SYSTEMS, METHODS, AND DEVICES UTILIZING CUMULITIVE SLEEP DATA TO PREDICT THE HEALTH OF AN INDIVIDUAL
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.
1 . A computer-system-implemented method, the computer system having at least one programmed processor to implement the method, the method comprising:
continuously collecting data components with respect to an individual from a wearable sensor device; and
collecting another set of data components with respect to the individual from a source separate from the wearable device;
the computer system—
(i) determining a type for the individual based on at least one of a match and a similarity between the collected data components for the individual and data components for at least one other individual;
(ii) determining a state related to sleep based on at least one of data from the wearable sensor device and the collected another set of data components; and
(iii) based on the sleep-related state, the determined type, and the data collected from the wearable sensor device, predicting the individual's state of health.
2 . The method of claim 1 , wherein the sleep-related state is determined 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 set of data components from a wearable sensor device is selected from the group consisting of: derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, continuous 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.