Dataset Information

Poor sleep quality is associated with nocturnal glycaemic variability and fear of hypoglycaemia in adults with type 1 diabetes.

ABSTRACT:

Aims

To examine sleep quality and its associations with glycaemic control, glycaemic variability (GV), and fear of hypoglycaemia (FOH) in adults with type 1 diabetes.

Background

Poor sleep quality has negative health consequences and is a frequent complaint among adults with type 1 diabetes. Sleep quality in adults with type 1 diabetes is likely affected by glucose levels as well as stressors associated with managing a chronic condition.

Design

A retrospective secondary analysis of pooled data from two previous cross-sectional studies was conducted.

Methods

We examined subjective sleep quality, FOH; objective measures of glycaemic control (HbA1c); and GV (3-day continuous glucose monitoring) in 48 men and women aged 18-45 years with type 1 diabetes. The data were collected over 3 years in 2013-2016.

Results/findings

Poor sleep quality was reported by 46% of patients. Those with poor sleep quality had significantly greater nocturnal GV and FOH. Nocturnal GV and FOH were significantly associated with poor sleep quality. The interaction effect of GV and FOH was significant.

Conclusion

These findings suggest that glycaemic control and FOH are targets for intervention to improve sleep quality in those with type 1 diabetes.

SUBMITTER: Martyn-Nemeth P

PROVIDER: S-EPMC7778470 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

Poor sleep quality is associated with nocturnal glycaemic variability and fear of hypoglycaemia in adults with type 1 diabetes.

Martyn-Nemeth Pamela P Phillips Shane A SA Mihailescu Dan D Farabi Sarah S SS Park Chang C Lipton Rebecca R Idemudia Esema E Quinn Laurie L

Journal of advanced nursing 20180719 10

<h4>Aims</h4>To examine sleep quality and its associations with glycaemic control, glycaemic variability (GV), and fear of hypoglycaemia (FOH) in adults with type 1 diabetes.<h4>Background</h4>Poor sleep quality has negative health consequences and is a frequent complaint among adults with type 1 diabetes. Sleep quality in adults with type 1 diabetes is likely affected by glucose levels as well as stressors associated with managing a chronic condition.<h4>Design</h4>A retrospective secondary ana ...[more]

PMID: 29917259

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Project description:Aims/hypothesisSuboptimal subjective sleep quality is very common in adults with type 1 diabetes. Reducing glycaemic variability is a key objective in this population. To date, no prior studies have examined the associations between objectively measured sleep quality and overnight glycaemic variability in adults with type 1 diabetes. We aimed to test the hypothesis that poor sleep quality would be associated with greater overnight glycaemic variability.MethodsData were collected in the home setting from 20 adults (ten male and ten female participants) with type 1 diabetes who were current insulin pump users. Simultaneous assessments of objective sleep quality (Zmachine Insight+) and continuous glucose monitoring (CGM) were performed over multiple nights (up to 15 nights) in each participant. Due to the real-life nature of this study, the participants kept their usual CGM alerts for out-of-range glucose values. Sleep quality was categorised as 'good' or 'poor' using a composite of objective sleep features (i.e. sleep efficiency, wake after sleep onset and number of awakenings) based on the National Sleep Foundation's consensus criteria. Glycaemic variability was quantified using SD and CV of overnight glucose values based on overnight CGM profiles.ResultsA total of 170 nights were analysed. Overall, 86 (51%) nights were categorised as good sleep quality, and 84 (49%) nights were categorised as poor sleep quality. Using linear mixed-effects models, poor sleep quality was significantly associated with greater glycaemic variability as quantified by SD (coefficient: 0.39 [95% CI 0.10, 0.67], p = 0.009) and CV (coefficient: 4.35 [95% CI 0.8, 7.9], p = 0.02) of overnight glucose values, after accounting for age, sex, BMI and overnight insulin dose. There was large inter- and intra-individual variability in sleep and glycaemic characteristics. Both pooled and individual-level data revealed that the nights with poor sleep quality had larger SDs and CVs, and, conversely, the nights with good sleep quality had smaller SDs and CVs. No associations were found between sleep quality and time spent in the target glucose range, or above or below the target glucose range, where CGM alarms are most likely to occur.Conclusions/interpretationObjectively measured sleep quality is associated with overnight glycaemic variability in adults with type 1 diabetes. These findings highlight an important role of sleep quality in overnight glycaemic control in type 1 diabetes. They also provide a strong incentive to both patients and healthcare providers for considering sleep quality in personalised type 1 diabetes glycaemic management plans. Future studies should investigate the mechanisms linking sleep quality to glycaemic variability.

Project description:AimsTo examine maternal fear of hypoglycaemia, glycaemia and pregnancy outcomes in women with impaired and normal awareness of hypoglycaemia.MethodsA pre-planned sub-study of 214 pregnant women with type 1 diabetes who participated in the CONCEPTT trial. Participants completed hypoglycaemia fear surveys (HFS-II) at baseline. Logistic regression and Poisson regression analyses were used to obtain an adjusted estimate for the rate ratio relating awareness to the number of severe hypoglycaemic episodes, and for several neonatal outcomes in relation to the total HFS-II score. The role of continuous glucose monitoring (CGM) use was examined.ResultsOverall, 30% of participants reported impaired awareness of hypoglycaemia (n = 64). Women with impaired awareness of hypoglycaemia had more episodes of severe hypoglycaemia (mean 0.44 vs. 0.08, p < 0.001) (12-34 weeks gestation) and scored higher on the HFS-II scale (43.7 vs. 36.0, p 0.008), indicating more fear of hypoglycaemia. They spent more time below range (CGM <3.5 mmol/L) and exhibited more glycaemic variability at 12 weeks gestation. Higher overall HFS-II scores were associated with a higher risk of maternal severe hypoglycaemia episodes (Rate Ratio 1.78, 95% CI 1.39-2.27). Women with impaired awareness of hypoglycaemia had less maternal weight gain but there were no differences in neonatal outcomes between women with impaired awareness of hypoglycaemia and normal hypoglycaemia awareness. Higher HFS-II scores were associated with more nephropathy (Odds Ratio 1.91, 95% CI 1.06-3.4). CGM use after 12 weeks was not associated with the number of episodes of severe hypoglycaemia (RR 0.75, 95% CI 0.49-1.15; p = 0.18).ConclusionsIn pregnant women with type 1 diabetes, impaired awareness of hypoglycaemia is associated with more maternal severe hypoglycaemia episodes and more fear of hypoglycaemia. Having impaired awareness of hypoglycaemia and/or fear of hypoglycaemia should alert clinicians to this increased risk. Reassuringly, there was no increase in adverse neonatal outcomes.

Project description:AimTo determine whether continuous glucose monitoring (CGM) can reduce hypoglycaemia in patients with post-bariatric hypoglycaemia (PBH).Materials and methodsIn an open-label, nonrandomized, pre-post design with sequential assignment, CGM data were collected in 22 individuals with PBH in two sequential phases: (i) masked (no access to sensor glucose or alarms); and (ii) unmasked (access to sensor glucose and alarms for low or rapidly declining sensor glucose). Twelve participants wore the Dexcom G4 device for a total of 28 days, while 10 wore the Dexcom G6 device for a total of 20 days.ResultsParticipants with PBH spent a lower percentage of time in hypoglycaemia over 24 hours with unmasked versus masked CGM (<3.3 mM/L, or <60 mg/dL: median [median absolute deviation {MAD}] 0.7 [0.8]% vs. 1.4 [1.7]%, P = 0.03; <3.9 mM/L, or <70 mg/dL: median [MAD] 2.9 [2.5]% vs. 4.7 [4.8]%; P = 0.04), with similar trends overnight. Sensor glucose data from the unmasked phase showed a greater percentage of time spent between 3.9 and 10 mM/L (70-180 mg/dL) (median [MAD] 94.8 [3.9]% vs. 90.8 [5.2]%; P = 0.004) and lower glycaemic variability over 24 hours (median [MAD] mean amplitude of glycaemic excursion 4.1 [0.98] vs. 4.4 [0.99] mM/L; P = 0.04). During the day, participants also spent a greater percentage of time in normoglycaemia with unmasked CGM (median [MAD] 94.2 [4.8]% vs. 90.9 [6.2]%; P = 0.005), largely due to a reduction in hyperglycaemia (>10 mM/L, or 180 mg/dL: median [MAD] 1.9 [2.2]% vs. 3.9 [3.6]%; P = 0.02).ConclusionsReal-time CGM data and alarms are associated with reductions in low sensor glucose, elevated sensor glucose, and glycaemic variability. This suggests CGM allows patients to detect hyperglycaemic peaks and imminent hypoglycaemia, allowing dietary modification and self-treatment to reduce hypoglycaemia. The use of CGM devices may improve safety in PBH, particularly for patients with hypoglycaemia unawareness.

Project description:Study objectivesWe aimed (1) to investigate the effect of a 12-week exercise training on sleep quality and heart rate variability in middle-aged and older adults with poor sleep quality and (2) to examine the factors associated with the improvements in sleep quality and heart rate variability parameters.MethodsForty adults aged ≥ 40 years with poor sleep quality (mean age = 62 years; 82.5% female) participated in this study. They were randomized into the exercise group or control group. Each exercise training program consisted of 40 minutes of supervised aerobic exercise training and 10 minutes of stretching class, 3 times a week for 12 weeks. Outcome measures included both subjective (Pittsburgh Sleep Quality Index) and objective (actigraphy recordings) sleep quality assessments, a cardiopulmonary exercise test, and heart rate variability assessment.ResultsThe exercise group showed significant improvements in the global score (P = .003), on all subscales of Pittsburgh Sleep Quality Index (P < .05), and in some heart rate variability parameters compared to the control group. Multiple regression analysis indicated that exercise participation was associated with either the sleep quality (β = -0.617, R² = .407; F = 6.226, P < .001) or heart rate monitor high frequency normalized units (β = 0.503, R² = .225; F = 3.200, P = .003) after adjustment for basic characteristics. However, the statistical significance between exercise participation and heart rate monitor high frequency normalized units diminished after controlling for the Pittsburgh Sleep Quality Index.ConclusionsOur results indicated that moderate-intensity exercise training had a beneficial effect on sleep quality and cardiac autonomic function. Middle-aged and older adults with poor sleep quality should be encouraged to engage in a moderate-intensity aerobic exercise training to improve their sleep quality and cardiac autonomic function.Clinical trial registrationRegistry: ClinicalTrials.gov; Name: The Effects of Exercise Training in Community-dwelling Elderly With Sleep Disturbances With Follow-up; URL: https://clinicaltrials.gov/ct2/show/NCT03005990; Identifier: NCT03005990.

Dataset Information

Poor sleep quality is associated with nocturnal glycaemic variability and fear of hypoglycaemia in adults with type 1 diabetes.

Aims

Background

Design

Methods

Results/findings

Conclusion

Publications

Poor sleep quality is associated with nocturnal glycaemic variability and fear of hypoglycaemia in adults with type 1 diabetes.

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