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Published on: June 13, 2025
Sleep Efficiency Predicts Next-Day Glycaemia and Daytime Glycaemia Influences Sleep in Free-Living Adults at Risk of
Antonio Clavero-Jimeno1, Juan J Martin-Olmedo2, Jairo H Migueles1,3
1Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain.
Aim:
To investigate how sleep features influence next-day diurnal glucose homeostasis and vice versa in free-living adults at risk of type 2 diabetes.
Materials And Methods:
This observational repeated-measures study included 388 adults aged 25-65 years (50% women) with overweight or obesity (BMI ≥ 25.0-< 40.0 kg/m2). Sleep and glucose homeostasis were simultaneously assessed over 14 days using wrist-worn accelerometers and continuous glucose monitors. Linear mixed models evaluated day-level associations between sleep metrics-wake-up time, sleep period time (i.e., time from sleep onset to wake-up) and sleep efficiency ([total sleep time/sleep period time] × 100)-and diurnal glucose metrics, including mean glucose and its standard deviation (glycaemic variability).
Results:
We analysed 3942 valid person-days. Each 1% increase in sleep efficiency was associated with lower next-day mean glucose (B [95% CI] = -0.05 [-0.08, -0.01] mg/dL; p = 0.007). Each 1 h delay in wake-up time was linked to reduced next-day glucose variability (-0.24 [-0.38, -0.10] mg/dL; p = 0.001). Conversely, each 1 mg/dL increase in daytime mean glucose was associated with later wake-up time (0.008 [0.002, 0.014] h; p = 0.008), longer sleep period time (0.006 [0.000, 0.012] h; p = 0.039) and lower sleep efficiency (-0.05% [-0.08%, -0.01%] %; p = 0.005) the subsequent night. Each 1 mg/dL increase in glucose variability was associated with earlier wake-up time (-0.02 [-0.03, -0.01] h; p < 0.001).
Conclusions:
This study provides evidence that sleep and glucose dynamics are temporally associated in free-living adults at risk of type 2 diabetes. These findings underscore the potential of combining sleep and glucose metrics to inform cardiometabolic risk prevention strategies.
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