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Article

Sleep Quality and Glycemic Control in Type 1 Diabetes: A Retrospective Cohort Study Using Advanced Technological Devices

by
Paola Pantanetti
1,†,
Federico Biondini
2,†,
Stefano Mancin
3,
Marco Sguanci
4,
Alice Masini
5,*,
Massimiliano Panella
5,*,
Sara Morales Palomares
6,
Gaetano Ferrara
7,
Fabio Petrelli
8,‡ and
Giovanni Cangelosi
1,‡
1
Unit of Diabetology, Ast Fermo, 63900 Fermo, Italy
2
Units of Psychiatry, Ast Fermo, 63900 Fermo, Italy
3
IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Italy
4
A.O. Polyclinic San Martino Hospital, Largo R. Benzi 10, 16132 Genova, Italy
5
Department of Translation Medicine, University of Eastern Piedmont, 28100 Novara, Italy
6
Department of Pharmacy, Health and Nutritional Sciences (DFSSN), University of Calabria, 87036 Rende, Italy
7
Nephrology and Dialysis Unit, Ramazzini Hospital, 41012 Carpi, Italy
8
School of Pharmacy, Polo Medicina Sperimentale e Sanità Pubblica “Stefania Scuri”, 62032 Camerino, Italy
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors also contributed equally to this work.
Diabetology 2025, 6(3), 21; https://doi.org/10.3390/diabetology6030021
Submission received: 31 December 2024 / Revised: 21 February 2025 / Accepted: 12 March 2025 / Published: 14 March 2025
Browse Figures
Review Reports Versions Notes

Abstract

:
Introduction/Aim: Type 1 diabetes (T1D) challenges glycemic control, with sleep disturbances affecting insulin sensitivity and glucose variability. This study aimed to observe sleep quality in T1D patients and glycemic outcomes, particularly at bedtime hours. Methods: This retrospective observational study, conducted at an Italian clinical center, included T1D patients using Medtronic devices. Sleep quality was assessed using the Italian version of the Pittsburgh Sleep Quality Index (PSQI), and glycemic outcomes were analyzed with CGM data. Descriptive statistics and non-parametric tests were applied for statistical comparisons. Results: Of 45 patients, four were excluded, leaving 41 for analysis. The mean PSQI score was 6.0 ± 4.1, with 36.6% showing poor sleep quality. No significant differences in age, sex, BMI, or diabetes duration were found. Poor sleepers had a higher time above range level 2 (TAR2) (6.3 ± 6.2%) compared to good sleepers (4.1 ± 5.0%). During bedtime hours, poor sleepers showed a significantly higher TAR2 (6.7 ± 7.2% vs. 3.3 ± 6.2%, p = 0.013). Conclusions: Poor sleep quality is associated with increased nocturnal hyperglycemia in T1D patients. Enhancing sleep quality may contribute to improved glycemic control, particularly during nighttime. Future research should explore targeted sleep interventions in diabetes care, and specific lifestyle-based healthcare programs are recommended to optimize glycemic outcomes.

1. Introduction

Diabetes is one of the most significant global public health challenges [1,2,3]. According to the World Health Organization (WHO), over 460 million people worldwide are affected by this condition, with projections indicating an exponential increase in the coming decades, primarily due to shifts in dietary and lifestyle habits [4,5,6,7,8,9,10]. While type 1 diabetes (T1D) is less prevalent than type 2 diabetes (T2D), it remains a major concern, particularly among younger populations, given its chronic nature and the therapeutic challenges that it poses [11,12,13,14,15].
Diabetes can significantly impact patients’ daily lives, often leading to emotional and psychological distress, including an increased prevalence of sleep disturbances and depression [16,17,18,19]. Studies suggest that individuals with diabetes are at a higher risk of experiencing poor sleep quality and mood disorders compared to the general population. Key contributing factors include fluctuating blood glucose levels, the psychological burden of disease management, and diabetes-related complications [20,21,22,23,24,25]. Moreover, research indicates that sleep disturbances are highly prevalent among individuals with diabetes, highlighting the importance of addressing these concerns to improve overall health outcomes [26,27,28].
The epidemiology of diabetes, both nationally and globally, underscores an increasing burden on healthcare systems, driven by factors such as aging populations, rising obesity rates, and the growing prevalence of sedentary lifestyles [29,30,31]. Although T2D affects a larger proportion of individuals worldwide, T1D presents distinct challenges that necessitate specialized and innovative therapeutic approaches, particularly given its increasingly social complex and technologically advanced management [32,33,34]. Over the past few decades, diabetes management—especially for T1D—has undergone significant advancements, shifting decisively toward personalized therapeutic strategies supported by sophisticated medical technologies [35,36]. Among these innovations, continuous glucose monitoring (CGM) systems and insulin pumps (IP) have represented a major breakthrough in optimizing glycemic control and improving patients’ quality of life. These devices not only facilitate better glucose regulation but also help to mitigate the emotional burden and reduce symptoms of depression and sleep disturbances, which are prevalent among individuals with diabetes [37,38]. For individuals with T1D, who must manage insulin administration daily to maintain stable blood glucose levels, technologies such as CGM, IP, and “smart pens” have been transformative. Beyond enhancing glycemic control, these technologies contribute to overall well-being by alleviating the psychological strain associated with disease management [39,40]. The reduction in emotional stress, alongside improvements in sleep quality and mood, is a critical component in enhancing quality of life, further reinforcing the importance of technological advancements in the holistic management of T1D patients [41,42,43].
In recent years, increasing attention has been given to the relationship between sleep and glycemic regulation in individuals with T1D [43]. Sleep disturbances, such as a reduced duration or poor-quality sleep, are common among T1D patients and can negatively impact glycemic control [44]. Sleep deprivation, for instance, may alter insulin sensitivity and increase glycemic variability, making it more challenging to maintain stable blood glucose levels both during the day and overnight. In particular, poor sleep quality can lead to greater glucose fluctuations, increasing the risk of hypoglycemia or hyperglycemia [45]. These issues are especially significant for T1D patients, who must manage insulin on a daily basis to maintain stable glucose levels [43,44,45,46]. While technologies such as continuous glucose monitoring (CGM) and IP have improved glycemic control, they do not always fully compensate for the negative effects of disturbed sleep [47]. Recent studies suggest that optimizing sleep quality could be an important strategy to improve glycemic control in T1D patients, particularly in the pediatric population [48,49]. However, integrating sleep management with the use of technological devices remains a challenge, and further investigation is needed to better understand how sleep quality may influence long-term glycemic regulation, particularly during the night [43,44,45,46,47,48,49]. In this context, our study aims to support new research on this topic and expand the debate on an extremely crucial aspect of T1D management, particularly through modern technological tools, which are now common in all chronic diseases in general [50].

1.1. Aims

1.1.1. Primary Aims

The primary objectives of this observation were to characterize the sleep quality of patients with T1D based on their scores on the Italian version of the Pittsburgh Sleep Quality Index (PSQI) [51] (Supplementary File S1) and compare the patient characteristics between groups.

1.1.2. Secondary Aim

We aimed to observe glycemic outcomes during the 30 days preceding the PSQI assessment between groups, as with the primary objective, but considering bedtime hours only.

2. Materials and Methods

2.1. Study Design

A single-center retrospective observational clinical study was conducted at the Diabetes and Nutrition Clinic of the tertiary hospital in Ast Fermo (Italy). The STROBE checklist was adopted for study reporting (Supplementary File S2) [52,53].

2.2. Ethical Considerations

The study was conducted in full compliance with the ethical principles outlined in the Declaration of Helsinki and in accordance with institutional and national research ethics guidelines. Ethical approval was granted by the Institutional Review Board of Ast Fermo (INF 03/2024, date 3 March 2024). All participants provided informed consent for the collection and use of their data.

2.3. Sample and Criteria

Among the 790 individuals with T1D attending the center and receiving personalized care, patients using the Medtronic Smart MDI (Simplera™ + InPen™) [https://www.medtronic-diabetes.com/en-gb/SmartMDI] (Accessed on 20 December 2024) or the MiniMed™ 780G system [https://www.medtronicdiabetes.com/products/minimed-780g-insulin-pump-system] (Accessed on 20 December 2024) were included in the study. The sample was selected for convenience at the recruiting center. To ensure the valid interpretation of the collected data and minimize potential confounding factors related to the overall glycemic response, carefully defined inclusion and exclusion criteria were applied. The statistical methods used, as described in the following sections, were selected to maximize the generalizability of the results.
The inclusion criteria were as follows: T1D diagnosis, male or female, aged ≥ 18 years, therapy requirement determined by the physician’s judgment, at least 70% sensor usage during the 30 days prior to the PSQI assessment, and provision of signed informed consent.
The exclusion criteria included the following: refusal to consent to data use, presence of concomitant or suspected malignant diseases, pregnancy or breastfeeding, recent acute illnesses (within 3 months of enrollment) excluding viral illnesses, renal impairment (eGFR < 60 mL/min), severe liver failure, congestive heart failure (NYHA class IV), proliferative diabetic retinopathy, cholelithiasis, chronic or ongoing acute pancreatitis, and adherence to a ketogenic diet or other nutritional interventions considered supplementary therapeutic treatments. Furthermore, failure to meet even a single inclusion criterion resulted in automatic exclusion from the study.

2.4. Endpoints and Tool

Patients’ sleep quality was described in terms of the PSQI score, analyzed both as a continuous variable and a categorical one (PSQI ≤ 5, PSQI > 5). The PSQI is a tool primarily used in clinical settings to assess an individual’s sleep quality over the past four weeks. It consists of nine questions (with Question 5 containing 10 subitems) that evaluate various aspects of sleep, including the duration, latency, efficiency, disturbances, and medication use. Through a specific coding system, the responses are converted into an overall score, where a score greater than 5 indicates poor sleep quality, while a score of 5 or lower signifies good sleep quality. Statistical comparisons were conducted between the groups based on their age, sex, T1D duration, body mass index (BMI), smoking status, and type of diabetes management device used. Additionally, statistical comparisons of the glycemic outcomes between groups were performed, including the following:
a.
Sensor glucose (SG) mean, standard deviation (SD), and coefficient of variation (CV);
b.
Time below range level 2 (TBR2): percentage of time spent below 54 mg/dL;
c.
Time below range level 1 (TBR1): percentage of time spent between 54 and 69 mg/dL;
d.
Time below range (TBR): percentage of time spent below 70 mg/dL;
e.
Time in range (TIR): percentage of time spent within 70–180 mg/dL;
f.
Time above range level 1 (TAR1): percentage of time spent between 181 and 250 mg/dL;
g.
Time above range level 2 (TAR2): percentage of time spent above 250 mg/dL;
h.
Time above range (TAR): percentage of time spent above 180 mg/dL.

2.5. Statistical Analysis

2.5.1. General Methodology

Descriptive statistics were used to summarize the results, including the mean, standard deviation (SD), minimum, maximum, and median with interquartile range (IQR) for continuous variables, with counts and percentages for categorical variables. Summary statistics were reported with a maximum of two decimal places, as appropriate. Bar charts were produced to visually represent some of the results. Comparisons of continuous variables between groups were performed using the Wilcoxon rank-sum test, while categorical variables were compared using Fisher’s exact test. All tests were two-sided, and p-values < 0.05 were considered statistically significant. Statistical analyses were conducted using the SAS software, version 9.4 (SAS Institute Inc., Cary, NC, USA). The statistical analysis performed was exploratory and based on the experience of a single center with a limited sample size. For this reason, statistical tests suitable for small sample sizes were employed.

2.5.2. Data Analysis

All data were collected by the physicians from device data files, CareLinkTM reports [54], clinical records, and other relevant sources. Data were shared via Excel spreadsheets using the SharePoint platform. Raw CGM data were utilized to calculate glycemic outcomes, as detailed in the following sections.

2.5.3. Derived Variables

The Medtronic CGM systems track SG levels throughout the day and night and measure SG (mg/dL) at regular intervals, as frequently as every 5 min. Thus, 288 measurements are taken daily for a patient who keeps the system activated continuously. Table 1 summarizes the criteria used in a previous study [55] to derive glycemic outcomes from CGM data.

2.5.4. Bedtime Hours

Bedtime hours were defined as the period between when a patient went to bed and when they woke up in the morning. To analyze glycemic outcomes exclusively within bedtime hours, a patient-specific time range was determined. This range was established by reviewing the patient’s responses to the PSQI questions about the usual time at which they went to bed and woke up during the past month.

2.5.5. Handling of Missing Data and Outliers and Validation Requirements

Potential outliers were retained in the analysis, and no imputation methods were applied to handle missing data. To ensure accuracy and reliability, all analyses were independently reviewed and validated by a second statistician.

3. Results

3.1. Patient Disposition

Out of the 45 patients considered, four were excluded from the analysis due to using the sensor for less than 70% of the time during the 30 days preceding the PSQI assessment. Consequently, the results presented in this manuscript refer to the remaining 41 subjects.

3.2. Sleep Quality

The mean PSQI score was 6.0 ± 4.1, and 15 (36.6%) patients had a PSQI score greater than 5, indicating poor sleep quality [56] (Table 2).

3.3. Patient Characteristics

Table 3 shows the patient characteristics, comparing poor and good sleepers. No statistically significant differences were observed for the variables examined. The patients’ mean age was approximately 52 years in both groups. The proportion of females was equal to 46.7% (7/15) among poor sleepers and 38.5% (10/26) among good sleepers. Although a true scientific interpretation of the data was not performed, poor sleepers had a shorter mean diabetes duration (19.2 ± 12.0 years) compared to good sleepers (28.8 ± 15.1 yrs.). The mean BMI was 25.0 ± 4.1 kg/m2 in the entire cohort (poor sleepers: 25.6 ± 3.6 kg/m2; good sleepers: 24.6 ± 4.4 kg/m2). Of the 15 subjects classified as poor sleepers, nine (60%) managed their T1D using the MiniMed™ 780G, while the remaining six (40%) used the Medtronic Smart MDI. Among the 26 subjects who reported good sleep quality, 19 (73.1%) were supported by the MiniMed™ 780G, whereas seven (26.9%) used the Medtronic Smart MDI.

3.4. Glycemic Outcomes

Table 4 shows the glycemic outcomes of the cohort during the 30-day period preceding the PSQI assessment, and Figure 1 represents the average TIR, TBR, and TAR during bedtime hours in the 30 days preceding the PSQI assessment by sleep quality. No statistically significant differences were observed between good and poor sleepers. The average SG mean and SD were 153.9 ± 18.3 mg/dL and 47.4 ± 10.7 mg/dL for the entire sample, respectively. The average CV was within the recommended target of ≤ 36% in both groups (poor sleepers: 31.2 ± 5.6%; good sleepers: 30.4 ± 4.6%). The mean TIR for the entire sample was 72.0 ± 11.5%, meeting the recommended target of > 70%. Poor sleepers demonstrated a slightly lower TIR (70.0 ± 13.1%) compared to good sleepers (73.2 ± 10.6%). The mean TBR for the entire sample was 1.7 ± 2.4%, well within the target of < 4%, with the TBR2 also meeting the recommended target of < 1% (0.3 ± 0.7%). However, the mean TAR for the entire sample was 26.2 ± 12.3%, slightly exceeding the target of < 25%. Poor sleepers exhibited a higher TAR (28.5 ± 14.0%) than good sleepers (24.9 ± 11.3%). The mean TAR2 was 6.3 ± 6.2% for poor sleepers, exceeding the recommended target of < 5%, while it was 4.1 ± 5.0% for good sleepers [41].

3.5. Glycemic Outcomes During Bedtime Hours

Table 5 shows the glycemic outcomes of the patients during the 30 days preceding the PSQI assessment, considering bedtime hours only, and the average TBR, TIR, and TAR values during bedtime hours are shown in Figure 2. Bedtime hours were defined for each patient based on the PSQI responses, as outlined in Table 5. Poor sleepers had an average number of bedtime hours of 7.3 ± 1.3 per night, while this figure was 7.6 ± 1.0 for good sleepers.
The mean TAR2 was significantly higher in poor sleepers compared to good sleepers (6.7 ± 7.2% vs. 3.3 ± 6.2%, p = 0.013), with median values of 4.9% and 1.1%, respectively. No other statistically significant differences were observed.

4. Discussion

This study examined 41 T1D patients treated with the Medtronic Smart MDI system (Simplera™ + InPen™) or the MiniMed™ 780G system, all of whom underwent a PSQI assessment and had at least 70% sensor use during the 30 days preceding the assessment. A total of 15 patients (36.6%) had a PSQI score > 5, indicating poor sleep quality. No statistically significant differences were observed between good and poor sleepers in terms of their age, gender, BMI, diabetes duration, smoking status, the device used for diabetes management, or glycemic outcomes during the 30 days preceding the PSQI assessment. However, when glycemic outcomes were analyzed exclusively during bedtime hours, the mean TAR2 (> 250 mg/dL) was significantly higher in poor sleepers compared to good sleepers (6.7 ± 7.2% vs. 3.3 ± 6.2%, p = 0.013), with median values of 4.9% and 1.1%, respectively.
This finding aligns with previous studies highlighting the negative impact of poor sleep quality on glycemic control in T1D patients [57,58,59,60]. Several mechanisms may explain this relationship, including the effects of sleep disturbances on insulin sensitivity and hormonal regulation [50]. During sleep, the body undergoes physiological processes essential for metabolic regulation and glucose homeostasis [61]. Sleep disruptions, particularly during the night, may alter the secretion of insulin and counter-regulatory hormones, such as cortisol, leading to increased insulin resistance and hyperglycemia [60,61]. Furthermore, the autonomic nervous system, which plays a key role in glucose metabolism regulation, is influenced by sleep quality [62,63]. Poor sleep has been associated with increased sympathetic nervous system activity, further impairing glycemic control. This vicious cycle, where hyperglycemia and insulin resistance contribute to more sleep disturbances, makes it increasingly difficult to maintain optimal glucose levels [57,58,59,60,61,62,63]. Given these findings, monitoring sleep quality could serve as a valuable intervention to optimize glycemic control, particularly during nighttime hours, and prevent hyperglycemic episodes. Clinicians should consider incorporating sleep assessments into routine diabetes care, as improving sleep quality may not only enhance overall health but also support better diabetes management [64]. Potential therapeutic approaches include sleep hygiene education, cognitive behavioral therapy for insomnia, and screening for underlying sleep disorders such as obstructive sleep apnea [65,66,67,68]. Additionally, CGM and other advanced technological devices could help to elucidate the relationship between sleep patterns and glucose fluctuations, enabling personalized treatment strategies that address both sleep quality and glycemic control [69,70].
Our results align with a recent study by Passanini et al. [71], which examined the use of the MiniMed™ 780G system for the treatment of T1D. Their findings demonstrated that the use of an automated insulin delivery system led to improvements in both glycemic control and sleep quality, while also reducing psychological distress associated with diabetes. While both studies emphasize the importance of sleep quality for glycemic control, key differences emerge in their focus. Our study primarily investigated the effect of sleep quality on the TAR2, revealing a significant difference in patients with good and poor sleep quality. In contrast, Passanini et al. [71] reported that MiniMed™ 780G use was associated with a reduction in severe hypoglycemia and an improvement in the TIR, without detecting a significant increase in the TAR. These findings suggest that advanced insulin delivery systems like the MiniMed™ 780G may provide additional benefits, enhancing both glycemic control and sleep quality, with potential positive implications for overall diabetes management. Moreover, our findings are consistent with other studies [72,73] that identified a correlation between poor sleep quality and greater nocturnal glycemic variability, measured through the SD and CV. Specifically, nights with poor sleep quality were associated with higher glycemic variability, underscoring the negative impact of sleep disturbances on nocturnal glycemic stability. These results further reinforce the importance of monitoring and optimizing sleep quality to enhance glycemic control in patients with T1D. On the other hand, the study by Martin-Nemeth et al. [74] explored the role of a fear of hypoglycemia (FOH) as a contributing factor to poor sleep quality in T1D patients. Anxiety related to the hypoglycemia risk may thus represent an additional variable affecting sleep quality and glycemic control. While our study did not directly investigate psychological factors, it is important to acknowledge the interplay between FOH, sleep disturbances, and glycemic management, which warrants further exploration. Additional studies [75,76,77] have reported that poor sleep quality, as measured by the PSQI, is associated with higher HbA1c levels and suboptimal glycemic control. Several sleep-disrupting factors—such as pain, coughing, snoring, and difficulty maintaining continuous sleep—were significantly correlated with poorer glycemic management. These findings align with our results, indicating that multiple sleep-related issues may contribute to glycemic instability in T1D patients. Lastly, a recent study [78] investigated the effects of seasonal transitions on sleep quality and glycemic parameters in T1D patients using CGM systems. Although no significant changes in sleep quality were observed, the study reported a deterioration in glycemic control, with an increase in the glycemic management indicator (GMI) and a reduction in TIR during seasonal changes. These results suggest that, in addition to sleep quality, environmental factors—such as seasonal transitions—may negatively influence glycemic regulation. Our findings, which highlight the impact of sleep quality on glycemic control, contribute to this broader context, emphasizing the need to monitor multiple factors, including environmental influences, for optimal diabetes management with technological devices.

4.1. Limitations

This analysis was conducted on a selected set of patients, and, given the retrospective observational nature of the data, selection bias may be present. Furthermore, the available patient characteristics were limited, potentially excluding factors such as stress, physical activity, or comorbid conditions that could influence sleep or glycemic control. A notable limitation of this study was the lack of subgroup analysis based on the diabetes management device used, as well as the reliance on a self-administered questionnaire for sleep assessment, which may have introduced recall bias. Another important limitation to highlight is the use of convenience sampling, which limits the generalizability of the findings. Future studies should aim to incorporate a broader range of patient characteristics, a larger sample size, and adjustments for potential confounders to generate more robust and generalizable evidence.

4.2. Future Perspectives for Clinical Practice

The future of diabetes management will increasingly rely on a holistic, patient-centered approach, integrating advanced technology, lifestyle modifications, and targeted interventions to improve sleep hygiene [79,80]. Clinicians must be well trained in the use of modern diabetes technologies and their integration into personalized care plans to optimize patient outcomes [55]. As the link between sleep quality and glycemic control becomes more evident, healthcare providers should actively assess and address sleep disturbances as a component of comprehensive diabetes management. This involves not only promoting sleep hygiene practices but also recognizing the broader influence of lifestyle factors, including physical activity, nutrition, and stress management, on metabolic health and glycemic stability [81]. By adopting a multidisciplinary approach, clinicians can deliver more effective, individualized care, helping patients with T1D and their caregivers to achieve better glycemic control, a reduced disease burden, and improved long-term well-being [82,83].

5. Conclusions

High-quality sleep plays a critical role in maintaining optimal glycemic control, particularly for individuals managing chronic conditions such as T1D. Sleep disturbances have been shown to negatively affect glycemic regulation, increasing the risk of nocturnal hyperglycemia and greater glucose variability. These findings emphasize the need for clinicians to recognize sleep as a modifiable factor that can significantly impact diabetes outcomes. By incorporating sleep assessments into routine diabetes care, healthcare providers can offer more comprehensive support, helping patients to maintain stable blood glucose levels and prevent nocturnal fluctuations. A holistic approach to diabetes management—one that considers physiological, psychological, and environmental factors—is essential in improving both glycemic control and overall well-being. Integrating targeted interventions to enhance sleep quality may contribute to reducing long-term complications, ultimately improving the quality of life of individuals with diabetes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diabetology6030021/s1, File S1. Italian version of Pittsburgh Sleep Quality Index (PSQI); File S2. STROBE Statement—checklist.

Author Contributions

Conceptualization, G.C. and P.P.; methodology, G.C. and S.M.; software, G.C.; validation, S.M. and G.C.; formal analysis, G.C.; investigation, G.C. and F.B.; data curation, G.C.; writing—original draft preparation, G.C., S.M., M.S., P.P., F.B. and S.M.P.; writing—review and editing, G.C., S.M.P, S.M. and F.P.; visualization, P.P., F.B., S.M., M.S., A.M., M.P., S.M.P., G.F., F.P. and G.C. supervision, G.C. and F.P.; project administration, G.C. and F.P.; P.P. and F.B. contributed equally as first authors; F.P. and G.C. contributed equally as second authors. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study adhered to the principles outlined in the Helsinki Declaration. Ethical approval was granted by the Institutional Review Board of Ast Fermo with authorization code INF 03/2024, date 3 March 2024.

Informed Consent Statement

All participants were informed about the study’s objectives, and consent was obtained in compliance with all privacy regulations (Art. 13 EU Regulation 679/2016) before survey administration. The data were processed anonymously.

Data Availability Statement

The data that support the findings of this study are available in the text and Supplementary Materials.

Acknowledgments

The authors wish to thank the following Medtronic employees for their technical and statistical support of this study: Ivan Merlo and Claudio Carrara.

Conflicts of Interest

The authors declare no competing interests.

References

  1. Ahmad, E.; Lim, S.; Lamptey, R.; Webb, D.R.; Davies, M.J. Type 2 diabetes. Lancet 2022, 400, 1803–1820. [Google Scholar] [CrossRef] [PubMed]
  2. Magliano, D.J.; Boyko, E.J. Committee IDFDAtes. IDF diabetes atlas. In IDF Diabetes Atlas; International Diabetes Federation: Brussels, Belgium, 2021; Volume 2021. [Google Scholar]
  3. Wong, N.D.; Sattar, N. Cardiovascular risk in diabetes mellitus: Epidemiology, assessment and prevention. Nat. Rev. Cardiol. 2023, 20, 685–695. [Google Scholar] [CrossRef]
  4. World Health Organization (WHO). Diabetes. Available online: https://www.who.int/news-room/fact-sheets/detail/diabetes (accessed on 25 November 2024).
  5. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021. Results. Institute for Health Metrics and Evaluation. Available online: https://vizhub.healthdata.org/gbd-results/ (accessed on 1 November 2024).
  6. GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: A systematic analysis for the Global Burden of Disease Study 2021. Lancet 2023, 402, 203–234. [Google Scholar] [CrossRef]
  7. Petrelli, F.; Cangelosi, G.; Scuri, S.; Nguyen, C.T.T.; Debernardi, G.; Benni, A.; Vesprini, A.; Rocchi, R.; De Carolis, C.; Pantanetti, P.; et al. Food knowledge of patients at the first access to a Diabetology center. Acta Biomed. 2020, 91 (Suppl. S3), 160–164. [Google Scholar] [CrossRef]
  8. Cangelosi, G.; Grappasonni, I.; Nguyen, C.T.T.; Acito, M.; Pantanetti, P.; Benni, A.; Petrelli, F. Mediterranean Diet (MedDiet) and Lifestyle Medicine (LM) for support and care of patients with type II diabetes in the COVID-19 era: A cross-observational study. Acta Biomed. 2023, 94 (Suppl. S3), e2023189. [Google Scholar] [CrossRef]
  9. GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024, 403, 2133–2161. [Google Scholar] [CrossRef]
  10. Cangelosi, G.; Acito, M.; Grappasonni, I.; Nguyen, C.T.T.; Tesauro, M.; Pantanetti, P.; Morichetti, L.; Ceroni, E.; Benni, A.; Petrelli, F. Yoga or Mindfulness on Diabetes: Scoping Review for Theoretical Experimental Framework. Ann. Ig. 2024, 36, 153–168. [Google Scholar] [CrossRef]
  11. Jacobsen, L.M.; Sherr, J.L.; Considine, E.; Chen, A.; Peeling, S.M.; Hulsmans, M.; Charleer, S.; Urazbayeva, M.; Tosur, M.; Alamarie, S.; et al. Utility and precision evidence of technology in the treatment of type 1 diabetes: A systematic review. Commun. Med. 2023, 3, 132. [Google Scholar] [CrossRef]
  12. Mallik, R.; Kar, P.; Mulder, H.; Krook, A. The future is here: An overview of technology in diabetes. Diabetologia 2024, 67, 2019–2026. [Google Scholar] [CrossRef]
  13. Handelsman, Y.; Hellman, R.; Lajara, R.; Roberts, V.L.; Rodbard, D.; Stec, C.; Unger, J. American Association of Clinical Endocrinology Clinical Practice Guideline: The use of advanced technology in the management of persons with diabetes mellitus. Endocr. Pract. 2021, 27, 505–537. [Google Scholar] [CrossRef]
  14. American Diabetes Association Professional Practice Committee. 7. Diabetes Technology: Standards of Care in Diabetes-2025. Diabetes Care 2025, 48 (Suppl. S1), S146–S166, Erratum in: Diabetes Care 2025, 48 (Suppl. S1), dc25er04b. https://doi.org/10.2337/dc25-er04b. [Google Scholar] [CrossRef] [PubMed]
  15. Cangelosi, G.; Mancin, S.; Morales Palomares, S.; Pantanetti, P.; Quinzi, E.; Debernardi, G.; Petrelli, F. Impact of school nurse on managing pediatric type 1 diabetes with technological devices support: A systematic review. Diseases 2024, 12, 173. [Google Scholar] [CrossRef]
  16. Li, H.; Ren, J.; Li, Y.; Wu, Q.; Wei, J. Oxidative stress: The nexus of obesity and cognitive dysfunction in diabetes. Front. Endocrinol. 2023, 14, 1134025. [Google Scholar] [CrossRef]
  17. Masenga, S.K.; Kabwe, L.S.; Chakulya, M.; Kirabo, A. Mechanisms of oxidative stress in metabolic syndrome. Int. J. Mol. Sci. 2023, 24, 7898. [Google Scholar] [CrossRef]
  18. Maina, J.G.; Balkhiyarova, Z.; Nouwen, A.; Pupko, I.; Ulrich, A.; Boissel, M.; Bonnefond, A.; Froguel, P.; Khamis, A.; Prokopenko, I.; et al. Bi-directional Mendelian randomization and multiphenotype GWAS show causality and shared pathophysiology between depression and type 2 diabetes. Diabetes Care 2023, 46, 1707–1714. [Google Scholar] [CrossRef] [PubMed]
  19. Winkley, K.; Upsher, R.; Stahl, D.; Pollard, D.; Kasera, A.; Brennan, A.; Heller, S.; Ismail, K. Psychological interventions to improve self-management of type 1 and type 2 diabetes: A systematic review. Health Technol. Assess. 2020, 24, 1–232. [Google Scholar] [CrossRef]
  20. Antza, C.; Kostopoulos, G.; Mostafa, S.; Nirantharakumar, K.; Tahrani, A. The links between sleep duration, obesity and type 2 diabetes mellitus. J. Endocrinol. 2021, 252, 125–141. [Google Scholar] [CrossRef]
  21. Lee, J.H.; Cho, J. Sleep and obesity. Sleep Med. Clin. 2022, 17, 111–116. [Google Scholar] [CrossRef] [PubMed]
  22. Zhu, B.; Abu Irsheed, G.M.; Martyn-Nemeth, P.; Reutrakul, S. Type 1 diabetes, sleep, and hypoglycemia. Curr. Diab. Rep. 2021, 21, 55. [Google Scholar] [CrossRef]
  23. Ni, M.H.; Yang, Y.S.; Huang, J.Y.; Lo, S.C.; Huang, C.N.; Kornelius, E. The association of depression and sleep disorders in patients with type 1 diabetes in Taiwan. Medicine 2024, 103, e38969. [Google Scholar] [CrossRef]
  24. Perfect, M.M. Sleep-related disorders in patients with type 1 diabetes mellitus: Current insights. Nat. Sci. Sleep 2020, 12, 101–123. [Google Scholar] [CrossRef] [PubMed]
  25. Ji, X.; Wang, Y.; Saylor, J. Sleep and Type 1 Diabetes Mellitus Management Among Children, Adolescents, and Emerging Young Adults: A Systematic Review. J. Pediatr. Nurs. 2021, 61, 245–253. [Google Scholar] [CrossRef] [PubMed]
  26. Abualula, N.A.; Jacobsen, K.H.; Milligan, R.A.; Rodan, M.F.; Conn, V.S. Evaluating Diabetes Educational Interventions with a Skill Development Component in Adolescents with Type 1 Diabetes: A Systematic Review Focusing on Quality of Life. Diabetes Educ. 2016, 42, 515–528. [Google Scholar] [CrossRef]
  27. Davidson, P.; LaManna, J.; Davis, J.; Ojeda, M.M.; Hyer, S.; Dickinson, J.K.; Todd, A.; Hammons, T.M.; Mohammed Fahim, S.; McDaniel, C.C.; et al. The Effects of Diabetes Self-Management Education on Quality of Life for Persons with Type 1 Diabetes: A Systematic Review of Randomized Controlled Trials. Sci. Diabetes Self Manag. Care 2022, 48, 111–135. [Google Scholar] [CrossRef]
  28. Kavookjian, J.; LaManna, J.B.; Davidson, P.; Davis, J.W.; Fahim, S.M.; McDaniel, C.C.; Ekong, G.; Todd, A.; Yehl, K.; Cox, C. Impact of Diabetes Self-Management Education/Support on Self-Reported Quality of Life in Youth with Type 1 or Type 2 Diabetes. Sci. Diabetes Self Manag. Care 2022, 48, 406–436. [Google Scholar] [CrossRef] [PubMed]
  29. Jayedi, A.; Zargar, M.S.; Emadi, A.; Aune, D. Walking speed and the risk of type 2 diabetes: A systematic review and meta-analysis. Br. J. Sports Med. 2024, 58, 334–342. [Google Scholar] [CrossRef]
  30. Cao, L.; An, Y.; Liu, H.; Jiang, J.; Liu, W.; Zhou, Y.; Shi, M.; Dai, W.; Lv, Y.; Zhao, Y.; et al. Global epidemiology of type 2 diabetes in patients with NAFLD or MAFLD: A systematic review and meta-analysis. BMC Med. 2024, 22, 101. [Google Scholar] [CrossRef]
  31. Gregory, G.A.; Robinson, T.I.; Linklater, S.E.; Wang, F.; Colagiuri, S.; de Beaufort, C.; Donaghue, K.C.; Harding, J.L.; Wander, P.L.; Zhang, X.; et al. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: A modelling study. Lancet Diabetes Endocrinol. 2022, 10, 741–760. [Google Scholar] [CrossRef]
  32. Anandhakrishnan, A.; Hussain, S. Automating insulin delivery through pump and continuous glucose monitoring connectivity: Maximizing opportunities to improve outcomes. Diabetes Obes. Metab. 2024, 26, 27–46. [Google Scholar] [CrossRef]
  33. Farhat, I.; Drishti, S.; Bochner, R.; Bargman, R. Do hybrid closed loop insulin pump systems improve glycemic control and reduce hospitalizations in poorly controlled type 1 diabetes? J. Pediatr. Endocrinol. Metab. 2024, 37, 1028–1035. [Google Scholar] [CrossRef]
  34. Scuri, S.; Tesauro, M.; Petrelli, F.; Argento, N.; Damasco, G.; Cangelosi, G.; Nguyen, C.T.T.; Savva, D.; Grappasonni, I. Use of an Online Platform to Evaluate the Impact of Social Distancing Measures on Psycho-Physical Well-Being in the COVID-19 Era. Int. J. Environ. Res. Public Health 2022, 19, 6805. [Google Scholar] [CrossRef] [PubMed]
  35. Petrovski, G.; Al Khalaf, F.; Campbell, J.; Umer, F.; Almajaly, D.; Hamdan, M.; Hussain, K. One-year experience of hybrid closed-loop system in children and adolescents with type 1 diabetes previously treated with multiple daily injections: Drivers to successful outcomes. Acta Diabetol. 2021, 58, 207–213. [Google Scholar] [CrossRef] [PubMed]
  36. McAuley, S.A.; Lee, M.H.; Paldus, B.; Vogrin, S.; de Bock, M.I.; Abraham, M.B.; Bach, L.A.; Burt, M.G.; Cohen, N.D.; Colman, P.G.; et al. Australian JDRF Closed-Loop Research Group. Six months of hybrid closed-loop versus manual insulin delivery with fingerprick blood glucose monitoring in adults with type 1 diabetes: A randomized, controlled trial. Diabetes Care 2020, 43, 3024–3033. [Google Scholar] [CrossRef]
  37. Cobry, E.; Kanapka, L.; Cengiz, E.; Carria, L.; Ekhlaspour, L.; A Buckingham, B.; Hood, K.; Hsu, L.J.; Messer, L.; Schoelwer, M.; et al. 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, 401–409. [Google Scholar] [CrossRef] [PubMed]
  38. Benioudakis, E.; Karlafti, E.; Kalaitzaki, A.; Kaiafa, G.; Savopoulos, C.; Didangelos, T. Technological developments and quality of life in type 1 diabetes mellitus patients: A review of the modern insulin analogues, continuous glucose monitoring and insulin pump therapy. Curr. Diabetes Rev. 2022, 18, e031121197657. [Google Scholar] [CrossRef]
  39. National Health Service (NHS) Digital. National Diabetes Audit 2021–2022, Type 1 Diabetes—Overview. Available online: https://digital.nhs.uk/data-and-information/publications/statistical/national-diabetes-audit-type-1-diabetes/nda-type-1-2021-22-overview (accessed on 24 November 2024).
  40. Foster, N.C.; Beck, R.W.; Miller, K.M.; Clements, M.A.; Rickels, M.R.; DiMeglio, L.A.; Maahs, D.M.; Tamborlane, W.V.; Bergenstal, R.; Smith, E.; et al. State of type 1 diabetes management and outcomes from the T1D exchange in 2016–2018. Diabetes Technol. Ther. 2019, 21, 66–72. [Google Scholar] [CrossRef]
  41. Tejera-Pérez, C.; Chico, A.; Azriel-Mira, S.; Lardiés-Sánchez, B.; Gomez-Peralta, F.; Área de Diabetes-SEEN. Connected insulin pens and caps: An expert’s recommendation from the Area of Diabetes of the Spanish Endocrinology and Nutrition Society (SEEN). Diabetes Ther. 2023, 14, 1077–1091. [Google Scholar] [CrossRef]
  42. Nimri, R.; Nir, J.; Phillip, M. Insulin pump therapy. Am. J. Ther. 2020, 27, e30–e41. [Google Scholar] [CrossRef]
  43. Cernea, S.; Raz, I. Insulin therapy: Future perspectives. Am. J. Ther. 2020, 27, e121–e132. [Google Scholar] [CrossRef]
  44. Chakrabarti, A.; Trawley, S.; Kubilay, E.; Alipoor, A.M.; Vogrin, S.; Fourlanos, S.; Lee, M.H.; O’Neal, D.N.; O’Regan, N.A.; Sundararajan, V.; et al. Closed-Loop Insulin Delivery Effects on Glycemia During Sleep and Sleep Quality in Older Adults with Type 1 Diabetes: Results from the ORACL Trial. Diabetes Technol. Ther. 2022, 24, 666–671. [Google Scholar] [CrossRef]
  45. Cobry, E.C.; Karami, A.J.; Meltzer, L.J. Friend or Foe: A Narrative Review of the Impact of Diabetes Technology on Sleep. Curr. Diabetes Rep. 2022, 22, 283–290. [Google Scholar] [CrossRef]
  46. Perez, K.M.; Hamburger, E.R.; Lyttle, M.; Williams, R.; Bergner, E.; Kahanda, S.; Cobry, E.; Jaser, S.S. Sleep in Type 1 Diabetes: Implications for Glycemic Control and Diabetes Management. Curr. Diab. Rep. 2018, 18, 5. [Google Scholar] [CrossRef] [PubMed]
  47. Abu Irsheed, G.; Martyn-Nemeth, P.; Baron, K.G.; Reutrakul, S. Sleep Disturbances in Type 1 Diabetes and Mitigating Cardiovascular Risk. J. Clin. Endocrinol. Metab. 2024, 109, 3011–3026. [Google Scholar] [CrossRef] [PubMed]
  48. Hilliard, M.E.; Levy, W.; Anderson, B.J.; Whitehouse, A.L.; Commissariat, P.V.; Harrington, K.R.; Laffel, L.M.; Miller, K.M.; Van Name, M.; Tamborlane, W.V.; et al. Benefits and Barriers of Continuous Glucose Monitoring in Young Children with Type 1 Diabetes. Diabetes Technol. Ther. 2019, 21, 493–498. [Google Scholar] [CrossRef] [PubMed]
  49. Jaser, S.S.; Foster, N.C.; Nelson, B.A.; Kittelsrud, J.M.; DiMeglio, L.A.; Quinn, M.; Willi, S.M.; Simmons, J.H. T1D Exchange Clinic Network. Sleep in children with type 1 diabetes and their parents in the T1D Exchange. Sleep Med. 2017, 39, 108–115. [Google Scholar] [CrossRef]
  50. Sguanci, M.; Mancin, S.; Gazzelloni, A.; Diamanti, O.; Ferrara, G.; Morales Palomares, S.; Parozzi, M.; Petrelli, F.; Cangelosi, G. The Internet of Things in the Nutritional Management of Patients with Chronic Neurological Cognitive Impairment: A Scoping Review. Healthcare 2025, 13, 23. [Google Scholar] [CrossRef]
  51. Curcio, G.; Tempesta, D.; Scarlata, S.; Marzano, C.; Moroni, F.; Rossini, P.M.; Ferrara, M.; De Gennaro, L. Validity of the Italian version of the Pittsburgh Sleep Quality Index (PSQI). Neurol. Sci. 2013, 34, 511–519. [Google Scholar] [CrossRef]
  52. Cuschieri, S. The STROBE guidelines. Saudi J. Anaesth. 2019, 13 (Suppl. S1), S31–S34. [Google Scholar] [CrossRef]
  53. von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P.; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. J. Clin. Epidemiol. 2008, 61, 344–349. [Google Scholar] [CrossRef]
  54. Medtronic. Care Link Report. Available online: https://www.medtronicdiabetes.com/customer-support/carelink-software-support/carelink-reports (accessed on 30 November 2024).
  55. Pantanetti, P.; Cangelosi, G.; Palomares, S.M.; Ferrara, G.; Biondini, F.; Mancin, S.; Caggianelli, G.; Parozzi, M.; Sguanci, M.; Petrelli, F. Real-World Life Analysis of a Continuous Glucose Monitoring and Smart Insulin Pen System in Type 1 Diabetes: A Cohort Study. Diabetology 2025, 6, 7. [Google Scholar] [CrossRef]
  56. Buysse, D.J.; Reynolds, C.F., 3rd; Monk, T.H.; Berman, S.R.; Kupfer, D.J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989, 28, 193–213. [Google Scholar] [CrossRef] [PubMed]
  57. Botella-Serrano, M.; Velasco, J.M.; Sánchez-Sánchez, A.; Garnica, O.; Hidalgo, J.I. Evaluating the Influence of Sleep Quality and Quantity on Glycemic Control in Adults with Type 1 Diabetes. Front. Endocrinol. 2023, 14, 998881. [Google Scholar] [CrossRef] [PubMed]
  58. Griggs, S.; Hickman, R.L.; Strohl, K.P.; Redeker, N.S.; Crawford, S.L.; Grey, M. Sleep-Wake Characteristics, Daytime Sleepiness, and Glycemia in Young Adults with Type 1 Diabetes. J. Clin. Sleep Med. 2021, 17, 1865–1874. [Google Scholar] [CrossRef]
  59. Griggs, S.; Strohl, K.P.; Grey, M.; Barbato, E.; Margevicius, S.; Hickman, R.L., Jr. Circadian Characteristics of the Rest-Activity Rhythm, Executive Function, and Glucose Fluctuations in Young Adults with Type 1 Diabetes. Chronobiol. Int. 2021, 38, 1477–1487. [Google Scholar] [CrossRef]
  60. Briançon-Marjollet, A.; Weiszenstein, M.; Henri, M.; Thomas, A.; Godin-Ribuot, D.; Polak, J. The Impact of Sleep Disorders on Glucose Metabolism: Endocrine and Molecular Mechanisms. Diabetol. Metab. Syndr. 2015, 7, 25. [Google Scholar] [CrossRef]
  61. Griggs, S.; Grey, M.; Strohl, K.P.; Crawford, S.L.; Margevicius, S.; Kashyap, S.R.; Li, C.-S.R.; Rajagopalan, S.; Hickman, R.L. Variations in Sleep Characteristics and Glucose Regulation in Young Adults with Type 1 Diabetes. J. Clin. Endocrinol. Metab. 2022, 107, e1085–e1095. [Google Scholar] [CrossRef]
  62. Güemes, A.; Georgiou, P. Review of the Role of the Nervous System in Glucose Homeostasis and Future Perspectives Towards the Management of Diabetes. Bioelectron. Med. 2018, 4, 9. [Google Scholar] [CrossRef] [PubMed]
  63. Carnagarin, R.; Matthews, V.B.; Herat, L.Y.; Ho, J.K.; Schlaich, M.P. Autonomic Regulation of Glucose Homeostasis: A Specific Role for Sympathetic Nervous System Activation. Curr. Diab. Rep. 2018, 18, 107. [Google Scholar] [CrossRef]
  64. Sousa, A.; Kalra, S. Sleep Hygiene and Diabetes: Suggestions for Primary Care. J. Pak. Med. Assoc. 2017, 67, 814–815. [Google Scholar]
  65. Zhang, H.-Z.; Zhang, P.; Chang, G.-Q.; Xiang, Q.-Y.; Cao, H.; Zhou, J.-Y.; Dong, Z.-M.; Qiao, C.; Xu, C.-R.; Qin, Y.; et al. Effectiveness of Cognitive Behavioral Therapy for Sleep Disturbance and Glycemic Control in Persons with Type 2 Diabetes Mellitus: A Community-Based Randomized Controlled Trial in China. World J. Diabetes 2021, 12, 292–305. [Google Scholar] [CrossRef]
  66. Zuo, X.; Dong, Z.; Zhang, P.; Zhang, P.; Chang, G.; Xiang, Q.; Zhu, X.; Zhou, J.; Qiao, C.; Yang, Y.; et al. Effects of Cognitive Behavioral Therapy on Sleep Disturbances and Quality of Life among Adults with Type 2 Diabetes Mellitus: A Randomized Controlled Trial. Nutr. Metab. Cardiovasc. Dis. 2020, 30, 1980–1988. [Google Scholar] [CrossRef]
  67. Berk, E.; Çelik, N. Sleep Quality and Glycemic Control in Children and Adolescents with Type 1 Diabetes Mellitus. Eur. Rev. Med. Pharmacol. Sci. 2023, 27, 4633–4638. [Google Scholar] [CrossRef] [PubMed]
  68. Donbaloğlu, Z.; Barsal Çetiner, E.; İnan Yüksel, A.; Singin, B.; Aydın Behram, B.; Bedel, A.; Parlak, M.; Tuhan, H. Sleep Disturbances in Children and Adolescents with Type 1 Diabetes Mellitus: Prevalence, and Relationship with Diabetes Management. Sleep Med. 2024, 115, 55–60. [Google Scholar] [CrossRef] [PubMed]
  69. Malone, S.K.; Peleckis, A.J.; Grunin, L.; Yu, G.; Jang, S.; Weimer, J.; Lee, I.; Rickels, M.R.; Goel, N. Characterizing Glycemic Control and Sleep in Adults with Long-Standing Type 1 Diabetes and Hypoglycemia Unawareness Initiating Hybrid Closed Loop Insulin Delivery. J. Diabetes Res. 2021, 2021, 6611064. [Google Scholar] [CrossRef]
  70. Malone, S.K.; Matus, A.M.; Flatt, A.J.; Peleckis, A.J.; Grunin, L.; Yu, G.; Jang, S.; Weimer, J.; Lee, I.; Rickels, M.R.; et al. Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia. J. Diabetes Sci. Technol. 2024, 18, 1416–1423. [Google Scholar] [CrossRef] [PubMed]
  71. Passanisi, S.; Lombardo, F.; Mameli, C.; Bombaci, B.; Macedoni, M.; Zuccotti, G.; Dovc, K.; Battelino, T.; Salzano, G.; Delvecchio, M. Safety, Metabolic and Psychological Outcomes of Medtronic MiniMed 780GTM in Children, Adolescents and Young Adults: A Systematic Review. Diabetes Ther. 2024, 15, 343–365. [Google Scholar] [CrossRef]
  72. Brandt, R.; Park, M.; Wroblewski, K.; Quinn, L.; Tasali, E.; Cinar, A. Sleep Quality and Glycaemic Variability in a Real-Life Setting in Adults with Type 1 Diabetes. Diabetologia 2021, 64, 2159–2169. [Google Scholar] [CrossRef]
  73. Martyn-Nemeth, P.; Duffecy, J.; Quinn, L.; Steffen, A.; Baron, K.; Chapagai, S.; Burke, L.; Reutrakul, S. Sleep-Opt-In: A Randomized Controlled Pilot Study to Improve Sleep and Glycemic Variability in Adults with Type 1 Diabetes. Sci. Diabetes Self Manag. Care 2023, 49, 11–22. [Google Scholar] [CrossRef]
  74. Sinisterra, M.; Hamburger, S.; Tully, C.; Hamburger, E.; Jaser, S.; Streisand, R. Young Children with Type 1 Diabetes: Sleep, Health-Related Quality of Life, and Continuous Glucose Monitor Use. Diabetes Technol. Ther. 2020, 22, 639–642. [Google Scholar] [CrossRef]
  75. Martyn-Nemeth, P.; Phillips, S.A.; Mihailescu, D.; Farabi, S.S.; Park, C.; Lipton, R.; Idemudia, E.; Quinn, L. Poor Sleep Quality Is Associated with Nocturnal Glycaemic Variability and Fear of Hypoglycaemia in Adults with Type 1 Diabetes. J. Adv. Nurs. 2018, 74, 2373–2380. [Google Scholar] [CrossRef]
  76. Mehrdad, M.; Azarian, M.; Sharafkhaneh, A.; Alavi, A.; Zare, R.; Hassanzadeh Rad, R.; Dalili, S. Association Between Poor Sleep Quality and Glycemic Control in Adult Patients with Diabetes Referred to Endocrinology Clinic of Guilan: A Cross-Sectional Study. Int. J. Endocrinol. Metab. 2021, 20, e118077. [Google Scholar] [CrossRef] [PubMed]
  77. Suteau, V.; Saulnier, P.-J.; Wargny, M.; Gonder-Frederick, L.; Gand, E.; Chaillous, L.; Allix, I.; Dubois, S.; Bonnet, F.; Leguerrier, A.-M.; et al. Association Between Sleep Disturbances, Fear of Hypoglycemia, and Psychological Well-Being in Adults with Type 1 Diabetes Mellitus: Data from the Cross-Sectional VARDIA Study. Diabetes Res. Clin. Pract. 2020, 160, 107988. [Google Scholar] [CrossRef] [PubMed]
  78. Angelino, S.; Longo, M.; Caruso, P.; Scappaticcio, L.; Di Martino, N.; Di Lorenzo, C.; Forestiere, D.; Paglionico, V.A.; Bellastella, G.; Maiorino, M.I.; et al. Sleep Quality and Glucose Control in Adults with Type 1 Diabetes during the Seasonal Daylight Saving Time Shifts. Diabetes Res. Clin. Pract. 2024, 217, 111859. [Google Scholar] [CrossRef] [PubMed]
  79. Cangelosi, G.; Mancin, S.; Pantanetti, P.; Nguyen, C.T.T.; Morales Palomares, S.; Biondini, F.; Sguanci, M.; Petrelli, F. Lifestyle Medicine Case Manager Nurses for Type Two Diabetes Patients: An Overview of a Job Description Framework—A Narrative Review. Diabetology 2024, 5, 375–388. [Google Scholar] [CrossRef]
  80. Jaser, S.S.; Hamburger, E.R.; Bergner, E.M.; Williams, R.; Slaughter, J.C.; Simmons, J.H.; Malow, B.A. Sleep coach intervention for teens with type 1 diabetes: Randomized pilot study. Pediatr. Diabetes 2020, 21, 473–478. [Google Scholar] [CrossRef]
  81. Jaser, S.S.; Bergner, E.M.; Hamburger, E.R.; Bhatia, S.; Lyttle, M.; E Bell, G.; Slaughter, J.C.; A Malow, B.; Simmons, J.H. Pilot Trial of a Sleep-Promoting Intervention for Children With Type 1 Diabetes. J. Pediatr. Psychol. 2021, 46, 304–313. [Google Scholar] [CrossRef]
  82. Morrow, T.; Bhatia, S.H.; Parmar, A.M.; Baker, L.; Abadula, F.; Williamson, D.; Choudhary, A.; Jaser, S.S. Sleep Habits of Early School-Aged Children with Type 1 Diabetes and Their Parents: Family Characteristics and Diabetes Management. Behav. Sleep Med. 2022, 20, 649–658. [Google Scholar] [CrossRef]
  83. Bisio, A.; A Brown, S.; McFadden, R.; Pajewski, M.; Yu, P.L.; DeBoer, M.D.; Schoelwer, M.J.; Bonner, H.G.; A Wakeman, C.; Cherñavvsky, D.R.; et al. Sleep and diabetes-specific psycho-behavioral outcomes of a new automated insulin delivery system in young children with type 1 diabetes and their parents. Pediatr. Diabetes 2021, 22, 495–502. [Google Scholar] [CrossRef]
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Figure 1. Average TIR, TBR, and TAR in the 30 days preceding the PSQI assessment by sleep quality.
Figure 1. Average TIR, TBR, and TAR in the 30 days preceding the PSQI assessment by sleep quality.
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Figure 2. Average TIR, TBR, and TAR during bedtime hours in the 30 days preceding the PSQI assessment by sleep quality.
Figure 2. Average TIR, TBR, and TAR during bedtime hours in the 30 days preceding the PSQI assessment by sleep quality.
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Table 1. Criteria that were used to derive glycemic outcomes from CGM data.
Table 1. Criteria that were used to derive glycemic outcomes from CGM data.
VariableDerivation for a Given Period
Sensor usage (%)(Number of CGM measurements/(Number of minutes in the period of interest/5) × 100
SG mean, SD, and CVMean, SD, and CV of CGM measurements
TIR metrics(Number of CGM measurements in the range of interest/Number of CGM measurements) × 100
Legend: SG: sensor glucose; SD: standard deviation; CV: coefficient of variation; TIR: time in range; CGM: continuous glucose monitoring.
Table 2. Sleep quality in T1D patients.
Table 2. Sleep quality in T1D patients.
Summary
Statistic		Total
(N = 41)
PSQIAvailable Measures (%)41 (100.0%)
Mean ± SD6.0 ± 4.1
Median (IQR)4.0 (3.0–8.0)
Min–Max1.0–17.0
Sleep quality
Poor sleepers
(PSQI greater than 5)		% (n/Available Measures)36.6% (15/41)
Good sleepers
(PSQI lower or equal to 5)		% (n/Available Measures)63.4% (26/41)
Legend. PSQI: Pittsburgh Sleep Quality Index; SD: standard deviation; IQR: interquartile range.
Table 3. Patient characteristics by sleep quality.
Table 3. Patient characteristics by sleep quality.
Summary
Statistic		Total
(N = 41)		Poor Sleepers
(N = 15)		Good Sleepers
(N = 26)		p-Value
Age (years)Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.850
Mean ± SD51.9 ± 11.652.2 ± 12.351.7 ± 11.5
Median (IQR)51.0 (43.0–63.0)51.0 (43.0–63.0)50.5 (42.0–64.0)
Min–Max26.0–72.026.0–72.029.0–70.0
Female% (n/Available Measures)41.5% (17/41)46.7% (7/15)38.5% (10/26)0.745
Diabetes duration (years)Available Measures (%)39 (95.1%)15 (100.0%)24 (92.3%)0.058
Mean ± SD25.1 ± 14.619.2 ± 12.028.8 ± 15.1
Median (IQR)25.0 (14.0–34.0)17.0 (10.0–32.0)27.0 (21.5–37.0)
Min–Max1.0–61.01.0–40.05.0–61.0
BMI (kg/m2)Available Measures (%)39 (95.1%)15 (100.0%)24 (92.3%)0.516
Mean ± SD25.0 ± 4.125.6 ± 3.624.6 ± 4.4
Median (IQR)24.9 (21.7–28.7)24.9 (23.0–28.7)24.6 (20.7–28.6)
Min–Max18.0–32.419.8–32.218.0–32.4
Smoking habit
Current smoker% (n/Available Measures)22.5% (9/40)26.7% (4/15)20.0% (5/25)0.204
Former smoker% (n/Available Measures)15.0% (6/40)26.7% (4/15)8.0% (2/25)
Non-smoker% (n/Available Measures)62.5% (25/40)46.7% (7/15)72.0% (18/25)
Device
MiniMedTM 780G% (n/Available Measures)68.3% (28/41)60.0% (9/15)73.1% (19/26)0.492
Medtronic Smart MDI% (n/Available Measures)31.7% (13/41)40.0% (6/15)26.9% (7/26)
Legend. BMI: body mass index; SD: standard deviation; IQR: interquartile range.
Table 4. Glycemic outcomes during the 30 days preceding the PSQI assessment by sleep quality.
Table 4. Glycemic outcomes during the 30 days preceding the PSQI assessment by sleep quality.
Summary
Statistic		Total
(N = 41)		Poor Sleepers
(N = 15)		Good Sleepers
(N = 26)		p-Value
SG mean (mg/dL)Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.310
Mean ± SD153.9 ± 18.3157.5 ± 20.4151.8 ± 17.1
Median (IQR)150.7 (142.0–168.6)159.0 (142.0–172.4)149.0 (142.0–162.9)
Min–Max117.0–197.5120.8–194.5117.0–197.5
SG SD (mg/dL)Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.228
Mean ± SD47.4 ± 10.749.4 ± 12.146.3 ± 9.8
Median (IQR)44.9 (40.0–51.4)49.5 (42.5–53.5)42.1 (39.9–48.9)
Min–Max29.9–81.229.9–81.235.3–77.3
SG CV (%)Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.598
Mean ± SD30.7 ± 5.031.2 ± 5.630.4 ± 4.6
Median (IQR)30.2 (27.3–32.4)30.6 (26.1–33.6)29.2 (27.3–31.7)
Min–Max21.6–44.823.2–44.821.6–43.6
TBR2 (%):
Time at < 54 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.250
Mean ± SD0.3 ± 0.70.2 ± 0.30.4 ± 0.8
Median (IQR)0.1 (0.0–0.3)0.0 (0.0–0.4)0.1 (0.0–0.3)
Min–Max0.0–3.90.0–1.00.0–3.9
TBR1 (%):
Time at 54–69 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.167
Mean ± SD1.4 ± 1.91.3 ± 2.11.5 ± 1.7
Median (IQR)0.8 (0.2–1.7)0.4 (0.1–1.4)1.0 (0.4–2.0)
Min–Max0.0–7.50.0–6.90.0–7.5
TBR (%):
Time at < 70 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.137
Mean ± SD1.7 ± 2.41.5 ± 2.41.9 ± 2.4
Median (IQR)0.9 (0.2–2.1)0.5 (0.1–1.8)1.1 (0.5–2.3)
Min–Max0.0–11.40.0–7.50.0–11.4
TIR (%):
Time at 70–180 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.473
Mean ± SD72.0 ± 11.570.0 ± 13.173.2 ± 10.6
Median (IQR)73.3 (65.0–81.7)70.3 (59.3–81.9)75.5 (68.4–81.7)
Min–Max43.5–92.743.5–92.744.9–85.8
TAR1 (%):
Time at
181–250 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.490
Mean ± SD21.3 ± 7.922.2 ± 9.220.8 ± 7.2
Median (IQR)21.3 (15.7–26.9)23.2 (16.3–30.5)21.2 (15.7–25.9)
Min–Max5.3–36.25.3–36.26.7–34.2
TAR2 (%):
Time at > 250 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.120
Mean ± SD4.9 ± 5.56.3 ± 6.24.1 ± 5.0
Median (IQR)2.6 (1.4–6.5)5.1 (1.7–8.4)2.4 (1.0–4.9)
Min–Max0.1–20.70.1–20.30.1–20.7
TAR (%):
Time at > 180 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.365
Mean ± SD26.2 ± 12.328.5 ± 14.024.9 ± 11.3
Median (IQR)23.7 (17.2–35.0)29.7 (17.9–40.5)23.6 (16.9–31.0)
Min–Max5.4–56.15.4–56.17.3–54.9
GMI (%)Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.310
Mean ± SD7.0 ± 0.47.1 ± 0.56.9 ± 0.4
Median (IQR)6.9 (6.7–7.3)7.1 (6.7–7.4)6.9 (6.7–7.2)
Min–Max6.1–8.06.2–8.06.1–8.0
Legend. SG: sensor glucose; SD: standard deviation; IQR: interquartile range; Min–Max: minimum–maximum; CV: coefficient of variation; TBR2: time below range level 2; TBR1: time below range level 1; TBR: time below range; TIR: time in range; TAR1: time above range level 1; TAR2: time above range level 2; TAR: time above range; GMI: glycemic management indicator.
Table 5. Glycemic outcomes during bedtime hours in the 30 days preceding the PSQI assessment by sleep quality.
Table 5. Glycemic outcomes during bedtime hours in the 30 days preceding the PSQI assessment by sleep quality.
Summary
Statistic		Total
(N = 41)		Poor Sleepers
(N = 15)		Good Sleepers
(N = 26)		p-Value
SG mean (mg/dL)Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.379
Mean ± SD153.0 ± 22.5157.5 ± 24.5150.5 ± 21.3
Median (IQR)147.5 (141.0–164.5)150.5 (142.0–174.0)147.1 (135.3–161.0)
Min–Max121.3–213.9121.3–204.5122.0–213.9
SG SD (mg/dL)Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.113
Mean ± SD44.1 ± 12.648.1 ± 13.541.7 ± 11.6
Median (IQR)41.4 (35.5–49.8)49.8 (34.4–54.9)40.1 (35.5–45.8)
Min–Max26.8–84.627.8–72.926.8–84.6
SG CV (%)Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.091
Mean ± SD28.7 ± 5.930.6 ± 7.327.5 ± 4.8
Median (IQR)28.3 (24.2–31.6)29.9 (24.2–35.0)28.0 (23.7–29.5)
Min–Max17.4–42.517.4–42.519.0–39.6
TBR2 (%):
Time at < 54 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.977
Mean ± SD0.3 ± 0.80.3 ± 0.70.3 ± 0.9
Median (IQR)0.0 (0.0–0.1)0.0 (0.0–0.1)0.0 (0.0–0.2)
Min–Max0.0–4.20.0–1.90.0–4.2
TBR1 (%):
Time at
54–69 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.691
Mean ± SD1.2 ± 2.21.5 ± 3.11.0 ± 1.5
Median (IQR)0.5 (0.0–1.2)0.4 (0.0–0.9)0.6 (0.0–1.4)
Min–Max0.0–9.90.0–9.90.0–5.5
TBR (%):
Time at
< 70 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.711
Mean ± SD1.5 ± 2.81.8 ± 3.81.4 ± 2.2
Median (IQR)0.5 (0.0–1.3)0.4 (0.0–1.0)0.6 (0.0–1.7)
Min–Max0.0–11.80.0–11.80.0–9.5
TIR (%):
Time at
70–180 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.163
Mean ± SD73.7 ± 15.769.6 ± 16.976.0 ± 14.9
Median (IQR)77.3 (66.8–85.3)71.0 (60.1–84.2)78.7 (70.3–86.5)
Min–Max32.6–95.337.8–95.132.6–95.3
TAR1 (%):
Time at 181–250 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.636
Mean ± SD20.2 ± 10.821.8 ± 12.719.2 ± 9.6
Median (IQR)19.1 (12.1–26.2)19.5 (12.1–30.1)17.9 (12.1–24.0)
Min–Max3.8–53.63.8–53.64.6–43.6
TAR2 (%):
Time at > 250 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.013
Mean ± SD4.6 ± 6.76.7 ± 7.23.3 ± 6.2
Median (IQR)1.8 (0.5–5.6)4.9 (1.4–10.6)1.1 (0.5–2.8)
Min–Max0.0–27.70.3–27.70.0–27.3
TAR (%):
Time at > 180 mg/dL (%)		Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.310
Mean ± SD24.8 ± 16.028.5 ± 17.722.6 ± 14.9
Median (IQR)20.6 (13.2–33.2)24.5 (13.5–39.9)19.8 (12.6–27.0)
Min–Max4.1–65.04.1–61.24.7–65.0
GMI (%): Available Measures (%)41 (100.0%)15 (100.0%)26 (100.0%)0.379
Mean ± SD7.0 ± 0.57.1 ± 0.66.9 ± 0.5
Median (IQR)6.8 (6.7–7.2)6.9 (6.7–7.5)6.8 (6.5–7.2)
Min–Max6.2–8.46.2–8.26.2–8.4
Legend. SG: sensor glucose; SD: standard deviation; IQR: interquartile range; Min–Max: minimum–maximum; CV: coefficient of variation; TBR2: time below range level 2; TBR1: time below range level 1; TBR: time below range; TIR: time in range; TAR1: time above range level 1; TAR2: time above range level 2; TAR: time above range; GMI: glycemic management indicator.
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MDPI and ACS Style

Pantanetti, P.; Biondini, F.; Mancin, S.; Sguanci, M.; Masini, A.; Panella, M.; Palomares, S.M.; Ferrara, G.; Petrelli, F.; Cangelosi, G. Sleep Quality and Glycemic Control in Type 1 Diabetes: A Retrospective Cohort Study Using Advanced Technological Devices. Diabetology 2025, 6, 21. https://doi.org/10.3390/diabetology6030021

AMA Style

Pantanetti P, Biondini F, Mancin S, Sguanci M, Masini A, Panella M, Palomares SM, Ferrara G, Petrelli F, Cangelosi G. Sleep Quality and Glycemic Control in Type 1 Diabetes: A Retrospective Cohort Study Using Advanced Technological Devices. Diabetology. 2025; 6(3):21. https://doi.org/10.3390/diabetology6030021

Chicago/Turabian Style

Pantanetti, Paola, Federico Biondini, Stefano Mancin, Marco Sguanci, Alice Masini, Massimiliano Panella, Sara Morales Palomares, Gaetano Ferrara, Fabio Petrelli, and Giovanni Cangelosi. 2025. "Sleep Quality and Glycemic Control in Type 1 Diabetes: A Retrospective Cohort Study Using Advanced Technological Devices" Diabetology 6, no. 3: 21. https://doi.org/10.3390/diabetology6030021

APA Style

Pantanetti, P., Biondini, F., Mancin, S., Sguanci, M., Masini, A., Panella, M., Palomares, S. M., Ferrara, G., Petrelli, F., & Cangelosi, G. (2025). Sleep Quality and Glycemic Control in Type 1 Diabetes: A Retrospective Cohort Study Using Advanced Technological Devices. Diabetology, 6(3), 21. https://doi.org/10.3390/diabetology6030021

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