SleepFit 360 2.0.8

Neurobit Technologies ❘ Logiciel gratuit ❘
iOS

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SleepFit 360 offers comprehensive insights into individual health by analyzing sleep-related biomarkers. It captures and examines vital parameters such as heart rate, respiration, temperature, and movements at a frequency of up to one thousand times per second during sleep, enabling detailed assessments of both physical and mental health status. Utilizing sleep as a window into current and future well-being, the platform provides tailored recommendations aimed at improving overall health outcomes.

The data collected is processed through Neurobit’s proprietary AI technology, Z3Pulse, which is supported by extensive research and trained on trillions of health data points. This advanced system enables personalized analysis by contextualizing your metrics against both population norms and your unique physiological profile. Ongoing efforts focus on integrating new insights derived from rigorous scientific validation to deepen understanding and support healthier lifestyles for individuals and their families.

By leveraging sophisticated AI-driven analyses, users are empowered to take proactive control of their health with customized suggestions designed specifically for their needs.

DISCLAIMER:

The SleepFit 360 application provides analysis based on data collected from the SleepFit sensor. The information presented within the app or associated reports is not intended to diagnose, treat, cure, or prevent any disease. It should not substitute professional medical advice; rather, it serves as a supplementary resource to facilitate discussions with healthcare providers.

Clinical Validations:

<ul>
<li>Pini, N., Ong, J. L., Yilmaz, G., Chee, N. I., Siting, Z., Awasthi, A., ... & Lucchini, M. (2021). An automated heart rate-based algorithm for sleep stage classification: validation using conventional PSG and innovative wearable ECG device. medRxiv.</li>
<li>Chen, Y. J., Siting, Z., Kishan, K., & Patanaik, A. (2021). Instantaneous Heart Rate-based sleep staging using deep learning models as a convenient alternative to Polysomnography.</li>
<li>Siting, Z., Chen, Y. J., Kishan, K., & Patanaik, A. (2021). Automated sleep apnea detection from instantaneous heart rate using deep learning models.</li>
</ul>

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