CGM Metrics in Adolescents with Type 1 Diabetes and Diabetes Distress in the Promoting Resilience in Stress Management (PRISM-T1D) Trial

Abstract

Introduction:

Diabetes distress is associated with suboptimal glycemic outcomes in adolescents with type 1 diabetes. Promoting Resilience in Stress Management (PRISM-T1D) is a skills-based intervention that improved behavioral outcomes, but not HbA1c, in adolescents with elevated diabetes distress.

Materials and Methods:

In this secondary analysis, we explored whether PRISM-T1D impacted continuous glucose monitor (CGM) metrics. Ample CGM data were available from 60% of the trial sample (n = 104/172). Wilcoxon rank-sum tests compared CGM values between intervention and usual care over time.

Results:

Less time was spent in Level 1 hypoglycemia (54–69 mg/dL) in the intervention group compared with usual care at 6 months [0.8% (0.3%, 2.1%) vs. 1.4% (0.7%, 3.4%), P = 0.03]. This difference equated to approximately 1 h less in Level 1 hypoglycemia per week, a clinically meaningful difference. Median percent time in range (TIR) (70–180 mg/dL) at 12 months favored the intervention group but was not statistically significant [56.9% (36.3%, 67.5%) vs. 47.7% (39.4%, 59.7%), P = 0.16]. This difference equated to approximately 2 h more TIR per day, a clinically meaningful difference. Limited CGM data were available for longitudinal within-person analysis from baseline to 6 months (n = 46) and baseline to 12 months (n = 42), and longitudinal analysis did not reveal any significant results in this limited subset.

Conclusion:

Results suggest PRISM-T1D’s potential glycemic benefit and highlight the value of CGM metrics with behavioral interventions.

Introduction

Among adolescents with type 1 diabetes, diabetes distress is associated with worse glycemic outcomes.^1,2 Resilience resources, or adaptive skills individuals use to manage distress (e.g., stress management, coping skills, self-efficacy, family support), have been associated with type 1 diabetes self-management and optimal glycemic outcomes.^3–6 The Promoting Resilience in Stress Management (PRISM-T1D) intervention trial, which taught resilience resources to adolescents with type 1 diabetes and elevated diabetes distress, demonstrated improved diabetes distress and self-management behaviors at 12 months post-randomization.⁷ Though there was no significant difference in HbA1c between groups at 6 or 12 months, patterns of within-person change in HbA1c indicated a nonsignificant trend favoring the intervention group suggesting possible glycemic benefits (mean change in HbA1c from baseline to 12 months of −0.3% in intervention group vs. 0.1% in usual care group, P = 0.09).⁷ Continuous glucose monitor (CGM) metrics were not previously examined, though time in range (TIR) and other CGM metrics are associated with diabetes complication risk and are recommended as clinical trial outcome measures.^8,9 We performed an exploratory analysis of available CGM data from the PRISM-T1D trial, hypothesizing favorable glycemic metrics among participants receiving the intervention compared with usual care.

Materials and Methods

The PRISM-T1D trial (ClinicalTrials.gov: NCT03847194), as previously described,^7,10,11 was approved via a reliance agreement by the Seattle Children’s Hospital and Baylor College of Medicine institutional review boards and was conducted between January 1, 2020 and November 30, 2022. Briefly, this two-site phase 3, parallel, 1:1 randomized controlled trial compared PRISM-T1D with usual care. Participants were adolescents aged 13–18 years with diagnosed type 1 diabetes and elevated diabetes distress as indicated by Problem Areas in Diabetes-Teen screener score ≥30.¹²

In addition to standard diabetes care, intervention participants received two 45- to 90-min, individual intervention sessions and a 30-min family session with a trained PRISM-T1D coach. Intervention sessions introduced skills for stress management, goal setting, cognitive reframing, and meaning-making. Brief telephone booster sessions were offered and participants had access to worksheets and a digital app for additional practice. The usual care group did not receive intervention sessions and did not have access to the app until after the final study timepoint at 12 months post-randomization.^7,10

As part of routine type 1 diabetes care, many study participants used CGM for diabetes management, including Dexcom G6 (Dexcom Inc., San Diego, California, USA), FreeStyle Libre (Abbott Diabetes Care Inc., Alameda, California, USA), and Guardian (Medtronic Minimed, Northridge, California, USA) devices. Data from the 14 days preceding each study data collection timepoint (baseline, 6, and 12 months) were extracted from digital data portals and categorized into the standard glycemic metrics: percent TIR (70–180 mg/dL), percent time in tight range (TITR) (70–140 mg/dL), percent Level 1 time below range (TBR) (54–69 mg/dL), percent Level 2 TBR (<54 mg/dL), percent Level 1 time above range (TAR) (181–250 mg/dL), and percent Level 2 TAR (>250 mg/dL).¹³ We used a data sufficiency threshold of ≥70% CGM wear time for each timepoint. Information on the use of automated insulin delivery/hybrid closed-loop (AID/HCL) systems was obtained from the electronic health record at baseline and subsequent study points. Participants self-reported demographic and medical data (e.g., duration of diabetes) at baseline.

Statistical analysis

This was a post hoc, exploratory sub-analysis; the trial was not powered for CGM metrics. An “available data” analysis was performed, as not all participants used CGM and not all CGM users had data at all three data collection timepoints. We first directly compared percent time in standard glucose ranges between the usual care and PRISM-T1D arms among all CGM users with data at each timepoint. We then analyzed CGM metric changes in individual participants over time (within-person changes) from baseline to 6 months and baseline to 12 months, using only participants with sufficient data at multiple timepoints and comparing by treatment arm. We used Wilcoxon rank-sum tests and Fisher’s exact tests for continuous outcomes and categorical outcomes, respectively, with P < 0.05 indicating statistical significance. We considered a change in TIR of ≥5% (>1 h/day) or any reduction in TBR as clinically important differences.¹³

Data and Resource Availability

Deidentified data from the clinical trial are available from the trial PI upon reasonable request.

Results

Participants’ medical and demographic characteristics are summarized in Table 1.⁷ Of the 172 trial participants, 124 (72%) had at least some CGM data available, and 104 (60% of the total sample) had ≥70% data sufficiency for at least one timepoint. There were sufficient data from 69, 72, and 87 CGM users at baseline, 6, and 12 months, respectively. At their initial data points, 99/104 (95%) participants used Dexcom, 4/104 (4%) used Guardian, and 1/104 (1%) used FreeStyle Libre. By study end, one Guardian user and the FreeStyle Libre user switched to using Dexcom.

Table 1.

Baseline Participant Demographic and Clinical Characteristics

	CGM users	Non-CGM users	P	Usual care CGM users	PRISM-T1D CGM users	P
	(n = 104)	(n = 68)	P	(n = 53)	(n = 51)	P
Age, years	15.5 ± 1.6	16.0 ± 1.7	0.044	14.9 ± 1.5	15.0 ± 1.6	>0.9
Gender			0.3			0.8
Female	62 (60)	34 (50)		31 (58)	31 (61)
Male	39 (38)	29 (43)		21 (40)	18 (35)
Other	3 (3)	5 (7)		1 (2)	2 (4)
Race			0.045			0.5
African American or Black	10 (10)	16 (24)		3 (6)	7 (14)
American Indian or Alaska Native	1 (1)	3 (4)		1 (1)	0 (0)
Asian	4 (4)	3 (4)		3 (6)	1 (2)
White	83 (80)	42 (62)		43 (81)	40 (78)
Other or multiple races	6 (6)	4 (6)		3 (6)	3 (6)
Ethnicity			0.4			0.1
Hispanic	18 (17)	15 (22)		6 (11)	12 (24)
Non-Hispanic	86 (83)	53 (78)		47 (89)	39 (76)
Insurance			0.037			0.3
Private	84 (81)	45 (66)		45 (85)	39 (76)
Public	20 (19)	22 (32)		8 (15)	12 (24)
Other	0 (0)	1 (2)		0 (0)	0 (0)
Baseline HbA1c, %	8.1 ± 1.5	9.6 ± 2.5	<0.001	7.8 ± 1.3	8.5 ± 1.6	0.033
Type 1 diabetes duration, years	6.6 ± 4.2	7.0 ± 3.7	0.3	6.5 ± 4.3	6.8 ± 4.4	0.8
Insulin pump use	80 (77)	35 (51)	<0.001	45 (85)	35 (69)	0.049
Hybrid closed-loop use	19 (18)	N/A		7 (13)	12 (24)	0.2

Bold font indicates significance.

CGM users include only those who had ≥70% data sufficiency at one of the study visits. Presented as no. (%) or mean ± SD. CGM, continuous glucose monitor.

CGM users had lower baseline mean HbA1c than the non-CGM users [8.1 ± 1.5% vs. 9.6 ± 2.5%, P < 0.001], and CGM users in the usual care arm had lower baseline HbA1c than CGM users in the intervention arm [7.8% ± 1.3% vs. 8.5% ± 1.6%, P = 0.033] and were more likely to use insulin pumps [45/53 (85%) vs. 35/51 (69%), P = 0.049]. CGM users were slightly younger [15.5 ± 1.6 vs. 16.0 ± 1.7 years, P = 0.044], more likely to be White [83/104 (80%) vs. 42/68 (62%), P = 0.045], and more likely to have private insurance [84/104 (81%) vs. 45/68 (66%), P = 0.037] than non-CGM users. There were no other significant baseline differences between CGM users and non-users or between CGM users in different study arms.

At baseline, no CGM metrics were statistically different between treatment arms. At 6 months, the PRISM-T1D group had significantly less Level 1 TBR compared with the usual care group [median (IQR): 0.8% (0.3%, 2.1%) vs. 1.4% (0.7%, 3.4%), P = 0.03]. This difference equated to approximately 1 h less in Level 1 hypoglycemia per week, a clinically meaningful difference. No other metric reached statistical significance. At 12 months, the PRISM-T1D arm had 9.2% higher TIR versus usual care (56.9% vs. 47.7%). Although not statistically significant (P = 0.16), this equated to approximately 2 more hours of TIR per day in the intervention group compared with usual care, a clinically meaningful difference (Table 2). A sensitivity analysis controlling for baseline HbA1c and pump use and did not detect further significant differences between groups.

Table 2.

Comparison of Median (IQR) Percent (%) Time in Each CGM Metric Between Usual Care and PRISM-T1D Arms at Each Study Timepoint

	Baseline		P	6 months		P	12 months		P
	Usual care	PRISM-T1D		Usual care	PRISM-T1D		Usual care	PRISM-T1D
	(n = 39)	(n = 35)		(n = 40)	(n = 37)		(n = 48)	(n = 41)
Level 2 hyperglycemia	10.7 (5.3, 29.0)	13.2 (5.3, 30.1)	0.81	15.5 (8.4, 29.3)	16.1 (8.6, 36.4)	0.47	20.5 (11.5, 33.2)	16.1 (9.7, 27.5)	0.26
(Readings >250 mg/dL)
Level 1 hyperglycemia	23.5 (17.8, 30.0)	23.0 (18.8. 27.1)	0.85	23.1 (18.3, 29.6)	22.3 (19.5, 28.6)	0.73	25.7 (20.6, 29.7)	23.7 (19.4, 29.4)	0.35
(Readings 181–250 mg/dL)
Time in range	56.0 (38.9, 72.5)	58.2 (40.8, 71.7)	0.94	52.5 (38.2, 68.5)	54.8 (39.0, 66.9)	0.79	47.7 (39.4, 59.7)	56.9 (36.3, 67.5)	0.16
(Readings 70–180 mg/dL)
Time in tight range	33.6 (23.4, 48.7)	33.7 (21.2, 47.3)	0.80	30.9 (20.8, 46.9)	34.6 (21.2, 46.0)	0.80	27.8 (19.6, 39.7)	34.0 (19.0, 45.3)	0.34
(Readings 70–140 mg/dL)
Level 1 hypoglycemia	1.8 (0.4, 3.1)	1.5 (0.28, 2.7)	0.32	1.4 (0.7, 3.4)	0.8 (0.3, 2.1)	0.03	1.1 (0.5, 3.4)	1.0 (0.3, 1.6)	0.10
(Readings 54–69 mg/dL)
Level 2 hypoglycemia	0.4 (0.1, 0.6)	0.2 (0.0, 0.8)	0.60	0.2 (0.1, 0.7)	0.1 (0.0, 0.6)	0.15	0.3 (0.0, 0.7)	0.1 (0.0, 0.3)	0.10
(Readings <54 mg/dL)

Bold font indicates significance.

Of the 104 CGM users, only 46 had sufficient data at both baseline and 6 months and 42 had sufficient data at both baseline and 12 months. The within-person changes in TIR, TAR, and TBR from baseline to 6 months and 12 months were not statistically different between the study arms. At 12 months, TIR decreased and TAR increased compared with baseline for most individuals in both study arms (Table 3).

Table 3.

Median (IQR) Within-Person Differences in Percent (%) Time in CGM Ranges from Baseline to 6 Months and from Baseline to 12 Months

	Change from baseline to 6 months		P	Change from baseline to 12 months		P
	Usual care	PRISM-T1D		Usual care	PRISM-T1D
	(n = 20)	(n = 26)		(n = 19)	(n = 23)
Level 2 hyperglycemia	1.2 (−2.7, 5.7)	1.3 (−4.8, 8.5)	0.85	4.0 (0.0, 11.2)	1.3 (−1.9, 7.5)	0.22
(Readings >250 mg/dL)
Level 1 hyperglycemia	−1.5 (−3.8, 3.4)	−1.2 (−6.5, 2.2)	0.47	−0.5 (−4.8, 6.4)	2.2 (−1.9, 4.2)	0.58
(Readings 181–250 mg/dL)
Time in range	0.9 (−5.9, 6.2)	1.5 (−6.7, 5.8)	0.90	−5.2 (−14.9, 1.4)	−4.1 (−9.1, 6.2)	0.29
(Readings 70–180 mg/dL)
Level 1 hypoglycemia	0.3 (−1.1, 0.9)	0.0 (−0.3, 0.3)	0.49	−0.1 (−0.9, 1.0)	0.0 (−1.0, 0.6)	0.67
(Readings 54–69 mg/dL)
Level 2 hypoglycemia	0.0 (−0.2, 0.4)	0.0 (−0.2, 0.1)	0.31	0.0 (−0.4, 0.3)	0.0 (−0.2, 0.0)	0.32
(Readings <54 mg/dL)

Positive values represent additional time spent in that metric; negative values represent less time.

Discussion

These data illustrate the potential for resilience-promoting behavioral interventions to promote favorable glycemic metrics compared with usual care among adolescents with type 1 diabetes and elevated diabetes distress. The statistically significant finding of less hypoglycemia (i.e., Level 1 TBR) at 6 months equates to 1 h less TBR per week in the PRISM-T1D group, a clinically important reduction in hypoglycemia and its associated dangers.¹³ The 9.2% difference in median TIR between groups at 12 months did not reach statistical significance in this exploratory analysis (P = 0.16). However, the difference of 2 h more TIR per day in the PRISM-T1D group is clinically meaningful and exceeds the 5% threshold for clinical significance.¹³ The within-person analyses did not reach statistical significance in part due to the incomplete availability of CGM data in our limited sample, but the increased TAR and decreased TIR over the study period in both arms align with the well documented increase in HbA1c during adolescence.¹⁴

Given the documented associations between diabetes distress and glycemic outcomes in youth with type 1 diabetes,¹ the intervention’s significant findings regarding improvements in diabetes distress may be particularly relevant to understanding the CGM metrics reported here.⁷ The improvement in diabetes distress noted in the PRISM-T1D intervention is a plausible mechanism for our exploratory findings pointing toward favorable CGM metrics in that arm, as reductions in diabetes distress may have downstream impacts on self-management behaviors and ultimately glycemic outcomes. Indeed, the PRISM-T1D trial also showed intervention benefits for self-management behaviors.⁷ Among this sample of adolescents with elevated diabetes distress, improving diabetes distress and self-management, with potential impacts on CGM metrics of glycemia, may be clinically relevant for both mental health and glycemic markers. However, the specific portion of glycemic benefit attributable to diabetes distress may have varied by individual.^2,7 To confirm this mechanism, further studies exploring the relationship between reducing diabetes distress and improving glycemic outcomes are necessary.

At baseline, the usual care CGM users had lower HbA1c and were more likely to use insulin pumps, although no difference in baseline CGM metrics was noted. The lack of difference in CGM metrics could be due to limited power but also suggests that HbA1c and CGM are useful tools offering related but distinct glycemic data. That CGM users had a lower HbA1c at baseline is unsurprising as CGM use has been associated with lower HbA1c,¹⁵ although this confirms that this association persists in youth with elevated diabetes distress as well. Similar to prior studies, White participants and those with private insurance were more likely to use CGM compared with participants from other racial backgrounds or with public insurance.¹⁴ Most participants in this study used a Dexcom CGM, but differences in accuracy and data reporting between CGM models could influence results. Larger, future studies should adjust for CGM model in the analyses.

With usual care participants more likely to be pump users, it is conceivable that glycemic effects of the PRISM-T1D intervention would be partially obscured given the association of pump use with lower HbA1c.¹⁶ Despite this, the CGM metric differences noted above favoring PRISM-T1D at later study visits were still detectable. Given the timing of this trial (January 2020–November 2022), few AID/HCL systems were commercially available, so many pump users operated their pumps in manual mode. Given the low prevalence of reported AID/HCL system utilization at baseline and the exploratory nature of this study, our analyses did not control for their use. It is conceivable that changing pump technology over time could act as a confounder, although the effect, if present, would have been likely to equally affect both study arms given the randomized study design.¹⁴ Future studies should collect data on and adjust for AID/HCL use longitudinally and evaluate for synergistic effects between AID/HCL system use and behavioral interventions in optimizing glycemic outcomes.

Strengths of this study include the randomized controlled trial design and the use of CGM data collected as part of routine clinical care. Although some studies have suggested that CGM may reduce diabetes distress,^17–19 others have not shown improvement with CGM use.²⁰ The participants in this study all had elevated diabetes distress by design, despite using CGM. The quantification of real-world CGM data in adolescents with type 1 diabetes and elevated diabetes distress is novel and adds to the understanding of glycemia in this population. A limitation of this exploratory data analysis was low power due to the smaller subset of the sample with CGM data at each time point, especially for within-participant differences over time, increasing the risk of type 2 error. Future trials with behavioral interventions in type 1 diabetes assessing CGM metrics as a prespecified outcome should be powered to detect a treatment difference of 5% TIR between treatment groups.^21–23 As CGM access continues to increase, research with larger samples of CGM users should be feasible in the near future.

The findings of this study have important implications for the delivery of diabetes care to youth with type 1 diabetes who experience diabetes distress. The American Diabetes Association Standards of Care and International Society of Pediatric and Adolescent Diabetes Clinical Practice Guidelines recommend screening for psychosocial stressors and diabetes distress and providing appropriate behavioral and mental health interventions.^24,25 This trial’s data demonstrated the benefits of the PRISM-T1D intervention in reducing distress and improving diabetes self-management behaviors among adolescents with type 1 diabetes with elevated distress,⁷ and the current findings suggest that it may also have a beneficial effect in preserving glycemic outcomes. Diabetes care team members should consider integrating the PRISM-T1D principles of stress management, goal setting, cognitive reframing, and meaning-making into routine care for youth with diabetes who are experiencing distress. Diabetes psychologists and mental health professionals are well versed in these principles and are well positioned to both deliver this care to individuals with type 1 diabetes and to teach medical team members strategies for integration into routine diabetes care.^26–28

Conclusions

The findings of this exploratory analysis suggest the potential for the PRISM-T1D intervention to have glycemic benefits as measured by CGM data, in addition to its established psychosocial benefits. Future studies of resilience-building behavioral interventions addressing diabetes distress in adolescents with type 1 diabetes should be adequately powered to assess CGM metrics alongside HbA1c. Integration of behavioral and medical or device-focused interventions may synergistically improve both psychosocial outcomes and glycemic outcomes in this population.

Footnotes

Authors’ Contributions

M.E.H., J.P.Y.-F., and A.R.R. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. M.L.F.: Conceptualization, Methodology, Formal analysis, Writing—Original Draft, Visualization; D.J.D.: Conceptualization, Methodology, Writing—Review and Editing, Supervision, Project administration; C.Z.: Conceptualization, Methodology, Formal analysis, Data Curation, Writing—Review and Editing; M.C.B.: Conceptualization, Methodology, Formal analysis, Data Curation, Writing—Review and Editing; F.M.: Conceptualization, Methodology, Writing—Review and Editing, Project administration; M.B.O.: Investigation, Writing—Review and Editing, Supervision, Project administration; C.P.: Conceptualization, Methodology, Investigation, Writing—Review and Editing, Supervision, Project administration; A.R.R.: Conceptualization, Methodology, Investigation, Writing—Review and Editing, Funding acquisition, Project administration; J.P.Y.-F.: Conceptualization, Methodology, Investigation, Writing — Review and Editing, Supervision, Project administration, Funding acquisition; M.E.H.: Conceptualization, Methodology, Investigation, Writing—Review and Editing, Supervision, Project administration.

Author Disclosure Statement

D.J.D. serves as an independent consultant for Dexcom and Insulet outside of this work. No other authors report disclosures.

Funding Information

Supported by NIH Grants R01DK121224 and 3R01DK119246-03S1. M.E.H. also received support from NIH 1K26DK138332.

Abbreviations Used

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