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.

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.FIELD OF THE INVENTION[0002]The present invention relates to the field of sleep analysis, and in particular, to the field of sleep analysis performed by an individual without clinician input.BACKGROUND OF THE INVENTION[0003]The use of (wearable) sleep trackers is being more popular and widespread, and are often used to perform sleep analysis or assessment without the input of a clinician / physician.[0004]Sleep trackers often execute a software application, running on the processor, to perform a sleep analysis or assessment process, e.g. based upon biometric data gathered by the sleep tracker itself or an external sensor, such as movement data from an accelerometer (e.g. mounted on the individual's wrist) or heartrate data from a heartrate monitor.[0005]However, despite their wi...

AI Technical Summary

Benefits of technology

The patent text describes a way to assess and reduce the likelihood of orthosomnia in individuals. This is done by controlling the information provided to the individual through a software application. By changing or supplementing the information, the likelihood of orthosomnia can be reduced. The text also mentions that explaining the difference between the individual's perception of sleep and the software application's prediction can help reduce the likelihood of orthosomnia. This approach provides a more accurate way to address the issue of orthosomnia.

Problems solved by technology

However, despite their widespread use, the accuracy of sleep trackers has been brought into question.

Studies evaluating the ability of wearable trackers to estimate sleep have generally found high sensitivity, comparatively low specificity, and invalidity around wake times during sleep.

Additionally, even when users are presented with accurate sleep tracking data within a healthy normal range, users may lack the domain knowledge to appropriately interpret the information provided to them.

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