Optimized effectiveness based sleep aid management

Abstract

A melatonin optimization system detects users' hormone sensitivity through sleep architecture monitoring and recommends a dose of melatonin personalized to each user in relation to the user's behaviors, needs and health conditions. Determining an optimized melatonin dose requires accurate prediction of non-intervention sleep onset latency for the upcoming sleep period so that the dose can be based on the difference between the user's desired sleep onset latency and the predicted non-intervention sleep onset latency. The system can use either a general population-based sleep onset latency prediction model or a machine learning model trained to be personalized for each user.

Description

CROSS-REFERENCE TO PRIOR APPLICATIONS[0001]This application claims the benefit of U.S. Provisional Application No. 63 / 054,197, filed on 20 Jul. 2020. This application is hereby incorporated by reference herein.BACKGROUND OF THE INVENTION1. Field of the Invention[0002]The disclosed concept pertains to methods and systems for effectively administering sleep aids, and, in particular, to methods and systems for optimizing sleep through the administration of exogenous melatonin.2. Description of the Related Art[0003]In 2016, 27 percent of people in a new Consumer Reports survey of 4,023 U.S. adults said they had trouble falling asleep or staying asleep most nights, and 68 percent—or an estimated 164 million Americans—struggled with sleep at least once a week. Of interest, approximately 5.2% of the 2002 NHIS Alternative Health / Complementary and Alternative Medicine supplement survey respondents reported using melatonin and 27.5% of those users reported insomnia as a reason for taking the ...

AI Technical Summary

Benefits of technology

The present invention provides a melatonin optimization system to help users achieve better sleep outcomes. The system includes a user interface to gather information about the user's health, behavior, and desired sleep outcome. A sleep architecture detection module monitors the user's sleep structure and detects their hormone sensitivity. A behavior detection module analyzes the user's behavior to define their detected behavior. An initial dose algorithm module recommends an initial melatonin dose for the user based on the information input. An effectiveness evaluation module determines the difference between the user's desired sleep outcome and their measured sleep outcome. A recommendation engine defines an intervention to reduce this outcome difference. The system can help users achieve better sleep outcomes by optimizing the effectiveness of melatonin.

Problems solved by technology

Despite the large global market and the millions of US adults who report using melatonin, the question of appropriate dose and formulation of melatonin for the adjustment of circadian rhythms and sleep problems has not been resolved.

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