Predicting orthosomnia in an individual

Abstract

A mechanism for identifying orthosomnia, being the preoccupation with sensor-derived sleep measures or automated sleep analysis processes, within an individual. Interactions between the individual and a software application performing a sleep analysis process are monitored and used to generate a predictive indicator that indicates a likelihood that the individual has orthosomnia. This predictive indicator may be used to control the software application, and in particular, to control the display of information provided by the software application.

1 . A computer-implemented method for generating a predictive indicator that indicates a likelihood of orthosomnia in an individual, the computer-implemented method comprising:
 obtaining user interaction data, being data responsive to the individual's interactions, via a user interface for a processor, with a software application running on the processor for performing a sleep analysis or assessment process; and   processing the obtained user interaction data to generate the predictive indicator that indicates a likelihood of orthosomnia in the individual.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the user interaction data comprises access data responsive to the individual accessing and/or opening the software application. 
     
     
         3 . The computer-implemented method of  claim 2 , wherein the access data comprises one or more measures of accessing and/or opening frequency and/or duration of access. 
     
     
         4 . The computer-implemented method of  claim 2 , wherein the step of processing the obtained user interaction data comprises:
 obtaining population access data, being data that is responsive to population trends of other individual's interactions with the software application running on the processor and/or one or more other versions of the software application running on one or more other processors; and   comparing the access data to the population access data to generate the predictive indicator.   
     
     
         5 . The computer-implemented method of  claim 2 , wherein the access data comprises time data indicating a time at which the individual interacts with the software application, and wherein the step of processing the obtained user interaction data comprises:
 obtaining, from a biometric sensor, biometric data of the user, the biometric data being responsive to changes in one or more physiological parameters of the individual at or during a time at which the individual accesses the and/or opens the software application, as indicated by the time data; and   processing the biometric data to generate the predictive indicator.   
     
     
         6 . The computer-implemented method of  claim 5 , wherein the one or more physiological parameters of the individual comprise a physiological parameter responsive to an autonomic response of the individual, such as a heartrate, sweating, a temperature, a respiratory rate, skin color, eye movement and/or an eye dilation. 
     
     
         7 . The computer-implemented method of  claim 2 , wherein the step of processing the obtained user interaction data comprises:
 obtaining historic access data, being data responsive to the individuals historic interactions, via the user interface, with the software application; and   comparing the access data to the historic access data to generate the predictive indicator.   
     
     
         8 . The computer-implemented method of  claim 1 , wherein:
 the step of obtaining the user interaction data comprises obtaining, via the user interface, user-derived sleep quality data representing a subjective perception of sleep quality of the individual; and   the step of processing the obtained user interaction data comprises:
 obtaining, from a sleep sensor, sleep sensor data responsive to one or more changes of physiological parameters of the individual during the individual's sleep; 
 processing the sleep sensor data to generate sensor-derived sleep quality data, being data representing an objective measure of the sleep quality of the individual; and 
 comparing the user-derived quality input data to the sensor-derived sleep quality data to generate the predictive indicator. 
   
     
     
         9 . A computer-implemented method of controlling a software application running on a processor for performing a sleep analysis or assessment process, the computer-implemented method comprising:
 generating a predictive indicator that indicates a likelihood of orthosomnia in an individual by performing the method of  claim 1 ; and   controlling the software application to modify and/or supplement information presented to the individual, via the user interface, during the sleep analysis or assessment process based on the predictive indicator.   
     
     
         10 . The computer-implemented method of  claim 9 , wherein the software application is configured to generate and display, at the user interface, a sleep quality measure during the sleep analysis or assessment process, and the step of controlling the software application comprises at least one of:
 modifying the sleep quality measure in response to the predictive indicator indicating that a likeliness of orthosomnia in the individual falls within a first predetermined range;   suppressing the display of the sleep quality measure in response to the predictive indicator indicating that a likeliness of orthosomnia falls within a second predetermined range; and/or   providing, at the display, supplementary information about sleep quality measures in response to the predictive indicator indicating that a likeliness of orthosomnia falls within a third predetermined range.   
     
     
         11 . The computer-implemented method of  claim 10 , wherein:
 the step of obtaining the user interaction data comprises obtaining, via the user interface, user-derived sleep quality data representing a subjective perception of sleep quality of the individual:   the computer-implemented method further comprises:
 obtaining, from a sleep sensor, sleep sensor data responsive to one or more changes of physiological parameters of the individual during the individual's sleep; and 
 processing the sleep sensor data to generate sensor-derived sleep quality data, being data representing an objective measure of the sleep quality of the individual, 
   wherein the supplementary information comprises information responsive to a difference between the user-derived sleep quality data and the sensor-derived sleep quality data.   
     
     
         12 . The computer-implemented method of  claim 1 , wherein the computer-implemented method is performed by the software application. 
     
     
         13 . The computer-implemented method of  claim 1 , wherein the predictive indicator comprise a binary indicator indicating a prediction of whether or not the individual has orthosomnia. 
     
     
         14 . A computer program product comprising computer program code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps of the method according to  claim 1 . 
     
     
         15 . A processing system for generating a predictive indicator that indicates a likelihood of orthosomnia in an individual, the processing system being configured to:
 obtain user interaction data, being data responsive to the individual's interactions, via a user interface for a processor, with a software application running on the processor for performing a sleep analysis or assessment process; and   process the obtained user interaction data to generate the predictive indicator that indicates a likelihood of orthosomnia in the individual.