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Sleep metrics; Glucose trends; Smartwatch; Morning glucose; Wearable technology; Sleep quality; Continuous glucose monitoring; Circadian rhythm; Sleep duration; Sleep stages; Sleep efficiency; Glucose regulation; Digital health; Personalized medicine; Blood sugar prediction; Sleep tracking; Metabolic health; Predictive analytics; Prospective cohort; Technology in healthcare

Introduction

The growing prevalence of wearable technology has opened new frontiers in health monitoring and chronic disease management. Smartwatches, which are now equipped with sophisticated sensors, can non-invasively track various physiological parameters, including sleep duration, sleep quality, heart rate variability, and movement patterns [1-5]. Sleep has long been recognized as a crucial regulator of metabolic processes, with both sleep deprivation and poor sleep quality being linked to impaired glucose regulation and increased risk of type 2 diabetes. Traditional methods of assessing sleep involve polysomnography, which, although accurate, is expensive and impractical for long-term use in the general population. With advancements in consumer-grade wearable devices, there is a growing opportunity to collect longitudinal sleep data and correlate it with other health parameters, including glucose levels [6-10].

Morning glucose trends, often measured via fasting glucose or continuous glucose monitoring (CGM), are indicative of overnight metabolic activity and can serve as an early biomarker for insulin resistance. Previous studies have shown associations between poor sleep and impaired glucose metabolism, but few have examined the predictive power of wearable-derived sleep data on real-time glucose trends. This study aims to bridge that gap by prospectively examining whether sleep metrics collected from commercially available smartwatches can predict morning glucose levels in a cohort of adults with varying glycemic statuses.

This prospective cohort study followed participants over a 30-day period, collecting daily sleep metrics through a standardized smartwatch and comparing these with their corresponding fasting or morning glucose readings obtained through CGM devices. By investigating the relationship between wearable-derived sleep variables (such as total sleep time, sleep efficiency, and REM sleep duration) and morning glucose levels, the study seeks to provide insights into the feasibility of integrating consumer wearables into metabolic health monitoring and early intervention strategies.

Discussion

The results of this prospective study revealed significant associations between specific smartwatch-derived sleep metrics and morning glucose levels. Most notably, shorter total sleep duration and lower sleep efficiency were consistently linked to higher fasting glucose the following morning. These findings support the hypothesis that inadequate or poor-quality sleep negatively affects overnight glucose metabolism, likely through dysregulation of insulin sensitivity and increased sympathetic nervous system activity. Additionally, prolonged latency to sleep onset and reduced time in REM sleep were also moderately correlated with glucose elevations, suggesting that not only quantity but also quality and architecture of sleep play a vital role in glycemic control.

One of the novel aspects of this study is the use of commercially available smartwatches rather than clinical-grade equipment, which enhances its applicability in real-world settings. While wearable devices may not provide the granularity of polysomnography, they offer practical advantages in terms of scalability, affordability, and user compliance. Our results suggest that these devices, when paired with CGM, can provide meaningful insights into daily glucose variability and potentially help identify individuals at risk for glucose dysregulation.

However, there are several limitations to consider. First, while smartwatches are increasingly accurate, discrepancies in sleep stage detection compared to polysomnography still exist. Second, factors such as diet, stress, and physical activity—which also influence glucose levels—were not controlled in this study. Future research should aim to include multivariate models to account for these confounding factors. Additionally, larger and more diverse cohorts are necessary to validate these findings and explore whether the predictive models can be generalized across different populations, including those with prediabetes or type 2 diabetes.

Nonetheless, the implications of this study are far-reaching. Integrating sleep data with glucose monitoring systems could enable early warnings for glycemic abnormalities and offer personalized feedback to improve metabolic health. This approach aligns well with the ongoing shift toward personalized, data-driven healthcare, where continuous lifestyle data informs clinical decision-making and self-management strategies.

Conclusion

In summary, this study demonstrates that sleep metrics derived from smartwatches are moderately predictive of morning glucose levels, with poor sleep quality and insufficient duration being linked to higher fasting glucose. These findings highlight the metabolic consequences of suboptimal sleep and reinforce the role of sleep as a modifiable factor in glucose regulation. The use of wearable technology offers a practical and scalable solution for continuous health monitoring, bridging the gap between lifestyle behaviors and metabolic outcomes.

Our results support the integration of smartwatch data into clinical and personal health frameworks for diabetes risk assessment and management. As wearable technology continues to evolve, its potential to serve as an adjunct tool in chronic disease prevention and intervention becomes increasingly promising. Future studies should aim to refine predictive algorithms, incorporate additional behavioral data, and assess long-term outcomes of using such integrated monitoring systems.

Overall, this research underscores the potential of leveraging digital health tools to enhance our understanding of the sleep-glucose relationship and promote proactive, personalized healthcare interventions.

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