Evidence mapPaperPMID 41902329Full record

ArticleJournal of sleep research2026

Associations Between Sleep Quality and Continuous Glucose Monitoring-Derived Metrics in Individuals With Type 2 Diabetes.

Soohyun Nam, Minjung Lee, Sangchoon Jeon, Garrett Ash, Stuart Weinzimer, Robin Whittemore

Abstract read
In one paragraph

Article in Journal of sleep research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Soohyun NamSchool of Nursing, Yale University, Orange, Connecticut, USA.
Minjung LeeSchool of Nursing, Yale University, Orange, Connecticut, USA.
Sangchoon JeonSchool of Nursing, Yale University, Orange, Connecticut, USA.
Garrett AshSchool of Medicine, Yale University, New Haven, Connecticut, USA.ORCID https://orcid.org/0000-0002-8655-7525
Stuart WeinzimerSchool of Medicine, Yale University, New Haven, Connecticut, USA.
Robin WhittemoreSchool of Nursing, Yale University, Orange, Connecticut, USA.

Funding

National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases R01DK132069
6 · The paper itself

Abstract

Sleep is a critical component of cardiometabolic health, yet individuals with Type 2 diabetes (T2D) experience disproportionately poor sleep quality. While extensive research links sleep duration and quality with HbA1c, less is known about the relationship between sleep and continuous glucose monitoring (CGM)-derived metrics, which capture short-term glycemic variability (GV) and time in range (TIR). Growing evidence suggests that CGM-derived metrics, particularly GV and TIR, are strongly associated with diabetes-related complications and all-cause mortality, underscoring their clinical importance. We conducted a study to examine associations between self-reported sleep quality and CGM-derived metrics among 137 adults with T2D. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and wore blinded CGM devices for 14 days. CGM-derived metrics included intraday- and interday-GV (coefficient of variation, J-index, high/low blood glucose indices, mean of daily differences [MODD]), TIR, time above range (TAR) and time below range (TBR). Multivariable linear regression adjusted for age, sex, body mass index, diabetes duration, depressive symptoms and race/ethnicity. Overall, 69% of participants reported poor sleep. Poor sleep quality was independently associated with higher TAR (daytime β = 0.18, p = 0.04; nighttime β = 0.13, p = 0.04), lower TIR (daytime β = -0.09, p = 0.04; nighttime β = -0.05, p = 0.04) and greater day-to-day GV (β = 0.22, p = 0.03) and higher hyperglycemia risk (β = 0.23, p = 0.04). These findings suggest that poor sleep quality in T2D is linked to increased hyperglycemia exposure, reduced TIR and unstable day-to-day GV, independent of clinical factors. Addressing sleep as a modifiable lifestyle factor and integrating sleep assessments with CGM may provide actionable insights to guide personalised diabetes management.

Indexed as

Blood GlucoseContinuous Glucose MonitoringDiabetes Mellitus, Type 2Sleep QualityAgedBlood Glucose Self-MonitoringFemaleHumansMaleMiddle AgedSleep DurationBlood Glucosecontinuous glucose monitoringglucose variabilitysleep qualitytime above rangetime in rangeType 2 diabetes

Identifiers

PMID41902329
PMCPMC13357892

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.