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HOUSTON, June 5, 2024 /PRNewswire/ -- Fullpower Technologies, in collaboration with Stanford Sleep Medicine and UCSF Department of Psychiatry and Behavioral Sciences, presented two major studies at the SLEEP 2024 conference, furthering our understanding of sleep fragmentation and heart rate variability (HRV) during sleep. These studies utilized the advanced Sleeptracker-AI^® Monitor, showcasing the solution's ability to gather extensive data and offer valuable insights into sleep health.

"We are proud of our continued collaboration in advancing Sleep Science research with Stanford Sleep Medicine and UCSF. Together, we are sharing these new findings with the sleep research community leveraging the Sleeptracker.ai platform and network of sleepers," said Philippe Kahn, CEO of Fullpower.ai.

Study on Sleep Fragmentation in a Large US Sample

The first study, led by Dr. Clete Kushida from Stanford University, investigated sleep fragmentation across different age groups and genders using Sleeptracker-AI^® data from over 117,000 participants and over 10 million recorded nights. The Sleeptracker-AI^® Monitor's noninvasive, under-mattress sensors provided reliable data on arousals and sleep-disordered breathing (SDB).

Key findings include:

• Greater Sleep Fragmentation in Men: Men exhibited significantly higher sleep fragmentation during REM sleep compared to women across all ages above 24. However, fragmentation decreased with age after 34.
• Age-related Differences: Sleep fragmentation increased with age during NREM sleep for both genders but showed a complex pattern during REM sleep, initially increasing up to 25-34 years and then decreasing with age.

These results highlight the potential of home monitoring devices in identifying sleep disturbances and their variations across demographics, emphasizing the need for further research to understand the underlying mechanisms.

Study on Heart Rate Variability (HRV) During Sleep

The second study, led by Dr. Yue Leng from the University of California, San Francisco, examined HRV in over 38,000 individuals, focusing on the impact of obstructive sleep apnea (OSA). Using the Sleeptracker-AI^® Monitor, the study collected HRV data from 2.7 million recorded nights.

Key findings include:

• HRV and OSA Severity: Individuals with moderate to severe OSA had significantly higher overall HRV variability (SDNN) and lower parasympathetic activity (RMSSD) than those without OSA. These differences were more pronounced in middle-aged adults.
• Age and Gender Differences: Both HRV metrics decreased with age. SDNN was consistently lower in females, while RMSSD showed greater reductions in individuals with severe OSA, particularly among younger and middle-aged adults.

This study underscores the importance of continuous, noninvasive monitoring of HRV to understand its relationship with sleep disorders and autonomic nervous system balance.

Conclusion

Fullpower Technologies' participation in SLEEP 2024 highlights the impactful research facilitated by the Sleeptracker-AI^® platform. These studies demonstrate the solution's capability to gather large-scale, real-world data, offering valuable insights into sleep health and paving the way for improved sleep disorder diagnostics and personalized treatment approaches.

For more information on these studies and the Sleeptracker-AI^® platform, please visit sleeptracker.ai

About Fullpower Technologies

Fullpower-AI^® is the provider of a generative AI deep-learning biosensing platform. The Fullpower-AI^® platform is a remote real-time biosensing edge-to-cloud solution vetted and successfully deployed in 60+ countries. Fullpower-AI^® customers are in sleep, life sciences, health, wellness, and biotechnology. A portfolio of 135+ patents backs the platform. Fullpower-AI^® is ISO 27001 certified.

For more information, contact BusDev@fullpower.com

SOURCE Fullpower