm design of overnight intensification of glycemic control.
Technology Use
The system was used in automated insulin delivery mode on average 92.6% ± 7.0% of the time during the AID study phase. Use of the system was temporarily suspended in March 2019 in one participant due to a software error and subsequently resumed after a software update. There were no adverse events during the trial.
Table 1 (Part B) presents the participants’ reported experience with AID use with five selected questions of technology acceptance that were rated on a scale of 1 to 5. Participants reported high ratings indicating favorable or positive responses related to ease of use, trust, and usefulness of the technology. However, participants reported neutral feelings regarding whether or not use of the system gave them more peace of mind and whether they would not want to give up the system at the end of the study.
Patient-Reported Outcomes (PRO)
PRO variables across study phases for these measures are reported in Table 2. DDS total scores decreased significantly during the AID phase (P = .046). Taking a closer look at the DDS scores, the interpersonal distress subscale was the major contributor to this decrease (P = .028). HFS-II scores and PSQI scores did not change significantly during AID. Depression scores appeared to show a slight increase from baseline until the end of the study, but this difference was not significant (P = .105).
Table 2.
Patient-Reported Outcomes.
| PROs variables: mean score (SD) | Study phase |
P value | ||
|---|---|---|---|---|
| Baseline |
SAP |
CLC |
||
| N = 15 | N = 15 | N = 15 | ||
| HFS—total score* | 26.29 (14.599) | 24.21 (12.448) | 27.64 (14.107) | NS |
| HFS—behavioral subscale* | 13.79 (5.632) | 13.5 (5.431) | 14.57 (5.694) | NS |
| HFS—worry subscale* | 12.5 (9.952) | 10.71 (8.588) | 13.07 (9.434) | NS |
| DDS—total score** | 1.40 (0.36) | 1.50 (0.36) | 1.35 (0.25) | .046 |
| DDS—emotional subscale** | 1.53 (0.49) | 1.64 (0.55) | 1.47 (0.42) | NS |
| DDS—regimen subscale** | 1.44 (0.42) | 1.57 (0.48) | 1.40 (0.33) | NS |
| DDS—physician subscale** | 1.23 (0.31) | 1.28 (0.21) | 1.27 (0.32) | NS |
| DDS—interpersonal subscale** | 1.36 (0.46) | 1.42 (0.48) | 1.20 (0.33) | .028 |
| Perceived burdens** | — | 21.46 (16.06) | 20.62 (22.36) | NS |
| Perceived benefits* | — | 61.93 (18.39) | 70.17 (23.90) | .024 |
| CESD—R** | 3.5 (4.014) | 6 (7.952) | 6.43 (6.88) | NS |
| PSQI** | 5.86 (3.009) | 5.71 (4.232) | 5.5 (3.107) | NS |
Abbreviations: CESD—R: Center for Epidemiologic Studies Depression Scale—Revised; CLC, closed-loop control; DDS, Diabetes Distress Survey; HFS-II, Hypoglycemia Fear Survey; PRO, patient-reported outcomes; PSQI, Pittsburgh Sleep Quality Index; SAP, sensor-augmented pump therapy.
Note. *Variable normally distributed, analyzed with paired t-test.
Non-parametric variable, analyzed with Wilcoxon Signed Rank test.
The user experience with this new system yielded mixed results. When comparing AID to SAP, users rated the perceived benefits of AID significantly higher (P = .024) whereas ratings of perceived burdens were similar for the two treatment phases.
Sleep Measurements
We were unable to analyze sleep data from two out of 15 individuals because of inadequate data. One participant never wore the actigraphy watch at night, and the other only wore it twice during the SAP phase and once during the closed-loop control (CLC) phase of the trial. Therefore, the results presented below are based on data from 13 participants (260 nights in total).
When comparing actigraphy data collected while using SAP versus AID, we were not able to identify any statistically significant differences (Table 3). Total sleep time (TST), sleep efficiency (SE), and Wake After Sleep Onset (WASO) remained the same in the two treatment phases. Only three participants were not sleeping the recommended sleep duration according to NSF guidelines. While using Control-IQ, shorter sleepers (<six hours sleep/night) spent an average of eight additional minutes asleep when using the AID system. Average SE was well above the NSF suggested minimum (85%) in the group, but the analysis of awakenings after sleep onset revealed that many of our participants did not have good sleep quality, with awakenings after sleep onset longer than 30 minutes occurring in 54% of older adults. These percentages remain unchanged from the SAP to the AID phase. The number of significant nocturnal awakenings (defined by the National Sleep Foundation as awakenings lasting at least five minutes) decreased during the CLC phase of the trial, but this result did not reach statistical significance (P = .102).
Table 3.
Actigraphy Outcomes.
| Sleep related variable [mean (SD)] | Older adults (over
65 y.o.) |
|
|---|---|---|
| SAP | CLC | |
| Total sleep time (hh:mm)* | 6:59 (1:04) | 7:03 (1:03) |
| Sleep efficiency (%)* | 91.75 (3.86) | 91.87 (5.33) |
| Wake after sleep onset (min)* | 36.28 (14.17) | 36.35 (21.74) |
| Number of awakenings (>5 minutes)** | 8 (11) | 6 (14) |
Abbreviation: CLC, closed-loop control; SAP, sensor-augmented pump therapy.
Note. *Variable normally distributed, analyzed with paired t-test.
Non-parametric variable, analyzed with Wilcoxon Signed Rank test.
All P values are NS.
Discussion
These findings demonstrate that older adults achieved improved glycemic outcomes using a new AID system compared to SAP therapy. These results are clinically relevant because this age group represents a unique and potentially vulnerable population for managing glycemic risks in T1D. Although this study is limited to one-month duration of use and sequential use of SAP and AID, we have previously reported that glycemic improvements with this system can occur after only one month of use. 6 Because the study design was not randomized and did not include a wash-out period, it is important to look at change in weekly glycemic outcomes, which indicated a rapid improvement during the first week of AID use compared to the last week of SAP use. This would suggest that the lack of a wash-out period did not impact our CGM outcomes. In addition to improved glycemic outcomes, participants reported positive experiences in the usefulness and ease of use of the system as well as their ability to trust the system.
The older adults who participated in this study had a long history of diabetes, which in some cases was over 60 years. Overall, the PRO indicated that, at baseline, these participants did not show high levels of emotional distress. Fear of hypoglycemia and diabetes distress scores were generally low and did not show any increase with technology use. However, use of the AID system was associated with significantly lower diabetes distress levels, particularly for interpersonal distress. This finding indicates that participants experienced more support (or fewer signs of lack of support) and more attention from their family and friends when using the AID system. Although clinical trials of AID use in children and adolescents often include parents in order to investigate the impact of treatment on the family, this does not typically occur in studies of adults. Future clinical trials of AID in adult populations should include data collection from partners, caregivers, and significant others, including qualitative interviews, in order to examine the impact of these devices on relationships.
Depression as measured by the CESD-R showed an increasing trend, although it was not significantly higher than the SAP phase. Taking a closer look at the depression data, it appears that two participants who qualified as outliers were responsible for this increase. One of these individuals had a clinical diagnosis of depression that was being treated during the study, while the other individual reported many unexpected problems while using the study devices that might have increased negative feelings about the study and/or their life situation, which had in turn a negative impact on their general mood state. While more research into possible interactions between depression and AID user experience is needed, these results may indicate that pre-existing depression should be well-controlled prior to beginning use of these systems, and that negative experiences with devices may trigger a negative emotional reaction.
Sleep did not deteriorate with AID use compared to SAP and even showed some slight beneficial effects in shorter sleepers, although these differences were not significant and only occurred in participants who did not meet NSF recommendations for minimum amount and quality of sleep. Overall, this study confirmed previous findings that people with T1D tend to have a poorer quality of sleep and lower quantity of sleep. More research is needed to examine the impact of AID on sleep, especially in individuals who have disturbed sleep patterns.
There are several methodological imitations of this study including the non-randomized design, absence of a control group, and limited number of participants that may have reduced the statistical power of the study. It also seems likely that the older adults willing to participate in this study were more comfortable and experienced with technology, including the use of diabetes technology, which may limit the potential generalization of the results to this age group.
Conclusion
This AID system appears to be a safe and easy-to-use tool for older adults that results in significant improvement in glycemic parameters. This system did not worsen sleep in users and appears to have a beneficial effect on adults’ diabetes distress especially distress associated with social support.
Acknowledgments
Tandem Diabetes Care (San Diego, CA) and Dexcom, Inc. (San Diego CA) for providing the equipment for this study at no-cost. The entire personnel of the Center for Diabetes Technology that supported this study and to all the participants and their families.
Footnotes
Abbreviations: AP, Artificial Pancreas; AID, Automated Insulin Delivery; CLC, Closed-Loop Control; CGM, Continuous Glucose Monitoring; CSII, Continuous Subcutaneous Insulin Infusion; SAP, Sensor-Augmented Pump Therapy; T1D, Type 1 Diabetes; UVA, University Of Virginia; TIR, Time In Range; HFS-II, Hypoglycemia Fear Survey; DDS, Diabetes Distress Survey; CESD-R, Center For Epidemiologic Studies Depression Scale-Revised; PSQI, Pittsburgh Sleep Quality Index; PRO, Patient-Reported Outcomes; TST, Total Sleep Time; SE, Sleep Efficiency; WASO, Wake After Sleep Onset; NSF, National Sleep Foundation.
Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This study was funded by a grant from the Virginia Research Investment Fund and received study supplies from Tandem Diabetes Care and Dexcom.
SAB reports grants from Virginia Research Investment Fund, non-financial support from Tandem Diabetes Care, non-financial support from Dexcom during the conduct of the study; grants and non-financial support from Tandem Diabetes Care, non-financial support from Dexcom, non-financial support from Roche Diagnostics, grants from Insulet, grants from Tolerion, outside the submitted work.
RM and AB report grants from Virginia Research Investment Fund, non-financial support from Dexcom Inc., non-financial support from Tandem Diabetes Care, during the conduct of the study.
MV reports grants from DexCom Medtronic, Insulet, and Tolerion outside the submitted work.
DRC was a part-time assistant professor of the UVA-CDT when the trial was in progress; now he is a full-time Dexcom employee affiliated with the UVA-CDT as an adjunct professor. DRC reports grants from State Council of Higher Education for Virginia (Virginia Research Investment Fund), during the conduct of the study.
LGF reports grants from Virginia Research Investment Fund, non-financial support from Dexcom Inc., non-financial support from Tandem Diabetes Care, during the conduct of the study. LGF has been licensed by the University of Virginia to form an LLC in partnership with the university to distribute and change licensing costs for the use of the Hypoglycemia Fear Survey in studies conducted by for-profit entities including pharmaceutical companies. However, there were no licensing fees involved for the use of the survey in this study. The survey is always available to use free of costs to non-profit entities.
PY, HB, and MP have nothing to disclose.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by a grant from the State Council of Higher Education for Virginia (SCHEV) through a Virginia Research Investment Fund (VRIF) grant. Tandem Diabetes Care provided insulin pumps and related supplies. Dexcom provided CGM and related supplies. All funding bodies had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
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