Systems, methods, and devices utilizing cumulative sleep data to predict the health of an individual

Abstract

The methods and systems described herein may involve determining at least one lifeotype of at least one individual, analyzing the at least one lifeotype, and delivering content to at least one individual based on the analysis. The methods and systems described herein may involve providing a game, determining at least one lifeotype of at least one player of the game, analyzing the at least one lifeotype, and affecting the game play based on the analysis. The methods and systems described herein may involve providing an interactive space, determining at least one lifeotype of at least one individual in the space, analyzing the at least one lifeotype, and modifying at least one attribute of the space based on the analysis.

What is claimed is: 
     
         1 . A computer-system-implemented method, the computer system having at least one programmed processor to implement the method, the method comprising:
 continuously collecting physiological data components with respect to an individual from a wearable sensor device;   the computer system—
 (i) obtaining context information for the individual based at least in part on output from a contextual sensor; 
 (ii) based on a determination that the context is a sleep-related context and the physiological data collected from the wearable sensor device, determining that the individual is in a sleep-related state; and 
 (iii) based on the determined sleep-related state and the sleep-related context, predicting the individual's state of health. 
   
     
     
         2 . The method of  claim 1 , wherein the context is also determined at least in part based on the output of a plurality of sensors of the wearable sensor device. 
     
     
         3 . The method of  claim 1 , wherein the sleep-related state is a stage of sleep, readiness for sleep, quality of sleep, duration of sleep, duration of a stage of sleep, a pre-sleep state, a post-sleep state, a sleep-dependent health state, a sleep-dependent performance state, and a physiological state during sleep. 
     
     
         4 . The method of  claim 1 , wherein said physiological data collected from the wearable sensor device is selected from the group consisting of: derived data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, sensed data, continuously monitored data, and real-time data. 
     
     
         5 . The method of  claim 1 , wherein at least one data component collected from a wearable sensor device is a data component that is derived from a plurality of sensors that is distinct from the output of any single sensor. 
     
     
         6 . The method of  claim 1 , wherein the prediction of the individual's state of health is based on basal readings during sleep. 
     
     
         7 . The method of  claim 1 , wherein the predicted state of health is a long term health condition. 
     
     
         8 . The method of  claim 7 , wherein the long term health condition is at least one of apnea, a heart condition, elevated blood pressure, diabetes, chronic pain, depression, or a general change in health over time. 
     
     
         9 . The method of  claim 1 , wherein the predicted state of health is a short term condition. 
     
     
         10 . The method of  claim 9 , wherein the short term condition is fatigue.