Systems, devices, and methods for evaluating and predicting sleep-related disorders

Abstract

The devices, systems, and methods described herein relate to the assessment and prediction of sleep-related disorders. In some variations, a method for graphically representing a user's health may include the steps of using a computing device to output notifications for user input of sleep, physical health, and mental health assessments, and generating a user health graphic on the computing device's graphical user interface. For example, multiple shapes, each containing a corresponding color, may be generated based on each of the sleep, physical health, and mental health assessments. The positions of the multiple shapes on a first vertical axis of the user health graphic may be arranged based on the sleep, physical health, and mental health assessments.

Description

Technical Field

[0004] , , , , ,

[0001] Cross - Reference to Related Applications This application claims the benefit of U.S. Provisional Application No. 63 / 454,599, filed on Mar. 24, 2023, and U.S. Provisional Application No. 63 / 543,888, filed on Oct. 12, 2023, the contents of each of which are hereby incorporated by reference in their entirety.

[0002] The devices, systems, and methods herein relate to remotely evaluating and predicting medical conditions, including but not limited to sleep - related disorders.

Background Art

[0003] Millions of people suffer from chronic medical conditions, including sleep - related disorders such as sleep apnea, hypopnea, and insomnia. Chronic medical conditions on average cost the U.S. healthcare system approximately $7,000 per person per year in direct costs for each condition, and sleep disorders alone cost the U.S. healthcare system approximately $7,000 per person per year in direct costs. Approximately 47% of insured Americans have multiple chronic medical conditions. Therefore, there is a desire to provide new and useful methods for monitoring and actively addressing chronic medical conditions.

Summary of the Invention

Means for Solving the Problems

[0004] What is described herein are systems, devices, and methods for remotely evaluating and predicting medical conditions such as sleep - related disorders. These systems and methods can, for example, receive user - input data and sleep measurement data and analyze the received data for trends and / or the risk of sleep disorders. In some variations, a user health graphic can be generated on a graphical user interface based on user data to graphically represent the user's health state. This can provide insights into the user's health and well - being, for example, on a continuous or semi - continuous real - time basis.

[0005] In some variations, a method for graphically representing a user's health may include using a computing device to output notifications in response to user inputs of sleep assessment, physical health assessment, and mental health assessment, and generating a user health graphic on the computing device's graphical user interface. Generating may include generating a first shape with a first color based on the sleep assessment, a second shape with a second color based on the physical health assessment, and a third shape with a third color based on the mental health assessment, and positioning the first, second, and third shapes with respect to a first axis of the user health graphic based on the sleep assessment, physical health assessment, and mental health assessment.

[0006] In some variations, the first axis may be the vertical axis of the user health graphic. In some variations, the first shape may include the first center, the second shape may include the second center, and the third shape may include the third center. The first center, the second center, and the third center may be positioned on the first axis.

[0007] In some variations, the first shape includes a first center, the second shape includes a second center, and the third shape includes a third center, with two of the first, second, and third centers located on the first axis, and the third center being offset from the first axis.

[0008] In some variations, the first shape includes a first center, the second shape includes a second center, and the third shape includes a third center, with one of the first, second, and third centers positioned on the first axis, and the other two of the first, second, and third centers offset from the first axis and located on opposite sides of the first axis. In some variations, the first axis may be a sloping axis between the vertical axis and the horizontal axis of the user health graphic. In some variations, the center-to-center distance between the first and second shapes may be based on the difference between two of the sleep assessment, physical health assessment, and mental health assessment.

[0009] In some variations, two or more centers of the first, second, and third shapes may be spaced apart from each other along the first axis. In some variations, two or more centers of the first, second, and third shapes may be offset from each other with respect to the first axis. In some variations, the first shape may include the first center, the second shape may include the second center, and the third shape may include the third center, with the first, second, and third centers being laterally offset from each other. In some variations, the first, second, and third shapes may partially overlap along the first axis. In some variations, the first shape may include the first center, the second shape may include the second center, and the third shape may include the third center, with the first, second, and third centers being spaced apart by an equal intercenter distance along the first axis.

[0010] In some variations, generating a user health graphic may involve generating multiple lines, each line overlapping two of the first, second, and third shapes. In some variations, the multiple lines may include a first line between the center of the first shape and the center of the second shape, and a second line between the center of the second shape and the center of the third shape.

[0011] In some variations, sleep assessments, physical health assessments, and mental health assessments may correspond to specific scales. In some variations, the specific scales may be Likert scales.

[0012] In some variations, each of the first, second, and third shapes may include a two-dimensional shape. In some variations, each of the first, second, and third colors may include a color gradient. In some variations, each of the first, second, and third colors may include an opacity gradient. In some variations, the background of the graphical user interface may include a color gradient.

[0013] In some variations, the method may further include periodically updating the user health graphic. In some variations, the method may further include generating an animation that includes the updated user health graphic and one or more previous user health graphics. In some variations, notifications may be output at predetermined intervals.

[0014] In some variations, the method may further include modifying computing device settings based on a user health graphic. In some variations, the method may further include introducing sleep services to the user based on a user health graphic.

[0015] Furthermore, methods for predicting sleep disorders are described here. In some variations, the methods for predicting sleep disorders may include receiving user data from a computing device, including sleep assessments, physical health assessments, and mental health assessments; receiving sleep measurement data from a measurement device; predicting the risk of one or more sleep disorders based on the user data and sleep measurement data; and referring sleep services to the user based on the predicted risk of one or more sleep disorders.

[0016] In some variations, the method may further include outputting notifications for user data input using a computing device. In some variations, sleep measurement data may include one or more sleep parameters. In some variations, one or more sleep parameters may include one or more of the following: sleep onset time, sleep end time, sleep duration, time to fall asleep, number of awakenings, length of awakening time, snoring status, snoring duration, exercise duration, exercise end time, number of alcoholic beverages consumed, time of last alcohol consumption, number of caffeinated beverages consumed, time of last caffeine consumption, daytime sleepiness status, daytime sleepiness severity, daytime sleepiness status, daytime sleepiness severity, mean sleep oxygen saturation, mean sleep heart rate variability, minimum sleep heart rate, maximum sleep heart rate, mean sleep heart rate, list of medications taken, and user demographic data. In some variations, one or more sleep parameters may further include user wake-up notes and user bedtime notes. In some variations, user demographic data may include one or more of medical history and test results.

[0017] In some variations, the sleep assessment, physical health assessment, and mental health assessment may correspond to a predetermined scale. In some variations, the predetermined scale may be a Likert scale. In some variations, the sleep assessment may include the quality of sleep, the physical health assessment may include physical condition, and the mental health assessment may include emotional condition. In some variations, the quality of sleep may include the reason for arousal. In some variations, physical condition may include the type of physical discomfort, the location of physical discomfort, and the intensity of physical discomfort. In some variations, emotional condition may include feelings upon waking and feelings before going to sleep.

[0018] In some variations, the method may further include generating sleep data based on user data and sleep measurement data. In some variations, generating sleep data may include matching user data with sleep measurement data. In some variations, the method may further include generating one or more sleep tendencies by analyzing the sleep data. In some variations, the risk of one or more sleep disorders may be based on one or more sleep tendencies.

[0019] In some variations, the method may further include generating a graphical user interface that includes one or more of the sleep tendencies and sleep disorder risks. In some variations, the method may further include modifying computing device settings based on one or more of the sleep tendencies and sleep disorder risks. In some variations, sleep service recommendations may be made based on one or more of the sleep tendencies and sleep disorder risks. In some variations, user data and sleep measurement data may be received at predetermined intervals.

[0020] In some modifications, the specified interval may be at least once a day. In some modifications, the specified interval may be at waking and going to bed. In some modifications, the sleep disorder may include one or more of the following: obstructive sleep apnea, central sleep apnea, hypopnea, orthopnea, nocturnal atrial fibrillation, nocturnal hypertension, nocturnal discomfort, chronic obstructive pulmonary disease worsening in the evening, heart failure, asthma, sleep quality, and waking discomfort.

[0021] Furthermore, this document describes a method for graphically representing a user's health, which may include receiving user data from a computing device, including sleep assessments, multiple physical health parameters, and mental health assessments, and receiving sleep measurement data from a measurement device. The sleep quality assessment may be generated based on the sleep assessment and sleep measurement data, the physical health quality assessment may be generated based on multiple physical health parameters, and the mental health quality assessment may be generated based on the mental health assessment. The user health graphic may be generated on the graphical user interface of the computing device and may include generating a first shape based on the sleep quality assessment, a second shape based on the physical health quality assessment, and a third shape based on the mental health quality assessment. The positions of the first, second, and third shapes may be positioned relative to a first axis of the user health graphic based on the sleep quality assessment, the physical health quality assessment, and the mental health quality assessment.

[0022] In some variations, the method may further include periodically generating assessments of sleep quality, physical health quality, and mental health quality. In some variations, the sleep measurement data may include one or more of the following: sleep duration, sleep efficiency, sleep latency, number of awakenings, wake duration, and sleep quality. In some variations, the sleep quality assessment may be based on a weighted sum of the sleep assessment and the sleep measurement data. In some variations, the weighting may be based on time.

[0023] In some variations, multiple physical health parameters may include one or more of the following: physical health assessment, number of physical discomforts, number of maximum physical discomforts, and mean severity of physical discomforts. In some variations, the assessment of physical health quality may be based on a weighted sum of multiple physical health parameters. In some variations, the weighting may be based on time. In some variations, multiple physical health parameters may each correspond to a predetermined scale. In some variations, the predetermined scale may be a Likert scale.

[0024] In some variations, generating the user health graphic may include alphanumeric representations of the evaluation of the quality of sleep, the evaluation of the quality of physical health, and the evaluation of the quality of mental health. In some variations, the user data and the sleep measurement data may be received at predetermined intervals. In some variations, the predetermined interval may be at least once a day.

[0025] In some variations, the first axis may be the vertical axis of the user health graphic. In some variations, the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, and the first center, the second center, and the third center may be arranged on the first axis. In some variations, the first shape may include a first center, the second shape may include a second center, and the third shape may include a third center. Two of the first center, the second center, and the third center may be arranged on the first axis, and the third one of the first center, the second center, and the third center may be offset from the first axis.

[0026] In some variations, the first shape may include a first center, the second shape may include a second center, and the third shape may include a third center. One of the first center, the second center, and the third center may be arranged on the first axis, and the other two of the first center, the second center, and the third center may be offset from the first axis and located on opposite sides of the first axis.

[0027] In some variations, the first axis may be an inclined axis between the vertical axis and the horizontal axis of the user health graphic. In some variations, the center-to-center distance between the first shape and the second shape may be based on the difference between two of the sleep evaluation, the physical health evaluation, and the mental health evaluation.

[0028] In some variations, two or more centers of the first shape, the second shape, and the third shape may be spaced apart from each other along the first axis. In some variations, two or more centers of the first shape, the second shape, and the third shape may be offset from each other with respect to the first axis.

[0029] In some variations, the first shape may include a first center, the second shape may include a second center, and the third shape may include a third center. The first center, the second center, and the third center may be offset horizontally from each other. In some variations, the first shape, the second shape, and the third shape may partially overlap along a first axis. In some variations, the first shape may include a first center, the second shape may include a second center, and the third shape may include a third center. The first center, the second center, and the third center may be separated by an equal center-to-center distance along the first axis.

[0030] In some variations, generating a user health graphic may include generating a plurality of lines. Each line of the plurality of lines may overlap two of the first shape, the second shape, and the third shape. In some variations, the plurality of lines may include a first line between the center of the first shape and the center of the second shape and a second line between the center of the second shape and the center of the third shape. In some variations, each of the first shape, the second shape, and the third shape may include a two-dimensional shape. In some variations, each of the first color, the second color, and the third color may include a color gradient. In some variations, each of the first color, the second color, and the third color may include an opacity gradient.

[0031] In some variations, the background of the graphical user interface may include a color gradient. In some variations, the method may further include periodically updating the user health graphic. In some variations, an animation including the updated user health graphic and one or more previous user health graphics may be generated. In some variations, a notification for the input of user data using a computing device may be output. The notification may be output at a predetermined interval.

[0032] In some variations, the method may further include modifying computing device settings based on a user health graphic. In some variations, the method may further include introducing sleep services to the user based on a user health graphic.

[0033] This patent or application file includes at least one drawing created in color. Copies of this patent or patent application publication, including the color drawing, are available from the Patent Office upon request and payment of the necessary fees. [Brief explanation of the drawing]

[0034] [Figure 1] This is a block diagram of a modified sleep evaluation system.

[0035] [Figure 2] This is a block diagram of a modified computing device.

[0036] [Figure 3A] This is an illustrative flowchart of a modified version of the sleep disorder prediction process. [Figure 3B] This is an illustrative flowchart of a modified version of the first evaluation process. [Figure 3C] This is an illustrative flowchart of a modified version of the second evaluation process.

[0037] [Figure 4] This is an illustrative flowchart of a modified process for graphically representing a user's health status.

[0038] [Figure 5A] This is a set of examples and variations of graphical user interfaces for use upon waking up. [Figure 5B] This is a set of examples and variations of graphical user interfaces for use upon waking up. [Figure 5C]This is a set of examples and variations of graphical user interfaces for use upon waking up. [Figure 5D] This is a set of examples and variations of graphical user interfaces for use upon waking up.

[0039] [Figure 6A] This is a set of exemplary and modified examples of objective sleep data graphical user interfaces. [Figure 6B] This is a set of exemplary and modified examples of objective sleep data graphical user interfaces. [Figure 6C] This is a set of exemplary and modified examples of objective sleep data graphical user interfaces. [Figure 6D] This is a set of exemplary and modified examples of objective sleep data graphical user interfaces.

[0040] [Figure 7A] This is a set of exemplary and modified examples of subjective sleep data graphical user interfaces. [Figure 7B] This is a set of exemplary and modified examples of subjective sleep data graphical user interfaces. [Figure 7C] This is a set of exemplary and modified examples of subjective sleep data graphical user interfaces. [Figure 7D] This is a set of exemplary and modified examples of subjective sleep data graphical user interfaces. [Figure 7E] This is a set of exemplary and modified examples of subjective sleep data graphical user interfaces. [Figure 7F] This is a set of exemplary and modified examples of subjective sleep data graphical user interfaces. [Figure 7G] This is a set of exemplary and modified examples of subjective sleep data graphical user interfaces. [Figure 7H] This is a set of exemplary and modified examples of subjective sleep data graphical user interfaces.

[0041] [Figure 8] This is an example and modification of a graphical user interface for waking up.

[0042] [Figure 9A] This is a set of exemplary and modified examples of a graphical user interface for bedtime. [Figure 9B] This is a set of exemplary and modified examples of a graphical user interface for bedtime.

[0043] [Figure 10A] This is a set of exemplary and modified versions of a sleep insight graphical user interface. [Figure 10B] This is a set of exemplary and modified versions of a sleep insight graphical user interface. [Figure 10C] This is a set of exemplary and modified versions of a sleep insight graphical user interface. [Figure 10D] This is a set of exemplary and modified versions of a sleep insight graphical user interface. [Figure 10E] This is a set of exemplary and modified versions of a sleep insight graphical user interface. [Figure 10F] This is a set of exemplary and modified versions of a sleep insight graphical user interface. [Figure 10G] This is a set of exemplary and modified versions of a sleep insight graphical user interface.

[0044] [Figure 11A] This is a set of exemplary and modified examples of graphical user interfaces for predicting sleep disorders. [Figure 11B] This is a set of exemplary and modified examples of graphical user interfaces for predicting sleep disorders.

[0045] [Figure 12] This is an example and variation of a feasible proposed graphical user interface.

[0046] [Figure 13] This is an example and variation of a graphical user interface.

[0047] [Figure 14A] This is a set of exemplary variations of health status reports. [Figure 14B] This is a set of exemplary variations of health status reports.

[0048] [Figure 15A] This is a set of exemplary and modified user health graphics. [Figure 15B] This is a set of exemplary and modified user health graphics.

[0049] [Figure 16] This is a schematic diagram of one variation of the key to the user health graphic.

[0050] [Figure 17A] This is another set of illustrative variations of user health graphics. [Figure 17B] This is another set of illustrative variations of user health graphics. [Figure 17C] This is another set of illustrative variations of user health graphics. [Figure 17D] This is another set of illustrative variations of user health graphics. [Figure 17E] This is another set of illustrative variations of user health graphics. [Figure 17F] This is another set of illustrative variations of user health graphics. [Figure 17G] This is another set of illustrative variations of user health graphics. [Figure 17H] This is another set of illustrative variations of user health graphics. [Figure 17I]This is another set of illustrative variations of user health graphics. [Figure 17J] This is another set of illustrative variations of user health graphics. [Figure 17K] This is another set of illustrative variations of user health graphics. [Figure 17L] This is another set of illustrative variations of user health graphics. [Figure 17M] This is another set of illustrative variations of user health graphics. [Figure 17N] This is another set of illustrative variations of user health graphics. [Figure 17O] This is another set of illustrative variations of user health graphics. [Figure 17P] This is another set of illustrative variations of user health graphics. [Figure 17Q] This is another set of illustrative variations of user health graphics. [Figure 17R] This is another set of illustrative variations of user health graphics. [Figure 17S] This is another set of illustrative variations of user health graphics. [Figure 17T] This is another set of illustrative variations of user health graphics. [Figure 17U] This is another set of illustrative variations of user health graphics. [Figure 17V] This is another set of illustrative variations of user health graphics. [Figure 17W] This is another set of illustrative variations of user health graphics. [Figure 17X] This is another set of illustrative variations of user health graphics. [Figure 17Y] This is another set of illustrative variations of user health graphics. [Figure 17Z] This is another set of illustrative variations of user health graphics. [Figure 17AA]This is another set of illustrative variations of user health graphics.

[0051] [Figure 18] This is an example and variation of a user health graphic user interface.

[0052] [Figure 19] This is another example and variation of a graphical user interface for user health insights. [Modes for carrying out the invention]

[0053] This document describes systems, devices, and methods for providing users with actionable suggestions when predicting the risk of chronic conditions such as sleep disorders. These systems, devices, and methods may, for example, acquire sleep data from one or more users and / or measurement devices for analysis and / or display, generate trends, insights, and predictions, and present these, along with one or more suggestions for action the user can take in light of the trends, insights, and predictions, to the user and / or a designated group of contacts (e.g., healthcare professionals, family, friends, caregivers). This allows, for example, the user to gain a comprehensive understanding of their health and take concrete steps toward improvement. For some users, reviewing sleep data and trends can be complex and burdensome. However, the systems and methods described herein may provide intuitive insights into sleep data and trends (e.g., summaries in plain language). For example, a user's health status, represented by a set of parameters (e.g., sleep, physical, and mental), may be graphically visualized on a graphical user interface (GUI) to facilitate a comprehensive and intuitive understanding of the user's health. In some variations, actionable suggestions may be generated based on data and / or trends. These suggestions may include one or more steps that the user, computing device, and / or a referenced service (e.g., a healthcare professional) can take for the user's health.

[0054] Sleep provides a valuable opportunity to monitor and assess sleep-related and non-sleep-related health conditions. Sleep, wakefulness, and / or recumbent states may provide high signal-to-noise ratios for symptoms of many non-sleep health conditions (e.g., heart failure, COPD). For example, reducing external stimuli at bedtime and / or during sleep (e.g., reduced light, reduced activity) may facilitate user recognition of health conditions that may be present throughout the day. Therefore, sleep tracking (e.g., self-reported sleep-related symptoms, multimodal sensor measurements) can be useful for tracking overall health. Furthermore, some sleep-related disorders overlap with many non-sleep-related disorders. For example, obstructive sleep apnea and insomnia may coexist with various chronic physical and mental / behavioral health conditions. Sleep disorders in general may indicate chronic physical and / or mental health conditions (e.g., early morning awakening is a sign of depression).

[0055] Generally, the systems and methods described herein may receive data from measuring devices (e.g., sleep trackers, cardiac monitors, activity trackers) configured to measure one or more user health characteristics during and / or related to sleep. Data generated from each of these devices may be automatically uploaded at predetermined intervals to the user's computing device (e.g., smartphone, laptop, PC) and / or database (e.g., cloud-based storage). The data may be matched (e.g., integrated) with user data (e.g., subjective user-input sleep data) for trend analysis and / or the generation of insights. For example, trends may be presented to the user on any device (e.g., sleep measurement device, smartphone, laptop) at selectable levels of complexity (e.g., detailed, concise, summary, plain language, long-term, medium-term, short-term). By presenting trends to the user in an accessible and / or customizable manner, the user may gain a better understanding of how behaviors (e.g., sleep, physical health, mental health) correlate with health. Healthcare professionals may also be permitted access to the user's sleep data and trend data.

[0056] Furthermore, the system and method may generate actionable suggestions in response to one or more of the sleep data, trends, and predictions. The suggestions may include actions that the user can take themselves (e.g., reduce caffeine and / or alcohol consumption, set up a nighttime sleep routine) and / or actions performed by the computing device (e.g., present sleep disorder information, encourage healthy behaviors). The actionable suggestions may be output on the computing device as prompts that the user can select to confirm the execution of the actionable suggestions. In some variations, the suggested actions may be performed automatically without user input. In some variations, the actionable suggestions may be based on user data (e.g., subjective sleep data from user input), sleep measurement data, and findings derived from trends. In some variations, the actionable suggestions may include referrals to sleep services, which may be useful if the user is predicted to be at high risk of sleep disorders.

[0057] Generally, the systems described herein include one or more of a computing device and a sleep measurement device. The sleep measurement device may be configured to generate measurement data (e.g., sleep data, cardiac data) that can be transmitted for processing and analysis. Data analysis may include trend analysis to find relationships between user data (e.g., subjective user input data) and sleep data measured by the measurement device, and relationships using derived values ​​such as sleep quality assessments, physical health quality assessments, and mental health quality assessments. Trend analysis of different datasets related to one or more of sleep, physical health, and mental health (e.g., user data, sleep measurement data, sleep quality assessments, physical health quality assessments, and mental health quality assessments) may provide comprehensive insights into the user's overall health. The analysis results may be used to generate one or more prompts to output to the user on a graphical user interface. For example, data analysis showing that the user has a tendency (e.g., high risk) for a sleep disorder (e.g., sleep apnea) may be used to output a user prompt referring the user to a medical professional and / or to add actionable suggestions (e.g., reduce caffeine consumption) to promote desirable behaviors (e.g., better quality sleep). The user may receive prompts from the system to change computing device settings (e.g., add reminders, measure additional parameters) based on one or more of the following: data analysis, trend analysis, and / or predictions. As another example, data analysis showing that the user has a positive trend may be used to generate prompts that provide the user with positive reinforcement.

[0058] In this specification, a specific output and its corresponding data or signal are referred to as a “prompt.” A specific user input and its corresponding data or signal are referred to as a “command.” In some variations, a prompt may suggest a command to the user. For example, a prompt may output a command (e.g., an actionable suggestion, recommendation, or finding) that the user actively inputs into the device. For example, a prompt may be displayed on the user’s device (e.g., the touchscreen of a computing device) and may suggest that the user discuss the risk of anticipated sleep disturbances with a healthcare professional, along with displaying a prompt suggesting an appointment with a healthcare professional (e.g., “Would you like to consult a professional?”). The user may confirm the execution of the prompt prompting them to schedule an appointment by inputting a selection into the device (e.g., selecting the corresponding icon on the touchscreen of a computing device), thereby providing the command.

[0059] I. system A sleep assessment system may include one or more components necessary for measuring and analyzing user data using the devices described herein. Furthermore, the system may be configured to generate and output predictions and / or prompts to encourage healthy behaviors based on the data and / or analysis thereof. Figure 1 is a block diagram of one modified example of a sleep assessment system (100). The system (100) includes a computing device (110) (e.g., a user device) and a measuring device (120) (e.g., a sleep measurement device) configured to measure one or more of the user's sleep parameters, such as sleep onset time, sleep end time, sleep duration, minutes to fall asleep, number of awakenings, and sleep quality, which are described in more detail herein. The computing device (110) and the measuring device (120) may be communicated via one or more wired or wireless communication channels. For example, the computing device (110) and the measuring device (120) may be connected via one or more wired or wireless communication channels to one or more of the following: a network (130), a database (140), a server (150), and a healthcare professional (HCP) device (160). The processing and analysis may be performed on any one of the devices of the system (100), or it may be distributed across multiple devices.

[0060] Figure 2 is a block diagram of one modified example of the computing device (200). For example, the computing device (200) may correspond to the computing device (110) of the sleep assessment system (100) described with respect to Figure 1. Although a single computing device is schematically depicted, it should be understood that the systems described herein may include multiple (e.g., two, three, four, five, or more) computing devices. In some modifications, the computing device (200) (or any other device of system (100)) may be configured to assess and predict sleep disorders and may provide one or more of the following: actionable suggestions and referrals to sleep services. The computing device (200) may include the integration of full-stack techniques including one or more of the following: raw data collection from a group of devices (e.g., computing devices, measurement devices, servers, databases), data storage, feature extraction, and analysis to identify trends and predict the risk of sleep disorders.

[0061] The computing device (200) may include a processor (210), memory (220), and at least one input / output interface (240). The memory (220) may be configured to store instructions related to one or more of the following: an evaluation module (222) configured to receive user data; a data processing module (224) configured to process raw data; a visualization module (226) configured to generate visual graphics; a tendency module (228) configured to generate sleep tendencies; a prediction module (230) configured to predict the risk of sleep disorders; a recommendation module (232) configured to generate actionable suggestions; and a referral module (234) configured to introduce sleep services.

[0062] In some variations, the assessment module (222) may be configured to receive user input data in response to a set of sleep and / or health-related questions from one or more of the computing devices and sleep measurement devices described herein. The questions may be based on a set of clinically recognized sleep questionnaires, such as those described herein. In some variations, the assessment module (222) may be configured to output notifications to the user at predetermined intervals for user input of user data, including, for example, sleep assessments, physical health assessments, and mental health assessments. In some variations, the sleep assessments, physical health assessments, and mental health assessments may each correspond to a predetermined scale. For example, the predetermined scale may be a Likert scale (e.g., a 3-point scale, a 5-point scale, a 10-point scale, an n-point scale). In some variations, the assessment module (222) may be configured to receive sleep measurement data from one or more of the measurement devices and data sources (e.g., database 140, server 150, HCP device 160).

[0063] In some variations, the sleep assessment may include the quality of sleep, the physical health assessment may include the physical condition, and the mental health assessment may include the emotional state. In some variations, the quality of sleep may include the reason for arousal. In some variations, the physical condition may include the type of physical discomfort, the location of the physical discomfort, and the intensity of the physical discomfort. In some variations, the emotional state may include feelings upon waking and feelings before going to sleep.

[0064] In some variations, user data and sleep measurement data may be received at predetermined intervals. For example, the predetermined intervals may be at least once a day, at least twice a day, at least three times a day, at least four times a day, at least five times a day, between once and twice a day, between once and three times a day, between once and four times a day, between once and five times a day, between two and three times a day, between two and four times a day, or between two and five times a day. In some variations, the predetermined intervals may correspond to specific times during the day. For example, in some variations, the predetermined intervals may be wake-up and bedtime times set by the user.

[0065] In some variations, user data and sleep measurement data may be integrated into user data that includes one or more sleep parameters. For example, sleep parameters may include one or more of the following: sleep onset time, sleep end time, sleep duration, sleep stage, time to fall asleep, number of awakenings, length of awakening, snoring status, snoring duration, exercise duration, exercise end time, number of alcoholic beverages consumed, time of last alcohol consumption, number of caffeinated beverages consumed, time of last caffeine consumption, daytime sleepiness status, daytime sleepiness severity, daytime sleepiness status, daytime sleepiness severity, mean sleep oxygen saturation, mean sleep heart rate variability, minimum sleep heart rate, maximum sleep heart rate, mean sleep heart rate, list of medications taken, and user demographic data. In some variations, user demographic data may include one or more of the following: medical history and test results. In some variations, sleep parameters may further include user wake-up notes and user bedtime notes. The processes related to the assessment of user health are described in more detail in this specification (for example, in Figures 3A-3C and 5A-9B).

[0066] In some variations, the data processing module (226) may be configured to process raw data (e.g., user data, sleep measurement data) from one or more of the computing devices and sleep measurement devices described herein to generate sleep data. In some variations, the data processing may include one or more of filtering, outlier removal, missing value handling, and data integration (e.g., matching). For example, a user may match (e.g., edit, correct, update) one or more measured sleep parameters, such as wake time, number of wakes, or any of the other sleep parameters described herein, to ensure data integrity and generate sleep data. In some variations, the data processing module (226) may be configured to output processed sleep data that can be input into one or more of the trend module (228), prediction module (230), recommendation module (232), introduction module (234), and visualization module (224).

[0067] In some variations, the visualization module (226) may be configured to generate a graphical representation of the user's health (e.g., a user health graphic) based on one or more of the user data, sleep measurement data, and / or sleep data, as described herein. In some variations, generating a user health graphic may include generating a user health graphic on the graphical user interface of a computing device and generating a first shape including a first color based on sleep assessment, a second shape including a second color based on physical health assessment, and a third shape including a third color based on mental health assessment. The positions of the first, second, and third shapes may be aligned with respect to a first axis of the user health graphic based on sleep assessment, physical health assessment, and mental health assessment.

[0068] In some variations, the center-to-center distance between the first and second shapes may be based on the difference between two of the sleep assessment, physical health assessment, and mental health assessment. In some variations, the first, second, and third shapes may partially overlap. For example, the first, second, and third shapes may overlap along the first axis (or any axis). In some variations, generating the user health graphic may optionally include generating multiple lines that overlap at least two of the first, second, and third shapes. In some variations, each of the first, second, and third shapes may include a two-dimensional shape. In some variations, each of the first, second, and third colors may include a color gradient. In some variations, each of the first, second, and third colors may include an opacity gradient. In some variations, the multiple lines may include a first line between the center of the first shape and the center of the second shape, and a second line between the center of the second shape and the center of the third shape. The processes related to graphically representing user health are described in more detail herein (for example, Figures 4, 15A to 17AA).

[0069] In some variations, the trend module (228) may be configured to generate one or more sleep trends by analyzing sleep data generated by the data processing module (224). Furthermore, the trend module (228) may be configured to generate a graphical user interface that includes one or more of the sleep trends and the risk of one or more sleep disorders. The processes related to trend generation are described in more detail herein (for example, in Figures 10A to 10G).

[0070] In some variations, the prediction module (230) may be configured to predict the risk of one or more sleep disorders based on user data and sleep measurement data. In some variations, the risk of one or more sleep disorders may be based on one or more sleep tendencies. In some variations, a graphical user interface may be generated based on the sleep tendencies and one or more of the risks of one or more sleep disorders. In some variations, the one or more sleep disorders may include one or more of the following: obstructive sleep apnea, central sleep apnea, hypopnea, orthopnea, nocturnal atrial fibrillation, nocturnal hypertension, nocturnal discomfort, chronic obstructive pulmonary disease worsening in the evening, heart failure, asthma, sleep quality, and waking discomfort. The processes related to prediction generation are described in more detail herein (e.g., Figures 11A-11B). In some variations, the prediction module (230) may include one or more machine learning models, as described in more detail herein.

[0071] In some variations, the recommendation module (232) may be configured to generate one or more actionable suggestions. In some variations, the actionable suggestions may include modifying computing device settings based on one or more of user data, sleep measurement data, processed data, and user health graphics. The process related to recommendation generation is described in more detail herein (for example, in Figure 12).

[0072] In some variations, the referral module (234) may be configured to refer sleep services to users of a computing device based on their sleep tendencies and one or more risks of one or more sleep disorders. The process related to generating referrals is described in more detail herein (for example, in Figures 13 to 14B).

[0073] Measuring devices As used herein, a measuring device may refer to any device configured to measure, receive, and / or analyze one or more characteristics of a user. A measuring device may measure, for example, sleep parameters, user activity, and / or nutrition. Non-limiting examples of measuring devices include sleep trackers, wearable activity devices (e.g., pedometers or other activity trackers), hydration trackers, blood pressure monitors, heart rate monitors, ultrasound (e.g., sonar) sensors, cholesterol monitors, scales, geolocation devices (e.g., GPS, GLONASS), smartphones, refrigerators, PCs, implantable diagnostic devices, ingestible diagnostic devices, and other diagnostic devices. A measuring device may include one or more sensors configured to measure one or more user parameters. Measurable parameters of a sensor generally include, but are not limited to, sleep, hydration, cholesterol, oxygen saturation, carbon dioxide saturation, pH, respiratory rate, respiratory sounds, vocalizations (e.g., cough), ultrasound voice, accelerometers with step count and / or location data, impedance, resistance, temperature, air quality, weight, blood pressure, heart rate, heart rate variability, etc. In some variations, ultrasonic audio (e.g., sonar data) may be generated by a sonar system configured to emit ultrasonic audio signals and receive reflected ultrasonic audio signals. Furthermore, sleep parameters may include one or more of the following: sleep onset time, sleep end time, sleep duration, time to fall asleep, number of awakenings, length of awakening time, snoring status, voice, snoring duration, exercise duration, exercise start time, exercise end time, number of alcoholic beverages consumed, time of last alcohol consumption, number of caffeinated beverages consumed, time of last caffeine consumption, daytime sleepiness status, daytime sleepiness severity, mean sleep oxygen saturation, mean sleep heart rate variability, minimum sleep heart rate, maximum sleep heart rate, mean sleep heart rate, list of medications taken, and user demographic data. For example, snoring status may indicate whether the user snored during the sleep period.

[0074] Measurement data generated by the measurement device includes, but is not limited to, sleep duration, sleep quality, mood / feelings, stress, nutritional data (e.g., meal markings, carbohydrate intake, calorie intake, etc.), activity or exercise (e.g., calories burned, steps taken, degree or intensity of activity (e.g., based on heart rate level), activity time, etc.), user weight, oral or other medications, hydration, etc. The data generated by the measurement device may be transmitted to any of the devices of System (100), which may include one or more of the features, elements, and / or functions of a computing device as described herein. For example, the measurement device may include a controller including a processor and memory for performing data analysis on the measurement data generated by the measurement device, and a communication interface configured to transmit the measurement data to other devices. The measurement device may connect to the devices using any known wired or wireless connection method and communication protocol.

[0075] Furthermore, it should be understood that in some modifications, the measurement device may be a wearable device as described above, while in other modifications, the measurement device may be configured as a non-wearable device. Examples of non-wearable measurement devices for measuring one or more sleep parameters include implantable devices, external monitors such as bedside monitors or home virtual assistant devices (e.g., similar to Amazon Echo® or Google Home® devices), set-top box services (e.g., similar to Apple TV®), or other smart home appliances such as clocks or radios. In some modifications, the systems described herein may include multiple measurement devices, one or more of which may be wearable devices, and one or more of which may be configured as non-wearable devices.

[0076] Computing devices Generally, the computing devices described herein may comprise a processor (e.g., a CPU) and a controller including memory (which may include one or more computer-readable storage media). The processor may incorporate data received from memory and user input to control one or more components of the system (e.g., a measuring device (120), a database (140), a server (150), an HCP device (160)). The memory may further store instructions for causing the processor to execute modules, processes, and / or functions related to the methods described herein. As used herein, a computing device may refer to any of the computing device (110), database (140), server (150), and HCP device (160) as shown in Figure 1. In some variations, the memory and processor may be implemented on a single chip. In other variations, they may be implemented on separate chips.

[0077] A computing device (110, 200) may be configured to receive and process user input to the computing device and measurement data from one or more measuring devices. The computing device may be configured to receive, compile, store, and access data. In some variations, the computing device may be configured to access and / or receive data from different sources. The computing device may be configured to receive data directly entered by a user and / or to receive data from a separate device (e.g., a measuring device, a smartphone, a tablet, a computer) and / or from a storage medium (e.g., a flash drive, a memory card). The computing device may receive data via a network connection or via a physical connection to a device or storage medium (e.g., via a Universal Serial Bus (USB) or other type of port), as will be discussed in more detail herein. Computing devices may include any of the following types of devices: mobile phones (e.g., smartphones), tablet computers, laptop computers, desktop computers, portable media players, wearable digital devices (e.g., digital glasses, wristbands, watches, brooches, armbands, virtual / augmented reality headsets, jewelry (e.g., bracelets, necklaces, rings)), televisions, set-top boxes (e.g., cable boxes, video players, video streaming devices), and game systems.

[0078] A computing device may be configured to receive various types of data. For example, a computing device may be configured to receive personal data (e.g., gender, weight, date of birth, age, height, date of diagnosis, anniversary of device use, etc.), user data (e.g., sleep assessment, physical health assessment, and mental health assessment), nutritional data (e.g., what the user had to eat each day, number of alcoholic beverages consumed, amount of carbohydrates consumed, etc.), activity data (e.g., if the user exercised, when the user exercised, duration of exercise, type of exercise performed (e.g., cycling, swimming, running, etc.), exercise intensity level (e.g., low, medium, high), heart rate during exercise, etc.), measurement data from measuring devices, or any other information. In some variations, a computing device may be configured to create, receive, and / or store user profiles. A user profile may include any of the user-specific information described above.

[0079] While the information described above may be received by a computing device, in some variations, the computing device may be configured to calculate any of the above data from the received information using software stored on the device itself or software stored externally. In some variations, the computing device may be configured to compare subjective data, measurement data, nutritional data, activity data, or any other relevant data with historical data (e.g., user's historical trends), data preloaded onto the computing device compiled from external sources (e.g., other devices), or data received from a set of separate devices (e.g., historical data or data compiled from external sources).

[0080] The processor may be any suitable processing device configured to execute and / or run a set of instructions or code, and may include one or more data processors, image processors, graphics processing units, physical processing units, digital signal processors, and / or central processing units. The processor may be, for example, a general-purpose processor, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc. The processor may be configured to execute and / or run application processes and / or other modules, processes and / or functions, related to the system and / or associated network. The underlying device technology may be provided in various component types (e.g., metal-oxide-semiconductor field-effect transistor (MOSFET) technology such as complementary metal-oxide-semiconductor (CMOS), bipolar technology such as emitter-coupled logic (ECL), polymer technology (e.g., silicon-conjugated polymers and metal-conjugated polymer-metal structures), analog and digital mixed technology, etc.).

[0081] In some variations, the memory may include a database (not shown) and may be, for example, random access memory (RAM), a memory buffer, a hard drive, erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), read-only memory (ROM), flash memory, etc. The memory may store instructions that cause the processor to execute modules, processes, and / or functions related to a communication device, such as measurement data processing, measurement device control, communication, and / or device configuration. Some variations described herein relate to computer storage products having a non-temporary computer-readable medium (sometimes also called a non-temporary processor-readable medium) having instructions or computer code thereon for performing various computer implementation operations. The computer-readable medium (or processor-readable medium) is non-temporary in the sense that it does not itself contain a temporary propagating signal (e.g., a propagating electromagnetic wave that carries information on a transmission medium such as space or a cable). The medium and computer code (sometimes also called code or algorithm) may be designed and constructed for a particular purpose or objective.

[0082] Examples of non-temporary computer-readable media include, but are not limited to, magnetic storage media such as hard disks, floppy disks, and magnetic tapes; optical storage media such as compact discs / digital video discs (CDs / DVDs), compact disc read-only memory (CD-ROMs), and holographic devices; magneto-optical storage media such as optical discs; solid-state storage devices such as solid-state drives (SSDs) and solid-state hybrid drives (SSHDs); and hardware devices specifically configured to store and execute program code, such as carrier signal processing modules, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), read-only memory (ROM), and random access memory (RAM) devices. Other modifications described herein relate to computer program products, which may include, for example, instructions and / or computer code disclosed herein.

[0083] The systems, devices, and / or methods described herein may be implemented by software (running on hardware), hardware, or a combination thereof. Hardware modules may include, for example, general-purpose processors (or microprocessors or microcontrollers), field-programmable gate arrays (FPGAs), and / or application-specific integrated circuits (ASICs). Software modules (running on hardware) may be expressed in a variety of software languages ​​(e.g., computer code), including C, C++, Java®, Python, Ruby, Visual Basic®, and / or other object-oriented, procedural, or other programming languages ​​and development tools. Examples of computer code include, but are not limited to, files containing microcode or microinstructions, machine instructions such as those generated by a compiler, code used to generate web services, and high-level instructions executed by a computer using an interpreter. Additional examples of computer code include, but are not limited to, control signals, encryption code, and compression code.

[0084] In some variations, the computing device (110) may further include a communication interface configured to allow a user to control one or more devices of the system. The communication interface may include a network interface configured to connect the computing device to other systems (e.g., the Internet, remote servers, databases) by wired or wireless connections. In some variations, the computing device (110) may communicate with other devices via one or more wired and / or wireless networks. In some variations, the network interface may include a radio frequency receiver, transmitter, and / or optical (e.g., infrared) receiver and transmitter configured to communicate with one or more devices and / or networks. The network interface may communicate with one or more of the measuring device (120), network (130), database (140), server (150), and HCP device (160) by wired and / or wireless means.

[0085] A network interface may include RF circuits that transmit and receive RF signals. RF circuits can convert electrical signals to and from electromagnetic signals and communicate with communication networks and other communication devices via electromagnetic signals. RF circuits may include, but are not limited to, antenna systems, RF transceivers, one or more amplifiers, tuners, one or more oscillators, digital signal processors, CODEC chipsets, subscriber identification module (SIM) cards, memory, and other well-known circuits for performing these functions.

[0086] Wireless communication by either computing devices or measuring devices may use multiple communication standards, protocols, and technologies, including, but are not limited to, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), High-Speed ​​Downlink Packet Access (HSDPA), High-Speed ​​Uplink Packet Access (HSUPA), Evolution Data Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPADA), Long-Term Evolution (LTE), Near Field Communication (NFC), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth®, Wireless Fidelity (WiFi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g This includes, but is not limited to, 802.11n, Voice over Internet Protocol (VoIP), Wi-MAX, email protocols (e.g., Internet Message Access Protocol (IMAP) and / or Post Office Protocol (POP)), instant messaging (e.g., Extensible Messaging and Presence Protocol (XMPP), Extensions Leveraging Instant Messaging and Presence by Session Initiation Protocol (SIMPLE), Instant Messaging and Presence Service (IMPS)), and / or Short Message Service (SMS), and any other suitable communication protocols. In some variations, the devices described herein may communicate directly with each other without transmitting data over a network (e.g., NFC, Bluetooth®, WiFi, RFID, etc.).

[0087] The communication interface may further include a user interface configured to enable a user (e.g., a designated contact person such as a user, partner, family member, medical professional, or coach) to control the computing device. The communication interface may enable a user to interact with the computing device directly and / or remotely, and / or control the computing device. For example, the user interface of a computing device may include an input device for the user to enter commands and an output device for the user to receive output (e.g., trends, insights, or prompts on a display device).

[0088] The user interface output device may output data analysis and actionable prompts corresponding to the user, and may include one or more of a display device and an audio device. For example, the display device of the computing device may be used to facilitate video conferencing between the user and a medical professional. The display device may allow the user to view trend analysis, predictions, insights, and / or other data processed by the controller. Data analysis generated by the server (150) may be displayed by the output device (e.g., a display) of the computing device (110). Measurement data from one or more measurement devices (120) may be received via a network interface and output visually and / or audibly via one or more output devices of the computing device (110). In some variations, the output device may include a display device that includes at least one of light-emitting diodes (LEDs), liquid crystal displays (LCDs), electroluminescent displays (ELDs), plasma display panels (PDPs), thin-film transistors (TFTs), organic light-emitting diodes (OLEDs), electronic paper / electronic ink displays, laser displays, and / or holographic displays.

[0089] The audio device may output user data, measurement data, predictions, system data, alarms, and / or notifications as audio. For example, the audio device may output an audible alarm if the user has not performed an assessment within a predetermined time (e.g., within a morning assessment performed within 15 minutes of the usual wake-up time). In some variations, the audio device may include at least one of a speaker, a piezoelectric audio device, a magnetostrictive speaker, and / or a digital speaker. In some variations, the user may communicate with other users using the audio device and communication channels. For example, the user may form an audio communication channel (e.g., a VoIP call) with a remote medical professional.

[0090] In some variations, the user interface may include input devices (e.g., touchscreens) and output devices (e.g., display devices) and may be configured to receive input data from one or more of the following: measuring devices (120), networks (130), databases (140), servers (150), and HCP devices (160). For example, user control of an input device (e.g., keyboard, buttons, touchscreen) may be received by the user interface and then processed by a processor and memory so that the user interface outputs control signals to one or more measuring devices (120). Some variations of the input device may include at least one switch configured to generate control signals. For example, the input device may have a touch surface for the user to provide input (e.g., finger contact to the touch surface) corresponding to a control signal. The input device with a touch surface may be configured to detect contact and movement on the touch surface using one of several touch sensitivity techniques, including capacitive, resistive, infrared, optical imaging, dispersed signal, acoustic pulse recognition, and surface acoustic wave techniques. In a variation of an input device including at least one switch, the switch may include, for example, at least one of the following: a button (e.g., a hard key, a soft key), a touch surface, a keyboard, an analog stick (e.g., a joystick), a directional pad, a mouse, a trackball, a jog dial, a step switch, a rocker switch, a pointer device (e.g., a stylus), a motion sensor, an image sensor, and a microphone. The motion sensor may receive user motion data from an optical sensor and classify the user's gestures as a control signal. The microphone may receive audio data and recognize the user's voice as a control signal.

[0091] To provide the user with additional sensory output (e.g., force feedback), a haptic device may be incorporated into one or more input and output devices. For example, a haptic device may generate a haptic response (e.g., vibration) to confirm user input to an input device (e.g., a touch surface). As another example, haptic feedback may be used to notify the user that user input has been overwritten by a computing device.

[0092] network In some variations, the systems and methods described herein may communicate with other computing devices, for example, through one or more networks, each of which may be any type of network (e.g., wired network, wireless network). The communication may or may not be encrypted. A wireless network can refer to any kind of digital network that is not connected by any kind of cable. Examples of wireless communication in a wireless network include, but are not limited to, cellular communication, wireless communication, satellite communication, and microwave communication. However, a wireless network may be connected to a wired network to connect to the Internet, the voice and data networks of other telecommunications carriers, business networks, and personal networks. Wired networks are typically transmitted by twisted-pair copper wire, coaxial cable, and / or fiber optic cable. There are many types of wired networks, such as wide area networks (WANs), metropolitan area networks (MANs), local area networks (LANs), Internet area networks (IANs), campus area networks (CANs), global area networks (GANs) like the Internet, and virtual private networks (VPNs). In the following, "network" refers to any combination of wireless, wired, public, and private data networks, which are typically interconnected via the Internet to provide a unified networking and information access system.

[0093] Cellular communications may encompass technologies such as GSM, PCS, CDMA or GPRS, W-CDMA, EDGE or CDMA2000, LTE, WiMAX, and 5G network standards. Some wireless network deployments may combine multiple cellular networks or mix cellular, Wi-Fi, and satellite communications.

[0094] II. Method This specification also describes methods for assessing and predicting a user's risk of chronic medical conditions using the systems and devices described herein. Analysis of user data and sleep measurement data may be used to generate one or more of the following: insights into the user's health (e.g., trends or other findings related to sleep, physical health, and / or mental health), predictions of the risk of medical conditions such as sleep disorders, and one or more recommendations (e.g., actionable prompts) that may suggest the user take specific steps, such as changing behavior and / or device settings. Insights generated from sleep data may increase awareness that sleep is fundamental to overall health and encourage users to continue self-reporting their outcomes. Recommendations may, in some cases, encourage users to translate sleep data into clinical action (e.g., seeking a referral to a sleep service).

[0095] In some variations, users may download and run a mobile application and / or register via a web portal. In some variations, user registration and onboarding may be automated or semi-automated. As part of the onboarding process, user demographics (e.g., age, gender, height, weight), medical history (e.g., comorbidities, symptoms, medications), and / or any other appropriate user information may be entered into the system and added to the user profile.

[0096] During user onboarding, users may optionally complete questionnaires or other appropriate questionnaire forms (e.g., dynamic or static questionnaires such as self-screening tools). Questionnaires may be tailored to, for example, sleep-related disorders. For example, users may be asked to answer questionnaires related to their personal medical history (e.g., symptoms, diagnoses of chronic diseases, medications), family medical history, and / or current sleep-related symptoms (e.g., insomnia). In addition, questionnaires may include, additionally or alternatively, generalized questions that are not necessarily specific to a medical condition (e.g., sleep habits, sleep goals, mental health goals, physical health goals, nutrition).

[0097] In general, the methods described herein may include performing objective and subjective assessments of a patient's sleep, generating sleep insights for the user, and providing sleep recommendations for the user's consideration and action. It should be understood that any of the systems and devices described herein may be used in the methods described herein.

[0098] Figure 3A is a flowchart that roughly illustrates the sleep disorder prediction process (300). The process (300) may include a sleep evaluation process (302), a sleep insight process (304), and a sleep recommendation process (306). The sleep evaluation process (302) may include using a computing device to output user input notifications (310) for user data (e.g., user input of subjective sleep data). For example, the computing device may display prompts at predetermined intervals as reminders for user input for evaluation (e.g., wake-up time, bedtime). Notifications may be provided regularly, such as at least once a day, twice a day (e.g., morning and evening), or three times a day (e.g., morning, noon, and evening). In some variations, notifications may be output around a predetermined wake-up time (e.g., around 6 a.m.) and a predetermined bedtime (e.g., around 10 p.m.) of the user, which may be set as the default or entered by the user. This specification describes two notifications and evaluations per day, but any number of notifications and evaluations may be performed on a daily basis. For example, the afternoon notification and evaluation may be performed to receive user data corresponding to a nap. In some modifications, notifications may be configured to be output at predetermined times for predetermined durations and / or frequencies, either additionally or alternatively. For example, notifications may include audible notifications (e.g., musical sounds, beeps, chirps, etc.), visual notifications (e.g., flashing lights, graphic displays), physical notifications (e.g., haptic feedback via a vibration motor), and any combination thereof. In some modifications, if the user does not respond to the notification (e.g., perform an evaluation) within a predetermined period after receiving the notification (e.g., 1 minute, 2 minutes, 5 minutes, 10 minutes, etc.), the notification may be provided again. Alternatively, in some modifications, notifications may be omitted.

[0099] User data and / or sleep measurement data may be received by devices (320) such as a computing device (110), a measurement device (120), a database (140), a server (150), and an HCP device (160), as described herein. In some variations, user data may include sleep assessments, physical health assessments, and mental health assessments. For example, the sleep assessment may include the state of sleep quality, the physical health assessment may include physical condition, and the mental health assessment may include emotional condition. Furthermore, the state of sleep quality may include reasons for arousal. Physical condition may include type of physical discomfort, location of physical discomfort, and intensity of physical discomfort. Emotional condition may include feelings upon waking and feelings before going to sleep. In some variations, the sleep assessment, physical health assessment, and mental health assessment may each correspond to a predetermined scale. For example, the predetermined scale may be a Likert scale (e.g., a 3-point scale, a 5-point scale, a 10-point scale, an n-point scale).

[0100] In some variations, user data may be entered on a computing device's GUI, as will be explained in more detail with respect to Figures 5A to 9B. In some variations, the user data entered by the user may consist of answers to a predetermined set of questions related to sleep and / or health. The questions may be based on a set of clinically recognized sleep-related questionnaires, including, but not limited to, the Functional Outcomes of Sleep (FOSQ), the Idiopathic Hypersomnia Severity Rating Scale (IHSS), the Epworth Sleepiness Scale, the Berlin Questionnaire for Sleep Apnea, the Short Morning-Evening Questionnaire (rMEQ), the Insomnia Severity Index, the Sleep Inertia Questionnaire (SIQ), the PHQ-9, the American Thoracic Society Sleep-Related Questionnaire, etc.

[0101] In some variations, the questions asked may be generated based on a predetermined list of questions. At least some of the questions asked to the user may be static or set to be asked by default. Additionally or alternatively, at least some of the questions asked to the user may be asked at different frequencies (e.g., daily, weekly, bi-weekly, monthly). Additionally or alternatively, at least some of the questions asked to the user may be dynamic or set to be asked in response to current user data (e.g., the user's answers to static questions). For example, in response to a prediction of a high risk of sleep disorder, dynamic questions may be generated to gather further information that helps characterize the risk of sleep disorder. In other words, dynamic questions may include one or more follow-up questions customized to correlate with different risk levels. The types of dynamic questions may, additionally or alternatively, be disease-specific and user-specific.

[0102] In some variations, one or more computing devices and measurement devices may be configured to measure sleep measurement data corresponding to one or more sleep parameters of a user. The sleep measurement data may be transmitted to the computing device (or any of the devices described herein) for processing and analysis. For example, a communication channel may be established between the measurement device and the computing device. The communication channel may be a wired or wireless connection and may use any communication protocol, including but not limited to those described herein. The communication channel may be established at predetermined intervals based on one or more of the following: time (e.g., hourly, daily, weekly), device usage (e.g., after wake-up time, when the device is powered on), connection request, etc.

[0103] In some variations, sleep measurement data may include one or more sleep parameters. One or more sleep parameters may include one or more of the following: sleep onset time, sleep end time, sleep duration, time to fall asleep, number of awakenings, length of awakening time, snoring status, snoring duration, exercise duration, exercise start time, exercise end time, number of alcoholic beverages consumed, time of last alcohol consumption, number of caffeinated beverages consumed, time of last caffeinated beverage consumption, daytime sleepiness status, daytime sleepiness severity, daytime sleepiness status, daytime sleepiness severity, mean sleep oxygen saturation, mean sleep heart rate variability, minimum sleep heart rate, maximum sleep heart rate, mean sleep heart rate, list of medications taken, and user demographic data. In some variations, sleep parameters may further include user wake-up notes and user bedtime notes. In some variations, user demographic data may include one or more of the following: medical history and test results.

[0104] Additionally or alternatively, user data may be transcribed and / or parsed using an appropriate speech-to-text transcription model or service (e.g., Google Voice). Optionally, the collection and storage of user data may be carried out in a HIPAA-compliant manner. Furthermore, in some variations, some or all user data may be encrypted and / or anonymized to further ensure user privacy. For example, in these variations, anonymization may provide an additional layer of security in combination with HIPAA-compliant practices to prevent other third parties from accessing identifiable user data.

[0105] In some variations, user data and sleep measurement data may be integrated to generate sleep data. For example, sleep data may be generated based on user data and / or sleep measurement data (330). In some cases, user data and sleep measurement data may not match due to one or more errors in the user and the measurement device. For example, the measurement device may measure the wake-up time as 7:00 a.m., but the user data may reflect the wake-up time as 6:45 a.m. Therefore, a user evaluating their wake-up or bedtime may reconcile the user data and sleep measurement data by verifying the correct wake-up time between the two in order to ensure data integrity and generate sleep data. As another example, the measured bedtime (e.g., 11 p.m.) (620) in the GUI (602) may be pre-entered in the sleep measurement data, but the user may manually verify it to reflect the actual bedtime. In some variations, sleep data generation (e.g., data integration, reconciliation) may include formatting the data to allow comparison and analysis between datasets generated from different devices. In some variations, data integration can be performed using, for example, one or more of the following: computing devices, measuring devices, and servers.

[0106] In some variations, the sleep assessment (302) may be performed at a predetermined time during the day. For example, in some variations, the sleep assessment (302) may be performed twice a day, once in the morning and once in the evening. In some variations, the sleep assessment (302) may include a wake-up assessment (321) (i.e., performed at or close to the user's wake-up time (e.g., within 30-60 minutes)) and a bedtime assessment (326) (i.e., performed at or close to the user's bedtime (e.g., within approximately 30-60 minutes)). Figure 3B is a flowchart roughly illustrating the wake-up assessment process (321). The wake-up assessment process (321) may include using a computing device (322) to output user input notifications of user data (e.g., user input of subjective sleep data). Sleep measurement data may be received from one or more measurement devices (323), and user data may be received by a computing device (324). Sleep data may be generated based on user data and / or sleep measurement data (325). In some variations, the user may be prompted to enter one or more of the following in the wake-up assessment: bedtime the previous night (e.g., bedtime the night before), time to fall asleep, time to wake up, number of awakenings, length of wake time (e.g., total wake time between bedtime and wake-up time), reason for wake-up, physical condition (e.g., how the body feels on a scale of 1 to 5, physical discomfort and intensity), emotional state (e.g., how the user feels today on a scale of 1 to 5, emotion, reason for the user feeling that emotion), and wake-up notes.

[0107] Figure 3C is a flowchart that roughly illustrates the bedtime assessment process (326). The bedtime assessment process (326) may include using a computing device to output user input notifications for user data (e.g., user input of subjective sleep data) (327). Sleep-related user data may be received by the computing device (328). Sleep data may be generated based on previously received user data and / or sleep measurement data (329). In some variations, the user may be prompted in the bedtime assessment to input one or more of the following: emotional state (e.g., how the user feels today on a scale of 1 to 5, emotions, and why the user feels those emotions), the level and severity of daytime sleepiness, and factors that may affect sleep (e.g., medication, alcohol, caffeine, exercise, frequency and / or length of naps), and bedtime notes.

[0108] Returning to Figure 3A, as shown there, the sleep insight process (304) may include generating one or more trends, and in some variations, optionally, generating a predicted risk of one or more sleep disorders. For example, one or more trends (e.g., sleep trends, health trends) may be generated based on sleep data (340). As shown in Figures 10A to 10G, sleep data may be analyzed to generate one or more trends that can be output on the GUI. For example, a sleep trend may include one or more of the following: sleep quality, average sleep duration, average actual sleep duration, average time to fall asleep, average wake time, average snoring duration, physical discomfort, sleep heart rate (e.g., maximum, minimum, and / or average), sleep disorders, average sleep oxygen saturation, average heart rate variability, sonar data, etc. In some variations, the user may select a period for trend analysis (e.g., 1 week, 2 weeks, 3 weeks, 1 month, 2 months, n weeks, n months). In some variations, trend analysis may incorporate user data (e.g., user perception of physical pain levels) and sleep measurement data (e.g., average heart rate, sonar data). Any combination of user data and sleep measurement data described herein may be used. In some variations, trends may be output using one or more of the following: plain language (e.g., "Your morning headaches are becoming more frequent"), graphical visualizations, charts, plots, summaries, etc. In some variations, one or more of the sleep data, trends, and predictions may be sent to designated contacts such as sleep services (e.g., clinicians, specialists, physicians, healthcare professionals) and / or designated contacts (e.g., family, friends, caregivers). In some variations, one or more of the GUI may include one or more selectable section navigation headers (not shown) to facilitate navigation to desired trends, data, and predictions.

[0109] In some variations, the risk of sleep disorders can be predicted based on sleep data (350). For example, the prediction step may include performing an analysis that evaluates the sleep data to predict the risk of one or more sleep disorders. In some variations, the sleep data may be analyzed via machine learning and / or other artificial intelligence techniques, and the output may be created as a prediction of the user's risk of sleep disorders (e.g., predicted severity, the likelihood of a medical condition such as sleep apnea being predicted). In some variations, sleep data across multiple users may be analyzed to characterize the risk of each user to sleep disorders or other conditions. In some variations, appropriate alert conditions may be selected, such as thresholds and / or other conditions to trigger notifications related to high-risk users.

[0110] In some variations, the methods, systems, and devices for predicting the risk of sleep disorders may utilize one or more predictive models (e.g., machine learning models) such as logistic regression, decision trees, and neural networks. For example, a system may receive sleep data (e.g., as described herein) from multiple users, process the data into encoded representations, and generate one or more predictions for the data. A neural network may be trained to process sleep data using a recurrent neural network and predict the risk of one or more sleep disorders. New data collected over time may improve and refine the predictive model(s). In some variations, the dimensionality of the sleep data may be reduced (e.g., using principal component analysis) to discover correlations between features. For example, the principal components may be accepted to the extent that 90% of the data variance is explained by the principal component mapping. Due to the nature of sleep data collection (e.g., forgetting to perform assessments), some users may fail to input at least some user data for the expected assessment. To compensate for missing user data, in some variations, the missing values ​​may be set as previous input values ​​or as the baseline mean values ​​for the user or users belonging to the same demographic group as the user. In some variations, the recurrent neural network model may be trained to predict the evaluation value for the following day, and if data is missing, the recurrent neural network may fill in the missing user data with predictions.

[0111] In some variations, a recurrent neural network may take sleep data as input and output the risk (e.g., probability) of a user having one or more sleep disorders. In some variations, one or more sleep disorders may include obstructive sleep apnea, central sleep apnea, hypopnea, orthopnea, nocturnal atrial fibrillation, nocturnal hypertension, nocturnal discomfort, chronic obstructive pulmonary disease worsening in the evening, heart failure, asthma, sleep quality, and waking discomfort. In some variations, after a training period with an initial dataset, the model may be deployed to predict the probability that a user has one or more sleep disorders. In some variations, the results of predictions using the model may be used to generate one or more of the following: notifications, actionable suggestions, and introductions.

[0112] To generate appropriate training data for training a machine learning model, one or more extracted features may be derived from a suitable training dataset. Generally, a training dataset may include user data, sleep measurement data, and information related to clinical diagnoses, and such information may be extracted as features for training a machine learning model. Any suitable portion of information from a training dataset (e.g., any information from the training dataset described above) may be extracted as features for use in training a machine learning model, and / or their format may be standardized. In some variations, some of the extracted features may be combined for training or otherwise correlated with each other.

[0113] In some variations, a trained machine learning model(s) may be tested to evaluate the performance of a model(s) trained on a different sleep dataset (e.g., consumer data, sample data). The model may then be modified to improve performance. Furthermore, the model may be validated using an independent sleep dataset to confirm its effectiveness. Once deployed, the model may be configured to generate predictions in real time (e.g., via a web or mobile application on a computing device). The performance of the deployed model may be monitored over time to identify potential problems and improve accuracy. The model may be periodically retrained with different sleep data to improve performance and accuracy over time.

[0114] Returning to Figure 3A, as an option, in some variations, the method may include generating a GUI that includes one or more trends and sleep disorder risks (360). As will be discussed in more detail with respect to Figures 10A–10G, 11A, 11B, 13, 14A, and 14B, the trends and predictions may provide user insights into one or more of sleep, physical health, and mental health.

[0115] In some variations, the sleep recommendation process (306) may include providing the user with one or more actionable suggestions and clinical referrals based on sleep insights. In some variations, actionable suggestions may be provided to the user (e.g., via a computing device) based on generated trends and predicted risk of sleep disorders (370). In some variations, actionable suggestions may include modifying computing device settings based on one or more of user data, sleep measurement data, sleep data, and user health graphics. For example, the microphone of a measurement device may be configured to record the sound of the user snoring while sleeping, depending on the user's selection of a corresponding actionable suggestion (e.g., a notification to reduce alcohol consumption).

[0116] As an option, in some variations, a clinical referral may be provided to the user (e.g., via a computing device) based on trends, sleep disorder risk, and / or user health graphics (460). In some variations, the clinical referral may include sleep services (380). For example, the user may be prompted to share a sleep and health report (e.g., GUI (1300)) generated by the computing device with a physician.

[0117] Continued user engagement with the systems, devices, and methods described herein can improve the accuracy and effectiveness of the insights and predictions generated. However, some users tend to be reluctant to engage with the system for continued reasons (e.g., stress and / or poor health, or other reasons). Therefore, in some variations, the display of graphical visualizations configured to enhance user engagement and motivation may be beneficial in methods for remotely assessing and predicting sleep-related disorders. Additionally or alternatively, users who successfully complete daily assessments may be provided with comforting (e.g., relaxing, soothing) content. For example, the method may include providing comforting audio and / or visual feedback (e.g., music clips, encouragement).

[0118] Figure 4 is a flowchart that broadly illustrates the graphical user health status process (400). The process (400) may include outputting user input notifications for user data using a computing device (410). For example, the computing device may display prompts at predetermined intervals as reminders for user input for evaluation (e.g., wake-up time, bedtime). User data and / or sleep measurement data may be received by devices such as a computing device (110), a measurement device (120), a database (140), a server (150), and an HCP device (160) as described herein (420). In some variations, user data may include sleep assessments, physical health assessments, and mental health assessments as described herein. For example, sleep assessments, physical health assessments, and mental health assessments may each correspond to a predetermined scale. For example, a predetermined scale may be a Likert scale (e.g., a 3-point scale, a 5-point scale, a 10-point scale, an n-point scale).

[0119] As shown in Figures 15A, 15B, and 17A-17AA, the process (400) may include generating a user health graphic on the graphical user interface of a computing device (430). In some variations, the user health graphic may be generated by generating a first shape based on sleep assessment, a second shape based on physical health assessment, and a third shape based on mental health assessment. Additionally or alternatively, the user health graphic may be generated by generating a first color based on sleep assessment, a second color based on physical health assessment, and a third color based on mental health assessment. The first shape may be associated with a first color, the second shape with a second color, and the third shape with a third color. For example, Figure 16 is a schematic diagram of a set of shapes having colors corresponding to different sleep and health assessments. For example, a negative (e.g., low-level) sleep rating may correspond to a first sleep pattern (1610) having a first color (e.g., red), a neutral (e.g., moderate) sleep rating may correspond to a second sleep pattern (1612) having a second different color (e.g., yellow), and a positive (e.g., good) sleep rating may correspond to a third sleep pattern (1614) having a third color different from the first and second colors (e.g., green). In some variations, a sleep quality rating of 1 or 2 on a 5-point Likert scale may correspond to a negative sleep rating, a sleep quality rating of 3 may correspond to a neutral rating, and a sleep quality rating of 4 or 5 may correspond to a positive sleep rating.

[0120] As discussed herein, sleep assessment, physical health assessment, and mental health assessment correspond to user input of user data, respectively. In some variations, the assessment of sleep quality may be based at least on sleep assessment and may include additional sleep parameters, as discussed herein. Similarly, the assessment of physical health quality may be based at least on physical health assessment and may include additional physical health parameters, as discussed herein.

[0121] In some variations, a negative sleep quality rating may be less than approximately 75% (e.g., out of 100% scale), less than approximately 70%, less than approximately 65%, or less than approximately 60%, encompassing all subranges and values ​​in between. In some variations, a neutral sleep quality rating may be between approximately 60% and approximately 85%, between approximately 65% ​​and approximately 80%, between approximately 70% and approximately 80%, between approximately 75% and approximately 80%, or between approximately 60% and approximately 80%, encompassing all subranges and values ​​in between. In some variations, a positive sleep quality rating may be greater than approximately 70%, greater than approximately 75%, greater than approximately 80%, or greater than approximately 85%, encompassing all subranges and values ​​in between. Sleep quality ratings may be determined based on multiple subjective and / or objective sleep parameters, such as sleep assessment, sleep duration, and sleep heart rate variability.

[0122] In some variations, the assessment of sleep quality may be determined based on multiple subjective and / or objective sleep parameters, such as sleep assessment (e.g., user input of subjective sleep quality), sleep duration, sleep efficiency, sleep latency, number of awakenings (e.g., awakenings exceeding a predetermined threshold (e.g., 4, 5, 6, 7 minutes, or more)), wake duration (e.g., sleep-onset wake time (WASO)), sleep heart rate variability, sleep duration consistency (e.g., sleep-onset consistency), sleep duration consistency (e.g., sleep duration consistency), and check-in consistency (e.g., consistency of user data input). In some variations, the number of sleep parameters used to determine the assessment of sleep quality may change over time as data is collected. For example, sleep duration consistency may be determined using multiple sleep durations over multiple nights and may be a sleep parameter added to the determination of the assessment of sleep quality after a predetermined number of nights (e.g., 3 nights, 5 nights, 7 nights, 14 nights).

[0123] In some variations, the assessment of sleep quality may be based on a weighted combination of multiple subjective and / or objective sleep parameters. For example, the assessment of sleep quality may be based on a weighted combination of two or more of the following: sleep duration, sleep efficiency, sleep latency, number of awakenings, wake duration, and sleep assessment. In some variations, the weights of each sleep parameter may be the same or different. For example, each sleep parameter such as sleep duration, sleep efficiency, sleep latency, number of awakenings, wake duration, and sleep heart rate variability may be given the same weight, while the sleep assessment may be given a relatively higher weight. As described above, the sleep assessment may correspond to a given scale, such as a Likert scale (e.g., a 5-point scale).

[0124] Exemplary sleep quality assessments can be determined as shown in Table 1. Here, user data and sleep measurement data are weighted to correspond to predetermined sleep evaluation values ​​in order to determine the sleep quality assessment. [Table 1]

[0125] In some variations, the time range x, percentage range y, numerical range z, evaluation a, and point value range (e.g., minimum, intermediate, maximum) may be predetermined. For example, sleep duration may have sleep quality point values ​​based on threshold sleep durations (e.g., less than 7 hours, between 7 and 9 hours, greater than 9 hours) for different age groups (e.g., young adults, adults, older adults). However, any sleep parameter may be subgrouped based on age or other demographic categories. Sleep efficiency may have sleep quality point values ​​based on predetermined scales (e.g., 0% to 100%) for different age groups, which may be the same as or different from the age groups of sleep duration. Sleep latency and wake time (e.g., sleep-onset wake time (WASO)) may each have sleep quality point values ​​based on predetermined time thresholds (e.g., up to 30 minutes, between 30 and 45 minutes). The number of awakenings may have sleep quality point values ​​based on predetermined ranges (e.g., 0 to 1, 2 to 3). Sleep quality can be subjectively assessed using a Likert scale of 1 to 5, and may have corresponding point values.

[0126] In some variations, the sleep quality assessment is the sum of the sleep quality values ​​for each sleep parameter, and the sleep quality assessment can be between a minimum and maximum number of points. In some variations, the sleep quality assessment may be scaled to correspond to a sleep quality assessment with a maximum number of points of 100. For example, as shown in Figure 19, a low level of health quality assessment (e.g., a low level of sleep quality assessment) may be less than approximately 60 (e.g., less than approximately 60%), a moderate health assessment (e.g., a moderate level of physical health quality assessment) may be between approximately 60 and approximately 80 (e.g., between approximately 60% and approximately 80%), and a good health assessment (e.g., a mental health quality assessment) may be above approximately 80 (e.g., above approximately 80%).

[0127] In some variations, sleep quality assessments may be determined regularly or at predetermined intervals (e.g., regularly, twice a day, daily, twice a week, weekly, twice a month, monthly). In some variations, the sleep quality assessments may be the same or different. For example, a weekly sleep quality assessment may include the average of daily sleep quality assessments over the most recent week (e.g., the most recent 7 days, the most recent calendar week), and a monthly sleep quality assessment may include a weighted average of weekly sleep quality assessments over the most recent month (e.g., the most recent 4 weeks, starting from the 1st of the calendar month) (e.g., more recent weeks are weighted more than older weeks).

[0128] In some variations, the assessment of physical health quality may be determined based on multiple subjective and / or objective physical health parameters, such as a physical health assessment (e.g., a user input of subjective physical health), the number of physical discomforts, the number of maximum physical discomforts (e.g., the maximum discomfort entered by the user, 5 out of 5), and the mean severity of physical discomforts. In some variations, the number of physical health parameters used to determine the assessment of physical health quality may change over time as data is collected.

[0129] In some variations, the weighting of each physical health parameter may be the same or different. For example, if no discomfort is reported, the assessment of physical health quality may correspond only to the physical health assessment, or if the maximum physical discomfort is not reported, the assessment of physical health quality may correspond to the physical health assessment, the number of physical discomforts, and the mean severity of the physical discomforts. As mentioned above, the physical health parameters may correspond to a predetermined scale, such as a Likert scale (e.g., a 5-point scale).

[0130] If discomfort is not accurately entered by the user, the assessment of the quality of physical health may be inaccurate. In some variations, if physical discomfort is not entered for a specified period of time (for example, 7 days, "Be sure to track specific discomforts in your morning check-in for more personalized insights into the state of your body"), the user may receive one or more additional prompts to enter physical discomfort.

[0131] Exemplary assessments of physical health quality can be determined as shown in Table 2, where user data corresponds to predetermined physical health assessment values ​​and is equally weighted to determine the assessment of physical health quality. [Table 2]

[0132] In some variations, the evaluation a, numerical ranges x, y, z, and point value ranges (e.g., minimum, intermediate, maximum) may be predetermined. For example, physical health assessment may be subjectively evaluated on a Likert scale from 1 to 5 and may have corresponding point values. The number of physical discomforts, the maximum number of physical discomforts, and the mean severity of physical discomforts may have different physical health quality point values ​​based on predetermined numerical ranges (e.g., 0, 1, 2, 3, 4, 5, 6 or higher, 0 to 2, 3 to 5, etc.) for different age groups (e.g., young adults, adults, older adults).

[0133] In some modifications, the physical health quality assessment may be the sum of the physical health quality values ​​for each physical health parameter, such that the physical health quality assessment falls between a minimum and a maximum score. In some modifications, the physical health quality assessment may be scaled such that a given maximum possible score corresponds to a physical health quality assessment of 100.

[0134] In some variations, the assessment of physical health quality may be determined at predetermined intervals (e.g., regularly, twice a day, daily, twice a week, weekly, twice a month, monthly). In some variations, the assessments of physical health quality may be the same or different. For example, a weekly assessment of physical health quality may include the average of daily assessments of physical health quality over the most recent week (e.g., the most recent 7 days, the most recent calendar week), and a monthly assessment of physical health quality may include a weighted average of weekly assessments of physical health quality over the most recent month (e.g., the most recent 4 weeks, starting from the 1st of the calendar month) (e.g., more recent weeks are weighted more than older weeks).

[0135] In some variations, one or more of the sleep quality assessment, physical health quality assessment, and mental health quality assessment may be output to the user independently of or in combination with the user health graphics described herein. Additionally or alternatively, one or more of the sleep quality assessment, physical health quality assessment, and mental health quality assessment may be used to generate one or more of the shapes and colors of the user health graphics.

[0136] In some variations, a negative physical health (e.g., physical) assessment may correspond to a first physical health shape (1620) having a first color (e.g., red), a neutral physical health assessment may correspond to a second physical health shape (1622) having a second different color (e.g., yellow), and a positive sleep assessment may correspond to a third physical health shape (1624) having a third color different from the first and second colors (e.g., green). In some variations, a physical health assessment of 1 or 2 on a 5-point Likert scale may correspond to a negative physical health assessment, a physical health assessment of 3 may correspond to a neutral assessment, and a physical health assessment of 4 or 5 may correspond to a positive physical health assessment.

[0137] In some variations, a negative mental health (e.g., heart) rating may correspond to a first mental health shape (1630) having a first color (e.g., red), a neutral mental health rating may correspond to a second mental health shape (1632) having a second different color (e.g., yellow), and a positive sleep rating may correspond to a third mental health shape (1634) having a third color different from the first and second colors (e.g., green). In some variations, a mental health rating of 1 or 2 on a 5-point Likert scale may correspond to a negative mental health rating, a mental health rating of 3 may correspond to a neutral rating, and a mental health rating of 4 or 5 may correspond to a positive mental health rating. In some variations, a given scale may be an n-point scale.

[0138] In the above explanation, the same color was used to indicate the same level (e.g., negative, neutral, positive) across the assessments of sleep, physical health, and mental health. However, please understand that this is not always the case, and each of the assessments of sleep, physical health, and mental health may have its own unique color scheme to indicate different levels.

[0139] In some variations, the arrangement of the first, second, and third shapes of the user health graphics on the graphical user interface may be based on their respective sleep and health assessments. For example, Figures 17A to 17AA show different colors and spatial arrangements of the first (1730), second (1740), and third (1750) shapes.

[0140] In some variations, each of the first shape (1730), the second shape (1740), and the third shape (1750) includes a two-dimensional shape. For example, the shapes can be any two-dimensional shapes such as circles, ellipses, squares, polygons, or combinations thereof. However, in some variations, one or more of the shapes may include a three-dimensional shape. For example, the three-dimensional shapes may include spheres, cylinders, cones, cubes, polyhedra, tori, pyramids, prisms, or combinations thereof.

[0141] For example, the positions of the first shape, the second shape, and the third shape may be positioned relative to a first axis of the user health graphic based on sleep assessment, physical health assessment, and mental health assessment. In some variations, the first axis may be the vertical axis (e.g., Y-axis) of the user health graphic. In some variations, the vertical axis of the user health graphic may be the vertical axis of the display, while in other variations it may be the horizontal axis (e.g., X-axis) of the display. The first shape may include a first center, the second shape may include a second center, and the third shape may include a third center. In some variations, the center-to-center distance between the first shape and the second shape may be based on the difference between two of the sleep assessment, physical health assessment, and mental health assessment. For example, in Figure 17H, the center-to-center distance between the first shape (1730) and the second shape (1740) is based on the difference between the assessment of the first shape and the assessment of the second shape. In other words, the longer the distance between the centers of the first shape (1730) and the second shape (1740), the greater the difference between the positive evaluation of the first shape (1730) (e.g., green) and the negative evaluation of the second shape (1740) (e.g., red) is reflected, compared to the distance and evaluation between the first shape (1730) and the third shape (1750).

[0142] In the modified example shown in Figure 17A, the first, second, and third centers are located on the first axis. Also, as shown in Figure 17A, two or more centers of the first, second, and third shapes are spaced apart from each other along the first axis. Furthermore, the first shape (1730), the second shape (1740), and the third shape (1750) partially overlap along the first axis. In Figure 17A, the first, second, and third centers are spaced equal apart along the first axis. Furthermore, in the modified example shown in Figure 17A, all three shapes have the same color, which may indicate that all three evaluations are within the same range (e.g., all are positive). Furthermore, the alignment of the three shapes vertically and in a straight line indicates that the three evaluations are within the same range.

[0143] In the modified examples shown in Figures 17B and 17C, the first and second centers of the first and second shapes (1730, 1740) are located on the first axis, while the third center of the third shape (1750) is offset from the first axis (for example, laterally offset from the first axis). For example, the first shape (1730) and the second shape (1740), which are green and have a corresponding positive evaluation, are located on the first axis, while the third shape (1750) in Figure 17B, which is yellow and has a neutral evaluation, is laterally offset from the first axis. Similarly, the third shape (1750) in Figure 17C, which is red and has a negative evaluation, is laterally offset from the first axis. The difference in the evaluation of the shapes can determine the degree of offset between the shapes. For example, the distance between the centers of the second shape (1740) and the third shape (1750) is greater in Figure 17C than in Figure 17B.

[0144] In some modifications, one of the first, second, and third centers is positioned on the first axis, while the other two of the first, second, and third centers are offset from the first axis and located on the same side or opposite side of the first axis. In the modifications shown in Figures 17F, 17N, 17V, and 17AA, the first axis is the inclination axis between the vertical axis and the horizontal axis of the user health graphic. For example, the inclination axis is shown as approximately 45 degrees, but it may be any angle between the vertical and horizontal axes (e.g., approximately 15 degrees, approximately 30 degrees, approximately 45 degrees, approximately 60 degrees, approximately 75 degrees).

[0145] As shown in Figures 17F, 17H, 17L, 17P, 17T, 17V, and 17AA, two or more centers of the first, second, and third shapes are offset from each other with respect to the first axis, and / or the centers of the first, second, and third shapes are offset from each other laterally. For example, each of the first, second, and third shapes may have a different color / evaluation.

[0146] Figures 18 and 19 are a set of exemplary variations of a health status graphic and sleep insight graphical user interface, configured to facilitate an intuitive and rapid understanding of the user's overall health (e.g., sleep, physical health, and mental health) through a combination of graphic and text formats.

[0147] Figure 18 shows the 62nd GUI (1800) which includes a health status graphic (1810) and insights (1820) that provide a text summary corresponding to the health status graphic (1810) for a given period. In some variations, the health status graphic (1810) may include an overlay of text (1830) indicating parameters (e.g., sleep, physical health, mental health) and ratings (e.g., low, moderate, good).

[0148] Figure 19 shows the 63rd GUI (1900) which includes a health status graphic (1910) and insights (1920) which provide a plain-language text summary corresponding to the health status graphic (1910) for a given period (e.g., months). In some variations, the health status graphic (1910) may include an overlay of text (1940) indicating parameters (e.g., sleep, physical health, mental health) and ratings (e.g., low, moderate, good). In some variations, the GUI 1900 may include a set of rating thresholds (1930) corresponding to the ratings of health quality (e.g., sleep quality rating, physical health quality rating, mental health quality rating).

[0149] Optionally, in some variations, multiple user health graphic animations can be generated (440). For example, an updated user health graphic and one or more previous user health graphics corresponding to a given period (e.g., one week, two weeks, three weeks, one month) may be displayed sequentially as an animation to help the user determine their health status over time.

[0150] Optionally, in some variations, process (400) may include providing the user with one or more actionable suggestions (450) and clinical referrals (460) based on the user health graphic. In some variations, the actionable suggestions may include modifying computing device settings based on the user health graphic. [Examples]

[0151] Figures 5A and 5B show exemplary graphical user interface (GUI) sets for waking up. The first GUI (500) in Figure 5A and the second GUI (502) in Figure 5B may include respective prompts (510, 512) configured to prepare and / or instruct the user to complete a wake-up assessment (e.g., morning check-in) upon waking up on a new day (e.g., after sleep) or immediately after waking up (e.g., within 15 minutes, 30 minutes, 60 minutes, 15-30 minutes, 15-60 minutes). User data collected during this time may include sleep data, physical health data, and mental health data, which can be used to predict the risk of one or more sleep-related disorders. Sleep measurement data may also be collected at this time, if available. A progress icon (520, 522) may be selected to proceed to the wake-up assessment GUI (e.g., Figures 6A-8). Upon completion of the evaluation, the third GUI (504) in Figure 5C and the fourth GUI (506) in Figure 5D may include prompts (514, 516) respectively, configured to indicate the completion of the evaluation and / or to encourage the completion of further evaluations. A progress icon (524, 526) may be selected to proceed to, for example, the insights GUI.

[0152] Figures 6A–6D, 7A–7H, and 8 are exemplary sets of wake-up assessment GUIs configured to track wake-up status based on subjective user reports and objective sleep measurement data to predict the risk of one or more sleep-related disorders. For example, the fifth GUI (600) in Figure 6A and the sixth GUI (602) in Figure 6B may include respective prompts (610, 612) for the user's sleep data. In some variations, the measurement data (630) received from the measurement device may include the user's sleep data, including bedtime (620), time to fall asleep (622), wake-up time (624), and total sleep time (626). These and other measured sleep parameters may be displayed and pre-entered in the fifth GUI (600) and the sixth GUI (602). However, the user may select and modify any of these sleep parameters (e.g., 620, 622, 624, etc.) to review and improve the accuracy of the sleep data. In this way, subjective user-reported sleep results can be entered in addition to measured sleep data from the measurement device. In some variations, the GUI may include a sleep data graphic (640) based on the sleep data. The user may select a progress icon (650) to proceed to the GUI for the next wake-up assessment, for example. Figure 6C shows a seventh GUI (604) including an awakening count prompt (660), an awakening time prompt (662), an awakening reason prompt (664), and a progress icon (650). The user may select and / or enter a predetermined awakening reason. In some variations, the user may select a customization icon (not shown) configured to receive user input for adding one or more user-defined awakening reasons. Figure 6D shows an eighth GUI (606) including a snoring status prompt (666) (e.g., Don't know, No, Yes) and a progress icon (650).

[0153] Figure 7A shows a ninth GUI (700) including a wake-up assessment physical condition prompt (720) and a progress icon (790). For example, the user may select their physical condition based on a five-point scale (e.g., terrible, bad, OK, good, great). Figure 7B shows a tenth GUI (702) including a body discomfort area prompt (730) and a progress icon (790). For example, the user may select one or more predetermined body parts that indicate physical discomfort (e.g., head, neck, chest, abdomen, arms and hands, legs and feet). Figure 7C shows an eleventh GUI (704) including a body discomfort type prompt (740) and a progress icon (790). For example, the user may select one or more predetermined types of physical discomfort associated with a selected body part (e.g., head) (e.g., headache, tension, dizziness, heat, sinusitis, eye pain, runny nose, double vision). In some variations, the user may select a customizable icon (not shown) configured to receive user input for adding one or more user-defined physical symptoms and / or emotions.

[0154] Figure 7D shows a twelfth GUI (706) including a physical discomfort intensity prompt (750) and a progress icon (790). For example, the user may select an intensity level (e.g., mild, tolerable, moderate, severe, and severe) corresponding to a selected physical discomfort (e.g., dizziness) and a selected body part (e.g., head). Figure 7E shows a thirteenth GUI (708) including a body discomfort area prompt (760) and a progress icon (790). GUI (708) can function as a confirmation screen that the selected body discomfort area, type, and intensity are accurately reflected. Figure 7F shows a fourteenth GUI (710) including a body discomfort area prompt (760) and a progress icon (790) containing previously entered user data. The user may add physical discomforts as needed. Figures 7G and 7H show a 15th GUI (712) and a 16th GUI (714), respectively, which include a physical discomfort type prompt (780, 782) and a progress icon (790). The GUIs (712, 714) may be configured to receive one or more of the following: a location of physical discomfort (e.g., back pain), a type of physical discomfort (e.g., eye pain), and a physical discomfort intensity (e.g., 2, 4).

[0155] Figure 8 shows a 17th GUI (800) that includes a wake-up memo prompt (810) and a progress icon (820) for optional user input. In some variations, the wake-up memo may include the user's thoughts upon waking that are not otherwise captured by sleep parameters. These thoughts may be quickly forgotten by the user if not entered upon waking. In some variations, one or more of the check-in GUIs may include therapy and / or medication prompts for the user to enter one or more therapies performed and / or completed by the user, and one or more medications taken by the user.

[0156] Figures 9A and 9B show the 18th GUI (900) and the 19th GUI (902), respectively, which include evaluation prompts (910, 912) that may indicate an evaluation status (e.g., complete, evaluation time, next evaluation time). In some variations, the user may select an edit icon (not shown) configured to receive user input to edit one or more check-ins. In some variations, one or more GUIs (e.g., Figures 5A–5D, 9A, 9B) may include a count of consecutive check-ins performed by the user, or other representative elements (e.g., icons), to prompt consistent input for check-ins.

[0157] Figures 10A–10G are a set of exemplary variations of a sleep insights GUI configured to provide user sleep and health trends, analysis, and insights based on sleep data. The sleep insights GUI can provide useful context and meaning for sleep data to users and healthcare professionals. For example, GUI(1000) 20 in Figure 10A may include prompts(1012) for a given time period(1010)(e.g., daily, weekly, bi-weekly, monthly, bi-monthly, yearly) corresponding to insights(e.g., trends, patterns, data analysis) in plain language. Using plain language facilitates quick, easy, and usable information transmission. Search icons(1020, 1022)(e.g., search fields) are configured to receive user input relevant to the professional. For example, the user may enter a query via text or voice by selecting the search icon(1020) in response to a prompt(1012). In some variations, the user may enter input as text or voice. The dashboard may be configured to provide one or more sleep trends including physical (e.g., bodily) discomfort (1030) and display a summary of a series of body discomfort locations (1032), body discomfort types (e.g., headache, eye pain), and body discomfort intensity (e.g., headache intensity levels 1, 1, 2, 3, 3, 4, and 5 for the week of January 15 to January 21).

[0158] Figure 10B shows a 21st GUI (1002) that includes a sleep trend dashboard, including physical discomfort (1030), mean sleep oxygen saturation level (1034), and mean sleep heart rate (1036) for a given period (e.g., January 15 to January 21). In some variations, the dashboard view may include data for a selectable given period (e.g., the past week, the past month, the past three months, the past six months, the past year). Figure 10C shows a 22nd GUI (1004) that includes plain language prompts (1012) summarizing the user's health status based on sleep data. A dashboard may be provided that includes one or more sleep trends, including average sleep quality (1038). One or more sleep trends may be shown using graphs, plots, and other visualizations. Figure 10D shows a 23rd GUI (1006) that includes a dashboard for one or more sleep tendencies, such as the location of physical discomfort (1040) and average time in bed (1042). Figure 10E shows a 24th GUI (1008) that includes a dashboard for one or more sleep tendencies, such as average snoring time (1044). Figure 10F shows a 25th GUI (1010) that includes a dashboard for one or more sleep tendencies, such as user input notes (1046) and average heart rate (1048). As described herein, sleep tendencies may include sleep data from subjective user data and objective sleep measurement data.

[0159] Figure 10G shows a 26th GUI (1011) that includes a prompt (1012) corresponding to a summary of trends (e.g., major sleep disruptors). In some variations, the plain language prompt may be followed by more detailed sleep trend data, such as a dashboard containing snoring data (1050) corresponding to an audio waveform recording that can be heard using a play icon (1052).

[0160] In some variations, any of the sleep insight graphical user interfaces described herein (e.g., Figures 10A to 10G) may include a default summary view and selectable and actionable prompts that provide one or more actionable suggestions and additional information (e.g., a detailed view) regarding a prompt (e.g., prompt 1012).

[0161] Figures 11A and 11B are a set of exemplary variations of a sleep disorder risk prediction GUI configured to facilitate the early identification of sleep-related disorders, improve awareness and education about sleep-related disorders, and provide recommendations for action.

[0162] Figures 11A and 11B show the 27th GUI (1100) and the 28th GUI (1102), respectively, which include mean sleep oxygen saturation levels (1132) and sleep disorder risk prediction prompts (1112), actionable suggestion prompts for additional sleep disorder information (1120), and mean heart rate variability (1134). In some variations, the actionable suggestion prompt (1120) may encourage the user to take proactive clinical action. In Figure 11B, the actionable suggestion prompt (1120) may include a link (1140) for installing a diagnostic application (e.g., an obstructive sleep apnea diagnostic application) on the computing device.

[0163] Figure 12 shows the 29th GUI (1200) which includes actionable suggestion prompts (1230) and a list of selectable actionable suggestions such as suggestions for earlier bedtime, snoring recording, oxygen saturation information, referral to clinical services (e.g., a specialist), and download of an HCP report (1240). In some variations, prompts (1230) may be generated based on sleep data (e.g., personalized for each user) to promote improved sleep and health. In some variations, actionable suggestions may encourage healthy behaviors (e.g., earlier bedtime, consultation with a specialist).

[0164] Figure 13 shows a 30th GUI (1300) for facilitating referral to sleep services and / or other clinical actions, including a prompt (1340) for sharing sleep and health reports for a specified period (1310) (e.g., weekly, monthly) with a healthcare professional. In some variations, a mobile application or web portal, as described herein, may generate the sleep and health reports. Figures 14A and 14B show a 31st GUI (1402) and a 32nd GUI (1404), respectively, which include a sleep and health report containing one or more sleep trends, sleep data, and user notes (e.g., wake-up notes, bedtime notes). In some variations, one or more of the content and format of the sleep and health report may be customized by a healthcare professional. The sleep and health report may include any of the user data and sleep measurement data described herein.

[0165] In some variations, the ultrasonic audio signal may be accessible (e.g., output, graph display) from one or more of the following: the sleep insight graphical user interface (e.g., Figures 10A-10G), the sleep disorder prediction graphical user interface (e.g., Figures 11A-11B), the introduction graphical user interface (e.g., Figure 13), and the health status report (e.g., Figures 14A-14B, sleep and health report).

[0166] Figures 15A and 15B are a set of exemplary variations of a health status graphical user interface. Figures 15A and 15B show the 33rd GUI (1500) and the 34th GUI (1502), respectively, which include a first shape (1510), a second shape (1512), and a third shape (1514). The first line (1520) connects the center of the first shape (1510) to the center of the second shape (1512), and the second line (1522) connects the center of the second shape (1512) to the center of the third shape (1514). In some variations, the health status graphic may be displayed on the introductory GUI and / or in combination with sleep tendency and sleep disorder risk predictions.

[0167] Figures 17A to 17AA are a set of exemplary variations of a health status graphic, configured to facilitate an intuitive and rapid understanding of a user's overall health (e.g., sleep, physical health, mental health). The health status graphic may include a first shape (1730) with a first color and a first center, a second shape (1740) with a second color and a second center, and a third shape (1750) with a third color and a third center. Optionally, a first line (1735) may connect the center of the first shape (1730) to the center of the second shape (1740), and a second line (1745) may connect the center of the second shape (1740) to the center of the third shape (1750). The first axis corresponds to the vertical axis of the health status graphic, and the second axis corresponds to the horizontal axis of the health status graphic. As discussed herein, colors may correspond to evaluations on a scale (e.g., negative, neutral, positive, 1, 2, 3, 4, 5). For example, green may be associated with positive evaluations of parameters (e.g., sleep, physical health, mental health), yellow with neutral evaluations, and red with negative evaluations. Thus, health evaluations may be based on the relative positions of colors and / or shapes. For example, health evaluations may be based on colors, as well as the relative positions of shapes with respect to each other and / or a given axis.

[0168] In some variations, as shown in Figures 17A to 17AA, each of the first color, second color, and third color may include a color gradient. However, in other variations, one or more of the above colors may not have a color gradient. In some variations, as shown in Figures 17A to 17AA, each of the first shape (1730), second shape (1740), and third shape (1750) may include an opacity gradient. However, in other variations, one or more of the shapes may not have an opacity gradient.

[0169] In some variations, the background of the graphical user interface may include a color gradient, as shown in Figures 17A to 17AA. However, the background may also include a non-color gradient background (e.g., a default background color).

[0170] Figure 17A shows the 35th GUI(1700), where the first shape (1730) corresponds to the positive color, the second shape (1740) corresponds to the positive color, and the third shape (1750) corresponds to the positive color. Each of the first shape (1730), the second shape (1740), and the third shape (1750) is positioned on the first axis (for example, the vertical axis of GUI(1700) passing through the midpoint of GUI(1700), which is not shown). Similarly, the centers of each of the first shape (1730), the second shape (1740), and the third shape (1750) are positioned on the first axis. Furthermore, the first line (1735) and the second line (1745) are also positioned on the first axis. The first line (1735) and the second line (1745) each overlap two of the first shape, the second shape, and the third shape. More specifically, the first line (1735) overlaps the first shape (1730) and the second shape (1740), and the second line (1745) overlaps the second shape (1740) and the third shape (1750). In some variations, two or more centers of the first, second, and third shapes may be spaced apart from each other along the first axis. For example, the first shape (1730), the second shape (1740), and the third shape (1750) may partially overlap along the first axis, but the centers of the first, second, and third shapes may be located at different points along the first axis. In some variations, the perimeter of one shape may intersect the center of another shape, or the other two of the three shapes. In particular, referring to the modified example shown in Figure 17A, the outer circumference (e.g., circle) of the second shape (1740) may intersect with the center of the first shape (1730) and the center of the second shape (1750). In other modified examples, such as the one shown in Figure 17B, the outer circumference of the second shape (1740) may intersect with the center of the first shape (1730), but may not intersect with the center of the third shape (1750). In yet another modified example, the outer circumference of the second shape (1740) may not intersect with the center of either the first or third shape. In Figure 17A, the first center, the second center, and the third center are separated by an equal intercenter distance along the first axis.Although not shown, in some variations, all three shapes may overlap each other (whether or not they are aligned with the axis).

[0171] Figure 17B shows the 36th GUI (1701), where the first shape (1730) corresponds to the positive color, the second shape (1740) corresponds to the positive color, and the third shape (1750) corresponds to the neutral color. In Figure 17B, the first center of the first shape (1730) and the second center of the second shape (1740) are located on the first axis, while the third center of the third shape (1750) is offset from the first axis. In other words, the first and second shapes (1730, 1750) are collinear but offset from the third shape (1750). In this way, the first line (1735) and the second line (1745) form a non-zero angle with respect to the first axis. Figure 17B demonstrates that the overlap of shapes is not limited. For example, the second shape (1740) and the third shape (1750) may partially overlap along an angle offset from the first axis. Figures 17D, 17F, 17H, 17J, and 17K show other examples of partial overlap of shapes.

[0172] In some variations, the center-to-center distance between the first and second shapes may be based on the difference between two of the following: sleep assessment, physical health assessment, and mental health assessment. Furthermore, the color and position of the third shape (1750) relative to the other shapes indicate to the user the difference in the respective assessments of the third shape (1750) relative to the first shape (1730) and the second shape (1740). For example, the first shape (1730) and the second shape (1740) correspond to positive assessments (e.g., positive sleep assessment, positive physical health assessment), while the third shape (1750) corresponds to a neutral assessment (e.g., a neutral mental health assessment).

[0173] Figure 17C shows the 37th GUI (1702), in which the first shape (1730) and the second shape (1740) are colored green, corresponding to positive evaluations, and the third shape (1750) is colored red, corresponding to negative evaluations. In Figure 17C, the first center of the first shape (1730) and the second center of the second shape (1740) are positioned on the first axis, and the third center of the third shape (1750) is offset from the first axis. The first line (1735) and the second line (1745) form an angle between the first axis and the horizontal axis, as described herein. The color and position of the third shape (1750) indicate to the user the difference in evaluation of the third shape (1750) compared to the first shape (1730) and the second shape (1740). The third shape (1750) in Figure 17C has a higher evaluation difference than the third shape (1750) in Figure 17B.

[0174] Figure 17D shows the 38th GUI (1703), in which the first shape (1730) corresponds to the positive color, the second shape (1740) corresponds to the neutral color, and the third shape (1750) corresponds to the positive color. In Figure 17D, the first center of the first shape (1730) and the third center of the third shape (1750) are located on the first axis, while the second center of the second shape (1740) is offset from the first axis.

[0175] Figure 17E shows the 39th GUI (1704), in which the first shape (1730) corresponds to an affirmative color, the second shape (1740) corresponds to a neutral color, and the third shape (1750) corresponds to a neutral color. In Figure 17E, the first center of the first shape (1730) is located on the first axis, and the second center of the second shape (1740) and the third center of the third shape (1750) are offset from the first axis. Figure 17E shows a modified example in which two or more centers of the first, second, and third shapes are offset from each other with respect to the first axis.

[0176] Figure 17F shows the 40th GUI (1705), in which the first shape (1730) corresponds to the positive color, the second shape (1740) corresponds to the neutral color, and the third shape (1750) corresponds to the negative color. In some variations, the first, second, and third centers are offset laterally from each other. The first line (1735) and the second line (1745) form angles (e.g., approximately 15 degrees, 30 degrees, 45 degrees, and 60 degrees) between the vertical axis and the horizontal axis of the user health graphic.

[0177] Figure 17G shows the 41st GUI (1706), in which the first shape (1730) corresponds to an affirmative color, the second shape (1740) corresponds to a negative color, and the third shape (1750) corresponds to an affirmative color. In Figure 17G, the first center of the first shape (1730) and the third center of the third shape (1750) are positioned on the first axis, while the second center of the second shape (1740) is offset from the first axis. The color and position of the second shape (1740) show the user the difference in the evaluation of the second shape (1740) relative to the first shape (1730) and the third shape (1750). The colors and positions of the first shape (1730) and the third shape (1750) indicate the agreement in their evaluations.

[0178] Figure 17H ​​shows the 42nd GUI (1707), where the first shape (1730) corresponds to the positive color, the second shape (1740) corresponds to the negative color, and the third shape (1750) corresponds to the neutral color. The first center of the first shape (1730) is located on the first axis, while the second center of the second shape (1740) and the third center of the third shape (1750) are offset from the first axis. The color and position of each shape show the user the difference in evaluations for each shape that does not match any of the evaluations.

[0179] Figure 17I shows the 43rd GUI (1708), where the first shape (1730) corresponds to an affirmative color, the second shape (1740) corresponds to a negative color, and the third shape (1750) corresponds to a negative color. The first center of the first shape (1730) is located on the first axis, while the second center of the second shape (1740) and the third center of the third shape (1750) are offset from the first axis. The color and position of the first shape (1730) show the user the difference in evaluations of the second shape (1740) and the third shape (1750) relative to the first shape (1730). The colors and positions of the second shape (1740) and the third shape (1750) show the agreement in their evaluations.

[0180] Figure 17J shows the 44th GUI (1709), where the first shape (1730) corresponds to a neutral color, the second shape (1740) corresponds to an affirmative color, and the third shape (1750) corresponds to an affirmative color. The first center of the first shape (1730) is offset from the first axis, while the second center of the first shape (1740) and the third center of the third shape (1750) are positioned on the first axis. The color and position of the first shape (1730) show the user the difference in the evaluation of the first shape (1730) relative to the second shape (1740) and the third shape (1750). The colors and positions of the second shape (1740) and the third shape (1750) show the agreement in their evaluations.

[0181] Figure 17K shows the 45th GUI (1710), where the first shape (1730) corresponds to a neutral color, the second shape (1740) corresponds to an affirmative color, and the third shape (1750) corresponds to a neutral color. In Figure 17K, the second center of the second shape (1740) lies on the first axis, while the first center of the first shape (1730) and the third center of the third shape (1750) are offset from the first axis. The color and position of the second shape (1740) show the user the difference in their respective evaluations of the first shape (1740) and the third shape (1750). The colors and positions of the first shape (1730) and the third shape (1750) indicate agreement in their evaluations (e.g., a neutral evaluation).

[0182] Figure 17L shows the 46th GUI (1711), in which the first shape (1730) corresponds to a neutral color, the second shape (1740) corresponds to an affirmative color, and the third shape (1750) corresponds to a negative color. In Figure 17L, the second center of the second shape (1740) is located on the first axis, and the first center of the first shape (1730) and the third center of the third shape (1750) are offset from the first axis.

[0183] Figure 17M shows the 47th GUI (1712), where the first shape (1730) corresponds to a neutral color, the second shape (1740) corresponds to a neutral color, and the third shape (1750) corresponds to an affirmative color. In Figure 17M, the third center of the third shape (1750) lies on the first axis, while the first center of the first shape (1730) and the second center of the second shape (1740) are offset from the first axis. The color and position of the third shape (1750) show the user the difference in their respective evaluations compared to the first shape (1730) and the second shape (1740). The colors and positions of the first shape (1730) and the second shape (1740) indicate agreement in their evaluations (e.g., a neutral evaluation).

[0184] Figure 17N shows the 48th GUI (1713), where a first shape (1730) corresponds to a neutral color, a second shape (1740) corresponds to a neutral color, and a third shape (1750) corresponds to a neutral color. When each shape has a neutral color, the shapes may be aligned at an angle (e.g., about 15 degrees, about 30 degrees, about 45 degrees, about 60 degrees, about 75 degrees) with respect to the vertical axis of the GUI to indicate the user's neutral health state. For example, a user's neutral health state may have a positive angle (Figure 17N), while a user's negative health state may have a negative angle (Figure 17AA). In some variations, the first axis may be a sloping axis between the vertical axis and the horizontal axis of the user health graphic.

[0185] Figure 17O shows the 49th GUI (1714), where the first shape (1730) corresponds to a neutral color, the second shape (1740) corresponds to a neutral color, and the third shape (1750) corresponds to a negative color.

[0186] Figure 17P shows the 50th GUI (1715), in which the first shape (1730) corresponds to a neutral color, the second shape (1740) corresponds to a negative color, and the third shape (1750) corresponds to a positive color. In Figure 17P, the third center of the third shape (1750) is located on the first axis, while the first center of the first shape (1730) and the third center of the third shape (1750) are offset from the first axis.

[0187] Figure 17Q shows the 51st GUI (1716), where the first shape (1730) corresponds to a neutral color, the second shape (1740) corresponds to a negative color, and the third shape (1750) corresponds to a neutral color.

[0188] Figure 17R shows the 52nd GUI (1717), where the first shape (1730) corresponds to a neutral color, the second shape (1740) corresponds to a negative color, and the third shape (1750) corresponds to a negative color. The color and position of the first shape (1730) show the user the difference in the evaluation of the first shape (1730) relative to the second shape (1740) and the third shape (1750). The colors and positions of the second shape (1740) and the third shape (1750) indicate agreement in their evaluations (e.g., a negative evaluation).

[0189] Figure 17S shows the 53rd GUI (1718), where the first shape (1730) corresponds to a negative color, the second shape (1740) corresponds to a positive color, and the third shape (1750) corresponds to a positive color. In Figure 17S, the second center of the second shape (1740) and the third center of the third shape (1750) are positioned on the first axis, while the first center of the first shape (1730) is offset from the first axis. The color and position of the first shape (1730) show the user the difference in the respective evaluations of the first shape (1730) relative to the second shape (1740) and the third shape (1750). The colors and positions of the second shape (1740) and the third shape (1750) indicate agreement in their evaluations (e.g., positive evaluation).

[0190] Figure 17T shows the 54th GUI (1719), in which the first shape (1730) corresponds to the negative color, the second shape (1740) corresponds to the positive color, and the third shape (1750) corresponds to the neutral color. In Figure 17T, the second center of the second shape (1740) is located on the first axis, while the first center of the first shape (1730) and the third center of the third shape (1750) are offset from the first axis on the opposite side of the axis.

[0191] Figure 17U shows the 55th GUI (1720), where the first shape (1730) corresponds to the negative color, the second shape (1740) corresponds to the positive color, and the third shape (1750) corresponds to the negative color. In Figure 17U, the second center of the second shape (1740) is located on the first axis, and the first center of the first shape (1730) and the third center of the third shape (1750) are offset from the first axis on the same side of the axis.

[0192] Figure 17V shows the 56th GUI (1721), in which the first shape (1730) corresponds to a negative color, the second shape (1740) corresponds to a neutral color, and the third shape (1750) corresponds to a positive color. In some modifications, the first axis may be a slope axis (e.g., having a negative slope) between the vertical axis and the horizontal axis of the user health graphic.

[0193] Figure 17W shows the 57th GUI (1722), where the first shape (1730) corresponds to a negative color, the second shape (1740) corresponds to a neutral color, and the third shape (1750) corresponds to a neutral color. The color and position of the first shape (1730) show the user the difference in the evaluation of the first shape (1730) relative to the second shape (1740) and the third shape (1750). The colors and positions of the second shape (1740) and the third shape (1750) indicate agreement in their evaluations (e.g., a neutral evaluation).

[0194] Figure 17X shows the 58th GUI (1723), where the first shape (1730) corresponds to a negative color, the second shape (1740) corresponds to a neutral color, and the third shape (1750) corresponds to a negative color. The color and position of the second shape (1740) show the user the difference in the evaluations of the second shape (1740) relative to the first shape (1730) and the third shape (1750). The colors and positions of the first shape (1730) and the third shape (1750) indicate the alignment of their evaluations (e.g., negative evaluation).

[0195] Figure 17Y shows the 59th GUI (1724), where the first shape (1730) corresponds to the negative color, the second shape (1740) corresponds to the negative color, and the third shape (1750) corresponds to the positive color. In Figure 17Y, the third center of the third shape (1750) lies on the first axis, while the first center of the first shape (1730) and the second center of the second shape (1740) are offset from the first axis.

[0196] Figure 17Z shows the 60th GUI (1725), where the first shape (1730) corresponds to the negative color, the second shape (1740) corresponds to the negative color, and the third shape (1750) corresponds to the neutral color.

[0197] Figure 17AA shows the 61st GUI (1726), in which the first shape (1730) corresponds to the negative color, the second shape (1740) corresponds to the negative color, and the third shape (1750) corresponds to the negative color. In some modifications, the first axis may be a slope axis (e.g., having a negative slope) between the vertical axis and the horizontal axis of the user health graphic.

[0198] In the foregoing description, for illustrative purposes, a specific nomenclature has been used to provide a complete understanding of the invention. However, it will be apparent to those skilled in the art that specific details are not necessary to carry out the invention. Accordingly, the foregoing description relating to specific embodiments of the invention is presented for illustrative and explanatory purposes only. These are not intended to be exhaustive or to limit the invention to the exact form disclosed, and obviously, many modifications and variations are possible in view of the foregoing teachings. The embodiments have been selected and described to illustrate the principles of the invention and its practical applications, so that those skilled in the art can utilize the invention and its various embodiments in various ways to suit a particular intended use. The following claims and equivalents are intended to define the scope of the invention.

Claims

1. A method of graphically representing a user's health, The system receives user data, including sleep assessments, multiple physical health parameters, and mental health assessments, from a computing device, and receives sleep measurement data from a measurement device. The process involves generating a sleep quality assessment based on the aforementioned sleep assessment and sleep measurement data, generating a physical health quality assessment based on the aforementioned multiple physical health parameters, and generating a mental health quality assessment based on the aforementioned mental health assessment. This includes generating user health graphics on the graphical user interface of the computing device, and the generation of these graphics is Based on the evaluation of sleep quality, a first shape is generated; based on the evaluation of physical health quality, a second shape is generated; and based on the evaluation of mental health quality, a third shape is generated. The method, comprising arranging the positions of the first shape, the second shape, and the third shape with respect to the first axis of the user health graphic based on the evaluation of the quality of sleep, the evaluation of the quality of physical health, and the evaluation of the quality of mental health.

2. The method according to claim 1, further comprising periodically generating the evaluation of the quality of sleep, the quality of physical health, and the quality of mental health.

3. The method according to claim 1, wherein the sleep measurement data includes one or more of the following: sleep duration, sleep efficiency, sleep latency, number of awakenings, wake duration, and sleep quality.

4. The method according to claim 1, wherein the evaluation of sleep quality is based on a weighted sum of the sleep evaluation and the sleep measurement data.

5. The method according to claim 4, wherein the load is based on time.

6. The method according to claim 1, wherein the plurality of physical health parameters include two or more of the following: physical health assessment, number of physical discomforts, number of maximum physical discomforts, and average severity of physical discomfort.

7. The method according to claim 1, wherein the evaluation of the quality of physical health is based on a weighted sum of the plurality of physical health parameters.

8. The method according to claim 7, wherein the weight is based on time.

9. The method according to claim 1, wherein the plurality of physical health parameters each correspond to a predetermined scale.

10. The method according to claim 9, wherein the predetermined scale is a Likert scale.

11. The method according to claim 1, wherein generating the user health graphic includes alphanumeric representations of the evaluation of sleep quality, the evaluation of physical health quality, and the evaluation of mental health quality.

12. The method according to claim 1, wherein the user data and the sleep measurement data are each received at one or more predetermined intervals.

13. The method according to claim 12, wherein the predetermined interval is at least once a day.

14. The method according to claim 1, wherein the first axis is the vertical axis of the user health graphic.

15. The method according to claim 1, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, and the first center, the second center, and the third center are arranged on the first axis.

16. The method according to claim 1, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, two of the first center, the second center, and the third center are located on the first axis, and the third of the first center, the second center, and the third center is offset from the first axis.

17. The method according to claim 1, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, one of the first center, the second center, and the third center is located on the first axis, and the other two of the first center, the second center, and the third center are offset from the first axis and located on opposite sides of the first axis.

18. The method according to claim 1, wherein the first axis is an inclined axis between the vertical axis and the horizontal axis of the user health graphic.

19. The method according to claim 1, wherein the distance between the centers of the first shape and the second shape is based on the difference between two of the sleep evaluation, the physical health evaluation, and the mental health evaluation.

20. The method according to claim 1, wherein two or more centers of the first shape, the second shape, and the third shape are spaced apart from each other along the first axis.

21. The method according to claim 1, wherein two or more centers of the first shape, the second shape, and the third shape are offset from each other with respect to the first axis.

22. The method according to claim 1, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, and the first center, the second center, and the third center are offset from each other in the lateral direction.

23. The method according to claim 22, wherein the first shape, the second shape, and the third shape partially overlap along the first axis.

24. The method according to claim 22, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, and the first center, the second center, and the third center are spaced apart by an equal intercenter distance along the first axis.

25. The method according to claim 1, wherein generating the user health graphic includes generating a plurality of lines, each of which overlaps with two of the first shape, the second shape, and the third shape.

26. The method according to claim 25, wherein the plurality of lines include a first line between the center of the first shape and the center of the second shape, and a second line between the center of the second shape and the center of the third shape.

27. The method according to claim 1, wherein each of the first shape, the second shape, and the third shape includes a two-dimensional shape.

28. The method according to claim 1, wherein each of the first color, the second color, and the third color includes a color gradient.

29. The method according to claim 1, wherein each of the first color, the second color, and the third color includes an opacity gradient.

30. The method according to claim 1, wherein the background of the graphical user interface includes a color gradient.

31. The method according to claim 1, further comprising periodically updating the user health graphic.

32. The method according to claim 31, further comprising generating an animation that includes the updated user health graphic and one or more previous user health graphics.

33. The method according to claim 1, further comprising outputting a notification for the input of user data using the computing device, wherein the notification is output at predetermined intervals.

34. The method according to claim 1, further comprising modifying computing device settings based on the user health graphic.

35. The method according to claim 1, further comprising introducing sleep services to the user based on the user health graphic.

36. Based on the user data and the sleep measurement data, predict the risk of one or more sleep disorders. The method according to claim 1, further comprising introducing a sleep service to a user based on the predicted risk of one or more sleep disorders.

37. The method according to claim 36, wherein the sleep measurement data includes one or more sleep parameters.

38. The method according to claim 37, wherein the one or more sleep parameters include one or more of the following: time of sleep onset, time of sleep end, duration of sleep, sleep stage, time of falling asleep, number of awakenings, length of awakening, snoring status, duration of snoring, duration of exercise, time of exercise end, number of alcoholic beverages consumed, time of last alcohol consumption, number of caffeinated beverages consumed, time of last caffeine consumption, daytime sleepiness status, severity of daytime sleepiness, daytime sleepiness status, severity of daytime sleepiness, mean sleep oxygen saturation, mean sleep heart rate variability, minimum sleep heart rate, maximum sleep heart rate, mean sleep heart rate, list of medications taken, and user demographic data.

39. The method according to claim 38, further comprising one or more sleep parameters including a user wake-up memo and a user bedtime memo.

40. The method according to claim 38, wherein the user demographic data includes one or more of medical history and test results.

41. The method according to claim 6, wherein the sleep assessment, the physical health assessment, and the mental health assessment each correspond to a predetermined scale.

42. The method according to claim 41, wherein the predetermined scale is a Likert scale.

43. The method according to claim 41, wherein the sleep evaluation includes the state of sleep quality, the physical health evaluation includes the physical state, and the mental health evaluation includes the emotional state.

44. The method according to claim 43, wherein the state of sleep quality includes the reason for arousal.

45. The method according to claim 43, wherein the physical condition includes a type of physical discomfort, a location of physical discomfort, and an intensity of physical discomfort.

46. The method according to claim 43, wherein the aforementioned emotional state includes waking emotions and sleeping emotions.

47. The method according to claim 45, further comprising generating sleep data based on the user data and the sleep measurement data.

48. The method according to claim 47, wherein generating sleep data includes comparing the user data with the sleep measurement data.

49. The method according to claim 47, further comprising generating one or more sleep trends by analyzing the aforementioned sleep data.

50. The method according to claim 49, wherein the risk of one or more sleep disorders is based on one or more sleep tendencies.

51. The method according to claim 36, further comprising generating a graphical user interface that includes one or more of the aforementioned sleep tendencies and the aforementioned risk of sleep disorders.

52. The method according to claim 36, further comprising modifying computing device settings based on one or more of the aforementioned sleep tendencies and the risks of the aforementioned sleep disorders.

53. The method according to claim 36, wherein the introduction of the sleep service is based on one or more of the sleep tendencies and the risk of sleep disorders.

54. The method according to claim 36, wherein the user data and the sleep measurement data are received at predetermined intervals.

55. The method according to claim 54, wherein the predetermined interval is at least once a day.

56. The method according to claim 54, wherein the predetermined interval is the time of waking up and the time of going to bed.

57. The method according to claim 36, wherein the sleep disorder includes one or more of the following: obstructive sleep apnea, central sleep apnea, hypopnea, orthopnea, nocturnal atrial fibrillation, nocturnal hypertension, nocturnal discomfort, chronic obstructive pulmonary disease that worsens in the evening, heart failure, asthma, sleep quality, and discomfort upon waking.

58. A method of graphically representing a user's health, In response to user input for sleep assessment, physical health assessment, and mental health assessment, a computing device will be used to output notifications. This includes generating user health graphics on the graphical user interface of the computing device, and the generation of these graphics is Based on the sleep evaluation, a first shape including a first color is generated; based on the physical health evaluation, a second shape including a second color is generated; and based on the mental health evaluation, a third shape including a third color is generated. The method, comprising arranging the positions of the first shape, the second shape, and the third shape with respect to the first axis of the user health graphic based on the sleep evaluation, the physical health evaluation, and the mental health evaluation.

59. The method according to claim 58, wherein the first axis is the vertical axis of the user health graphic.

60. The method according to claim 58, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, and the first center, the second center, and the third center are arranged on the first axis.

61. The method according to claim 58, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, two of the first center, the second center, and the third center are located on the first axis, and the third of the first center, the second center, and the third center is offset from the first axis.

62. The method according to claim 58, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, one of the first center, the second center, and the third center is located on the first axis, and the other two of the first center, the second center, and the third center are offset from the first axis and located on opposite sides of the first axis.

63. The method according to claim 58, wherein the first axis is an inclined axis between the vertical axis and the horizontal axis of the user health graphic.

64. The method according to claim 58, wherein the center-to-center distance between the first shape and the second shape is based on the difference between two of the sleep evaluation, the physical health evaluation, and the mental health evaluation.

65. The method according to claim 58, wherein two or more centers of the first shape, the second shape, and the third shape are spaced apart from each other along the first axis.

66. The method according to claim 58, wherein two or more centers of the first shape, the second shape, and the third shape are offset from each other with respect to the first axis.

67. The method according to claim 58, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, and the first center, the second center, and the third center are offset from each other in the lateral direction.

68. The method according to claim 67, wherein the first shape, the second shape, and the third shape partially overlap along the first axis.

69. The method according to claim 67, wherein the first shape includes a first center, the second shape includes a second center, the third shape includes a third center, and the first center, the second center, and the third center are spaced apart by an equal intercenter distance along the first axis.

70. The method according to claim 58, wherein generating the user health graphic includes generating a plurality of lines, each of which overlaps with two of the first shape, the second shape, and the third shape.

71. The method according to claim 70, wherein the plurality of lines include a first line between the center of the first shape and the center of the second shape, and a second line between the center of the second shape and the center of the third shape.

72. The method according to claim 58, wherein the sleep evaluation, the physical health evaluation, and the mental health evaluation each correspond to a predetermined scale.

73. The method according to claim 72, wherein the predetermined scale is a Likert scale.

74. The method according to claim 58, wherein each of the first shape, the second shape, and the third shape includes a two-dimensional shape.

75. The method according to claim 58, wherein each of the first color, the second color, and the third color includes a color gradient.

76. The method according to claim 58, wherein each of the first color, the second color, and the third color includes an opacity gradient.

77. The method according to claim 58, wherein the background of the graphical user interface includes a color gradient.

78. The method according to claim 58, further comprising periodically updating the user health graphic.

79. The method according to claim 78, further comprising generating an animation that includes the updated user health graphic and one or more previous user health graphics.

80. The method according to claim 58, wherein the notification is output at predetermined intervals.

81. The method according to claim 58, further comprising modifying computing device settings based on the user health graphic.

82. The method according to claim 58, further comprising introducing sleep services to the user based on the user health graphic.

83. A method for predicting sleep disorders, The system receives user data, including sleep assessments, physical health assessments, and mental health assessments, from a computing device, and receives sleep measurement data from a measurement device. Based on the user data and the sleep measurement data, predict the risk of one or more sleep disorders. The method, comprising referring a user to a sleep service based on the predicted risk of one or more sleep disorders.

84. The method according to claim 83, further comprising outputting a notification for the input of user data using the computing device.

85. The method according to claim 83, wherein the sleep measurement data includes one or more sleep parameters.

86. The method according to claim 85, wherein the one or more sleep parameters include one or more of the following: time of sleep onset, time of sleep end, duration of sleep, sleep stage, time of falling asleep, number of awakenings, length of awakening, snoring status, duration of snoring, duration of exercise, time of exercise end, number of alcoholic beverages consumed, time of last alcohol consumption, number of caffeinated beverages consumed, time of last caffeine consumption, daytime sleepiness status, severity of daytime sleepiness, daytime sleepiness status, severity of daytime sleepiness, mean sleep oxygen saturation, mean sleep heart rate variability, minimum sleep heart rate, maximum sleep heart rate, mean sleep heart rate, list of medications taken, and user demographic data.

87. The method according to claim 86, further comprising one or more sleep parameters including a user wake-up memo and a user bedtime memo.

88. The method according to claim 86, wherein the user demographic data includes one or more of medical history and test results.

89. The method according to claim 83, wherein the sleep assessment, the physical health assessment, and the mental health assessment each correspond to a predetermined scale.

90. The method according to claim 89, wherein the predetermined scale is a Likert scale.

91. The method according to claim 83, wherein the sleep evaluation includes the state of sleep quality, the physical health evaluation includes the physical state, and the mental health evaluation includes the emotional state.

92. The method according to claim 91, wherein the state of sleep quality includes the reason for wakefulness.

93. The method according to claim 91, wherein the physical condition includes a type of physical discomfort, a location of physical discomfort, and an intensity of physical discomfort.

94. The method according to claim 91, wherein the aforementioned emotional state includes waking emotions and sleeping emotions.

95. The method according to claim 83, further comprising generating sleep data based on the user data and the sleep measurement data.

96. The method according to claim 95, wherein generating sleep data includes comparing the user data with the sleep measurement data.

97. The method according to claim 95, further comprising generating one or more sleep trends by analyzing the aforementioned sleep data.

98. The method according to claim 97, wherein the risk of one or more sleep disorders is based on one or more sleep tendencies.

99. The method according to claim 83, further comprising generating a graphical user interface that includes one or more of the aforementioned sleep tendencies and the aforementioned risk of sleep disorders.

100. The method according to claim 83, further comprising modifying computing device settings based on one or more of the aforementioned sleep tendencies and the aforementioned risk of sleep disorders.

101. The method according to claim 83, wherein the introduction of the sleep service is based on one or more of the sleep tendencies and the risk of sleep disorders.

102. The method according to claim 83, wherein the user data and the sleep measurement data are received at predetermined intervals.

103. The method according to claim 83, wherein the predetermined interval is at least once a day.

104. The method according to claim 83, wherein the predetermined interval is the time of waking up and the time of going to bed.

105. The method according to claim 83, wherein the sleep disorder includes one or more of the following: obstructive sleep apnea, central sleep apnea, hypopnea, orthopnea, nocturnal atrial fibrillation, nocturnal hypertension, nocturnal discomfort, chronic obstructive pulmonary disease that worsens in the evening, heart failure, asthma, sleep quality, and discomfort upon waking.

106. A method for predicting a user's health, The system receives user data, including sleep assessments, multiple physical health parameters, and mental health assessments, from a computing device, and receives sleep measurement data from a measurement device. The method comprising generating a sleep quality assessment based on the sleep assessment and the sleep measurement data, generating a physical health quality assessment based on the plurality of physical health parameters, and generating a mental health quality assessment based on the mental health assessment.

107. The method according to claim 106, further comprising introducing a sleep service to a user based on one or more of the evaluation of sleep quality, the evaluation of physical health quality, and the evaluation of mental health quality.

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