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.

What is claimed is: 
     
         1 . A melatonin optimization system for optimizing the effectiveness of exogenous melatonin in achieving a desired sleep outcome for a user, the system comprising:
 a user interface configured to accept information input to the user interface regarding health conditions, self-reported behavior, and a desired sleep outcome of the user;   a sleep architecture detection module configured to perform monitoring of a sleep architecture of the user and to detect a hormone sensitivity of the user through the monitoring;   a behavior detection module configured to detect and collect information about behavior of the user in order to define a detected behavior of the user;   an initial dose algorithm module configured to define an initial advised dose of melatonin for the user;   an effectiveness evaluation module configured to determine an outcome difference between the desired sleep outcome of the user and a measured sleep outcome of the user; and   a recommendation engine configured to define an intervention for the user to reduce the outcome difference,   wherein the initial dose algorithm module is configured to define the initial advised dose of melatonin based on the information input to the user interface, and   wherein the recommendation engine is configured to define the intervention based on the outcome difference, the monitoring of the sleep architecture, the detected behavior of the user, and the initial advised dose of melatonin.   
     
     
         2 . The melatonin optimization system of  claim 1 , wherein the desired sleep outcome is a sleep onset latency. 
     
     
         3 . The melatonin optimization system of  claim 1 , wherein the self-reported behavior includes information about food intake, alcohol intake, and caffeine intake. 
     
     
         4 . The melatonin optimization system of  claim 1 , wherein the behavior detection module includes a stress detector configured to detect user physiological data comprising at least one of a heart rate variability and a skin conductance. 
     
     
         5 . The melatonin optimization system of  claim 1 ,
 wherein the behavior detection module includes a stress detector, the stress detector comprising a device configured to communicate via an application programming interface (API) with remote stress detection software,   wherein the remote stress detection software is configured to determine a stress level of the user.   
     
     
         6 . The melatonin optimization system of  claim 1 , wherein the behavior detection module includes a light sensor configured to determine the duration, intensity, and timing of both sunlight and artificial light to which a user is exposed throughout the day and up to the user's bed time. 
     
     
         7 . The melatonin optimization system of  claim 1 ,
 wherein the intervention includes a change to a current dose of melatonin being recommended to the user,   wherein the change to the current dose of melatonin being recommended to the user is based on: an effectiveness of any previous melatonin intervention, the user's behavior for the current day as detected by the behavior detection module, and the user's sleep architecture from the previous night as detected by the sleep architecture detection module.   
     
     
         8 . The melatonin optimization system of  claim 1 , wherein, if a current recommended melatonin dose has reached a predetermined maximum level, the recommendation engine will define the intervention to only include changes to the user's behavior. 
     
     
         9 . The melatonin optimization system of  claim 1 ,
 wherein the effectiveness evaluation module comprises a machine learning model,   wherein the machine learning model is trained to provide a non-intervention sleep onset latency prediction for an upcoming sleep period of the user based on data collected by the sleep architecture detection module regarding a most recent sleep period of the user.   
     
     
         10 . The melatonin optimization system of  claim 9 ,
 wherein the machine learning model has been provided training to personalize the non-intervention sleep onset latency prediction for the user,   wherein the training of the machine learning model comprises providing sleep architecture data of the user from a baseline period when the user was not using exogenous melatonin to the machine learning model and providing sleep architecture data of the user from a testing period when the user was using varying doses of exogenous melatonin to the machine learning model.   
     
     
         11 . The melatonin optimization system of  claim 9 , wherein the training of the machine learning model further comprises providing behavior data of the user from the behavior detection module associated with the baseline period to the machine learning model and providing behavior data of the user from the behavior detection module associated with the testing period to the machine learning model. 
     
     
         12 . The melatonin optimization system of  claim 9 ,
 wherein the machine learning model has been provided training to predict a non-intervention sleep onset latency for the user,   wherein the training of the machine learning model comprises providing sleep architecture data of research study subjects from a baseline period when the research study subjects were not using exogenous melatonin to the machine learning model and providing sleep architecture data of the research study subjects from a testing period when the research study subjects were using varying doses of exogenous melatonin to the machine learning model.   
     
     
         13 . The melatonin optimization system of  claim 9 , wherein the recommendation engine is configured to define the intervention based on the non-intervention sleep onset latency prediction provided by the machine learning model. 
     
     
         14 . The melatonin optimization system of  claim 10 , wherein the recommendation engine is configured to define the intervention based on the non-intervention sleep onset latency prediction provided by the machine learning model. 
     
     
         15 . The melatonin optimization system of  claim 12 , wherein the recommendation engine is configured to define the intervention based on the non-intervention sleep onset latency prediction provided by the machine learning model.