Beyond Metabolic Control: Psychosocial Benefits of Automated Insulin Delivery Systems in Pediatric Type 1 Diabetes,a Systematic Review

Abstract

Introduction:

Type 1 diabetes (T1D) affects approximately 1.2 million children and adolescents worldwide. Despite advances in therapy, insulin remains the primary treatment, requiring tight glycemic control to prevent long-term complications. Automated insulin delivery (AID) systems, combining continuous glucose monitoring and insulin pumps with algorithms, have opened a new paradigm in T1D management. This review aims to synthesize data on psychosocial outcomes and quality of life (QoL) from randomized controlled trials (RCTs) comparing AID systems to standard diabetes care in children and adolescents.

Methods:

A systematic search following PRISMA guidelines was conducted across Medline, Embase, PubMed, and Cochrane databases. RCTs evaluating QoL of children with T1D using AID systems were included. Data extracted included participant demographics, AID system types, main outcomes, QoL scales, and results. Studies had to involve children and/or adolescents with T1D using AID systems and/or their caregivers and assess QoL or psychosocial outcomes using validated scales.

Results:

The search identified 3373 studies, with 14 meeting inclusion criteria, encompassing 1251 participants, mostly children, adolescents and young adults. Studies used various AID systems, including Control-IQ, CamAPS FX, Diabeloop DBL4K, Medtronic MiniMed^®670G and Advanced Hybrid Closed-Loop, and compared them to standard care. They showed variable improvements in treatment satisfaction and expectations, sleep quality, and QoL, along with a reduction in fear of hypoglycemia when using AID systems. However, the effect on emotional burden and diabetes distress was inconsistent.

Conclusions:

RCTs indicate that AID systems enhance treatment satisfaction, sleep quality, and hypoglycemia confidence in children with T1D, contributing to better diabetes-specific QoL. Although their impact on emotional burden and diabetes distress outcomes is inconsistent, AID systems generally offer significant benefits over traditional insulin delivery methods, underscoring their importance in pediatric T1D management.

Introduction

Type 1 diabetes (T1D) is an autoimmune condition leading to insulin deficiency and lifelong treatment. It affects over 1.2 million children and adolescents worldwide.¹ Despite recent therapeutic advances such as continuous glucose monitoring (CGM) and intensive insulin therapy via multiple daily injections (MDI) or insulin pumps (continuous subcutaneous insulin infusion [CSII]) many patients still fail to meet glycemic targets, and disease management continues to impact quality of life and mental health.² Therefore, the risk of developing psychiatric comorbidities, especially depressive disorders, increases with earlier onset of T1D.³ These diabetes-induced psychological conditions are significantly associated with poor treatment adherence, inadequate glycemic control, and earlier onset of long-term complications.³

To improve diabetes control and QoL, automated insulin delivery (AID) systems have been developed. These systems combine a CGMS, an insulin pump, and an algorithm and allow continuous adaption of insulin delivery to glucose levels. The landscape of diabetes care for children has recently been transformed with the advent of hybrid closed-loops (HCL), which use the AID algorithm to adapt insulin to blood glucose between meals, while still requiring users to manually enter the amount of carbohydrates ingested during meals. This technological synergy represents a paradigm shift from traditional approaches, with many real-world studies and randomized controlled trials (RCTs), showing that HCL improve diabetes control by lowering glycated hemoglobin (HbA1c), increasing the time spent within normal glycemic range, and decreasing the time spent in hyperglycemia, without increasing the frequency of hypoglycemia.⁴ The aim of this review is to synthesize existing data on psychosocial outcomes and QoL obtained from RCTs comparing AID systems to standard diabetes care in children and adolescents with T1D.

Review method

A systematic search was done according to the PRISMA guidelines.⁵ In February 2023, Medline, Embase, PubMed, and Cochrane databases were searched with the following search term combination: “child” AND “type 1 diabetes” AND “Automated Insulin Delivery” AND “quality of life” (see supplementary data for detailed search terms). The research was repeated in July 2024, which identified one relevant study published during the interval, and again in January 2025, revealing one additional study. No language limitations were used. We included RCTs investigating the QoL among children and/or adolescents with T1D using AID systems as well as QoL among their caregivers.

Eligibility criteria

Studies included in the review met the following criteria: −

RCTs exploring the use of AID systems in T1D management. Studies using predictive low glucose suspend (PLGS) algorithms were not included, as our aim was to focus on most recent technologies with autonomous insulin delivery capabilities, and to reduce heterogeneity in the mechanisms and expected psychosocial impacts of the interventions.

−

Involvement of children and/or adolescents with T1D and their caregivers, if applicable.

−

Outcomes related to QoL and/or other psychosocial outcomes, assessed through validated scales and questionnaires.

Study selection

Initial search results were reviewed for relevance by two independent reviewers (F.I.L. and P.K.) based on title and abstract of the articles. Among the remaining articles, the full text was read to assess the article for eligibility. Disagreements among two reviewers was resolved through consensus.

The following information was extracted from the included studies: author, publication year, country, number of participants, age of participants, type of AID system, main outcomes, type of scale used to quantify QoL or other psychosocial outcomes, population in which psychosocial information was analyzed (patient, caregivers, or both), and main results.

Review results

Our search initially identified a total of 3373 studies; 2084 remained after the removal of duplicates. Following the screening process, 1769 studies were excluded based on title, and 281 based on abstract; 34 studies underwent further evaluation, and an additional study was discovered through manual searching of references (total of 35 studies). Two additional studies were included during the second and third round of research in July 2024 and January 2025. After review of the full articles, 14 studies met the predefined inclusion criteria. A summary of the study selection process is presented in Figure 1. The 14 included studies (see Table 1) investigated a combined total of 1251 participants, with at least 565 being children and adolescents with T1D, using either an AID system (only studies involving HCL systems met the eligibility criteria) or standard care such as MDI, CSII or sensor-augmented pump (SAP) therapy. The participant numbers in each study ranged from 17 to 302 individuals. Notably, all the studies were conducted in industrialized countries, including United States (six studies),^{9,11,13,14,17,18} Australia (four studies),^6,8,10,19 United Kingdom (three studies),^7,12,14 Austria (two studies),^7,12 Belgium (two studies),^12,15 France (two studies),^12,15 Germany (two studies),^7,9 Canada (one study),¹⁷ Israel (one study),⁹ Luxembourg (one study),¹² New Zealand (one study),¹⁶ and Slovenia (one study).⁹ The duration of outcome evaluation across the studies varied from 4 nights to 44 weeks. The following HCL algorithms were investigated in the included studies: Control-IQ (CIQ) (Tandem Diabetes Care, San Diego, CA) (three studies),^11,13,18 the Cambridge model predictive control algorithm in different configurations, such as the CamAPS FX or the FlorenceM configuration (CamDiab, Cambridge, UK) (three studies),^7,12,14 Diabeloop for kids DBL4K (Diabeloop, Grenoble, F) (one study),¹⁵ Medtronic MiniMed®670G (Medtronic, Minneapolis, MN) (three studies),^8,10,17 Medtronic MiniMed® Advanced Hybrid Closed-Loop (AHCL: Medtronic 760G 4.0 or 780G) (Medtronic, Minneapolis, MN) (three studies),^9,16,19 and Android-HCLS, an initial version of the Medtronic MiniMed®670G algorithm (Medtronic, Minneapolis, MN) (one study).⁶ As control groups, SAP was used in 12 studies, SAP with low glucose suspend (SAP-LGS) in 3 studies,^6,11,16 MDI in 2 studies,^8,18 and other HCL systems in 1 study.⁹ QoL and wellbeing were measured by the Pediatric-specific diabetes Quality of Life (PedsQL diabetes module),²⁰ the World Health Organization-Five Well-Being Index questionnaire (WHO-5)²¹ and the European Quality of Life 5 Dimensions (EQ-5D, a scale used to evaluate QoL indirectly by evaluation of the individual’s health status)²² in nine studies.^{8,11–16,18,19} Treatment satisfaction and expectations were measured by the Diabetes Treatment Satisfaction Questionnaires (DTSQ, DTSQs, and DTSQc)²³ and the Glucose Monitoring Satisfaction Survey (GMSS)²⁴ in eight studies.^{6–10,14,16,17} Fear of hypoglycemia and hypoglycemia confidence were measured by the Hypoglycemia Fear Survey version 2 (HFS-II),²⁵ the Children’s Hypoglycemia Fear Survey (CHFS),²⁶ the Hypoglycemia Confidence Scale (HCS)²⁷ and the Gold and Clarke score^28,29 in seven studies.^{10,12–14,16,18,19} Sleep quality was measured by the Epworth Sleepiness Scale (ESS)³⁰ and the Pittsburg Sleep Quality Index (PSQI)³¹ in five studies.^{11–13,16,18} Diabetes distress and emotional burden was measured by the Problem Areas in Diabetes (PAID, PAID-T),^32,33 the State Trait Anxiety Inventory (STAI),³⁴ the Center for Epidemiological Studies-Depression (CES-D),³⁵ the Pediatric Inventory for parents (PIP),³⁶ the Parent Diabetes Distress Scale³⁷ in six studies,^{8,11,13,14,18,19} the Generalized Anxiety Disorder 7-item (GAD-7)³⁸ in one study¹⁹ and the Primary Care Evaluation of Mental Disorders (PRIME-MD)³⁹ in one study.¹⁹

FIG. 1.

PRISMA flow diagram of the systematic review.

Table 1.

Studies Included in Systematic Review

Authoryearcountry	No of casesAge of participants	Study design	Duration of intervention	Type of AID	Controls	Primary and secondary outcomes	QoL scale used	Results
Sharifi et al., 2016⁶ Australia	28 participants 16 adults (>24 years) 12 adolescents (12–18 years)	Randomized crossover study	2 × 4 nights	Medtronic Android-HCLS	PLGS	1: Percent continuous glucose monitoring (CGM) time (00:00–08:00 h) within target range (72–144 mg/dL) 2: Percent CGM time above target; below target; glycemic variability; symptomatic hypoglycemia; adult treatment satisfaction; sleep quality; cognitive function	-DTSQ -DTSQs, -DTSQc -CHFS	Patient Satisfaction: • Adults were generally more satisfied with Android-HCLS (median DTSQc score: +10 points; P < 0.02). • Two participants reported lower satisfaction due to overnight insulin pump linkage failures, causing alarms and sleep disruptions. Cognitive Functioning and Sleep Quality: • No significant differences in cognitive performance or self-reported alertness between Android-HCLS and SAP-LGS in adults or adolescents. • Objective sleep parameters showed no clinically significant differences between the systems. • A small but statistically significant decline in sleep quality (PSQI score) was observed with Android-HCLS.
Barnard et al., 2017⁷ United Kingdom Germany Austria	58 participants 32 adults 26 children/adolescents (6–18 years)	Randomized crossover study	12 weeks Adults: 24/24 Children: Overnight	Cambridge model predictive control algorithm	SAP	1: Explore psychosocial experiences of closed loop technology for adults, children, and adolescents with type 1 diabetes and their parents	-DTQ -Interviews	DTQ Results: • Difference scores were more positive after the closed loop phase on 22 items, but more negative or unchanged on 8 items. • Adults had 17 negative items after the open loop phase and 6 after the closed loop phase. • Change item scores were moderately positive and similar for both phases, with adults having slightly more positive perspectives than pediatric participants. Usability Ratings: • Closed loop system was rated higher by adults (mean 3.94) than by pediatric participants (mean 3.65). • Lowest ratings: “Size, weight, appearance, and fashion issues” and “Use during sports, exercise, and bathing.” Interview Results: • 26 pediatric participants and 25 parents were interviewed, with fewer completing follow-up interviews. • Interviews were generally brief, with pediatric participants averaging 15 min and parents 22 min
Abraham et al., 2021⁸ Australia	135 participants (12–25 years)	Multicenter randomized study	6 months	MiniMed®670G	CSII or MDI +/− CGM	1: Percentage of time in range (TIR) 2: CGM metrics for hypoglycemia, hyperglycemia and glycemic variability; psychosocial measures	-PedsQL -PAID-T -DTSQ -STAI	• Improvement in diabetes-specific quality of life (+4.4 points; 95% CI: 0.4–8.4) and treatment satisfaction in the HCL group. • No change in diabetes distress or fear of hypoglycemia between groups.
Bergenstal et al., 2021⁹ United States Germany Israel Slovenia	113 participants 73 young adults (17–20 years) 40 in their twenties (21–29 years)	Open-label, two-period, randomized crossover study	2 × 12 weeks	Medtronic MiniMed® AHCL	MiniMed®670G	1: Proportion of time glucose levels were above 180 mg/dL (>10·0 mmol/L) during 06:00 h to 23:59 h, tested for superiority; Proportion of time glucose levels were below 54 mg/dL (<3·0 mmol/L) calculated over a full 24-h period, tested for non-inferiority (margin 2%). 2: Mean glucose concentration; coefficient of variation; proportion of time spent with range of glucose concentration; several prespecified quality-of-life endpoints	-GMSS -Broader quality-of-life questionnaires	Glucose Monitoring Satisfaction: • Mean scores: 2.76 at screening, 2.65 after 670G system, and 2.80 after the advanced hybrid closed-loop system (P = 0.003 for 670G vs. advanced hybrid closed-loop).
Burckhardt et al., 2021¹⁰ Australia	17 participants (12–55 years)	Randomized crossover study	2 × 8 weeks	MiniMed®670G	CSII or SAP	1: Plasma epinephrine response to experimentally induced hypoglycemia during the hyperinsulinemic hypoglycemic clamp study. 2: Hormones measured during the clamp study (norepinephrine, growth hormone, cortisol, and insulin) and symptoms of hypoglycemia. These were assessed with a questionnaire	-Gold -Clarke -HFS -Worry subscore -DTSQs	Gold Score • Lower after intervention (median 4) compared to control (median 5); P = 0.033. Clarke Score • No significant difference between intervention and control; P = 0.872. HFS Score and Worry Subscore • No change observed. DTSQ • Comparable between control and intervention; P = 0.783. Perceived Frequency of Hypoglycemia • Significantly lower after intervention compared with control; P = 0.004.
Cobry et al., 2021¹¹ United States	101 participants (6–13 years)	Multicenter unblended randomized study	16 weeksExtension 28 weeks	Control-IQ	SAP +/− PLGS	1: Assess different sources of psychological and behavioral burdens associated with living with and managing pediatric type 1 diabetes (T1D)	-Pediatric Hypoglycemia Fear Survey -PAID -PedQL -Insulin Delivery Systems: Perceptions, Ideas, Reflections, and Expectations questionnaire -PSQI	Parent Questionnaires • PHFS Scores: - CLC group: Decreased by −7.7. - SAP group: Decreased by −3.9. - Adjusted difference: −6.0, P = 0.07. • PAID Scores: - CLC group: Decreased by −7.8. - SAP group: Decreased by −0.9. - Adjusted difference: −7.4, P = 0.08. • PedsQL Scores: - CLC group: Increased by +4.3. - SAP group: No change. - Adjusted difference: +4.0, P = 0.13. • PSQI Scores: - CLC group: Improved from 5.8 to 4.3 (clinically significant). • Extension Phase: - SAP group (switched to Control-IQ): Similar changes as CLC group during RCT; no significant differences. - CLC group: Scores remained stable from 16 to 28 weeks. Child Questionnaires • CHFS Scores: - CLC group: Decreased by −3.7; Worry subscale by −4.3. - SAP group: Increased by +1.4; Worry subscale by +2.3 (P = 0.11). • Post-Switch Changes (SAP to CLC): - Similar changes in scores from 16 to 28 weeks. • Significant Changes in CLC Group (Baseline to 28 weeks): - CHFS total score and subscales: Worry, avoidance, helplessness, social consequences. - PedsQL total score and subscales: Diabetes, communication.
de Beaufort et al., 2022¹² Luxembourg Belgium UK Austria France	74 participants (1–7 years)	Multinational, open-label, randomized crossover study	2 × 16 weeks	Cambridge model predictive control algorithm	SAP	1: Evaluate the impact of CamAPS FX hybrid closed-loop (HCL) automated insulin delivery in very young children with type 1 diabetes (T1D) on caregivers’ wellbeing, fear of hypoglycemia, and sleepiness	-ESS -WHO-5-HFS	Hypoglycemia Fear (HFS Scores): • Caregivers reported significantly lower fear of hypoglycemia with HCL treatment compared to SAP. • Mean difference in total HFS score: −5 (95% CI: −9 to −2; P < 0.001). • Lower HFS score related to reduced behavior (−2; 95% CI: −3 to −1; P < 0.006) and worry subscales (−3; 95% CI: −6 to −1; P < 0.001). Daytime Sleepiness (ESS Scores): • Slightly lower adjusted mean ESS score after HCL compared to SAP, but not statistically significant (P = 0.09). • Baseline: 24.7% scored >9 (excessive sleepiness). • After HCL: 16.4% scored >9. • After SAP: 23.5% scored >9. Well-being (WHO-5 Scores): • Higher well-being reported after HCL treatment than SAP. • Adjusted mean difference: +8 (95% CI: 3 to 16; P < 0.001). • Baseline: 11% scored <50. • After HCL: 7.5% scored <50. • After SAP: 16.4% scored <50. • Notable improvement in “I woke up feeling fresh and rested” with mean scores of 2.9 at baseline and after SAP, and 3.5 after HCL.
Cobry et al., 2022¹³ United States	101 parent-child participants (6–13 years)	Multicenter randomized study	16-week randomized period followed by a 12-week extension, where the SAP group transitioned to CIQ and the treatment group remained on CIQ.	Control-IQ	SAP	1: Assess the association between subjective sleep in parents of youth using the Tandem Control-IQ (CIQ) HCL system and nocturnal glycemic and psychosocial outcomes, focusing on parents with baseline poor sleep quality.	-PSQI -HFS-II -PAID	Parent and Child PROs: • Significant improvements in all parent PROs after CIQ use (P < 0.001). • 27 of 49 poor sleepers improved to good sleepers. • Significant reduction in total HFS-C score. Parent Poor Sleepers vs. Good Sleepers: • Baseline PSQI: Poor sleepers scored 7, good sleepers scored 4. • Poor sleepers had higher diabetes-related distress at baseline (P = 0.002), but no significant difference after CIQ use (P = 0.221). • Child PAID score higher in children of poor sleepers both before and after CIQ use (P = 0.015 and P = 0.038). Other Outcomes: • No significant changes in glycemic outcomes or other PROs.
Hood et al., 2022¹⁴ United Kingdom United States	98 participants (6–18 years)	Open-label, single period, randomized, parallel design study	6 months	Cambridge model predictive control algorithm	CSSI +/− CGM	1: Examine changes in the lived experience of type 1 diabetes after use of hybrid closed loop, including the CamAPS FX CL system.	-CES-D -Parent Diabetes Distress Scale -PAID-T -HCS -HFS-II -GMSS -Attitudes about diabetes specific technology -PedsQL	No Significant Effects: No statistically significant effects overall; small sample sizes likely affected results. CamAPS FX System: • High use rate (>90%) showed: • Improved quality of life for those over 11 (P < 0.05). • Reduced parent diabetes distress (P < 0.001) and fear of hypoglycemia (P < 0.05). FlorenceM System: • Low use rate (57%); no additional analysis. Focus Group Insights: • CamAPS FX improved sleep, reduced worry, and enhanced glycemic control. • Challenges: device burden and connectivity issues, especially during activities.
Kariyawasam et al., 2022¹⁵ France Belgium	21 participants (6–12 years)	Multicentre, open-label, randomized, controlled, non-inferiority, two-session crossover study	2 × 6 weeks(1 week wash out)	Diabeloop for Kids DBL4K hybrid closed-loop system	SAP	1: Mean proportion of time spent in hypoglycemia 2: Mean glycemia, the low blood-glucose index and high blood-glucose index, TIR, the coefficient of variation of glucose, and the Blood Glucose Risk Index Exploratory outcomes : Proportion of time the closed-loop system was active during the 72-h hospital phase, the duration of glucose monitoring with both systems during the 72-h hospital phase, and patients’ and their guardians’ perceptions of lifestyle change, satisfaction, and diabetes management	-Diabetes Treatment Satisfaction Questionnaire -Diabetes Technology Questionnaire -PedsQoL -Artificial Pancreas Acceptance Questionnaire	Baseline Opinions: • Children had a positive opinion about the closed-loop device, with 65%–89% expecting benefits (fewer hypoglycemic episodes, better glucose control, easier management). Post-Use Opinions: • Positive opinion largely maintained, except for fewer hypoglycemic events (47% agreed) and easier social life management (53% agreed). • 50% of children and 67% of parents wanted to switch to the closed-loop system after the study. Quality of Life: • Median PedsQoL scores were above 75 for children and parents for both devices.
Wheeler et al., 2022¹⁶ New Zealand	59 total participants 16 children (7–12 years) 14 teenagers (13–17 years) 29 adults (18–65 years)	Dual-center, randomized, open-label, two-sequence crossover study	2 × 4 weeks	Medtronic MiniMed® AHCL	SAP + PLGS	Sub study to assess impacts on quality of life, sleep and treatment satisfaction were compared using seven age-appropriate validated questionnaires given to patients or caregivers.	-DTSQs -DTSQc -DTQ -PSQI -WHO-5 -HCS -HFS-II	Treatment Satisfaction: • Increased treatment satisfaction with the MiniMed™ AHCL system compared with SAP + PLGM, consistent with previous studies on similar systems. Subjective Sleep Quality: • Small but significant improvement in sleep quality (PSQI scores) with AHCL; participants on SAP + PLGM had poorer sleep quality. Well-being and Fear of Hypoglycemia: • No significant changes in well-being index or hypoglycemia fear/confidence, likely due to the effectiveness of the SAP + PLGM comparator. Psychosocial Benefits: • Benefits seen across adolescent and adult age groups, particularly in those with high diabetes burden.
Garg et al., 2023¹⁷ United States Canada	302 participants(2–80 years)	Randomized control study	6 months	MiniMed®670G	CSII	1: Evaluate safety and effectiveness of MiniMed™ 670G hybrid closed loop (HCL) in comparison with continuous subcutaneous insulin infusion (CSII) therapy for 6 months in persons with type 1 diabetes (T1D)	-DTSQs -DTSQc -Participant-reported diabetes treatment satisfaction questionnaire (status and change) and perceived frequency of hyperglycemia and hypoglycemia scores	Group 1: Included participants with baseline A1C >8.0%. • Diabetes Treatment Satisfaction: Higher total DTSQc scores with HCL compared to CSII control. • Perceived Frequency of Hyperglycemia: Lower with HCL intervention. • Perceived Frequency of Hypoglycemia: Not specified. Group 2: Included participants with baseline A1C ≤8.0%. • Diabetes Treatment Satisfaction: Lower total DTSQc scores with HCL compared with CSII control. • Perceived Frequency of Hyperglycemia: Lower with HCL intervention. • Perceived Frequency of Hypoglycemia: More noticeable reduction with HCL intervention.
Hood et al., 2024¹⁸ United States	102 participants (2–6 years)	Randomized control study	13 weeks +/− 13 weeks extension	Control-IQ	SAP or MDI + CGM	1: Examine patient-reported outcomes (PROs) after the use of t:slim X2 insulin pump with Control-IQ technology (CIQ) in young children with type 1 diabetes.	-HFS-II -HCS -PedsQL -PIP -PSQI -user experience interviews	Parent-Reported Outcomes (PROs): Baseline vs. 13 Weeks vs. 26 Weeks: • Parenting Stress: Decreased (P = 0.05). • Hypoglycemia Fear: Decreased (P = 0.02). • Hypoglycemia Confidence: Increased (P = 0.04). • Child Quality of Life: Increased (P = 0.02). • Sleep Quality: Decreased (P = 0.005) with CIQ vs. standard care. User Experience Interviews: • Improved nighttime glycemic control. • Increased time in range (TIR) during the day. • Reduced diabetes management burden. • Increased confidence in allowing children more independence. • Some found the system insufficiently aggressive for treating highs (n = 17). • Site changes were noted as more painful and slower to heal. Future Preferences: • 24 out of 28 guardians wanted to continue using CIQ. • 3 of the 4 remaining guardians expressed interest in alternative AID systems. • Most families (27/28) preferred continued use of an AID system.
Abraham et al., 2025¹⁹ Australia	42 participants (12–25 years)	Open-label, multicenter, parallel, randomized controlled study	24 weeks	Medtronic MiniMed® AHCL	CSSI +/- CGM	1: Evalutate HbA1c collected at 24 weeks. 2: CGM metrics Change in auxological parameters and change in insulin doses Validated questionnaires assessment to evaluate patient reported outcomes.	-DTSQ -PAID - HFS -GAD-7 -Gold score	Diabetes-Specific Quality of Life: • Outcome measures indicated minimal changes, though slightly better in the intervention group compared to the control. Not significant. Diabetes Distress: • Levels of diabetes distress remained stable. • The intervention group showed a minor, nonsignificant reduction in distress, compared with the control group. Anxiety and Depression: • Scores for anxiety and depression did not significantly differ between groups. • The results suggest a potential benefit of the intervention, but the effect size was small. Fear of Hypoglycemia: • No statistically meaningful improvement in the intervention group. • Point estimates in favor of the intervention, but confidence intervals overlapped substantially with those of the control group

DTSQ, Diabetes Treatment Satisfaction Questionnaire; DTSQs, Diabetes Treatment Satisfaction Questionnaire Status; DTSQc, DTSQ change version; PAID, Problem Areas in Diabetes; PAID-T, Adapted measure of diabetes-specific emotional distress for adolescents; HFS-II, Hypoglycemia Fear Survey, version 2; CHFS, Children’s Hypoglycemia Fear Survey; PedQL: Pediatric-specific diabetes quality of life; STAI, State Trait Anxiety Inventory; ESS, Epworth Sleepiness Scale; WHO-5, World Health Organization–Five Well-Being Index questionnaire; CES-D, Center for Epidemiological Studies-Depression; HCS, Hypoglycemia Confidence Scale; PSQI, Pittsburg Sleep Quality index; GMSS, Glucose monitoring satisfaction survey; PIP, Pediatric Inventory for Parents (common parenting concerns).

Results

Diabetes treatment satisfaction and expectations

Diabetes treatment satisfaction and expectations were measured using tools such as the DTSQ and the GMSS. Control groups included CSII, MDI, SAP, or another HCL algorithm. While none of the studies showed a negative effect of AID on treatment satisfaction and expectations, the results where heterogeneous: Most RCTs investigating treatment satisfaction were performed on algorithms by Medtronic. The impact of the MiniMed®670G algorithm was described by Abraham et al. (2021),⁸ Burckhardt et al. (2021),¹⁰ and Garg et al. (2023),¹⁷ who compared it with CSII or MDI, CSII or SAP, and CSII respectively. Abraham et al. reported a statistically significant improvement in treatment satisfaction scores when the algorithm was compared to CSII or MDI. Burckhardt et al. found a positive effect, only in terms of patient-perceived frequency of hypoglycemia and Garg et al. found higher scores of treatment satisfaction in patients or their caregivers but only in a patient population with HbA1c levels >8% at inclusion. The MiniMed® AHCL algorithm was investigated by Bergenstal et al. (2021)⁹ and Wheeler et al. (2022)¹⁶ who compared it with the MiniMed®670G algorithm or SAP + PLGS, respectively. A significantly higher treatment satisfaction was reported by patients using this algorithm compared with those using the MiniMed®670G system (Bergenstal et al.) or SAP + PLGS (Wheeler et al.).

The Cambridge model predictive control algorithm running on a tablet computer, was compared with SAP by Barnard et al. (2017).⁷ The authors found it to be associated with a favorable rating of most items of treatment satisfaction. Hood et al. (2022)¹⁴ compared the algorithm running on a modified Medtronic pump or Dana RS pump, with CSII with or without CGMS. No difference in treatment satisfaction could be shown in this study. Finally, the DBLK4 algorithm was shown by Kariyawasam et al. (2022)¹⁵ to be associated with similar treatment satisfaction scores to SAP.

Sleep quality

Sleep quality was measured using the ESS and the PSQI. Several studies have shown a positive impact of HCL on sleep quality or sleepiness. The use of CIQ was associated with improved sleep quality in parents (Cobry et al. 2021¹¹and 2022¹³) and children (Cobry et al. 2021,¹¹ Hood et al. 2024¹⁸) when compared with patients using SAP or PLGS. The use of the AHCL algorithm by Medtronic® led to improved sleep quality in adolescents and adults when compared with PLGS (Wheeler et al. 2022¹⁶). Finally, a slight nonsignificant reduction in sleepiness was noted in children using CamAPS FX as compared with SAP (De Beaufort et al. 2022¹²). Technical issues with AID systems result in sleep disruption, as illustrated by Sharifi et al. (2016)⁶ in a study investigating the impact of Android HCLS, an early version of the Medtronic MiniMed®670G algorithm.

Fear of hypoglycemia and hypoglycemia confidence

Fear of hypoglycemia and hypoglycemia confidence were measured using the HFS-II, the CHFS, and the Gold and Clarke score, where higher scores indicate greater fear of hypoglycemia, and the HCS, where higher scores indicate greater confidence. De Beaufort et al (2022)¹² investigated fear of hypoglycemia in caregivers of very young children aged 1–7 years using the CamAPS FX system. They observed a significant reduction of HFS scores after the HCL treatment period compared with SAP. This reduction in HFS total score was associated with lower levels of both behavioral action and worry in relation to fear of hypoglycemia. A statistically significant decrease in parental fear of hypoglycemia was also reported by Hood et al. (2022)¹⁴ who compared users of CamAPS FX aged 6–18 years with patients treated with CSII with or without CGMS.

Cobry et al. (2021)¹¹ provide insights into how CIQ may help reduce parents’ fear of hypoglycemia by showing a reduction in specific fear-related behaviors and in the score of the subscale “maintain high blood glucose,” probably reflecting a decreased perceived need to keep their child’s glucose higher to reduce the risk of hypoglycemia in certain situations. The study also highlights a decrease in concerns about the negative social impact of hypoglycemia on their child’s behavior. In this study, children aged 6–13 years were randomly assigned to either the CIQ group or the SAP/PLGS group. After 16 weeks, children using the CIQ system showed a decrease in HFS total scores by −7.7 compared with a −3.9 decrease in the SAP group, suggesting that the CIQ algorithm may reduce fear of hypoglycemia. However, this difference was not statistically significant (−4.9 [−10.8 to 0.9] P = 0.11 at the end of the 16-week RCT). In a separate study, Cobry et al. (2022)¹³ also reported that the CIQ algorithm positively impacted fear of hypoglycemia and enhanced hypoglycemia confidence in children using the system. Finally, a study by Hood et al. (2024)¹⁸ demonstrated a significant reduction in hypoglycemia-related fears (P = 0.02) among guardians of children using CIQ. There was also a significant increase in hypoglycemia confidence scores (P = 0.04), indicating that guardians felt more confident in managing and preventing hypoglycemia episodes in their children.

Burckhardt et al. (2021)¹⁰ compared the Medtronic MiniMed®670G HCL system with CSII or SAP in adolescents older than 12 years and adults with impaired hypoglycemia awareness. They found improved self-reported hypoglycemia awareness and reduced time spent in hypoglycemia in the HCL group, but no significant changes in fear of hypoglycemia. Wheeler et al. (2022)¹⁶ and Abraham et al. (2025)¹⁹ examined fear of hypoglycemia across children, adolescents, and adults using Medtronic’s AHCL. They found no significant difference in either participants or their caregivers compared to those treated with PLGS.

QoL, diabetes-specific quality of life, and well-being

QoL and well-being were measured using two questionnaires, where higher scores indicate better QoL: the pediatric-specific diabetes quality of life (PedsQL) questionnaire (maximum score: 100) and the World Health Organization-Five Well-Being Index questionnaire (WHO-5) (maximum score: 25 per item; overall maximum score: 100).

In Abraham’s 2021 study,⁸ diabetes-specific QoL, as measured by PedsQL (maximum 100 points), was improved by 4 points (95% CI, 0.4–8.4 points) in Minimed®670G users compared with CSII or MDI. Over the 6-month study period, users of the HCL system consistently demonstrated significantly higher scores compared with the control group. Abraham et al. (2025)¹⁹ could not demonstrate significant differences in quality of life scores between the AHCL and standard care (CSII ± CGM), but there was a trend favoring the AHCL group. Finally, another study, comparing Medtronic’s AHCL (Wheeler et al 2022¹⁶) with SAP or PLGS, could not demonstrate improved QoL in users of the HCL.

Hood et al. (2022)¹⁴ found that parents of children using the CamAPS FX HCL experienced various improvements in their QoL scores. Notably, there were significant enhancements in the communication subscale of the PedsQL for patients over 11 years old compared with the control group using CSII with or without CGMS. However, although promising, these findings should be interpreted cautiously due to small sample sizes and variable baseline survey scores. Also, they should be considered in light of the differential use of the investigated systems (HCL was active 93% of the time in the CamAPS FX but only 57% of the time in the FlorenceM configuration), the lower satisfaction with the FlorenceM configuration and the post hoc subgroup analysis. The same algorithm was investigated in De Beaufort et al.’s study (2022),¹² where caregivers reported higher level of well-being with an adjusted mean difference of 8 (3,16) on the WHO-5 scale (maximum 100 points) when their child used CamAPS FX compared with those treated with SAP. In the HCL group, only 7.5% participants scored below 50 as compared with 16.4% in the SAP group. These differences were significant across all WHO-5 items, with particularly noteworthy improvements in the item “I woke up feeling fresh and rested.”

Cobry et al. (2021)¹¹ used the PedsQL tool in both children aged 6–13 years and their parents. While CIQ did not negatively impact QoL compared with SAP, the study did not show a positive effect of HCL on these outcomes. In contrast, Hood et al. (2024)¹⁸ found that CIQ significantly enhanced the QoL for caregivers of children aged 2–6 years. Their QoL scores increased (between baseline and end of study at 26 weeks) from 74.4 ± 8.6 to 77.3 ± 7.8 for participants on standard care during 13 weeks followed by CIQ during 13 weeks and from 72.5 ± 11.8 to 78.1 ± 10.2 for patient on CIQ during 26 weeks (both P = 0.02), indicating a beneficial impact on overall well-being and daily functioning.

The study by Kariyawasam et al. (2022)¹⁵ described QoL in users of the DBLK4 algorithm and found similar median PedsQoL scores for children aged 5–12 years using the algorithm as compared with the controls using SAP.

Diabetes distress and emotional burden

Diabetes distress and emotional burden were measured using the PAID, PAID-T, STAI, CES-D, PIP, and the Parent Diabetes Distress Scale.

The findings varied across studies: Abraham et al. (2021)⁸ and Abraham et al. (2025)¹⁹ found no significant changes in anxiety levels among MiniMed®670G algorithm users compared with CSII or MDI. Cobry et al. (2021)¹¹ observed reductions in fear of hypoglycemia and overall distress among parents of children using CIQ compared with the SAP group, although these differences were not statistically significant. Cobry et al. (2022)¹³ reported higher PAID scores (indicating higher diabetes distress) among parents classified as poor sleepers (PSQI score > 5), with significant improvements noted with the use of CIQ. Hood et al. (2024)¹⁸ used the PIP and reported a decrease in parenting stress scores (P = 0.05) and a reduced burden associated with diabetes management, leading to lower emotional distress and enhanced overall mental health (P = 0.05) among guardians using CIQ.

In contrast, Hood et al. (2022)¹⁴ did not find significant psychosocial benefits between groups, although subgroup analysis revealed modest improvements in parent diabetes distress among users of the CamAPS FX system.

Discussion

This systematic review of 14 RCTs involves at least 565 children and adolescents with T1D and/or their caregivers. The included RCTs involved different commercially available algorithms, as well as various hardware configurations. While they provided robust evidence on the clinical efficacy of HCL systems (i.e., their impact on metabolic control of the disease), they often fell short in capturing the full spectrum of psychosocial outcomes and were not powered to detect significant changes in these measures, leading to heterogeneous results regarding QoL and other patient-reported outcomes that are inherently subjective and influenced by individual experience and expectations.

In addition to commercially available algorithms, other, open-source systems, have proven safe and beneficial in terms of metabolic control of T1D. However none of these studies met the inclusion criteria for this review, with the exception of the study of Burnside et al. (2022)⁴⁰ that investigated the impact of AndroidAPS on metabolic control of T1D and on QoL of children and adults with T1D. However, the data on QoL were not published at the time of writing this review and could therefore not be included.

Several RCTs included in this review showed higher treatment satisfaction with HCL systems such as the Minimed®670G and Medtronic’s AHCL compared with standard care. Several RCTs showed reductions in fear of hypoglycemia, mainly with CamAPS FX and CIQ, although statistical significance varied. Positive effects on sleep quality were reported with systems like CIQ and Medtronic’s AHCL. Improvement of QoL was described in several studies: Minimed®670G and CamAPS FX demonstrated improvements in specific QoL measures and CIQ proved beneficial for caregivers of children aged 2–6 years. On the contrary, studies involving Medtronic’s AHCL and CIQ in children aged 6–13 years could not demonstrate a significant impact of the HCL. The findings suggest that the impact of HCL systems on QoL varies, highlighting the need for further research to understand these differences across systems and age groups. Finally, studies showed inconsistent impacts on diabetes distress and emotional burden.

These heterogeneous findings even within the same HCL system can be partly attributed to contextual differences between studies, such as intervention duration, participant age, or the treatment used in the control group. For example, the MiniMed®670G system showed a significant improvement in quality of life (PedsQL + 4 points; 95% CI: 0.4 to 8.4) and treatment satisfaction in the study by Abraham et al. (2021),⁸ involving children and adolescents (mean age: 12.3 ± 2.9 years) for 6 months, with a control group using MDI or CSII. Conversely, in the study by Burckhardt et al (2021),¹⁰ involving adolescents and young adults with altered hypoglycemic awareness, the same system showed no clear benefit on fear of hypoglycemia or overall satisfaction, despite a perceived improvement in the frequency of hypoglycemia. The absence of significant result in the latter study could be explained by a smaller population (n = 46), by a shorter intervention duration (3 months), and/or by the use of CSII or SAP in the control group.

The studies investigating CIQ also illustrate this contextual variability. In children aged 6–13, Cobry et al (2021)¹¹ observed no significant effect on quality of life (PedsQL) after 16 weeks of use compared with SAP/PLGS. However, in another study, Hood et al. (2024)¹⁸ showed a significant improvement in the quality of life of parents of younger children (2 to 6 years) using CIQ for 26 weeks: parents’ PedsQL scores rose from 74.4 ± 8.6 to 77.3 ± 7.8 (P = 0.02) for those starting with standard care and from 72.5 ± 11.8 to 78.1 ± 10.2 (P = 0.02) for those on CIQ throughout the duration of the study. This difference suggests that the age of the children, and consequently greater parental role in younger age group, strongly influence the perception of the benefits of the system.

Similarly, with CamAPS FX, Hood et al. (2022)¹⁴ reported a significant improvement in the “communication” subscale of the PedsQL in patients over 11 years of age. However, this improvement was absent in the FlorenceM configuration, although based on the same algorithm, but with a different hardware and interface, and a significantly lower rate of active use of the closed loop (57% vs. 93% for CamAPS FX). These findings suggest that ergonomic features and adherence patterns directly influence satisfaction and quality of life outcomes, independent of algorithmic performance. This nuance is highlighted in Table 2, which summarizes both algorithmic characteristics and associated device features (e.g., commercial name, hardware configuration, usability aspects). In the remaining studies included in this review, hardware configurations were consistent within each algorithm, thereby allowing the algorithm itself to be considered the primary variable.

Table 2.

Comparative Overview of Algorithmic Approaches and Device Ergonomics

Algorithm	Control-IQ	Cambridge model predictive control algorithm			Diabeloop for kids	Medtronic MiniMed®670G	Medtronic MiniMed® advanced hybrid Closed-Loop	Android-HCLS
Algorithm	Control-IQ	Experimental	FlorenceM configuration	CamAPS FX configuration	Diabeloop for kids	Medtronic MiniMed®670G	Medtronic MiniMed® advanced hybrid Closed-Loop	Android-HCLS
Insulin pump and manufacturer	Tandem t:slimX2 (Tandem Diabetes Care)	Dana R Diabecare (Sooil)	Medtronic Minimed® 640G (Medtronic)	Dana Diabecare RS (Sooil)	Kaleido (ViCentra)	Medtronic MiniMed®670G (Medtronic)	Medtronic MiniMed®780G (Medtronic)	Medtronic VEO (Medtronic)
Glucose Sensor	Dexcom G6	FreeStyle Navigator II⁷	FreeStyle Libre Pro¹⁴	FreeStyle Libre Pro¹⁴ Dexcom G6¹²	Dexcom G6	Guardian Sensor 3	Guardian Sensor 3	Enlite
User interface	On insulin pump	On tablet computer	On smartphone¹⁴	On smartphone¹⁴	On smartphone	On insulin pump	On insulin pump	On smartphone

Hood K.K. et al., 2022.¹⁴

Barnard K.D. et al., 2017.⁷

De Beaufort C. et al., 2022.¹²

The intervention used by the control group also plays a crucial role. For example, studies comparing Medtronic’s AHCL algorithm with advanced systems such as SAP + PLGS (Wheeler et al., 2022)¹⁶ or the MiniMed®670G HCL (Bergenstal et al., 2021)⁹ show more modest, sometimes nonsignificant, differences in QoL or satisfaction, probably due to the ceiling effect associated with using an already optimized treatment.

The introduction of HCL systems in clinical practice represents a significant advancement in the management of T1D in children and adolescents. These systems have demonstrated clear benefits in terms of glycemic control, by reducing HbA1c, increasing time spent in target glycemic range, and reducing time spent in hyper- and hypoglycemia. However, the real-world effectiveness and the overall impact of this technology depends on long-term adherence of the patients to the different systems. Therefore, their acceptance and impact on the patient’s QoL need to be optimal to guarantee a durable impact on metabolic control and long-term complications.

The importance of patient adherence to HCL systems is highlighted by Hood et al. (2022)¹⁴ where the adherence to a same algorithm (Cambridge model predictive control) was dependent on the hardware configuration used (CamAPS FX versus FlorenceM). High adherence rates were associated with better glycemic outcomes, improved QoL, and higher patient satisfaction. Barriers to adherence to HCL systems can include the complexity of the technology, the complexity and reactivity of the user interface, and the frequency of technical issues. In addition to optimizing these components, effective adherence also requires ongoing support from health care providers, ensuring that patients and caregivers are confident and comfortable with the technology. This includes addressing technical difficulties promptly and providing encouragement and reassurance to maintain consistent use.

In this context, therapeutic education plays a pivotal role in enhancing adherence to HCL systems and improving overall diabetes management. Education programs should teach patients the strength and limitations of a given algorithm, so that their expectations can be met by the system. This can also improve the patient’s confidence in the HCL and reduce anxiety while using it. Practical training on how to operate the HCL system, interpret data, troubleshoot common issues, and switching back to manual mode is essential in addition to addressing the behavioral aspects of diabetes management, such as maintaining motivation and dealing with diabetes distress.

Strengths and limitations

Limitations of this study is the considerable heterogeneity in study designs, evaluated outcomes, and control measures, which complicates the aggregation and comparison of findings. In addition, many of the included studies focused on secondary outcomes of studies investigating the impact of HCL on metabolic control of T1D, potentially resulting in insufficient statistical power. The inability to blind participants or investigators introduces the risk of bias, particularly due to the novelty effect, which may influence responses. Another important limitation is the potential influence of floor and ceiling effects. For instance, reductions in diabetes distress may be difficult to demonstrate when baseline levels are already low, and improvements in hypoglycemic confidence may be hard to detect if participants already report high confidence at baseline. Some studies included both pediatric and adult participants. From those, we extracted and reported outcomes specifically related to the pediatric population when these data were available. However, in some cases, subgroup analyses or stratified results for children and adolescents were not clearly reported, which limits the generalizability of the findings to the pediatric population alone. Finally, we decided to include only RCTs. This decision was taken to ensure methodological rigor and comparability across studies. However, this excludes other study designs, including observational and qualitative studies that may explore quality of life more extensively and as a primary outcome. However, this study has notable strengths, including a rigorous methodology adhering to PRISMA guidelines. Only high-quality RCTs were included, with a significant proportion of double cross-over designs, thereby enhancing the robustness and validity of the findings.

Conclusions

HCL systems represent a significant advancement in T1D management for children and adolescents and consistently improve glycemic control. However, their impact on psychosocial outcomes such as treatment satisfaction, fear of hypoglycemia, sleep quality, and diabetes distress varies across studies, most of which were not designed to investigate these outcomes. Overall, the studies included in this review tend to show a positive impact of HCL on patient-reported outcomes. But the high variability of the results highlights the need for further RCTs with larger sample sizes, aiming at specifically investigating QoL and psychosocial outcomes using standardized and validated tools. A longer observation period may be necessary to capture long-term effects of HCL on these parameters. Finally, conducting subgroup analyses could identify specific populations of children and adolescent with T1D that may benefit more from one or the other HCL system.

Footnotes

Acknowledgments

The authors would like to thank the collaborators who contributed to the conceptual discussions and provided valuable feedback throughout the preparation of this review. This work was conducted as part of an academic project within the Hôpitaux Universitaires de Genève.

Authors’ Contributions

Conceptualization: F.I.L., P.K., L.F.P., V.M.S.; Methodology: F.I.L., P.K., L.F.P.; Writing original draft: F.I.L.; Writing—Review and Editing: F.I.L., P.K., L.F.P., V.M.S.; Supervision: P.K.

Author Disclosure Statement

No potential conflicts of interest relevant to this article were reported.

Funding Information

The study was supported by the HUG research encouragement grant (to F.I.L.).

Abbreviations Used

References

Magliano

, Boyko

, IDF Diabetes Atlas 10th edition scientific committee. IDF DIABETES ATLAS. 10th ed.. International Diabetes Federation: Brussels; 2021.

Wardian

, True

, Folaron

, et al. The choice should be yours: Diabetes-related distress by insulin delivery method for people with type 1 diabetes. Diabetes Technol Ther, 2020; 22(1):42–47; doi: 10.1089/dia.2019.0228

Tareen

, Tareen

. Psychosocial aspects of diabetes management: Dilemma of diabetes distress. Transl Pediatr, 2017; 6(4):383–396; doi: 10.21037/tp.2017.10.04

Peacock

, Frizelle

, Hussain

. A systematic review of commercial hybrid closed-loop automated insulin delivery systems. Diabetes Ther, 2023; 14(5):839–855; doi: 10.1007/s13300-023-01394-5

Page

, McKenzie

, Bossuyt

, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Bmj, 2021; 372:n71; doi: 10.1136/bmj.n71

Sharifi

, De Bock

, Jayawardene

, et al. Glycemia, treatment satisfaction, cognition, and sleep quality in adults and adolescents with type 1 diabetes when using a closed-loop system overnight versus sensor-augmented pump with low-glucose suspend function: A randomized crossover study. Diabetes Technol Ther, 2016; 18(12):772–783; doi: 10.1089/dia.2016.0288

Barnard

, Wysocki

, Ully

, et al. Closing the loop in adults, children and adolescents with suboptimally controlled type 1 diabetes under free living conditions: A psychosocial substudy. J Diabetes Sci Technol, 2017; 11(6):1080–1088; doi: 10.1177/1932296817702656

Abraham

, de Bock

, Smith

, et al.; Australian Juvenile Diabetes Research Fund Closed-Loop Research group. Effect of a hybrid closed-loop system on glycemic and psychosocial outcomes in children and adolescents with type 1 diabetes: A randomized clinical trial. JAMA Pediatr, 2021; 175(12):1227–1235; doi: 10.1001/jamapediatrics.2021.3965

Bergenstal

, Nimri

, Beck

, et al.; FLAIR Study Group. A comparison of two hybrid closed-loop systems in adolescents and young adults with type 1 diabetes (FLAIR): A multicentre, randomised, crossover trial. Lancet, 2021; 397(10270):208–219; doi: 10.1016/S0140-6736(20)32514-9

10.

Burckhardt

M-A

, Abraham

, Dart

, et al. Impact of hybrid closed loop therapy on hypoglycemia awareness in individuals with type 1 diabetes and impaired hypoglycemia awareness. Diabetes Technol Ther, 2021; 23(7):482–490; doi: 10.1089/dia.2020.0593

11.

Cobry

, Kanapka

, Cengiz

, et al.; iDCL Trial Research Group. Health-related quality of life and treatment satisfaction in parents and children with type 1 diabetes using closed-loop control. Diabetes Technol Ther, 2021; 23(6):401–409; doi: 10.1089/dia.2020.0532

12.

de Beaufort

, Schierloh

, Thankamony

, et al.; KidsAP Consortium. Cambridge hybrid closed-loop system in very young children with type 1 diabetes reduces caregivers’ fear of hypoglycemia and improves their well-being. Diabetes Care, 2022; 45(12):3050–3053; doi: 10.2337/dc22-0693

13.

Cobry

, Bisio

, Wadwa

, et al. Improvements in parental sleep, fear of hypoglycemia, and diabetes distress with use of an advanced hybrid closed-loop system. Diabetes Care, 2022; 45(5):1292–1295; doi: 10.2337/dc21-1778

14.

Hood

, Garcia-Willingham

, Hanes

, et al.; DAN05 Consortium. Lived experience of CamAPS FX closed loop system in youth with type 1 diabetes and their parents. Diabetes Obes Metab, 2022; 24(12):2309–2318; doi: 10.1111/dom.14815

15.

Kariyawasam

, Morin

, Casteels

, et al. Hybrid closed-loop insulin delivery versus sensor-augmented pump therapy in children aged 6-12 years: A randomised, controlled, cross-over, non-inferiority trial. Lancet Digit Health, 2022; 4(3):e158–e168; doi: 10.1016/S2589-7500(21)00271-5

16.

Wheeler

, Collyns

, Meier

, et al. Improved technology satisfaction and sleep quality with Medtronic MiniMed® advanced hybrid closed-loop delivery compared to predictive low glucose suspend in people with Type 1 Diabetes in a randomized crossover trial. Acta Diabetol, 2022; 59(1):31–37; doi: 10.1007/s00592-021-01789-5

17.

Garg

, Grunberger

, Weinstock

, et al.; Adult and Pediatric MiniMed™ HCL Outcomes 6-month RCT: HCL versus CSII Control Study Group. Improved glycemia with hybrid closed-loop versus continuous subcutaneous insulin infusion therapy: Results from a randomized controlled trial. Diabetes Technol Ther, 2023; 25(1):1–12; doi: 10.1089/dia.2022.0421

18.

Hood

, Schneider-Utaka

, Reed

, et al.; PEDAP Trial Study Group. Patient reported outcomes (PROs) and user experiences of young children with type 1 diabetes using t: Slim X2 insulin pump with control-IQ technology. Diabetes Res Clin Pract, 2024; 208:111114; doi: 10.1016/j.diabres.2024.111114

19.

Abraham

, Smith

, Dart

, et al. Glycemic and psychosocial outcomes of advanced hybrid closed-loop therapy in youth with high HbA1c: A randomized clinical trial. Diabetes Care, 2025; 48(1):67–75; doi: 10.2337/dc24-0276

20.

Varni

, Delamater

, Hood

, et al.; Pediatric Quality of Life Inventory 3.2 Diabetes Module Testing Study Consortium. PedsQL 3.2 diabetes module for children, adolescents, and young adults: Reliability and validity in type 1 diabetes. Diabetes Care, 2018; 41(10):2064–2071; doi: 10.2337/dc17-2707

21.

Topp

, Østergaard

, Søndergaard

, et al. The WHO-5 well-being index: A systematic review of the literature. Psychother Psychosom, 2015; 84(3):167–176; doi: 10.1159/000376585

22.

Rabin

, de Charro

. EQ-5D: A measure of health status from the EuroQol Group. Ann Med, 2001; 33(5):337–343; doi: 10.3109/07853890109002087

23.

Bradley

. Diabetes treatment satisfaction questionnaire. Change version for use alongside status version provides appropriate solution where ceiling effects occur. Diabetes Care, 1999; 22(3):530–532; doi: 10.2337/diacare.22.3.530

24.

Polonsky

, Fisher

, Hessler

, et al. Development of a new measure for assessing glucose monitoring device-related treatment satisfaction and quality of life. Diabetes Technol Ther, 2015; 17(9):657–663; doi: 10.1089/dia.2014.0417

25.

Gonder-Frederick

, Schmidt

, Vajda

, et al. Psychometric properties of the hypoglycemia fear survey-II for adults with type 1 diabetes. Diabetes Care, 2011; 34(4):801–806; doi: 10.2337/dc10-1343

26.

Gonder-Frederick

, Nyer

, Shepard

, et al. Assessing fear of hypoglycemia in children with Type 1 diabetes and their parents. Diabetes Manag (Lond), 2011; 1(6):627–639; doi: 10.2217/DMT.11.60

27.

Polonsky

, Fisher

, Hessler

, et al. Investigating hypoglycemic confidence in type 1 and type 2 diabetes. Diabetes Technol Ther, 2017; 19(2):131–136; doi: 10.1089/dia.2016.0366

28.

Clarke

, Cox

, Gonder-Frederick

, et al. Reduced awareness of hypoglycemia in adults with IDDM. A prospective study of hypoglycemic frequency and associated symptoms. Diabetes Care, 1995; 18(4):517–522; doi: 10.2337/diacare.18.4.517

29.

Gold

, MacLeod

, Frier

. Frequency of severe hypoglycemia in patients with type I diabetes with impaired awareness of hypoglycemia. Diabetes Care, 1994; 17(7):697–703; doi: 10.2337/diacare.17.7.697

30.

Johns

. A new method for measuring daytime sleepiness: The Epworth sleepiness scale. Sleep, 1991; 14(6):540–545; doi: 10.1093/sleep/14.6.540

31.

Smyth

. The Pittsburgh Sleep Quality Index (PSQI). J Gerontol Nurs, 1999; 25(12):10–11; doi: 10.3928/0098-9134-19991201-10

32.

Weissberg-Benchell

, Antisdel-Lomaglio

. Diabetes-specific emotional distress among adolescents: Feasibility, reliability, and validity of the problem areas in diabetes-teen version. Pediatr Diabetes, 2011; 12(4 Pt 1):341–344; doi: 10.1111/j.1399-5448.2010.00720.x

33.

Evans

, Weil

LEG

, Shapiro

, et al. Psychometric properties of the parent and child problem areas in diabetes measures. J Pediatr Psychol, 2019; 44(6):703–713; doi: 10.1093/jpepsy/jsz018

34.

Knowles

, Olatunji

. Specificity of trait anxiety in anxiety and depression: Meta-analysis of the state-trait anxiety inventory. Clin Psychol Rev, 2020; 82:101928; doi: 10.1016/j.cpr.2020.101928

35.

Carleton

, Thibodeau

, Teale

MJN

, et al. The center for epidemiologic studies depression scale: A review with a theoretical and empirical examination of item content and factor structure. PLoS One, 2013; 8(3):e58067; doi: 10.1371/journal.pone.0058067

36.

Lewin

, Storch

, Silverstein

, et al. Validation of the pediatric inventory for parents in mothers of children with type 1 diabetes: An examination of parenting stress, anxiety, and childhood psychopathology. Fam Syst Health, 2005; 23(1):56–65; doi: 10.1037/1091-7527.23.1.56

37.

Hessler

, Fisher

, Polonsky

, et al. Understanding the areas and correlates of diabetes-related distress in parents of teens with type 1 diabetes. J Pediatr Psychol, 2016; 41(7):750–758; doi: 10.1093/jpepsy/jsw002

38.

Spitzer

, Kroenke

, Williams

JBW

, et al. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med, 2006; 166(10):1092–1097; doi: 10.1001/archinte.166.10.1092

39.

Spitzer

, Kroenke

, Williams

. Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. JAMA, 1999; 282(18):1737–1744; doi: 10.1001/jama.282.18.1737

40.

Burnside

, Lewis

, Crocket

, et al. Open-source automated insulin delivery in type 1 diabetes. N Engl J Med, 2022; 387(10):869–881; doi: 10.1056/NEJMoa2203913