Assessment of cohort sleep performance

Abstract

Certain aspects and features of the present disclosure relate to assessing the sleep performance of a cohort of individuals sleeping in a shared environment (e.g., a single bed, a single room, adjacent rooms in a single example, or a single household). Individual or collaborative sleep performance scores, and other sleep performance metrics, can be determined. If one individual is receiving treatment for a sleep-related and / or breathing disorder (e.g., with a respiratory therapy device), an assessment of the sleep performance of the entire cohort may be useful. The assessment of the cohort helps determine what actions can be taken to improve the sleep performance of all individuals in the cohort. In some cases, parameters of the user therapy device are adjusted based on the monitored sleep performance of other individuals in the cohort.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 127,597, filed December 18, 2020, and entitled "Evaluation of Cohort Sleep Performance," the disclosure of which is incorporated herein by reference in its entirety. [Technical field]

[0002] The present disclosure relates to treating sleep conditions in general, and more specifically, to monitoring sleep performance in multiperson environments. [Background technology]

[0003] Many individuals suffer from sleep-related and / or respiratory disorders, such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), such as obstructive sleep apnea (OSA), central sleep apnea (CSA), other types of apnea, such as mixed apnea-hypopnea, respiratory effort-related arousals (RERA), Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), chest wall disease, or insomnia. Sleep-related respiratory disorders may be associated with one or more events that may occur during sleep, such as snoring, apnea, hypoventilation, restless legs syndrome, sleep disorders, choking, increased heart rate, dyspnea, asthma attacks, epilepsy, epileptic seizures, or any combination of these. Individuals affected by such sleep-related respiratory disorders are typically treated with one or more medical devices to improve sleep and reduce the likelihood of an event occurring during sleep. One example of such a device is a respiratory therapy system that can provide positive airway pressure to an individual, although other devices can be used.

[0004] Furthermore, many individuals suffering from sleep-related and / or breathing disorders sleep in the same environment as one or more other individuals. Any individual in the environment can affect the sleep performance of another individual in the environment. Examples include disruptions due to movement or noise (e.g., a first bed partner entering bed after a second bed partner has entered the early stages of sleep), disruptions due to normal sleep hygiene (e.g., timing of using digital devices or eating before sleep), and other factors. Not only does the treatment of an individual suffering from a sleep-related and / or breathing disorder affect the sleep performance of a healthy individual, but the actions of a healthy individual affect the sleep performance of an individual suffering from a sleep-related and / or breathing disorder.

[0005] There is a need to provide meaningful metrics regarding an individual's sleep performance in an environment that includes one or more other individuals, which may be used to improve the overall sleep quality of one or more individuals in the environment, such as, for example, improving compliance and user engagement with a sleep regimen, identifying actions that positively impact sleep performance, and otherwise improving sleep performance. Summary of the Invention

[0006] Certain aspects and features of the present disclosure include receiving sensor data from one or more sensors associated with a sleep session of an individual in an environment, determining first sleep performance data from the sensor data, receiving second sleep performance data associated with a sleep session of a different user of a respiratory treatment device in the environment than the individual, generating one or more sleep performance metrics using the first sleep performance data and the second sleep performance data, and presenting the one or more sleep performance metrics.

[0007] In some cases, the one or more sleep performance metrics include: i) a collaborative sleep performance score; ii) an individual sleep performance score associated with the individual; iii) an individual sleep performance score associated with the user; iv) a hypnogram associated with the individual; v) a hypnogram associated with the user; vi) a therapy score for the user; vii) a resonance score; or viii) any combination of i-vii. In some cases, the individual's sleep session and the user's sleep session overlap in time. In other cases, the individual's sleep session and the user's sleep session do not overlap in time, such as when either the individual or the user is a shift worker with a unique sleep-wake pattern. In some cases, the first sleep performance data includes sleep stage information or sleep state information, and the second sleep performance data includes respiratory therapy device usage information. In some cases, generating the one or more sleep performance metrics includes generating a collaborative sleep performance score, wherein generating the collaborative sleep performance score includes generating a first sleep performance score using the first sleep performance data, generating a second sleep performance score using the second sleep performance data, and generating a collaborative sleep performance score using the first sleep performance score and the second sleep performance score.

[0008] In some cases, the method further includes receiving goal information related to a first user and a second user, the goal information indicating goals related to i) the individual's sleep session, ii) the user's sleep session, or iii) a combination of i and ii, generating a goal state update including evaluating the goal information using i) the first sleep performance data, ii) the second sleep performance data, or iii) a combination of i and ii, and outputting the goal state update. In some cases, receiving the goal information includes generating a set of one or more suggested goals and accepting a selection of a selected goal from the set of suggested goals. In some cases, generating a set of suggested goals includes submitting a questionnaire including one or more questions, receiving response information in response to submission of the questionnaire, and using the received response information to generate a set of suggested goals. In some cases, generating a set of suggested goals includes accessing historical sleep performance data associated with historical sleep performance metrics, identifying one or more factors as influencing the historical sleep performance metrics, determining a suggested action for each of the one or more factors that is estimated to improve future sleep performance metrics, and using the suggested action for each of the one or more factors to generate a set of suggested goals. In some cases, generating a set of suggested goals includes receiving demographic information associated with the individual or user, and using the received demographic information to generate a set of suggested goals. In some cases, generating a set of suggested goals includes receiving historical respiratory treatment device usage information associated with the user, and using the received historical respiratory treatment device usage information to generate a set of suggested goals. In some cases, generating a set of suggested goals includes receiving subjective feedback associated with a plurality of historical sleep sessions, and using the subjective feedback to generate a set of suggested goals.In some cases, evaluating the goal further includes using the sensor data. In some cases, evaluating the goal using the sensor data includes estimating a distance between the individual and the user using the sensor data. In some cases, receiving the goal information includes receiving a target achievement date associated with the goal, and receiving the target achievement date includes automatically determining the target achievement date using i) the first sleep performance data, ii) the second sleep performance data, iii) the historical sleep performance data, or iv) any combination of i-iii. In some cases, the goal information includes goals related to start times of future sleep sessions of the individual and start times of future sleep sessions of the user. In some cases, the goal information includes goals related to a distance between the individual and the user in future sleep sessions. In some cases, the goal information includes goals related to future use of a respiratory treatment device. In some cases, after completing one goal, suggesting further selection of goals. Further selection of the goal may be based on one or more of the goals achieved, the time spent achieving the goals achieved, the user's sleep performance data and / or subjective data, personal sleep performance data and / or subjective data, or combinations thereof.

[0009] In some cases, the method further includes identifying coaching suggestions for improving future sleep performance metrics and providing the coaching suggestions after a first sleep session. In some cases, identifying the coaching suggestions includes receiving subjective feedback associated with a plurality of historical sleep sessions and generating a coaching suggestion using the subjective feedback. In some cases, identifying the coaching suggestions includes accessing historical sleep performance data associated with historical sleep performance metrics, identifying one or more factors as influencing the historical sleep performance metrics, determining a proposed action for each of the one or more factors that is estimated to improve future sleep performance metrics, and generating a coaching suggestion with the proposed change for each of the one or more factors.

[0010] In some cases, the method further includes providing an incentive based on the first sleep performance data and the second sleep performance data. In some cases, providing the incentive is further based on a comparison between the one or more sleep performance metrics and the historical sleep performance metrics. In some cases, generating the one or more sleep performance metrics includes generating a collaborative sleep performance score, the generating the collaborative sleep performance score includes generating a first sleep performance score using the first sleep performance data, generating a second sleep performance score using the second sleep performance data, and generating a collaborative sleep performance score using the first sleep performance score and the second sleep performance score, wherein the incentive is provided if the first sleep performance score exceeds a first threshold and the second sleep performance score exceeds a second threshold. In some cases, providing the incentive includes providing a first personal incentive associated with the individual and providing a second personal incentive associated with the user. In some cases, the method further includes providing a first personal incentive associated with the individual if the first sleep performance score exceeds the first threshold and providing a second personal incentive associated with the user if the second sleep performance score exceeds the second threshold. In some cases, the method further includes providing a first personal incentive associated with the user if the first sleep performance score exceeds the first threshold and providing a second personal incentive associated with the individual if the second sleep performance score exceeds the second threshold.

[0011] In some cases, the method further includes transmitting summary information based on the first sleep performance data, the summary information being usable when received by a user device associated with the user to generate an entry in a feed of historical summary information associated with the individual. In some cases, the method further includes receiving responsive feedback in response to generating the entry. In some cases, generating the one or more sleep performance metrics includes generating a first sleep performance score using the first sleep performance data, the summary information including the first sleep performance score. In some cases, the method further includes receiving summary information at a user device associated with the individual, the summary information being based on the second sleep performance data, and generating an entry in a feed of historical summary information associated with the user using the received summary information. In some cases, generating the one or more sleep performance metrics includes generating a second sleep performance score using the second sleep performance data, the summary information including the second sleep performance score. In some cases, the second sleep performance data is determined using the sensor data, the sensor data further relating to a sleep session of the user in an environment. In some cases, the second sleep performance data is determined using second sensor data from a second set of one or more sensors, the second sensor data relating to a sleep session of the user within the environment.

[0012] Certain aspects and features of the disclosure include a method that includes using the respiratory treatment device to supply air to a user interface worn by a user in an environment participating in a sleep session, receiving sleep session data related to an individual's sleep session in an environment different from the user, and adjusting parameters of the respiratory treatment device in response to the received sleep session data. In some cases, the parameters are dynamically adjusted during the user's sleep session and the individual's sleep session. In some cases, the sleep session data includes individual's sleep stage data, and the parameters of the respiratory treatment device are adjusted based on the sleep stage data. In some cases, adjusting the parameters of the respiratory treatment device includes adjusting the parameters to a first setting if the sleep session data indicates the individual is awake, and adjusting the parameters to a second setting if the sleep session data indicates the individual is asleep, the respiratory treatment device being quieter when the parameters are adjusted to the first setting than when the parameters are adjusted to the second setting.

[0013] In some cases, the method further includes receiving first sensor data associated with a sleep session of the user, receiving second sensor data associated with a sleep session of the individual, where sleep session data associated with the second sleep session is determined using the second sensor data, and synchronizing the first sensor data and the second sensor data. In some cases, the method further includes improving a signal-to-noise ratio of a signal of the first sensor data using the synchronized second sensor data. In some cases, the method further includes detecting a possible event using the first sensor data, and confirming the event using the synchronized second sensor data. In some cases, the method further includes estimating a posture of the user using the synchronized first sensor data and the synchronized second sensor data. In some cases, the method further includes establishing a wireless connection with a user device associated with the individual, where the sleep session data is received using the wireless connection, measuring a characteristic of the wireless connection, and determining location information of the individual based on the measured characteristic of the wireless connection. In some cases, the method further includes adjusting a parameter of a respiratory treatment device based on the location information. In some cases, the wireless connection is a Bluetooth connection.

[0014] In some cases, the environment is a building. In some cases, the environment is a pair of adjacent rooms. In some cases, the environment is a room. In some cases, the environment is a sleep surface.

[0015] Certain aspects and features of the present disclosure include generating a simulated respiratory treatment device sound, outputting the simulated respiratory treatment device sound, monitoring the outputted simulated respiratory treatment device sound with a microphone, and adjusting the output of the simulated respiratory treatment device sound based on the monitored outputted simulated respiratory treatment device sound.

[0016] In some cases, the method further includes accessing a set of predefined respiratory treatment settings, and generating the simulated respiratory treatment device sounds is based on the set of predefined respiratory treatment settings. In some cases, the method further includes accessing a set of therapy settings for the respiratory treatment device, and generating the simulated respiratory treatment device sounds is based on the set of therapy settings for the respiratory treatment device. In some cases, the method further includes receiving an adjustment command, adjusting a volume of the simulated respiratory treatment device in response to receiving the adjustment command, and providing a respiratory treatment recommendation based on the adjusted volume of the simulated respiratory treatment device. In some cases, the respiratory treatment recommendation includes i) a respiratory treatment device model, ii) a user interface type, iii) a user interface model, iv) a conduit type, v) a conduit model, or vi) any combination of i)-v. In some cases, the method further includes receiving sensor data from one or more sensors associated with a user participating in a sleep session, the simulated respiratory treatment device sound being output during the sleep session, determining the sleep performance information using the sensor data, and outputting the sleep performance information. In some cases, the method further includes modifying the output of the simulated respiratory treatment device sound during the sleep session. In some cases, the output of the simulated respiratory treatment device sound is modified based on the determined sleep performance information. In some cases, the generation of the simulated respiratory treatment device sound is associated with the first respiratory treatment device model, and the method further includes obtaining historical sleep performance information associated with the historical sleep sessions, the historical sleep sessions occurring during the output of additional simulated respiratory treatment device sounds associated with a second respiratory treatment device model, and generating a comparison between the sleep performance information and the historical sleep performance information. In some cases, the method further includes generating a recommendation for the first respiratory treatment device model or the second respiratory treatment device model based on the generated comparison.In some cases, the method further includes receiving medical information associated with an individual, and modifying the output of the simulated respiratory therapy device sounds based on the received medical information.

[0017] Certain aspects and features of the present disclosure relate to a system including a control system including one or more processors and a memory storing machine-readable instructions, the control system coupled to the memory, wherein any of the above methods are performed when the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system.

[0018] Certain aspects and features of the present disclosure relate to a system for shared sleep scoring, the system including a control system configured to implement any of the methods described above.

[0019] Certain aspects and features of the present disclosure relate to a system for controlling respiratory therapy, the system including a control system configured to implement any of the methods described above.

[0020] Certain aspects and features of the present disclosure relate to a system for simulating respiratory therapy, the system including a control system configured to implement any of the methods described above.

[0021] Certain aspects and features of the present disclosure relate to a computer program product comprising instructions that, when executed by a computer, cause the computer to perform any of the methods described above. In some cases, the computer program product is a non-transitory computer-readable medium. This application refers to the following drawings, in which the use of the same reference numbers in different drawings is intended to indicate the same or similar elements. [Brief description of the drawings]

[0022] [Figure 1]FIG. 1 is a functional block diagram of a system suitable for scoring sleep performance in accordance with certain embodiments of the present disclosure. [Diagram 2] FIG. 2 is a perspective view of the system, user, and bed partner of FIG. 1 according to certain embodiments of the present disclosure. [Diagram 3] FIG. 3 is a diagram illustrating an example timeline of a sleep session according to some implementations of the disclosure. [Figure 4] FIG. 4 illustrates an example hypnogram associated with the sleep session of FIG. 3 according to certain aspects of the present disclosure. [Diagram 5] FIG. 5 is a perspective view of a pair of cohort members including first and second cohort members according to certain embodiments of the present disclosure. [Figure 6] FIG. 6 is a flow chart illustrating a process for generating and presenting sleep performance metrics for a sleep cohort according to certain embodiments of the present disclosure. [Figure 7] FIG. 7 is a flow chart illustrating a process for tracking goals of a sleep cohort according to certain embodiments of the present disclosure. [Figure 8] FIG. 8 is a flow chart illustrating a process for generating coaching suggestions for a sleep cohort according to certain embodiments of the present disclosure. [Figure 9] FIG. 9 is a flow chart illustrating a process for generating simulated respiratory treatment device sounds, according to certain embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments thereof have been shown by way of example in the accompanying drawings and are herein described in detail. It is to be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but that the disclosure is intended to cover all modifications, equivalents and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0024] Certain aspects and features of the present disclosure relate to assessing the sleep performance of a cohort of individuals sleeping in a shared environment (e.g., a bed, a room, a set of adjacent rooms, or a household). Individual or collaborative sleep performance scores, and other sleep performance metrics, can be determined. An assessment of the sleep performance of the entire cohort can be useful if at least one individual is receiving treatment for a sleep-related and / or breathing disorder (e.g., a respiratory therapy system). The assessment of the cohort helps determine what actions can be taken to improve the sleep performance of all individuals in the cohort.

[0025] Certain aspects and features of the present disclosure also relate to using sleep data of one individual in an environment to adjust parameters of a respiratory treatment device used by another individual (e.g., a treatment user) in the same environment. In some cases, these adjustments, which can be performed automatically or manually, can improve the sleep quality of one or all of the individuals and improve compliance and engagement with the respiratory treatment system for users treated by the system.

[0026] Certain aspects and features of the present disclosure also relate to generating simulated respiratory treatment device sounds to help individuals acclimate to sleep in an environment in which the respiratory treatment device is used. This simulation can help acclimate a first individual to the sounds of the respiratory treatment system when a second individual sleeping in an environment shared with the first individual is using or about to start a respiratory treatment therapy. Similarly, the simulation can help acclimate a second individual who is an intended user of the respiratory treatment system before starting a respiratory treatment with the respiratory treatment system. In some cases, such a simulation can be tailored to the settings being used or to be used with the respiratory treatment system. In some cases, the simulation can be tailored to the sleep performance of the individual in the environment. The sleep performance of the individual in the environment can be tracked to determine the impact of the simulated respiratory treatment system sounds on the individual's sleep performance.

[0027] Certain aspects of the present disclosure may be used to determine sleep performance metrics (e.g., collaborative sleep performance scores and / or individual sleep performance scores) associated with a sleep session of a user (e.g., a therapy user) receiving respiratory therapy and sleep sessions of different individuals sleeping in the same environment. Other individuals sleeping in a shared environment with the therapy user may be referred to as therapy-adjacent individuals, bed partners (e.g., when the individuals share the same bed as the therapy user), or simply individuals. The therapy-adjacent individuals may themselves be receiving a sleep-related therapy, such as therapy with a respiratory therapy system, but this is not necessarily the case. The therapy-adjacent individuals may themselves be receiving a sleep-related therapy, such as therapy with a respiratory therapy system, but this is not necessarily the case. The therapy-adjacent individuals may be separated from the therapy user or may be in a different room from the therapy user. When used with respect to therapy-adjacent individuals, the term "therapy-adjacent" includes individuals who sleep in the same environment as the therapy user, whose sleep performance may affect or be affected by the therapy user.

[0028] The therapeutic user and the therapeutic adjacent individual may sleep in a shared environment. The shared environment may be any environment in which the actions of the therapeutic user or the therapeutic adjacent individual affect the sleep performance of the other person. In one example, the environment may be a bed, such as when the therapeutic user and the therapeutic adjacent individual are bed partners. In another example, the environment may be a room, such as when the therapeutic user and the therapeutic adjacent individual sleep in the same room but in different beds. In another example, the environment may be adjacent rooms, such as when the therapeutic user and the therapeutic adjacent individual sleep in adjacent rooms, such as rooms that share a common wall. In another example, the environment may be within a household, such as when the therapeutic user and the therapeutic adjacent individual sleep in spaced apart rooms within a single structure. Other environments may be used.

[0029] In one example, the therapeutic user and the therapeutic adjacent individual may be spouses, where one spouse's sleep-related actions may affect the other spouse's sleep performance (e.g., one spouse going to bed later than the other spouse may wake the other spouse from sleep). In another example, the therapeutic user may be a parent and the therapeutic adjacent individual may be a child sleeping in an adjacent room, where the parent's or child's sleep-related actions may affect the other spouse's sleep performance (e.g., the parent's loud snoring affects the child's ability to sleep). In some cases, a collaborative sleep performance score may be determined for the therapeutic user and multiple therapeutic adjacent individuals (e.g., the individual, the individual's spouse, and the individual's one or more children). As used herein, reference to a single therapeutic adjacent individual may appropriately include reference to multiple therapeutic adjacent individuals. For example, when certain aspects and features of the present disclosure describe sleep performance data of a therapeutic adjacent individual as being used to adjust parameters of a therapeutic device of a therapeutic user, in some cases, sleep performance data of multiple therapeutic adjacent individuals may be used to adjust parameters of a therapeutic device of a therapeutic user.

[0030] As used herein, a cohort of therapeutic users and therapeutic adjacent individuals may be referred to as a sleep cohort. A sleep cohort may include any number of members, including therapeutic users and any number of therapeutic adjacent individuals. Accordingly, certain aspects and features of the present disclosure relate to the calculation of sleep cohort sleep performance metrics (e.g., collaborative sleep performance scores) that include therapeutic users and one or more therapeutic adjacent individuals.

[0031] Various sleep performance metrics may include, for example, i) a collaborative sleep performance score, ii) an individual sleep performance score associated with the therapeutic adjacent individual, iii) an individual sleep performance score associated with the therapeutic user, iv) a hypnogram associated with the therapeutic adjacent individual, v) a hypnogram associated with the user's therapy, vi) a user's therapy score, vii) a resonance score (e.g., a score indicating consistency between the sleep sessions of the therapeutic user and the therapeutic adjacent individual), or viii) any combination of i-vii. Other sleep performance metrics may also be used. The sleep performance metric may be based on one or more aspects of the individual's sleep, such as one or more of total sleep time, sleep efficiency, number of awakenings and / or awakenings, total time in REM sleep, total time in deep sleep, sleep onset latency, REM latency, and wake after sleep (WASO).

[0032] The resonance score may be indicative of consistency between sleep sessions of two or more members of a sleep cohort, as measured by any suitable metric or combination of metrics. In some cases, consistency may be based on the closeness of the members' sleep onset times. In some cases, consistency may be based on similarity between sleep performance metrics, such as sleep performance scores and / or sleep quality scores. Other metrics may also be used to establish the resonance score.

[0033] The collaborative sleep performance score is indicative of the overall sleep performance of the sleep cohort. Generally, the collaborative sleep performance score increases when all sleep cohort members achieve improved sleep. In some cases, but not necessarily, the collaborative sleep performance score can be calculated based on the sleep performance score of the therapeutic user and the sleep performance score of the therapeutic adjacent individuals. In some cases, the collaborative sleep performance score may be the average or sum of the sleep performance score of the therapeutic user and the sleep performance score of the therapeutic adjacent individuals, although other formulas and measurements may be used. The individual's sleep performance score can be determined based on sensor data specific to the therapeutic user or sensor data specific to the therapeutic adjacent individuals. However, in some cases, the collaborative sleep performance score may be based directly on sensor data without including the determination of an individual's sleep performance score.

[0034] The sleep performance scores, including the individual sleep performance score and the collaborative sleep performance score, can be based on the sleep sessions of the sleep cohort members. Each member of the sleep cohort may undergo a separate sleep session, as described in further detail herein with reference to FIG. 3, which may or may not overlap with the other members' separate sleep sessions. In some cases, a cohort sleep session may be defined as a sleep session from the earliest initial start time of all cohort members' sleep sessions to the latest end time of all cohort members' sleep sessions. Thus, in an overall cohort sleep session, a therapeutic user may undergo a therapeutic user's sleep session, and a therapeutic adjacent individual may undergo a therapeutic adjacent individual's sleep session. The therapeutic user's sleep session and the therapeutic adjacent individual's sleep session can be completely separate, overlap, or be the same. Thus, the collaborative sleep performance score of the sleep cohort may be a score related to the cohort sleep sessions.

[0035] The sensor data may include data from one or more sensors, such as sensors in or around a cohort member, in or around the entire cohort of sleepers, or in or around the environment. Once processed, various sleep performance data may be extracted from the sensor data, which may be used to determine various sleep performance metrics. For purposes of determining sleep performance metrics, the sensor data may be processed to obtain sleep performance data, such as treatment information and sleep quality information. Treatment information may include information related to a treatment user's use of a treatment system, such as a respiratory treatment system. For example, treatment information may include usage information (e.g., data indicative of a treatment user's use of a treatment device) and event information (e.g., data indicative of sleep apnea or other events, such as user interface on / off events and leak events). Sleep quality information may include objective and / or subjective data regarding the sleep quality of a cohort member. The objective data may include data such as sleep state information (e.g., data indicative of whether a user is asleep) and / or sleep stage information (e.g., data indicative of a user's sleep stage), and subjective data may include subjective feedback provided by a given cohort member or other members of the cohort. For example, data provided by a therapeutic adjacent individual may be in response to the question, "On a scale of 1 to 5, how rested are you?" Upon waking, data provided by a therapeutic user may be in response to the question, "On a scale of 1 to 5, how well do you think your bed partner slept last night?" Thus, sensor data may include objective metrics obtained from sensors such as radio frequency sensors, microphones, pressure monitors, etc., but also data obtained (e.g., feedback responses) via sensors such as touch screens, interactive buttons, etc.

[0036] The therapeutic information may be data related to the use of a sleep-related therapeutic device by the therapeutic user. Any suitable sleep-related therapeutic device may be used. In some cases, the sleep-related therapeutic device may include adjustable parameters, such as dynamically adjustable parameters and / or manually adjustable parameters. In some cases, the sleep-related therapeutic device may include a respiratory therapeutic device, a mandibular positioning device, a sleep therapy implant (e.g., an implantable stimulator for stimulating the hypoglossal nerve in the neck), or other such devices. Certain aspects and features of the present disclosure may be particularly useful when the therapeutic device used by the therapeutic user is a respiratory therapeutic device.

[0037] The respiratory treatment system may include a respiratory treatment device that delivers pressurized air to a treatment user via a conduit and a user interface. Different models and types of conduits and different models and types of user interfaces may be used to fluidly couple the treatment user to the respiratory treatment device. While receiving respiratory treatment, the treatment user may participate in a sleep session during which first sensor data may be collected from a first set of one or more sensors, such as sensors in the respiratory treatment device, sensors in a user device (e.g., a smartphone), sensors in an activity tracker (e.g., a wearable activity tracker), or other sensors located in, on, or around the treatment user (e.g., an implantable device, a clothing integrated sensor, a mattress integrated sensor, a wall-mounted sensor, or a ceiling-mounted sensor, etc.). The first sensor data collected from the first set of one or more sensors may be used to determine treatment information associated with the treatment user (e.g., one or more usage variables associated with use of the respiratory treatment system). The first sensor data may also be used to determine sleep quality information associated with the treatment user (e.g., sleep state information, sleep stage information, and / or subjective feedback). The first sensor data may be used to determine other variables and / or information.

[0038] For therapeutically adjacent individuals, the second sensor data can be collected from a second set of one or more sensors. The second set of one or more sensors can be the same as the first set of one or more sensors, a subset of the first set of one or more sensors, a superset of the first set of one or more sensors, overlapping with the first set of one or more sensors (e.g., some sensors are shared with the first set of one or more sensors but not all sensors), or an exclusive set of one or more sensors (e.g., none of the sensors are shared with the first set of one or more sensors). Similarly, the second sensor data can be the same as the first sensor data, a subset of the first sensor data, a superset of the first sensor data, sharing some sensor data with the first sensor data, or none with the first sensor data. The second sensor data can be collected simultaneously with the first sensor data or in close temporal proximity to the first sensor data. In an example using an exclusive set of one or more sensors, the sleep performance of a therapeutic user can be assessed using sensor data from the therapeutic user's respiratory treatment device and, optionally, sensor data from the therapeutic user's smartphone, while the sleep performance of a therapeutic adjacent individual can be assessed using sensor data from the therapeutic adjacent individual's smartphone. Other devices and sensors can be used.

[0039] The therapy information may include usage variables related to use of the respiratory treatment system. Usage variables related to use of the respiratory treatment system may include any suitable variables related to how the therapy user uses the respiratory treatment system. Examples of suitable usage variables include usage time (e.g., duration of time the therapy user uses the respiratory treatment system), seal quality variables (e.g., an indication of seal quality between the therapy user and the user interface), leak flow rate variables (e.g., an indication of unintended leak flow rate, such as leaks due to poor quality seals or leaks due to mouth breathing when wearing a nasal pillow type user interface), event information (e.g., an indication of detected events that occurred during a sleep session, such as an apnea-hypopnea index (AHI)), user interface compliance information (e.g., an indication of detected user interface transformation events, such as putting on or taking off a user interface), multiple therapy sub-sessions during a sleep session (e.g., multiple separate blocks used consecutively by the respiratory treatment system), and user interface pressure. Other usage variables may also be used. A statistical summary (e.g., average, maximum, minimum, count, etc.) of one or more usage variables may be used as one or more additional usage variables. The one or more usage variables may include any suitable combination of usage variables.

[0040] Determining the usage variable may include processing the sensor data to identify one or more values ​​associated with the usage variable. The one or more values ​​may be a measurement or a calculated score associated with the usage variable. For example, the seal quality variable may be a measurement of leak flow rate (e.g., L / min) or a seal quality score (e.g., 18 out of 20). Determining the usage variable may include determining a single value or multiple values ​​(e.g., timestamp values). For example, in some cases, determining the seal quality variable may include determining a single value (e.g., 18 out of 20) that represents the overall (e.g., average) seal quality for the entire sleep session. However, in some cases, determining the seal quality variable may include determining a set of timestamp values ​​(e.g., in the range of 0 to 20, 18 at 10:00:00 pm, 18.1 at 10:00:05 pm, 18.2 at 10:00:10 pm, etc.) that represent changes in seal quality over time, e.g., the data for seal quality over a sustained period may be charted.

[0041] The sleep quality information may include objective information such as sleep state information, sleep stage information, and / or other such information derived from the sensor data. Subjective information may also be included, such as subjective feedback received in response to presentation of feedback questions to the user. The sleep state information is information indicative of the sleep state of the cohort member. The sleep state indicates whether the cohort member is asleep. The sleep stage information may include information indicative of the sleep stages experienced by the cohort member during the sleep session. Examples of sleep stages include awake stages, rapid eye movement stage (REM) stages, light sleep stages, and deep sleep stages. The sensor data may be processed to determine the times when the cohort member enters and exits various sleep stages. In some cases, determining the sleep stage information may include determining the total duration the cohort member spent in each sleep stage. In an exemplary 8-hour sleep session, the sleep stage information may indicate a total of 21 minutes of awake, 101 minutes of REM sleep, 267 minutes of light sleep, and 91 minutes of deep sleep. In some cases, however, determining the sleep state and / or sleep stage information may include generating time-stamped data indicative of the cohort member's sleep states and / or sleep stages at various times throughout the sleep session. The time-stamped sleep stage information may be graphed to generate a hypnogram of the cohort member's sleep session.

[0042] In some cases, the objective data may include various parameters extracted from the sensor data, such as total time in bed, total sleep time, sleep onset latency, post-sleep wake parameters, sleep efficiency, number of awakenings and / or arousals, fragmentation index, total time in REM sleep, REM latency, total time in deep sleep, or any combination thereof.

[0043] For therapeutic-adjacent individuals, the sleep performance metric can be based solely on sensor data that is sleep quality data (e.g., data related to the therapeutic-adjacent individual's sleep session but not specifically related to use of a therapeutic device) that can include subjective data in the form of subjective feedback. In this case, the sleep performance metric can include or be based on various factors related to the therapeutic-adjacent individual's sleep, such as sleep stage information including time spent in various sleep stages, and subjective feedback such as an indication of how rested the user felt at or after the end of a sleep session.

[0044] When subjective feedback is used, the subjective feedback of a particular cohort member (e.g., a therapy user) can include subjective feedback obtained from that particular cohort member (e.g., feedback provided by the therapy user) and / or subjective feedback obtained from other members of the cohort (e.g., feedback provided by therapy adjacent individuals).

[0045] The sleep quality data may be used to determine a sleep quality score or other sleep quality metrics. The sleep quality score may be an indication of the sleep quality experienced by the cohort member during the sleep session. For example, a sleep session with many awakenings or interruptions may have a low sleep quality score, and a sleep session with many awakenings or interruptions may have a high sleep quality score. In some cases, the sleep quality score may be based on subjective feedback (e.g., feedback from the cohort member indicates subjective calmness after the sleep session), objective data, or a combination of both. As used herein, subjective feedback may also include daytime information such as subjective energy levels, fatigue levels, mood (e.g., content, irritable, etc.). Some of such information may also be collected objectively by sensors, such as wearable sensors that detect cardiac, respiratory, and / or motion parameters that may estimate energy levels, fatigue levels, mood, etc.

[0046] In one example, the sleep quality score or components thereof can be objectively determined, for example, based on sleep stage information. In one example, the time spent in different sleep stages can be used to determine the sleep quality score. Additionally or alternatively, the pattern of sleep stages (e.g., sleep architecture) can be used to determine the sleep quality score. The sleep stage information can be divided into sleep stage segments indicating the time spent in each sleep stage (e.g., the total time spent in each sleep stage during a sleep session, or the duration of each successive sleep stage that occurred during a sleep session).

[0047] In some cases, the sleep quality score may be assessed based at least in part on i) respiratory rate, ii) heart rate, iii) heart rate variability, iv) movement data, v) electroencephalogram data, vi) oxygen saturation data, vii) respiratory rate variability, viii) respiratory depth, ix) tidal volume data, x) inspiration amplitude data, xi) expiration amplitude data, xii) inspiration volume data, xiii) expiration volume data, xiv) inspiration-expiration ratio data, xv) sweat data, xvi) temperature data, xvii) pulse elapsed time data, xviii) blood pressure data, xix) posture data, xx) pressure data, xxi) blood glucose data, or xxii) any combination of i-xxi.

[0048] For therapeutic users or therapeutic adjacent individuals using sleep-related therapeutic devices, sleep performance metrics can be based on sensor data including therapeutic information and / or sleep quality information. In some cases, therapeutic information and sleep quality information can be used in combination to determine sleep performance metrics. For example, tracking the total time that therapeutic users used a respiratory therapy device during a sleep session (genioglossus muscle tone is generally better) and sleep stage information can be informative and useful. In another example, since apnea and hypopnea events are more common during REM sleep (e.g., due to reduced tone of the genioglossus muscle of the tongue) and more harmful during REM and deep sleep (e.g., due to opportunities to interrupt REM sleep, negatively affect spatial memory, and / or reduce the amount of deep sleep), tracking the amount of time that the respiratory therapy device was used during REM sleep and / or deep sleep can be more useful. Thus, in addition to tracking the total time of use, the time of use of the respiratory therapy device in one sleep stage (e.g., REM sleep or deep sleep) can be emphasized (e.g., weighted more strongly) than the time of use of the respiratory therapy device in another sleep stage (e.g., wakefulness or light sleep). Thus, if a therapeutic user uses a respiratory therapy device for longer periods of time before going to bed, the sleep performance score may not increase significantly or at all, but if the same therapeutic user uses a respiratory therapy device to sleep for longer periods of time in the REM stage, the sleep performance score may increase significantly.

[0049] The treatment information may be used to determine a treatment score, which may be indicative of the use of the treatment by the treatment user, such as the effectiveness of the treatment and / or the treatment user's compliance with the treatment.

[0050] The sleep quality score and the treatment score can be components of a sleep performance score. In some cases, the sleep quality score and the treatment score can be further decomposed into subcomponents, such as a score of time in REM sleep, a score of the length of a sleep session, a score of time using a therapeutic device, and a score of whether or not there is unintentional leakage. Thus, the sleep quality score and / or the treatment score, and optionally one or more subcomponent scores, can be used as a sleep performance metric or can be used to generate a sleep performance metric. In some cases, the various component scores and any subcomponent scores of the cohort members can be used to generate an individual sleep performance score that can be presented as an individual sleep performance score, and / or can optionally generate a collaborative sleep performance score. In some cases, the various component scores and any subcomponent scores of the cohort members can be used to directly generate a collaborative sleep performance score, rather than first generating an individual sleep performance score.

[0051] In one example, the sleep performance score can be presented using a numerical score, but this is not necessarily the case. In some cases, the collaborative sleep performance score can be presented using a graphic device showing the balance between two or more components or subcomponents. For example, the collaborative sleep performance score can be represented as the balance between the individual sleep performance scores of two members in a sleep cohort. In one example, the graphic device is a bubble level or similar device, allowing an individual to quickly and easily see which component / subcomponent / member is relatively better than the other, and optionally how better. If one individual achieves a much higher sleep performance score than another individual, the graphic device may show a strong imbalance, while a slightly higher score may show only a slight imbalance or no imbalance. In some cases, multiple presentation techniques (e.g., presenting the sleep performance score as a digital score with a graphic device showing the balance) can be combined.

[0052] Calculating a collaborative sleep performance score (e.g., a combined sleep performance score) may include calculating scores for the therapeutic user, the therapeutic adjacent individuals, or may include calculating a single collaborative sleep performance score using various sensor data and / or subjective feedback received from the therapeutic user and the therapeutic adjacent individuals. The sleep performance score and other sleep performance metrics may be provided, for example, as a number within a range (e.g., a number within a range of 0-100), charted data (e.g., a hypnogram of sleep stage data), graphically represented information (e.g., a green check mark indicating no events were detected), or in any other suitable manner.

[0053] In one example, the collaborative sleep performance score can be presented as a number in the range of 0-100, with a higher number representing a higher quality sleep of the sleep cohort (e.g., a higher quality sleep of the therapeutic user and any number of therapeutic adjacent individuals as a whole). In some cases, the collaborative sleep performance score can be an average of the individual sleep performance scores. In one example, the therapeutic user can have a sleep performance score of 70 and the therapeutic adjacent individual can have a sleep performance score of 80, in which case the collaborative sleep performance score can be 75. In the same example, if the therapeutic user earns a subsequent sleep performance score of 78 and the therapeutic adjacent individual earns a subsequent sleep performance score of 76, the collaborative sleep performance score can be 76. This can result in a decrease in the sleep performance score of the cohort members and an increase in the collaborative sleep performance score. In some cases, other sleep performance metrics collaborative versions can be calculated, for example, in a manner similar to the manner in which the collaborative sleep performance score is calculated from the individual sleep performance scores. As described herein, the collaborative sleep performance score can also be presented in other ways or in addition, such as a graphical device showing the balance between the therapeutic user's sleep performance score and the therapeutic adjacent individual's sleep performance score.

[0054] The sleep performance metric may be presented to the cohort members in any suitable manner, for example, via a display device of the respiratory treatment device, a display device of the user device (e.g., a smartphone), or in other manner. Presenting any sleep performance metric may include presenting the sleep performance metric and the base component and / or sub-component scores. For example, presenting the sleep performance metric may include presenting i) a collaborative sleep performance score, ii) an individual sleep performance score of the given cohort member, iii) an individual sleep performance score of other members in the cohort, iv) one or more component and / or sub-component scores that make up the sleep performance scores of i-iii, or v) any combination of i-iv. Examples of component and / or sub-component scores include scores for each of the usage variables, sleep stage information, sleep state information, and / or sleep quality information. In some cases, presenting the sleep performance metric may include presenting a graphical representation of the component and / or sub-component scores that make up the sleep performance metric. For example, presenting the collaborative sleep performance score may include presenting a graphical representation of the individual sleep performance scores that make up the collaborative sleep performance score.

[0055] In some cases, presenting the sleep performance metric may include presenting the contribution of a particular component or sub-component to the overall sleep performance metric. In some cases, the components or sub-components, such as the usage variable, may be decomposed (e.g., binned) and / or sorted by sleep stage information. For example, a set (e.g., bins) of four sub-component scores may be presented as the usage variable, including a score for time of usage during wakefulness, a score for time of usage during REM sleep, a score for time of usage during light sleep, and a score for time of usage during deep sleep. It should be understood that each sub-component score may be a score calculated by applying a weighting value to the usage variable, as described herein with respect to the calculation of the overall sleep performance score.

[0056] Sleep performance metrics, such as sleep performance scores, can be used as objective metrics of cohort members' sleep sessions. In some cases, for therapeutic users, sleep performance metrics may be, but are not necessarily, limited to the portion of the therapeutic user's sleep session that uses respiratory therapy. Sleep performance metrics can provide information to therapeutic users to help monitor, maintain, and / or encourage self-compliance (e.g., using a respiratory therapy device as desired or prescribed) and / or to therapeutic adjacent individuals to help monitor, maintain, and / or encourage compliance of therapeutic users. In some cases, sleep performance metrics can provide information to healthcare providers, facilities, and / or healthcare-related companies (e.g., healthcare insurance providers) regarding the compliance and effectiveness of therapeutic users who use a respiratory therapy device while sleeping and / or the impact of therapeutic users or therapeutic adjacent individuals on the sleep of others. In some cases, sleep performance metrics can be used to provide objective measurements for research purposes and evaluation.

[0057] In some cases, goals can be set to improve overall sleep quality or to improve some aspect related to sleep quality (e.g., improve a certain sleep performance metric). In some cases, cohort members can set goals directly through a graphic user interface of an application running on a user device (e.g., a smartphone). In some cases, cohort members can select from a list of suggested goals, such as a list of global pre-defined goals (e.g., goals commonly selected by all users), a list of demographic-specific pre-defined goals (e.g., goals commonly selected by users who share a certain demographic with the cohort member), or a list of custom-generated goals (e.g., custom-generated goals for the cohort member). Custom-generated goals can be automatically generated based on sensor data (e.g., sensor data from historical sleep sessions) or based on input provided by the member. Once provided with a list of suggested goals, cohort members can select one or more goals to use.

[0058] In examples where custom generated goals are automatically based on sensor data, historical sleep performance metrics (e.g., historical collaborative sleep performance scores) can be analyzed to determine one or more factors that may affect a particular sleep performance metric. The analysis can include using the historical sensor data to identify factors discussed and then identifying suggested actions to take to improve future sleep performance metrics (e.g., future collaborative sleep performance scores). The suggested actions can include taking a given action (e.g., brushing teeth before bedtime) or avoiding taking a given action (e.g., avoiding consuming caffeine 2 hours before bedtime). The factors discussed can include factors that may affect a given sleep performance metric. Examples of factors include loud snoring, obstructive sleep apnea diagnosis, AHI, cohort members having different work shifts, cohort members that include young children, poor sleep hygiene, anxiety about a cohort member's own sleep or another cohort member's sleep, caffeine intake, alcohol intake, etc. In some cases, one or more factors may be determined from feedback provided in response to a questionnaire, such as a questionnaire asking about potential issues related to the cohort members who are therapy users. Exemplary issues include worries about bed partners having to sleep in separate rooms, health concerns, worries about determining whether therapy is adequate, worries about not being adequately administered (e.g., if the user interface is removed during a sleep session), therapy device settings (e.g., pressure levels, noise, leaks, etc.), worries about having to contact a medical device vendor or manufacturer. In some cases, the questionnaire may be based on a clinically validated questionnaire (e.g., the Epworth Sleepiness Scale, the Quality of Life Index, the Binary Adjustment Scale, or the Beck Depression Inventory).

[0059] The suggested actions may be related to these factors. For example, for poor sleep hygiene factors, the suggested actions may be to stop using electronic screens at least 30 minutes before going to bed, not to eat at least 60 minutes before going to bed, and other such actions. As another example, for factors that cause cohort members to feel anxious about other cohort members' sleep, the suggested actions may be to exercise to reduce anxiety, to discuss anxiety with other cohort members, etc. The list of suggested goals may be goals related to taking the suggested actions. For example, if an analysis of the historical collaborative sleep performance scores identifies that the collaborative sleep performance scores tend to be lower when the therapeutic adjacent individual goes to bed more than 30 minutes before the therapeutic adjacent individual goes to bed, the suggested action may be for the therapeutic adjacent individual to go to bed within 30 minutes after the therapeutic user goes to bed, and the goal may be for the therapeutic adjacent individual to go to bed within 30 minutes after the therapeutic user goes to bed in at least 75% of sleep sessions over the next two weeks.

[0060] In another example where the customized generated goal is automatically generated based on the sensor data, the historical treatment information can be analyzed to identify historical respiratory treatment device usage information. The historical respiratory treatment device usage information can be further analyzed to identify one or more targets. For example, if the analysis of the historical respiratory treatment device usage information identifies that using the respiratory treatment device for at least 5 hours during a sleep session improves the collaborative sleep performance score, the suggested action can be to use the respiratory treatment device for at least 5 hours during each sleep session, and the list of suggested goals can include a first goal to use the respiratory treatment device for at least 5 hours during each sleep session for the next 5 days, a second goal to use the respiratory treatment device for at least 8 hours during three sleep sessions next week, and a third goal to use the respiratory treatment device for at least 5 hours for 7 consecutive days during the next 3 months.

[0061] In some cases, cohort members may be provided with a survey that includes one or more questions. The cohort members' responses to these questions may be used to generate a list of suggested goals and / or to identify one or more particularly relevant goals. In some cases, responses from some or all members in the cohort may be used to generate a list of suggested goals.

[0062] In some cases, cohort members can provide input in the form of suggestive feedback related to one or more historical sleep sessions. Such subjective feedback can be used to determine one or more goals. For example, if feedback that the therapeutic adjacent individual feels insufficiently rested coincides with a sleep session in which the therapeutic user sleeps in a different room than the therapeutic adjacent individual, a goal can be suggested that the therapeutic user and the therapeutic adjacent individual sleep in the same room for at least a threshold number of nights per week. This exemplary goal can be evaluated by estimating the distance between the therapeutic user and the therapeutic adjacent individual based on sensor data (e.g., by analyzing patterns in audio data, analyzing signal strength of wireless signals, etc.).

[0063] Goals can be set for individual cohort members or for the entire cohort, for example, each individual cohort member may have an individual goal to stop viewing electronic screens by 9:30 pm, in which case the cohort member's goal may be tracked individually, while the entire cohort may have a goal to achieve a coordinated sleep performance score of at least 90 out of 100.

[0064] Once a goal is established, the goal may be monitored and evaluated after each sleep session of the cohort member for which the goal was set and / or after each cohort sleep session. Sensor data may be used in the monitoring and evaluation of the goal. An evaluation of the cohort member's progress or the cohort's progress against any given goal may be presented to one or more members of the cohort, for example, by one or more display devices. This presentation of progress helps to incentivize cohort members to improve sleep hygiene and improve overall sleep quality.

[0065] In one example, if a goal for cohort members is to go to bed at substantially the same time as each other, the start times of each of the cohort members' sleep sessions can be monitored to determine whether the cohort members begin their sleep sessions within a threshold time apart from each other.

[0066] In some cases, receiving the goal information may include receiving a target achievement date associated with the goal. The target achievement date may be received as a number of days from a specific date or a current date. The target achievement date may be manually specified, such as by user input, or may be automatically determined. The automatically determined target achievement date may then be automatically set or suggested to the cohort members for the given goal. The automatic determination of the target achievement date may be based on any suitable data, such as i) the first sleep performance data, ii) the second sleep performance data, iii) historical sleep performance data, or iv) any combination of i-iii. The target achievement date may be determined as a target achievement date that is achievable by a given cohort member or cohort. The achievability of the target achievement date may be based on the historical sleep performance data, for example, by identifying previous instances in which the goal was achieved, by identifying trends in sleep performance metrics, or by other analysis. For example, for a goal of not drinking caffeine within 2 hours of going to bed for 4 consecutive days, analysis of historical sleep performance data (e.g., including historical subjective feedback on caffeine use) can identify when cohort members were able to attempt the goal of not drinking caffeine within 2 hours of going to bed for 4 consecutive days. This allows the target achievement date to be set proactively relative to the previous attempt (e.g., within 2 weeks of the current date), equivalent to the previous attempt (e.g., within 2 weeks of the current date), or reserved relative to the previous attempt (e.g., within 2 weeks of the current date).

[0067] In some cases, the target achievement date can be updated based on an estimated duration to achieve the goal. Updating the target achievement date in this manner can be done to avoid feelings of discouragement felt by cohort members when they approach the target achievement date but do not make sufficient progress. The estimated duration to achieve the goal can be determined based on sleep performance data from the therapeutic user's current sleep session (e.g., last night's sleep session), sleep performance data from the therapeutic adjacent individual's current sleep session, and / or historical sleep performance data from one or all users. For example, if current and historical sleep performance data associated with the therapeutic user indicates that the therapeutic user is gradually using the respiratory therapy device for more hours each night, but less than expected for the near target achievement date of the set goal, an estimate can be made of when the therapeutic user is likely to achieve the goal. This estimate can then be used to update the target achievement date. The target achievement date can be updated proactively, equitably, or reserve against the estimate. In some cases, estimates can be generated for other purposes, such as to help incentivize cohort members to achieve their goals on time or early, or to evaluate whether cohort members are improving in their attempts to achieve their goals.

[0068] Examples of possible goals include using a therapeutic device for a certain amount of time each night, having the therapeutic user and the therapeutic adjacent individual sleep in the same environment for at least a threshold number of nights each week, improving sleep performance metrics of the therapeutic user and the therapeutic adjacent individual, improving collaborative sleep performance metrics, improving subjective feedback (e.g., improving responses to routine questions about how rested cohort members feel), stopping or minimizing snoring, losing weight, improving mood, reducing sleepiness between sleep sessions, improving compliance with using a therapeutic device, improving compliance with using a system for monitoring sleep performance (e.g., activating a sleep monitoring application on a smartphone every night), etc.

[0069] In some cases, an interactive feed may be provided to share sleep-related data (e.g., individual sleep performance scores) and / or goals among cohort members. The interactive feed may allow cohort members to comment on each other's entries, for example, by text-based comments, image-based comments, or reactions. The reactions may include any number of pre-defined reactions (e.g., "like," "thumbs up," various emoticons, etc.). In some cases, the reactions may be recorded by counting the number of reactions presented with the entry on the feed. In some cases, the interactive feed may provide further motivation to achieve a given goal. In some cases, the sleep-related data and / or goals of a cohort member or other members of the cohort may be shared with external individuals (e.g., individuals not within the sleep cohort, e.g., different sleep cohort members). Such data sharing may allow individuals to comment on each other's entries in a similar interactive feed, providing further motivation to achieve a given objective, and obtain good sleep hygiene and overall sleep quality.

[0070] A cohort member's user device (e.g., a smartphone) may transmit summary information based on the sleep performance data when sharing data to an interactive feed. The summary information may include sleep-related data, comments provided by the member, etc. In some cases, the summary information may be transmitted directly to the user devices of other cohort members. In other cases, the summary information may be transmitted to a network-accessible server (e.g., via a local area network, a wide area network, the cloud, or the Internet), which may then be accessed by another cohort member's user device. When a server is used, unique identifiers (UIDs) associated with each member of the cohort may be correlated directly or via the cohort's UID.

[0071] In some cases, coaching suggestions may be identified and provided to improve the cohort member's sleep performance metrics or the cohort's collaborative sleep performance metrics. The coaching suggestions may be recommendations to take or not take certain actions. In some cases, but not necessarily, these actions may be similar to actions associated with goal setting, as described herein. Coaching suggestions may be generated automatically based on an analysis of historical sensor data and / or historical sleep performance metrics, or may be generated manually, for example, in response to subjective feedback.

[0072] If automatically generated, the coaching suggestions are obtained by analyzing the sleep performance data and sleep performance metrics to identify factors that affect the history of a given sleep performance metric, and then identifying a suggested action related to the factors. The suggested action may be selected as an action that is expected to improve the given sleep performance metric in the future (e.g., improve future sleep performance metrics). The coaching suggestions can then be generated as suggestions designed to get the cohort members to perform the suggested action. The suggested action may be to perform the given action or to avoid performing the given action.

[0073] If manually generated, the coaching suggestions may be based on certain subjective feedback from the cohort members. For example, a cohort member may say that they prefer to fall asleep no later than 10 p.m., or may determine that they feel unrested when they fall asleep after 10 p.m. the previous night. In this case, a coaching suggestion may be automatically generated to encourage the cohort member to try to fall asleep by 10 p.m. or take other measures to encourage falling asleep by 10 p.m.

[0074] In some cases, manually generated coaching suggestions may be provided to individual cohort members, for example, coaching suggestions for remembering to use a respiratory therapy device may be provided only to therapy users.

[0075] Coaching suggestions may be direct or indirect. Direct coaching suggestions indicate a desired outcome. Indirect coaching suggestions do not necessarily represent an expected outcome, but are expected to achieve the desired outcome. Indirect suggestions may be implicit, implicit, or ambiguous. An implicit suggestion is designed to achieve a desired outcome without the individual recognizing that the implication is aimed at achieving the desired outcome. An implicit suggestion is designed to achieve a desired outcome by suggesting an action that may be related to the desired outcome. An implicit suggestion does not directly indicate the desired outcome, although an individual may recognize that the implicit suggestion is related to an action related to the desired outcome and recognize that performing the action will improve the desired outcome. In some cases, an indirect suggestion may be in the form of an explanation or question rather than explicitly indicating an action to be performed. For example, instead of explicitly suggesting a therapy user to use a respiratory therapy system in their next sleep session, an indirect coaching suggestion may be used as a reminder to review the suitability of the user interface.

[0076] In the example where the desired outcome is to use the respiratory treatment system on more days per week, various coaching suggestions can be used. A direct coaching suggestion is to sleep with the respiratory treatment on at least five days this week. An implicit coaching suggestion can be in the form of a series of prompts to the therapy user, such as providing a motivational prompt each morning after using the respiratory treatment system, which can subconsciously motivate the therapy user to use the respiratory treatment system more frequently during the week. An implicit suggestion can be presented as a notification indicating how much time the respiratory treatment system was used during the previous sleep session and can suggest that the therapy user try to reach or exceed the usage time of the previous sleep session. Thus, the implicit suggestion is intended to improve the length of usage, but has the effect of increasing the opportunities for the therapy user to use the respiratory treatment system not only during the next sleep session, but potentially also during subsequent sleep sessions.

[0077] Coaching suggestions based on subjective feedback or data related to a particular cohort member may be vague or explicit. In some cases, vague suggestions may be a form of indirect suggestions. In some cases, explicit suggestions may be a form of direct suggestions. An explicit coaching suggestion is a suggestion that is indicative of the underlying subjective feedback or underlying data. For example, if the subjective feedback indicates that the therapeutic adjacent individual wants the therapeutic user to use the respiratory treatment device, or that the therapeutic adjacent individual feels that the therapeutic user is not feeling well-rested after several days of sleep sessions without the respiratory treatment device, an explicit coaching suggestion may encourage the therapeutic adjacent individual to use the respiratory treatment device to ensure that the therapeutic adjacent individual gets quality sleep. This explicit coaching suggestion may indicate a potential sleep-related problem (e.g., treating the adjacent individual's sleep quality). Similarly, an implicit coaching suggestion may be to alert the therapeutic user to properly adjust the straps of the user interface, or to alert the therapeutic user that he or she used the respiratory treatment device for one minute during the last sleep session, perhaps hoping to improve during the next sleep session. Such vague coaching reminders may not directly indicate an underlying sleep-related concern, but may have the effect of improving the underlying sleep-related concern. Thus, while a suggestion to adjust the user interface strap may not recognize that the therapeutic proximate individual desires that the therapeutic user use the respiratory treatment device to achieve better quality sleep, the suggestion has the effect of bringing the respiratory treatment device to the forefront of the therapeutic user's mind, and may result in the desired outcome of the therapeutic user using the respiratory treatment device, especially if provided near the start of a sleep session.

[0078] In another example, an explicit coaching suggestion may be a suggestion to a therapeutic adjacent individual to encourage the therapeutic user to use the therapeutic device longer in the next sleep session because the previous sleep session only used it for 1 hour. This explicit coaching suggestion reveals data relevant to the therapeutic user, i.e., the length of time the therapeutic device was used during the previous sleep session. Similarly, an ambiguous coaching suggestion may be a suggestion to a therapeutic adjacent individual to encourage the therapeutic user to use the therapeutic device for at least 5 hours during the next sleep session. Such an ambiguous coaching suggestion may provide a similar effect without revealing the underlying data (e.g., the therapist's data).

[0079] Coaching suggestions may be personal in nature or may relate to an entire cohort, for example, an individual coaching suggestion may be to have an individual cohort member take or not take a particular action, and a cohort coaching suggestion may be to have all members of a cohort take or not take a particular action.

[0080] The coaching suggestions can be evaluated with subjective feedback or objective data. When subjective feedback is used, cohort members can indicate the success of the suggestions they attempted. For example, they can respond to prompts asking if they tried the suggestions and if they felt like they had a break by saying "yes, I tried the suggestions" and "yes, I felt like I had a break." When objective data is used, one or more sleep performance metrics from a previous sleep session can be analyzed and / or compared to historical sleep performance metrics to determine if an improvement occurred. The system can assume that the suggestions were made or can prompt the member to indicate if the suggestions were made. Thus, the effectiveness of the suggestions can be evaluated based on the detected changes in the sleep performance metrics.

[0081] In some cases, an incentive system can be used to provide additional incentives to individual members of a cohort or to the cohort as a whole. Incentives can be based on achieving a threshold sleep performance metric (e.g., a threshold sleep performance score), achieving a goal or goal milestone (e.g., quantified progress toward a goal), and / or implementing a coaching suggestion (e.g., confirmation to take or not take a particular action). In some cases, cohort incentives can be provided based on the performance of the entire cohort, for example, when a threshold collaborative sleep performance metric is reached or when a cohort goal is reached.

[0082] In some cases, incentives may be provided individually, such as when individual cohort members reach a threshold individual sleep performance score or when they implement a personalized coaching suggestion. In some cases, a combination of individual and cohort incentives may be provided. In one example, a therapeutic user may reach a threshold individual sleep performance score, but the therapeutic adjacent individuals do not reach their respective threshold individual sleep performance scores, and the cohort as a whole does not reach the threshold collaborative sleep performance score. In this example, only the therapeutic user receives the incentive, although this is not always the case.

[0083] In some cases, cohort members may earn individual incentives based on the sleep sessions of another cohort member. For example, a therapeutic user may earn a particular incentive only if a therapeutic neighbor achieves a sleep performance score higher than a threshold. In this case, cohort members may be encouraged to improve the sleep of other members in the cohort.

[0084] Incentives may be provided for individual sleep sessions or for longer durations (e.g., all sleep sessions in a week or all sleep sessions in a month). For longer durations, incentives may be based on achieving a desired result (e.g., achieving a threshold sleep performance score or achieving a goal) once during each sleep session, or for at least a threshold number of sleep sessions.

[0085] Incentives may be provided by cohort members, other cohort members, or a third party. Any suitable incentive may be used. For example, incentives may take the form of cash prizes, gift cards, gift items, discount codes to retailers, etc. In some cases, incentives may be sleep-related or related to goals or coaching suggestions.

[0086] In some cases, incentives can be dynamically changed based on historical data (e.g., historical sleep performance scores) and / or current data. For example, if a cohort member was offered an incentive worth $10, but analysis of the historical data determines that the cohort member did not improve their sleep performance score or deviated from a desired goal, the incentive can be automatically adjusted to increase (e.g., increase to $15) to provide a stronger incentive for improving sleep performance and overall sleep quality.

[0087] In some cases, sensor data from the therapeutic adjacent individual can be used to control parameters associated with the therapeutic user's therapeutic device (e.g., respiratory therapy device). This control can be dynamic (e.g., automatically during the same sleep session), automatic (e.g., automatically between a current sleep session and a subsequent sleep session), or manual (e.g., recommended parameter adjustments provided to the therapeutic user after a current sleep session). In one example of dynamic control, if the sensor data indicates that the therapeutic adjacent individual is in a particular sleep state or stage, the air pressure delivered by the respiratory therapy device can be automatically adjusted to increase (e.g., to a level that is more effective but may be louder). In one example of manual control, whenever the seal quality of the therapeutic user's user interface falls below a threshold amount, if the sensor data indicates that the therapeutic adjacent individual has a micro-arousal, a message can be presented to the therapeutic user to take action to improve seal quality before a subsequent sleep session (e.g., by adjusting or replacing the conduit or user interface).

[0088] In one example, the therapeutic user may begin using the respiratory treatment device before the therapeutic adjacent individual begins a sleep session. It may be identified that the therapeutic adjacent individual is not attempting to fall asleep, and the respiratory treatment device may act with normal parameters. However, if the sensor data indicates that the therapeutic adjacent individual is attempting to fall asleep, the respiratory treatment device may operate with adjusted parameters that operate the respiratory treatment device more quietly (e.g., operate the flow generator motor at a slower speed). The respiratory treatment device may continue to operate with the adjusted parameters until the sensor data indicates that the therapeutic adjacent individual has reached a particular sleep state (e.g., sleep) or sleep stage (e.g., light sleep). In some cases, the respiratory treatment device may continue to operate with the adjusted parameters for a preset duration while the therapeutic adjacent individual is still attempting to fall asleep, e.g., if the therapeutic adjacent individual does not fall asleep, before reverting to the previous or other parameters.

[0089] In some cases, sensor data associated with one cohort member may be collected from a different set of sensors of another cohort member. For example, sleep performance data of a therapeutic user may be obtained from one or more sensors incorporated in the therapeutic user's respiratory treatment device and user device, while sleep performance data of a therapeutic adjacent individual may be obtained from one or more sensors incorporated in the therapeutic user's user device. In this case, sensor data associated with the therapeutic user may be synchronized with sensor data associated with the therapeutic adjacent individual. Synchronizing the sensor data may include synchronizing the data according to a timestamp, synchronizing the data according to a commonly detected event (e.g., adjusting the data based on detection of a particularly loud snoring sound), or any combination thereof. The resulting synchronized sensor data may be used i) to improve the signal-to-noise ratio of certain raw sensor data (e.g., by identifying and filtering noise or by identifying and amplifying a desired signal), ii) to perform further analysis and / or obtain sleep performance metrics, iii) to confirm events that may have been detected using the raw sensor data, or iv) any combination of i-iii. In some cases, performing further analysis can include detecting the location of the cohort members relative to the various sensors used to collect the raw sensor data (e.g., sensors in the treatment user's smartphone and sensors in the treatment adjacent individual's smartphone). Such location detection can be used to identify the location of the treatment user or treatment adjacent individual within the environment (e.g., position on a bed or posture in a room). In some cases, parameters of the respiratory treatment device can be adjusted based on this location information.

[0090] Certain aspects and features of the present disclosure relate to the generation of simulated respiratory therapy device sounds. Such simulated sounds can be simulated by any suitable device, such as a user device (e.g., a smartphone) that includes a speaker and a microphone. The speaker can be used to output the simulated respiratory therapy device sounds, while the microphone can be used to monitor the output sounds and adjust as necessary to ensure accuracy of the simulation.

[0091] The simulated respiratory therapy device sounds can be generated on demand (e.g., programmatically generated via an electronic oscillator) or can be pre-recorded (e.g., a pre-recorded file including electronically generated sounds or a pre-recorded file including a recording of a physical respiratory therapy device). In some cases, the output simulated respiratory therapy device sounds can be selected by an individual using the respiratory therapy device simulator (e.g., a user receiving respiratory therapy, an individual who will begin respiratory therapy in the future, an individual sleeping in the same environment as a user receiving respiratory therapy, or an individual sleeping in the same environment as an individual who will begin respiratory therapy in the future). Selection of a particular simulated respiratory therapy device sound can include selecting a model and / or type of respiratory therapy device, and optionally selecting one or more settings or parameters. For example, an individual sleeping in the same environment as a future therapy user can select the model and default settings that the future therapy user will use. Based on this selection, the simulator can adjust the generated sounds or select a particular pre-recorded file to accurately simulate the simulated respiratory therapy device sounds associated with the individual's selection. In some cases, the simulated respiratory therapy device sounds can be adjusted based on an actual model of the therapy user and / or the actual settings of the respiratory therapy device.

[0092] In some cases, the individual can adjust the output of the simulated respiratory treatment device sound (e.g., adjust the volume of the simulated respiratory treatment device sound). Based on this adjustment (e.g., adjusted volume), a respiratory treatment recommendation can be provided. The respiratory treatment recommendation can include a respiratory treatment device model, a user interface type and / or model, a conduit type and / or model, a set of respiratory treatment device settings, or any combination thereof. For example, a maximum volume level that the individual would like to tolerate for a comfortable sleep can be set, and the simulator can provide a respiratory treatment recommendation in a particular user interface that achieves the desired result.

[0093] In some cases, the individual may perform a sleep session while outputting the simulated respiratory therapy device sound. In this case, the sensor data may be used to track each individual's sleep performance as they experience the simulated respiratory therapy device sound. Such sensor data may be used to determine sleep performance metrics that may be used to identify the individual's degree of tolerance to the simulated respiratory therapy device sound. The individual may be a future therapeutic user or a future therapeutic adjacent individual. In some cases, the sensor data may be used to automatically adjust the output simulated respiratory therapy device sound. For example, when the individual experiences a different sleep stage, the simulated respiratory therapy device sound may increase or decrease in volume or other characteristics of the sound (e.g., simulated unintentional leakage sound or other simulated sound associated with the use of the respiratory therapy device) may be automatically adjusted based on the individual's current sleep stage. In some cases, the simulated respiratory therapy device sound may be adjusted to be more invasive (e.g., at a higher volume or other characteristics that may disturb the individual's sleep) until the individual's sleep performance metric falls below a threshold. Thus, one or more sleep performance metrics may be used to objectively evaluate an individual's tolerance to different volumes and types of simulated respiratory therapy device sound.

[0094] In some cases, the simulated respiratory therapy device sound may be adjusted based on historical sleep performance information (e.g., historical sleep performance metrics and / or underlying sensor data) collected during a previous sleep session. In this case, the simulated respiratory therapy device sound used in the current sleep session may be different from the simulated respiratory therapy device sound used in the previous sleep session. The historical sleep performance information may then be compared to the sleep performance information from the current sleep session to generate a comparison between the two different simulated respiratory therapy device sounds. This comparison may be presented to the individual. In some cases, recommendations may be generated based on a comparison between the sleep performance information of the previous sleep session and the current sleep session. The difference in the simulated respiratory therapy device sound may be due to the use of a different respiratory therapy device model, a difference in volume, or a difference in other characteristics. This allows the individual to select a more desired configuration based on the sleep performance comparison. For example, a prospective therapy user may try the simulated respiratory therapy device sound for the first respiratory therapy device on the first night and the simulated respiratory therapy device sound for the second respiratory therapy device on the second night. If the future treatment user's sleep performance metrics improve on the second night, the future treatment user may choose to continue using the second respiratory treatment device instead of the first respiratory treatment device.

[0095] In some cases, the simulated respiratory therapy device sounds may be modified based on medical information associated with the individual. Such medical information may include height, weight, sex, diagnosis, or other such information. For example, when the simulator is first launched, the simulator may prompt the individual to answer certain questions (e.g., obstructive sleep apnea questions such as the STOP-Bang questionnaire), and the simulator may use these answers to modify (e.g., change and / or select) the simulated respiratory therapy device sounds. For example, if the individual answers the questions in a manner that indicates a high likelihood of obstructive sleep apnea, the simulated respiratory therapy device sounds may be modified to include characteristics associated with use of a respiratory therapy device by a person suffering from obstructive sleep apnea.

[0096] Certain aspects and features of the present disclosure also relate to an interactive system for identifying issues associated with a sleep cohort whose members are therapy users. Once identified, the system can provide information or lead to suggestions to alleviate the identified issues. For example, if a cohort member answers a questionnaire in a manner that indicates anxiety about the treatment and its impact on the cohort member's sleep quality, the system can provide information and / or coaching suggestions to alleviate the cohort member's anxiety. For example, the system can provide knowledge and tips, interactive content to resolve common questions about the treatment (e.g., the noise of the treatment device), interactive topics to help cohort members exchange points of interest with each other, and / or cross-orientation content related to the questionnaire responses of other members of the cohort. Such information and / or coaching suggestions may be in text, audio, video, or any other format. In some cases, historical answers to questions can be used to generate new questions for future questionnaires. The questionnaires can be used as part of subjective feedback corresponding to a sleep quality score or in other ways.

[0097] In some cases, the system may allow the user to respond in free text or speech (e.g., via a microphone) that can be interpreted by a natural language processor. This interpretation, optionally along with sentiment analysis of the input, leads to extracting useful information that can be stored in a structured format. In some cases, the system may allow the user to respond with fixed choices (e.g., multiple choice, Likert scale, or graphic choice).

[0098] In some cases, the system can provide coaching suggestions related to the concerns of other members in the cohort.

[0099] These illustrative examples are presented to introduce the reader to the general subject matter discussed herein and are not intended to limit the scope of the disclosed concepts. The following section describes various additional features and examples with reference to the drawings, in which like numerals indicate like elements and directional illustrations are for purposes of illustrating the illustrative examples, but as such are not intended to limit the disclosure. Elements included in the figures may not be to scale.

[0100] 1, a system 100 according to some implementations of the present disclosure is shown. The system 100 includes a control system 110, a memory device 114, an electronic interface 119, a respiratory treatment system 120, one or more sensors 130, one or more user devices 170, one or more light sources 180, and one or more activity trackers 190.

[0101] In some cases, a single system 100 can be used to monitor multiple members of a sleep cohort. In some such cases, a single system 100 may include multiple user devices 170 incorporating multiple instances of one or more sensors 130. In some cases, multiple iterations of the system 100 can be used to monitor multiple members of a sleep cohort (e.g., a separate system 100 for each member of the cohort). Aspects and features of the system 100 can be used to monitor sleep and interact with any member of the cohort, for example, therapeutic users and therapeutic adjacent users.

[0102] The control system 110 includes one or more processors 112 (hereinafter referred to as processor 112). The control system 110 is generally used to control (e.g., operate) various components of the system 100 and / or analyze data acquired and / or generated by the components of the system 100. The processor 112 may be a general-purpose or special-purpose processor or microprocessor. Although one processor 112 is shown in FIG. 1, the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that may be in a single housing or may be located remotely from one another. The control system 110 (or any other control system) or a portion of the control system 110, such as the processor 112 (or any other processor or portion of any other control system), may be used to perform one or more steps of the methods described and / or claimed herein. The control system 110 may be coupled to and / or located, for example, in a housing of the user device 170, a portion (e.g., a housing) of the respiratory system 120, and / or in a housing of one or more sensors 130. The control system 110 may be centralized (in one such housing) or distributed (in two or more such housings that are physically distinct). In such implementations that include two or more housings to house the control system 110, such housings may be located proximate to and / or distant from one another.

[0103] The memory device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The memory device 114 may be any suitable computer-readable storage device or medium, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. Although one memory device 114 is shown in FIG. 1, the system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 may be coupled to and / or located within the housing of the respiratory device 122, the housing of the user device 170, the housing of one or more sensors 130, or any combination thereof. Like the control system 110, the memory device 114 may be centralized (in one storage device) or distributed (in two or more physically distinct storage devices).

[0104] In some implementations, the memory device 114 (FIG. 1) stores a member profile associated with a cohort member. The member profile can include an identification of a sleep cohort to which the member belongs. The member profile can include, for example, demographic information associated with the cohort member, biometric information associated with the cohort member, medical information associated with the cohort member, self-reported feedback, sleep parameters associated with the cohort member (e.g., sleep-related parameters recorded from one or more early sleep sessions), or any combination thereof. The demographic information can include, for example, information indicative of the age of the cohort member, the sex of the cohort member, the race of the cohort member, the geographic location of the cohort member, relationship status, family history of insomnia, employment status of the cohort member, education status of the cohort member, socioeconomic status of the cohort member, or any combination thereof. The medical information can include, for example, indicative of one or more medical conditions associated with the cohort member, medication use of the cohort member, or both. The medical information data may also include a fall risk assessment associated with the user (e.g., a fall risk score using the Morse Fall Scale), a Multiple Sleep Onset Latency Test (MSLT) test result or score, and / or a Pittsburgh Sleep Quality Index (PSQI) score or value. The self-reported feedback may include information indicative of a self-reported subjective sleep score (e.g., poor, average, excellent), a cohort member's self-reported subjective stress level, a cohort member's self-reported subjective fatigue level, a cohort member's self-reported subjective health status, a life event recently experienced by a cohort member, or any combination thereof.

[0105] The electronic interface 119 is configured to receive data (e.g., physiological data and / or audio data) from one or more sensors 130, which is stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with the one or more sensors 130 using a wired or wireless connection (e.g., using an RF communication protocol, a WiFi communication protocol, a Bluetooth communication protocol, over a cellular network, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 may further include one or more processors and / or one or more memory devices the same as or similar to the processor 112 and memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated with the user device 170. In some other implementations, the electronic interface 119 is coupled to the control system 110 and / or the memory device 114 or is integrated with the control system 110 and / or the memory device 114 (e.g., within the housing).

[0106] As mentioned above, in some implementations, the system 100 optionally includes a respiratory system 120 (also referred to as a respiratory treatment system). The respiratory system 120 may include a respiratory pressure treatment device 122 (herein referred to as a respiratory device 122), a user interface 124 (also referred to as a mask or patient interface), a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidification tank 129, or any combination thereof. In some implementations, the control system 110, the memory device 114, the display device 128, one or more sensors 130, and the humidification tank 129 are part of the respiratory device 122. Respiratory pressure treatment refers to providing an air supply to an airway entrance of a treatment user at a controlled target pressure that is nominally positive relative to the atmosphere throughout the treatment user's respiratory cycle (as opposed to negative pressure treatments such as, for example, a tank ventilator or cuirass). The respiratory device 120 is commonly used to treat individuals suffering from one or more sleep-related breathing disorders (eg, obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0107] The respiratory device 122 is generally used to generate pressurized air that is delivered to a therapy user (e.g., using one or more motors (e.g., blower motors) driving one or more compressors). In some implementations, the respiratory device 122 generates a continuous constant air pressure, which is delivered to the therapy user. In other implementations, the respiratory device 122 generates two or more predetermined pressures (e.g., a first predetermined pressure and a second predetermined pressure). In yet another embodiment, the respiratory device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the respiratory device 122 can deliver at least about 6 cm H2O, at least about 10 cm H2O, at least about 20 cm H2O, between about 6 cm H2O and about 10 cm H2O, between about 7 cm H2O and about 12 cm H2O, etc. The respiratory device 122 can also deliver pressurized air at a predetermined flow rate, e.g., between about -20 liters / minute and about 150 liters / minute, while maintaining a positive pressure (relative to ambient pressure).

[0108] The user interface 124 engages a portion of the treatment user's face and delivers pressurized air from the breathing device 122 to the treatment user's airway to help prevent narrowing and / or collapse of the airway while sleeping. This may also increase the treatment user's oxygen intake while sleeping. Depending on the treatment being applied, the user interface 124 may form a seal with an area or portion of the user's face, for example, to facilitate delivery of gas at a pressure that is sufficiently variable with ambient pressure to effectively perform the treatment, for example, at a positive pressure of about 10 cmH2O relative to ambient pressure. For other forms of treatment, such as delivery of oxygen, the user interface may not include a sufficient seal to facilitate delivery of a gas supply to the airway at a positive pressure of about 10 cmH2O.

[0109] As shown in FIG. 2, in some implementations, the user interface 124 is a face mask that covers the treatment user's nose and mouth (e.g., as shown in FIG. 2). Alternatively, the user interface 124 may be a nasal mask that provides air to the treatment user's nose, or a nasal pillow mask that provides air directly to the treatment user's nostrils. The user interface 124 may include a number of straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing the interface on a portion of the treatment user (e.g., face) and a conformal cushion (e.g., silicone, plastic, foam, etc.) that facilitates an airtight seal between the user interface 124 and the treatment user. In some examples, the user interface 124 may be a tube-up mask, with the straps of the mask configured to act as conduits for delivering pressurized air to the face or nasal mask. The user interface 124 may also include one or more vents that allow carbon dioxide and other gases exhaled by the treatment user 210 to escape. In other implementations, the user interface 124 may include a mouthpiece (eg, a night guard mouthpiece, a jaw positioning device, etc., shaped to fit the treatment user's teeth).

[0110] The conduit 126 (also referred to as an air circuit or tube) allows air to flow between two components of the respiratory device 120 (e.g., the respiratory device 122 and the user interface 124). In some embodiments, there may be separate branches of this conduit for inhalation and exhalation. In other implementations, a single branch conduit is used for both inhalation and exhalation. Generally, the respiratory treatment system 120 forms an air path extending between the motor of the respiratory treatment device 122 and the user and / or the user's airway. As such, the air path generally includes at least the motor of the respiratory treatment device 122, the user interface 124, and the conduit 126.

[0111] One or more of the breathing device 122, the user interface 124, the conduit 126, the display device 128, and the humidification tank 129 may include one or more sensors, such as a pressure sensor, a flow sensor, or more generally, any other sensor 130 described herein. These one or more sensors may be used, for example, to measure the air pressure and / or the flow rate of the pressurized air provided by the breathing device 122.

[0112] The display device 128 is typically used to present images, including still images, video images, or both, and / or information related to the respiratory device 122 . For example, the display device 128 can provide information regarding the status of the breathing device 122 (e.g., whether the breathing device 122 is on / off, the pressure of the air exhaled by the breathing device 122, the temperature of the air exhaled by the breathing device 122, etc.) and / or other information (e.g., sleep performance metrics, sleep performance scores, sleep scores, or treatment scores (e.g., myAir® scores as described in WO 2016 / 061629 and US 2017 / 0311879, each of which is combined by reference in its entirety), the current date and time, personal information of a user receiving treatment, a user questionnaire, etc. In some implementations, the display device 128 functions as a human machine interface (HMI) including a graphic user interface (GUI) configured to display images as an input interface. The display device 128 may be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input by a human interacting with the breathing device 122.

[0113] The humidification tank 129 is coupled or integrated with the respiratory device 122 and includes a reservoir that can be used to humidify the pressurized air delivered from the respiratory device 122. The respiratory device 122 can include a heater that heats water in the humidification tank 129 to humidify the pressurized air delivered to the treatment user. Additionally, in some implementations, the conduit 126 can further include a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air delivered to the treatment user. The humidification tank 129 can be fluidly coupled to a water vapor inlet of the air passage and configured to deliver water vapor into the air passage via the water vapor inlet, or can be formed in line with the air passage as part of the air passage itself. In other implementations, the respiratory treatment device 122 or the conduit 126 can include a waterless humidifier. The waterless humidifier can incorporate a sensor that interfaces with other sensors located elsewhere in the system 100.

[0114] The breathing system 120 can be, for example, a mechanical ventilator or a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure (APAP) system, a bilevel or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to a treatment user. An APAP system automatically varies the air pressure delivered to a treatment user, for example, based on breathing data associated with the treatment user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure (e.g., expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure.

[0115] Referring to FIG. 2, a portion of the system 100 (FIG. 1) according to some implementations is shown. A therapy user 210 of the respiratory device 120 and a therapy adjacent individual 220 (e.g., a bed partner) are located in a bed 230 in an environment 280 and lie on a mattress 232. A user interface 124 (e.g., a full face mask) can be worn by the therapy user 210 during the therapy user's sleep session. The user interface 124 is fluidly coupled and / or connected to the respiratory device 122 via a conduit 126. Conversely, the respiratory device 122 delivers pressurized air to the therapy user 210 via the conduit 126 and the user interface 124 to increase air pressure in the therapy user's 210 throat, thereby helping to prevent airway closure and / or narrowing during sleep. The respiratory treatment device 122 may include a display device 128 that allows a user to interact with the respiratory treatment device 122. The respiratory treatment device 122 may further include a humidification tank 129 that stores water for humidifying the pressurized air. The respiratory treatment device 122 may be placed on a nightstand 240 directly adjacent to the bed 230, or more generally on any surface or structure generally adjacent to the bed 230 and / or the user 210, as shown in Figure 2. The user may also wear, for example, a blood pressure device and / or an activity tracker 190 while lying on the mattress 232 in the bed 230.

[0116] 2, the therapeutic user 210 may have a user device 170A and the therapeutic adjacent individual 220 may have their own user device 170B, although this is not necessarily the case. User devices 170A, 170B may be iterations of user device 170, with each device including any combination of one or more sensors 130 for acquiring sensor data that may be used to generate the sleep performance metrics disclosed herein.

[0117] Returning to Figure 1, the one or more sensors 130 of the system 100 may include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmography (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a humidity sensor 176, a laser radar sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data received and stored by the memory device 114 or one or more other memory devices.

[0118] Although the one or more sensors 130 are shown and described as including each of a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, an RF receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmography (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a humidity sensor 176, and a laser radar sensor 178, more generally, the one or more sensors 130 may include any combination and number of the sensors described and / or illustrated herein.

[0119] One or more sensors 130 can be used to generate sensor data, such as physiological data, audio data, or both. The control system 110 can use the physiological data generated by the one or more sensors 130 to determine a sleep-wake signal and one or more sleep-related parameters associated with the cohort members during a sleep session. The sleep-wake signal can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-arousals, rapid eye movement (REM) stage, a first non-REM stage (commonly referred to as "N1"), a second non-REM stage (commonly referred to as "N2"), a third non-REM stage (commonly referred to as "N3"), or any combination thereof. N1 and N2 can be considered light sleep stages, and N3 can be considered a deep sleep stage. Methods for determining sleep stages based on physiological data generated by one or more sensors, such as sensor 130, are described, for example, in WO 2014 / 047310, U.S. Pat. No. 10,492,720, U.S. Pat. No. 10,660,563, U.S. Pat. App. Pub. No. 2020 / 0337634, WO 2017 / 132726, WO 2019 / 122413, U.S. Pat. App. Pub. No. 2021 / 0150873, WO 2019 / 122414, U.S. Pat. App. Pub. No. 2020 / 0383580, each of which is incorporated herein by reference in its entirety. Additionally, the sleep-wake signal can be time-stamped to indicate the time a cohort member got into bed, the time a cohort member left the bed, the time a cohort member attempted to fall asleep, etc. During the sleep session, the sleep-wake signal may be measured by the sensor 130 at a predetermined sampling rate, e.g., one sample per second, one sample per 30 seconds, one sample per minute, etc. Examples of one or more sleep-related parameters that may be determined for a cohort member during a sleep session based on the sleep-wake signal include total time in bed, total time asleep, sleep onset latency, post-sleep wake parameters, sleep efficiency, fragmentation index, or any combination thereof.

[0120] The physiological and / or audio data generated by the one or more sensors 130 may also be used to determine a respiratory signal associated with a cohort member during a sleep session. The respiratory signal typically indicates the breathing or breathing of the cohort member during a sleep session. The respiratory signal may indicate, for example, respiratory rate, respiratory rate variability, inhalation amplitude, exhalation amplitude, inhalation to exhalation ratio, number of events per hour, patterns of events, pressure settings of the respiratory device 122, or any combination thereof. These events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, RERA, flow limitation (e.g., an event in which flow does not increase despite an increase in negative intrathoracic pressure indicating an increased effort), mask leak (e.g., from the user interface 124), restless legs, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, epileptic seizure, seizure, elevated blood pressure, hyperventilation, or any combination thereof. The event can be detected by any means known in the art, for example, as described in U.S. Pat. No. 5,245,995, U.S. Pat. No. 6,502,572, WO 2018 / 050913, WO 2020 / 104465, each of which is incorporated by reference in its entirety.

[0121] The pressure sensor 132 outputs pressure data that may be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is an air pressure sensor (e.g., an air pressure sensor) that generates sensor data indicative of the respiration (e.g., inhalation and / or exhalation) and / or ambient pressure of a therapeutic user using the respiratory system 120. In such embodiments, the pressure sensor 132 may be coupled to or integrated with the respiratory device 122. The pressure sensor 132 may be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, an electric potential sensor, or any combination thereof. In one example, the pressure sensor 132 may be used to determine the blood pressure of a cohort member.

[0122] The flow sensor 134 outputs flow data that may be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 is used to determine the airflow rate from the respiratory device 122, the airflow rate through the conduit 126, the airflow rate through the user interface 124, or any combination thereof. In such embodiments, the flow sensor 134 may be coupled to or integrated with the respiratory device 122, the user interface 124, or the conduit 126. The flow sensor 134 may be, for example, a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof.

[0123] The temperature sensor 136 outputs temperature data that may be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicative of a cohort member's core body temperature, a cohort member's skin temperature, the temperature of the air flowing from the respiratory device 122 and / or through the conduit 126, the temperature within the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon band gap temperature sensor or semiconductor based sensor, a resistance temperature detector, or any combination thereof.

[0124] The motion sensor 138 outputs motion data that may be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 may be used to detect the user's motion during a sleep session and / or the motion of any of the components of the respiratory treatment system 120, such as the respiratory treatment device 122, the user interface 124, or the conduit 126. The motion sensor 138 may include one or more inertial sensors, such as, for example, an accelerometer, a gyroscope, and a magnetometer. The motion sensor 138 may be used to detect motion or acceleration associated with arterial pulsations, such as pulsations in or around the user's face and proximal to the user interface 124, and is configured to detect pulsation shape, rate, amplitude, or volume characteristics. In some implementations, the motion sensor 138 optionally or additionally generates one or more signals representative of the user's body motion, from which a signal representative of the user's sleep state may be obtained, for example, via the user's respiratory motion.

[0125] The microphone 140 outputs audio data that may be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The audio data generated by the microphone 140 may be played as one or more sounds (e.g., sounds from cohort members such as the treatment users 210 and / or treatment adjacent individuals 220) during a sleep session. The audio data from the microphone 140 may also be used (e.g., with the control system 110) to identify events experienced by the cohort members during a sleep session, as described in further detail herein. The microphone 140 may be coupled to or integrated with the respiratory device 122, the user interface 124, the conduit 126, the user device 170A, or the user device 170B. For example, the microphone 140 may be located within the respiratory treatment device 122, the user interface 124, the conduit 126, or other components. The microphone 140 may also be located adjacent to or coupled to the exterior of the respiratory treatment device 122, the exterior of the user interface 124, the exterior of the conduit 126, or the exterior of any other component. The microphone 140 may also be a component of the user device 170 (e.g., the microphone 140 is a smartphone microphone). The microphone 140 may be integrated into the user interface 124, the conduit 126, the respiratory treatment device 122, or any combination thereof. In general, the microphone 140 may be located at any point within or adjacent to the air path of the respiratory treatment system 120, which includes at least the motor, the user interface 124, and the conduit 126 of the respiratory treatment device 122. The air path is therefore also referred to as the acoustic path.

[0126] The speaker 142 outputs sound waves that can normally be heard by a cohort member using the system 100 (e.g., therapy user 210 of FIG. 2). In one or more implementations, the sound waves may be audible to a user of the system 100 or inaudible to a user of the system (e.g., ultrasound). The speaker 142 can function, for example, as an alarm clock or to play an alarm or message to the cohort member (e.g., in response to an event). In some implementations, the speaker 142 may be used to transmit audio data generated by the microphone 140 to the cohort member. The speaker 142 may be coupled to or integrated with the respiratory device 122, the user interface 124, the conduit 126, the user device 170A, or the user device 170B.

[0127] The microphone 140 and the speaker 142 can be used as separate devices. In some embodiments, the microphone 140 and the speaker 142 can be combined into an acoustic sensor 141, for example as described in WO 2018 / 050913, which is incorporated herein by reference in its entirety. In such an implementation, the speaker 142 generates or emits sound waves at predetermined intervals, and the microphone 140 detects the reflection of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency that is inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the sleep of the therapeutic user 210 or the therapeutic adjacent individual 220 (FIG. 2). Based at least in part on data from the microphone 140 and / or speaker 142, the control system 110 can determine the location of the therapeutic user 210 or therapeutic adjacent individual 220 (FIG. 2) and / or one or more sleep-related parameters described herein, such as a respiratory signal, a respiratory rate, an inhalation amplitude, an exhalation amplitude, an inhalation exhalation ratio, a number of events per time, an event pattern, a sleep stage, a pressure setting of the respiratory treatment device 122, a mouth leak status, or any combination thereof. In this context, a SONAR sensor may be understood to involve active acoustic sensing, such as by generating / transmitting an ultrasonic or low frequency ultrasonic sensing signal (e.g., within a frequency range of about 17-23 kHz, 18-22 kHz, or 17-18 kHz) through the air. Such a system may be considered in connection with WO 2018 / 050913 and WO 2020 / 104465, mentioned above. In some implementations, the speaker 142 is a bone conduction speaker. In some implementations, the one or more sensors 130 include (i) a first microphone that is the same as or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone that is the same as or similar to microphone 140 but is independent and separate from the first microphone that is integrated into acoustic sensor 141.

[0128] In some implementations, sensor 130 includes (i) a first microphone that is the same as or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone that is different from the first microphone that is the same as or similar to microphone 140 and integrated into acoustic sensor 141.

[0129] The RF transmitter 148 generates and / or transmits radio waves (e.g., high frequency band, low frequency band, long wave signal, short wave signal, etc.) having a predetermined frequency and / or amplitude. The RF receiver 146 detects reflections of the radio waves transmitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the location of the cohort members and / or one or more sleep-related parameters described herein. The RF receiver (RF receiver 146 and RF transmitter 148 or another RF pair) may also be used for wireless communication between the control system 110, the respiratory device 122, one or more sensors 130, the user device 170A, the user device 170B, or any combination thereof. Although the RF receiver 146 and the RF transmitter 148 are shown in FIG. 1 as separate and distinct elements, in some implementations the RF receiver 146 and the RF transmitter 148 are combined as part of the RF sensor 147. In some such embodiments, the RF sensor 147 includes control circuitry. A specific form of RF communication may be WiFi, Bluetooth, etc.

[0130] In some embodiments, the RF sensor 147 is part of a mesh system. One example of a mesh system is a WiFi mesh system, which may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / movable or fixed. In such an embodiment, the WiFi mesh system includes a WiFi router and / or a WiFi controller, and one or more satellites (e.g., access points), each of which includes an RF sensor identical or similar to the RF sensor 147. The WiFi router and satellites continuously communicate with each other using WiFi signals. The WiFi mesh system can be used to generate motion data based on changes in the WiFi signal between the router and the satellite (e.g., differences in received signal strength) due to the movement of an object or person partially obstructing the signal. This motion data can be indicative of movement, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0131] Camera 150 outputs image data playable as one or more images (e.g., still images, motion images, thermal images, or combinations thereof) that can be stored in memory device 114. Control system 110 can use the image data from camera 150 to determine one or more sleep-related parameters described herein. For example, image data from camera 150 can be used to identify the location of cohort members, determine the time that a cohort member gets into bed, and determine the time that a cohort member gets out of bed.

[0132] Infrared (IR) sensor 152 outputs infrared image data that can be played back as one or more infrared images (e.g., still images, video images, or both) that can be stored in memory device 114. Infrared data from IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the temperature of the cohort members and / or the movement of the cohort members. IR sensor 152 can also be used in combination with camera 150 when measuring the presence, location, and / or movement of cohort members. For example, IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, while camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0133] The PPG sensor 154 outputs physiological data associated with the cohort member that may be used to determine one or more sleep-related parameters, such as heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiration amplitude, expiration amplitude, inspiration-expiration ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 may be worn by the cohort member, embedded in clothing and / or fabric worn by the cohort member, embedded in and / or coupled to the user interface 124 and / or its associated helmet (e.g., straps, etc.).

[0134] The ECG sensor 156 outputs physiological data related to the electrical activity of the cohort member's heart. In some implementations, the ECG sensor 156 includes one or more electrodes positioned on or around a portion of the cohort member during a sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more sleep-related parameters described herein.

[0135] The EEG sensor 158 outputs physiological data related to electrical activity of the cohort member's brain. In some implementations, the EEG sensor 158 includes one or more electrodes placed on or around the cohort member's scalp during the sleep session. The physiological data from the EEG sensor 158 may be used, for example, to determine the cohort member's sleep state at any given time during the sleep session. In some implementations, the EEG sensor 158 may be integrated into the user interface 124 and / or an associated helmet (e.g., a strap, etc.).

[0136] The capacitance sensor 160, the force sensor 162, and the strain gauge sensor 164 output data that may be stored in the memory device 114 and used by the control system 110 to determine one or more sleep-related parameters described herein. The EMG sensor 166 outputs physiological data related to electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicative of the oxygen concentration of a gas (e.g., in the conduit 126 or in the user interface 124). The oxygen sensor 168 may be, for example, an ultrasonic oxygen sensor, an electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, the one or more sensors 130 further include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, an oximeter sensor, or any combination thereof.

[0137] The analyte sensor 174 may be used to detect the presence of an analyte in the exhaled breath of a cohort member (e.g., the therapy user 210). Data output by the analyte sensor 174 may be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analytes in the therapy user 210's breath. In some implementations, the analyte sensor 174 is placed near the mouth of the therapy user 210 to detect analytes in breath exhaled from the therapy user's 210 mouth. For example, if the user interface 124 is a face mask that covers the nose and mouth of the therapy user 210, the analyte sensor 174 may be placed in the face mask to monitor the therapy user's 210 mouth breathing. In other implementations, for example, if the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 may be placed near the nose of the therapy user 210 to detect analytes in breath exhaled through the therapy user's nose. In other implementations, when the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 can be located near the mouth of the treatment user 210. In this implementation, the analyte sensor 174 can be used to detect whether there is air unintentionally leaking from the mouth of the treatment user 210. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some implementations, the analyte sensor 174 can also be used to detect whether the treatment user 210 is breathing through the nose or mouth. For example, if the presence of an analyte is detected by data output by the analyte sensor 174 located near the mouth or in the face mask of the treatment user 210 (in implementations where the user interface 124 is a face mask), the control system 110 can use the data as an indication that the treatment user 210 is breathing through his or her mouth.

[0138] The humidity sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The humidity sensor 176 can be used to detect humidity in various areas around the treatment user (e.g., in the conduit 126 or the user interface 124, near the face of the treatment user 210, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the respiratory device 122, etc.). Thus, in some embodiments, the moisture sensor 176 can be located in the user interface 124 or the conduit 126 to monitor the humidity of the pressurized air from the respiratory device 122. In other implementations, the humidity sensor 176 is located near any area where humidity levels need to be monitored. The humidity sensor 176 may also be used to monitor the ambient humidity of the air in the environment 280 around the treatment user 210 and / or the treatment adjacent individual 220, for example, in a bedroom.

[0139] A Light Detection and Ranging (LiDAR) sensor 178 can be used for depth sensing. Such optical sensors (e.g., laser sensors) can be used to detect objects and create a three-dimensional (3D) map of a surrounding environment, such as a living space. LiDAR typically utilizes a pulsed laser to measure time of flight. LiDAR is also referred to as 3D laser scanning. In one use case of such a sensor, a fixed or mobile device (such as a smartphone) with a LiDAR sensor 178 can measure and map an area more than 5 meters away from the sensor. For example, LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor. The LiDAR sensor 178 can also automatically create a geofence for a RADAR system by using artificial intelligence (AI) to detect and classify features in space that may cause problems for the RADAR system, such as glass windows (which may be highly reflective to RADAR). For example, LiDAR can also be used to estimate a person's height as well as changes in height that occur when a person sits down, falls, etc. LiDAR can be used to create a 3D mesh representation of the environment. In a further application, LiDAR can reflect off solid surfaces through which the radio waves pass (e.g., radio-transparent materials), thereby enabling classification of different types of obstacles.

[0140] 1, any combination of one or more sensors 130 may be integrated into and / or coupled to any one or more components of system 100, including breathing device 122, user interface 124, conduit 126, humidification tank 129, control system 110, user device 170 (e.g., user devices 170A, 170B of FIG. 2), or any combination thereof. For example, microphone 140 and speaker 142 are integrated into and / or coupled to user device 170, and pressure sensor 130 and / or flow sensor 132 are integrated into and / or coupled to breathing device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to the respiratory device 122, the control system 110, or the user device 170, and is typically positioned approximately adjacent to the therapeutic user 210 or therapeutic adjacent individual 220 during a sleep session (e.g., positioned on or in contact with a portion of the therapeutic user 210 or therapeutic adjacent individual 220, and worn by the therapeutic user 210 or therapeutic adjacent individual 220, coupled or positioned on a nightstand, coupled to a mattress, coupled to a ceiling, etc.).

[0141] For example, as shown in FIG. 2, one or more of the sensors 130 may be located at a first position 250a on the nightstand 240 adjacent to the bed 230 and the therapeutic user 210. Alternatively, one or more of the sensors 130 may be located at a second position 250B on and / or within the mattress 232 (e.g., the sensor is coupled and / or integrated with the mattress 232). Also, one or more of the sensors 130 may be located at a third position 250C on the bed 230 (e.g., the auxiliary sensor 140 is coupled and / or integrated with the headboard, footboard or other position on the frame of the bed 230). One or more of the sensors 130 may be located at a fourth position 250d on a wall or ceiling generally adjacent to the bed 230 and / or the user 210. One or more of the sensors 130 may be located at a fifth position, such as coupled to and / or located on and / or within the housing of the respiratory device 122 of the respiratory system 120. Additionally, one or more sensors 130 may be positioned at a sixth location 250F such that the sensor is coupled to and / or disposed on the therapeutic user 210 (e.g., the sensor is embedded or coupled to fabric or clothing worn by the therapeutic user 210 during a sleep session). Similarly, one or more sensors 130 may be positioned at a seventh location such that the sensor is coupled to and / or disposed on the therapeutic adjacent individual 220 (e.g., the sensor is embedded or coupled to fabric or clothing worn by the therapeutic adjacent individual 220 during a sleep session). Additionally, one or more sensors 130 may be positioned at an eighth location 250G on the nightstand adjacent to the bed 230 and may be positioned on the therapeutic adjacent individual 220. More generally, one or more sensors 130 may be positioned at any suitable location relative to the cohort member being monitored such that the sensor 140 can generate physiological data associated with the cohort member (e.g., the therapeutic user 210 and / or the therapeutic adjacent individual 220) during one or more sleep sessions.

[0142] The user device 170 (FIG. 1) includes a display device 172. The user device 170 may be a mobile device, such as, for example, a smartphone, a tablet, a laptop, etc. Alternatively, the user device 170 may be an external sensing system, a television (e.g., a smart television), or another smart home device (e.g., a smart speaker, such as Google Home®, Google Nest®, Amazon Echo®, Amazon Echo Show®, an Alexa®-enabled device, etc.). In some implementations, the user device is a wearable device (e.g., a smart watch). The display device 172 is generally used to display images, including still images, moving images, or both. In some embodiments, the display device 172 functions as a human-machine interface (HMI) that includes a graphical user interface (GUI) and an input interface configured to display images. The display device 172 may be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or touch sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input by a human interacting with user device 170. In some implementations, one or more user devices may be used by and / or included in system 100, such as a separate user device for each member of a sleep cohort.

[0143] The light source 180 is generally used to emit light having an intensity and wavelength (e.g., color). For example, the light source 180 can emit light at a wavelength between about 380 nm and about 700 nm (e.g., a wavelength in the visible light spectrum). The light source 180 may include, for example, one or more light emitting diodes, one or more organic light emitting diodes, a light bulb, a lamp, an incandescent light bulb, a CFL light bulb, a halogen light bulb, or any combination thereof. In some implementations, the intensity and / or wavelength (e.g., color) of the light emitted from the light source 180 may be changed by the control system 110. The light source 180 may also emit light in a predetermined emission pattern, such as continuous emission, pulsed emission, periodic emission of different intensities (e.g., including an emission cycle of gradually increasing intensity followed by decreasing intensity), or any combination thereof. The light emitted from the light source 180 may be directly observed by the cohort members, or may be reflected or refracted before reaching the cohort members. In some implementations, the light source 180 includes one or more light pipes.

[0144] In some implementations, the light source 180 is physically coupled to or integrated with the respiratory treatment system 120. For example, the light source 180 may be physically coupled to or integrated with the respiratory device 122, the user interface 124, the conduit 126, the display device 128, or any combination thereof. In some implementations, the light source 180 is physically coupled to or integrated with the user device 170. In some other implementations, the light source 180 is separate and distinct from each of the respiratory treatment system 120 and the user device 170, as well as the activity tracker 190. In such implementations, the light source 180 may be positioned to point toward the cohort members, for example, on a nightstand 240, a bed 230, other furniture, a wall, a ceiling, etc.

[0145] The activity tracker 190 is generally used to assist in generating physiological data for determining activity measurements associated with a cohort member (e.g., a therapeutic user 210 or a therapeutic adjacent individual 220). Activity measurements may include, for example, number of steps, distance traveled, number of steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiration rate, average respiration rate, resting respiration rate, maximum respiration rate, respiration rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, number of calories burned, blood oxygen saturation, electrodermal activity (also referred to as electrical skin conductance or galvanic skin response), or any combination thereof. The activity tracker 190 includes one or more sensors 130 described herein, such as a motion sensor 138 (e.g., one or more accelerometers and / or gyros), a PPG sensor 154, and / or an ECG sensor 156.

[0146] In some implementations, the activity tracker 190 is a wearable device that may be worn by a cohort member, such as a smart watch, wristband, ring, or patch. For example, referring to FIG. 2, the activity tracker 190 is worn on the wrist of the therapeutic user 210. The activity tracker 190 may also be coupled or integrated with clothing or apparel worn by the therapeutic user. In some cases, a similar activity tracker may be worn on the wrist of the therapeutic adjacent individual 220 or coupled or integrated with clothing or apparel worn by the therapeutic adjacent individual 220. Alternatively, the activity tracker 190 may be coupled or integrated with the user device 170A and / or the user device 170B (e.g., in the same storage device). More generally, the activity tracker 190 may be communicatively coupled to or physically integrated within (e.g., in a storage device) the control system 110, the memory 114, the respiratory system 120, the user device 170A and / or the user device 170B.

[0147] 1, although the control system 110 and the memory device 114 are described and shown in FIG. 1 as separate and distinct components of the system 100, in some implementations the control system 110 and / or the memory device 114 are integrated into the user device 170 and / or the respiratory device 122. Alternatively, in some implementations the control system 110 or portions thereof (e.g., the processor 112) may be located in the cloud (e.g., embedded in a server, embedded in an Internet of Things (IoT) device (e.g., smart TV, smart thermostat, smart appliance, smart lighting, etc.), connected to the cloud, processed by an edge cloud, etc.), located in one or more servers (e.g., a remote server, a local server, or any combination thereof, etc.).

[0148] Although the system 100 is shown to include all of the above components, in accordance with implementations of the present disclosure, a system for generating physiological data and determining a recommended notification or action for a cohort member may include more or fewer components. For example, a first alternative system includes the control system 110, the memory device 114, and at least one of the one or more sensors 130. As another example, a second alternative system includes the control system 110, the memory device 114, at least one of the one or more sensors 130, and a user device 170. As yet another example, a third alternative system includes the control system 110, the memory device 114, the respiratory system 120, at least one of the one or more sensors 130, and the first and second user devices 170. Thus, various systems may be formed using any portion of the components illustrated and described herein and / or in combination with one or more other components.

[0149] As used herein, a sleep session can be defined in a variety of ways, for example, based on an initial start time and end time. Referring to FIG. 3, an example timeline 301 for a sleep session is shown. The timeline 301 includes a time from when the user goes to bed (tベッド ), sleep onset time (t GTS ), initial sleep time ((t 睡眠 ), the first micro-awakening MA1 and the second micro-awakening MA2, the awakening time (t 覚醒 ), wake-up time (t 起床 ).

[0150] In some implementations, a sleep session is a duration during which a cohort member is asleep. In such implementations, a sleep session has a start time and an end time, during which the cohort member does not wake up until the end time. That is, any time during which the cohort member is awake is not included in the sleep session. According to the definition of the first sleep session, if a cohort member wakes up and falls asleep multiple times in the same night, each sleep session separated by a period of wakefulness is a sleep session.

[0151] Alternatively, in some implementations, a sleep session may have a start time and an end time, and a cohort member may end a sleep session as long as the cohort member's continuous sleep duration during the sleep session is below the sleep duration threshold without the sleep session ending. The wakefulness duration threshold may be defined as a percentage of the sleep session. The wakefulness duration threshold may be, for example, about 20% of the sleep session, about 15% of the sleep session duration, about 10% of the sleep session duration, about 5% of the sleep session duration, about 2% of the sleep session duration, etc., or any other threshold percentage. In some implementations, the wakefulness duration threshold is defined as a fixed amount of time, such as, for example, about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, or other amount of time.

[0152] In some implementations, a sleep session may be defined as the entire time between when a cohort member first gets into bed at night and when the cohort member last leaves the bed the next morning. That is, a sleep session may be defined as a period beginning on a first date (e.g., Monday, January 6, 2020) at a first time (e.g., 10:00 p.m.), referred to as the current night when the cohort member first gets into bed and attempts to go to sleep (e.g., not if the cohort member first attempts to watch television or use a smartphone before going to sleep), and ending on a second date (e.g., Tuesday, January 7, 2020) at a second time (e.g., 7:00 a.m.), referred to as the next morning when the cohort member first gets out of bed and attempts to go to sleep instead of returning home the next morning.

[0153] In some implementations, a cohort member can manually define the start of a sleep session and / or manually end a sleep session. For example, a cohort member can select (e.g., by clicking or tapping) a user-selectable element presented on display device 172 of user device 170 (FIG. 1) to manually start or end a sleep session.

[0154] bedtime t ベッド is associated with the time when a cohort member first occupies a bed (e.g., bed 230 in FIG. 2) before going to sleep (e.g., when the cohort member is lying down or sitting in bed). ベッド can be identified based on a bed threshold duration to distinguish between a time when a cohort member goes to bed and sleeps and a time when a cohort member goes to bed for other reasons (e.g., watching television). For example, the bed threshold duration can be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. Here, the bed time t ベッド is described herein in relation to a bed, but more generally, ベッドcan refer to the time when a cohort member first enters any position for sleeping (e.g., couch, chair, sleeping bag, etc.).

[0155] GTS was related to the time when cohort members first attempted to fall asleep after getting into bed (t ベッド For example, after getting into bed, a cohort member may engage in one or more activities to relax (e.g., reading, watching television, listening to music, using a user device 170, etc.) before attempting to fall asleep. 睡眠 ) is the time when a cohort member first went to bed. For example, the initial sleep time (t 睡眠 ) may be the time at which a cohort member first entered a non-REM sleep stage.

[0156] Awakening time t 覚醒 A cohort member may experience one of multiple short duration (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.) involuntary micro-arousals (e.g., micro-arousals MA1, MA2) after initially falling asleep. ... 覚醒 Conversely, cohort members return to sleep after each micro-arousal (MA1, MA2). Similarly, cohort members may have one or more conscious awakenings (e.g., awakening A) after initially falling asleep (e.g., after waking to go to the bathroom, after caring for a child or pet, after sleepwalking, etc.). However, cohort members may return to sleep again after awakening. Thus, at awakening time t 覚醒 may be defined, for example, based on an arousal threshold duration (e.g., at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0157] Similarly, the wake-up time t 起床refers to the time at which a cohort member leaves the bed and remains outside the bed to end a sleep session (as opposed to, for example, a cohort member waking up during the night to go to the bathroom, care for a child or pet, or sleepwalk). That is, the wake-up time t 起床 is the last time a cohort member wakes up without returning to bed before the next sleep session (e.g., the following night). Therefore, the wake-up time 起床 can be defined, for example, based on a wake threshold duration (e.g., a time at which cohort members fall asleep for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). ベッド The time may also be defined based on a wake threshold duration (eg, a cohort member is at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.).

[0158] As mentioned above, cohort members are ベッド From the last t 起床 In some embodiments, the patient may wake up and get out of bed one or more times during the night until the last awakening time t 覚醒 and / or last wake-up time t 起床 The threshold duration may be identified or determined based on a predetermined threshold duration following an event (e.g., falling asleep or getting out of bed). Such threshold duration may be customized for a cohort member. For a typical cohort member who goes to bed at night and then wakes up and gets out of bed in the morning, the threshold duration may be about 12 to about 18 hours (the cohort member's wakefulness (t 覚醒 ) or wake up (t 起床 ) and the time when cohort members go to bed (t ベッド ), falling asleep (t 入眠 ) or sleep (t 睡眠 ) can be used. For cohort members with long periods of bed rest, a shorter threshold time (e.g., from about 8 hours to about 14 hours) can be used. The threshold period can be initially selected and / or later adjusted based on the system monitoring the cohort members' sleep behavior.

[0159] Total time in bed (TIB) is the time from bedtime t ベッド From wake-up time t 起床 t . Total sleep time (TST) refers to the duration between the initial sleep time and the wake time, not including any conscious or unconscious awakenings and / or microarousals in between. Typically, total sleep time (TST) will be less than total time in bed (TIB) (e.g., 1 minute less, 10 minutes less, 1 hour less, etc.). For example, referring to timeline 301 of FIG. 3, total sleep time (TST) refers to the duration between the initial sleep time t sleep and the alarm time t wake , but excluding the duration of the first micro-awakening MA1, the second micro-awakening MA2, and awakening A. As shown, in this example, the total sleep time (TST) is less than the total time in bed (TIB).

[0160] In some implementations, total sleep time (TST) may be defined as total continuous sleep time (PTST). In such implementations, the continuous total sleep time does not include the period of the predetermined initial portion or the first non-REM stage (e.g., light sleep stage). For example, the predetermined initial portion may be about 30 seconds to about 20 minutes, about 1 minute to about 10 minutes, about 3 minutes to about 5 minutes, etc. Total continuous sleep time is one measure of continuous sleep and smooths out the sleep-wake hypnogram. For example, when a cohort member first falls asleep, the cohort member may be in the first non-REM stage for a very short time (e.g., about 30 seconds), then return to the wake stage for a short time (e.g., 1 minute), and then return to the first non-REM stage. In this example, the total continuous sleep time excludes the first instance (e.g., about 30 seconds) of the first non-REM stage.

[0161] In some embodiments, a sleep session is based on time to bed (t ベッド ) and wake-up time (t 起床 ), i.e., total time in bed (TIB). In some embodiments, a sleep session is defined as a sleep session that ends at initial sleep time (t 睡眠 ) and wake-up time (t 覚醒) In some embodiments, a sleep session is defined as total sleep time (TST). In some embodiments, a sleep session is defined as ending at sleep onset time (t GTS ) and wake-up time (t 覚醒 In some embodiments, a sleep session is defined as ending at a sleep onset time (t GTS ) and wake-up time (t 起床 In some embodiments, a sleep session is defined as ending at bedtime (t ベッド ) and wake-up time (t 覚醒 ) In some implementations, a sleep session is defined as ending at an initial sleep time (t 睡眠 ) and wake-up time (t 起床 ) is defined as ending in

[0162] 4, an example hypnogram 400 is shown corresponding to a timeline 400 (FIG. 4) according to some implementations. As shown, the hypnogram 400 includes a sleep-wake signal 401, a wake stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. An intersection between the sleep-wake signal 401 and one of the axes 410, 420, 430, 440 indicates a sleep stage at any given time during a sleep session.

[0163] The sleep-wake signal 401 can be generated based on physiological data associated with the cohort members (e.g., generated by one or more sensors 130 (FIG. 1) described herein). The sleep-wake signal can indicate one or more sleep states or stages including wakefulness, relaxed wakefulness, micro-arousal, REM stage, first non-REM stage, second non-REM stage, third non-REM stage, or any combination thereof. In some implementations, one or more of the first non-REM stage, second non-REM stage, and third non-REM stage may be grouped together and classified as a light sleep stage or a deep sleep stage. For example, a light sleep stage may include a first non-REM stage, while a deep sleep stage may include a second non-REM stage and a third non-REM stage. Although the hypnogram 400 shown in FIG. 4 includes a light sleep stage axis 430 and a deep sleep stage axis 440, in some embodiments, the hypnogram 400 may include an axis for each of the first non-REM stage, the second non-REM stage, and the third non-REM stage. In other embodiments, the sleep-wake signal may indicate a respiration signal, a respiration rate, an inhalation amplitude, an exhalation amplitude, an inhalation to exhalation ratio, a number of events per hour, a pattern of events, or any combination thereof. Information describing the sleep-wake signal may be stored in memory device 114.

[0164] The hypnogram 400 can be used to determine one or more sleep-related parameters, such as sleep onset latency (SOL), wake onset after sleep (WASO), sleep efficiency (SE), sleep fragmentation index, sleep blocks, or any combination thereof.

[0165] Sleep onset latency (SOL) is the time when sleep begins (t 入眠 ) and initial sleep time (t 睡眠) in other words, sleep onset latency indicates the time required for a cohort member to actually fall asleep after first attempting to fall asleep. In some embodiments, sleep onset latency is defined as sustained sleep onset latency (PSOL). The difference between sustained sleep onset latency and sleep onset latency is that sustained sleep onset latency is defined as the duration between the time of sleep onset and a predetermined amount of sustained sleep. In some embodiments, the predetermined amount of sustained sleep may include, for example, at least 10 minutes of sleep in the second non-REM stage, the third non-REM stage, and / or the REM stage, and no more than 2 minutes of awake REM stage, the first non-REM stage, and / or movement therebetween. In other words, sustained sleep onset latency requires, for example, up to 8 minutes of sustained sleep in the second non-REM stage, the third non-REM stage, and / or the REM stage. In other embodiments, the predetermined amount of sustained sleep may include at least 10 minutes of sleep in the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or the REM stage after the initial sleep time. In such an implementation, a predetermined amount of sustained sleep can preclude any micro-arousals (e.g., a 10 second micro-arousal does not restart a 10 minute period).

[0166] The wake after sleep onset (WASO) is related to the total duration of the wake of the cohort member between the initial sleep time and the wake time. Thus, the wake after sleep includes short wakes and micro-awakenings during the sleep session, whether conscious or unconscious (e.g., micro-awakenings MA1, MA2 shown in FIG. 4). In some embodiments, the wake after sleep onset (WASO) is defined as the persistent wake after sleep onset (PWASO) that includes only the total duration with wakes of a predetermined length (e.g., greater than 10 seconds, greater than 30 seconds, greater than 60 seconds, greater than about 5 minutes, greater than about 10 minutes, etc.).

[0167] Sleep efficiency (SE) is determined as the ratio of total time in bed (TIB) to total sleep time (TST). For example, if the total time in bed is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency for the sleep session is 93.75%. Sleep efficiency indicates the sleep hygiene of the cohort members. For example, if the cohort members go to bed before bedtime and spend time on other activities (e.g., watching television), the sleep efficiency decreases (e.g., the cohort members are penalized). In some implementations, sleep efficiency (SE) may be calculated based on the total time in bed (TIB) and the total time the cohort members attempt to sleep. In such implementations, the total time the cohort members attempt to sleep is defined as the duration between the time of sleep onset (GTS) and the time of wake-up as described herein. For example, if the total sleep time is 8 hours (e.g., between 11:00 PM and 7:00 AM), the time of sleep onset is 10:45 PM, and the time of wake-up is 7:15 AM, the sleep efficiency parameter is calculated to be about 94%.

[0168] The fragmentation index is determined based at least in part on the number of awakenings during the sleep session. For example, if a cohort member has two microawakenings (e.g., microawakening MA1 and microawakening MA2 as shown in FIG. 4), the fragmentation index can be expressed as 2. In some implementations, the fragmentation index is scaled between a predetermined integer range (e.g., between 0 and 10).

[0169] A sleep block relates to a transition between any stage of sleep (e.g., NREM stage 1, NREM stage 2, NREM stage 3, and / or REM) and a wakeful stage. Sleep blocks can be calculated, for example, with a resolution of 30 seconds.

[0170] In some embodiments, the systems and methods described herein generate or analyze a hypnogram including a sleep-wake signal to determine time to bed (t ベッド ), sleep onset time (t GTS ), initial sleep time (t 睡眠 ), one or more first microawakenings (e.g., MA1 and MA2), and the awakening time (t 覚醒 ), wake-up time (t 起床), or any combination thereof, based at least in part on a hypnogram sleep-wake signal.

[0171] In another embodiment, the one or more sensors 130 detect the time of getting to bed (t ベッド ), sleep onset time (t GTS ), initial sleep time (t 睡眠 ), one or more first microawakenings (e.g., MA1 and MA2), and the awakening time (t 覚醒 ), wake-up time (t 起床 ), or a combination thereof, which can be used to further define a sleep session. For example, a bedtime t ベッド may be determined based on data generated by, for example, the motion sensor 138, the microphone 140, the camera 150, or any combination thereof. Sleep time may be determined based on, for example, data from the motion sensor 138 (e.g., data indicating that the cohort member is not exercising), data from the camera 150 (e.g., data indicating that the cohort member is not exercising and / or that the cohort member has turned off the lights), data from the microphone 140 (e.g., data indicating use of off TV), data from the user device 170 (e.g., data indicating that the cohort member is no longer using the user device 170), data from the pressure sensor 132 and / or the flow sensor 134 (e.g., data indicating that the therapy user turns on the respiratory device 122, data indicating that the therapy user wears the user interface 124, etc.), or any combination thereof.

[0172] Although the hypnogram 400 depicts the REM stages gradually shortening as the sleep session progresses, this is not always the case: in some cases, the duration of the REM stages gradually increases as the sleep session progresses (e.g., the first REM stage is shorter than the last REM stage).

[0173] 5 is a perspective view of a cohort member pair including first and second cohort members 510, 520, according to some embodiments of the disclosure. In some cases, aspects and features of the disclosure can be used between two or more cohorts 510, 520 that do not use respiratory or other sleep-related treatment devices. In this case, the system (e.g., system 100 of FIG. 1) can continue to monitor the cohort members 510, 520 and determine sleep performance metrics, which can be used to determine collaborative sleep performance metrics, evaluate individual or cohort goals, generate individual or cohort coaching suggestions, or improve the sleep quality of the cohort members 510, 520.

[0174] Cohort member 510 and cohort member 520 sleep together in bed 530 on mattress 532. A system for tracking the sleep sessions of cohort members 510, 520 can be implemented via a first user device 570A and a second user device 570B. User device 570A may be a smartphone associated with cohort member 510, and user device 570B may be a smartphone associated with cohort member 520, although other devices may be used, such as a sonar-enabled and / or radar-enabled (optionally further including a microphone) bedside device configured to monitor physiological signals (e.g., cardiac, respiratory, and / or motion signals).

[0175] The distance between the first user device 570A and the second user device 570B can be estimated based on sensor data collected by one or more sensors in each user device 570A, 570B. In some cases, the distance can be estimated based on the signal strength of a wireless signal, such as a Bluetooth signal, transmitted between the user devices 570A, 570B. In some cases, the distance can be estimated based on echoes detected by microphones of the user devices 570A, 570B.

[0176] The distance between the user devices 570A, 570B can be used to estimate whether the cohort member 520 is sleeping in the same environment 500 as the cohort member 510. For example, as shown in FIG. 5, if the distance is determined to be relatively small, the cohort member 520 can be estimated to be sleeping in the same bed as the cohort member 510. At a smaller distance, the cohort members 510, 520 can be estimated to be sleeping in the same room. In some cases, the distance can indicate that the cohort members 510, 520 are sleeping in adjacent rooms. In some cases, the distance can indicate that the cohort members 510, 520 are sleeping in the same home (e.g., the same building). In some cases, the distance can indicate that the cohort member 520 is not sleeping in the same environment 500 as the cohort member 510.

[0177] In some cases, the distance between user devices 570A, 570B can be used to better identify the location of one or two of cohort members 510, 520, for example, by echolocation or by detection by other sensors. For example, knowledge of the distance between user devices 570A, 570B, combined with knowledge of the distance between cohort member 510 and each user device 570A, 570B, can be used to precisely locate cohort member 510 within environment 500.

[0178] 6 is a flow chart illustrating a process 600 for generating and presenting sleep performance metrics for a sleep cohort according to some aspects of the disclosure. Process 600 may be performed by system 100 of FIG. 1 or components thereof, or multiple instances or components of system 100 of FIG.

[0179] In block 602, sensor data is received. The sensor data can be received from one or more sensors. The sensor data received in block 602 is related to a sleep session of an individual in an environment. In other words, the sensor data received in block 602 includes acquired sensor data of an individual (e.g., a first cohort member, such as a treatment adjacent individual) participating in the sleep session. In some cases, the sensor data may also include sensor data acquired before or after the sleep session.

[0180] The environment may be a bed, a room, a set of adjacent rooms, or a house or other building. In some cases, the sensor data received in block 602 may relate to a sleep session of a second individual (e.g., a second cohort member, such as a therapeutic user) in the environment.

[0181] At block 604, the sensor data is used to determine first sleep performance data. The first sleep performance data includes data regarding performance of the first cohort member's sleep session. Determining the sleep performance data may include analyzing the sensor data to identify various metrics, such as sleep quality data, associated with the first cohort member's sleep session. If appropriate, the sleep performance data may include sleep stage information, sleep state information, and / or sleep performance data.

[0182] Second sleep performance data may be received at block 606. The second sleep performance data may relate to a second cohort member, and more specifically, to a sleep session of the second cohort member in the same environment as the first cohort member. The sleep session of the second cohort member may overlap fully or partially with the sleep session of the first cohort member.

[0183] In some cases, the second sleep performance data has been determined from individual sensor data associated with the second cohort members when the second sleep performance data is received in block 606. In some cases, receiving the second sleep performance data in block 606 may include determining the second sleep performance data from sensor data (e.g., the sensor data of block 602 or other sensor data).

[0184] At block 608, one or more sleep performance metrics may be generated from the first sleep performance data and the second sleep performance data. The sleep performance metric may be any useful metric used to measure performance of a sleep session. In some cases, the one or more sleep performance metrics include i) a collaborative sleep performance score, ii) an individual sleep performance score associated with the first cohort member, iii) an individual sleep performance score associated with the second cohort member, iv) a hypnogram associated with the first cohort member, v) a hypnogram associated with the second cohort member, vi) a treatment score for treating the user, vii) a resonance score, or viii) any combination of i-vii.

[0185] In some cases, the sleep performance metric may be a collaborative sleep performance score calculated using a first sleep performance score associated with a first cohort member and a second sleep performance score associated with a second cohort member.

[0186] In some cases, generating the sleep performance metric in block 608 may include synchronizing the first sensor data and the second sensor data. The first sensor data may be sensor data received in block 602 from a first set of one or more sensors associated with the first cohort member. The second sensor data may be sensor data received from a second set of one or more sensors associated with the second cohort member.

[0187] The sleep performance metrics may be presented at block 610. The presenting at block 610 may include presenting one or more sleep performance metrics to a first cohort member, to a second cohort member, to a third party, or any combination thereof. The presenting may include presenting component and / or sub-component scores associated with the sleep performance metrics, and optionally a presentation of the contribution of the component and / or sub-component scores to a given sleep performance metric.

[0188] In some cases, presenting the sleep performance metrics in block 610 may include generating an entry in a feed associated with the cohort member or cohort. The feed may be a social media feed or similar feed. The feed may include summary information, sleep performance metrics, or other such information. The feed may be interactive to allow other cohort members or third parties to interact with the entries on the feed, which may prompt the cohort member to improve their sleep quality.

[0189] In some cases, the first cohort members are therapeutic adjacent individuals and the second cohort members are therapeutic users, although in some cases the reverse may be true. In some cases, both the first cohort members and the second cohort members are therapeutic users. When a cohort member is a therapeutic user, the sensor data may include data from one or more sensors associated with the therapeutic user's therapeutic device, and the sleep performance data may include therapeutic data associated with use of the therapeutic device.

[0190] In an exemplary case, the process 600 may be performed by a user device (e.g., a smartphone) of a first cohort member. The user device may receive sensor data from one or more sensors of the user device and / or one or more sensors operatively coupled to the user device. The user device may then determine the first sleep performance data from the sensor data. The user device may then receive the second sleep performance data. In some cases, the user device may receive the sensor data and use the sensor data to determine the second sleep performance data. However, in some cases, the user device may receive the second sleep performance data determined from the sensor data (e.g., at a user device of a second cohort member).

[0191] In another example case, process 600 may be performed by a server (eg, a cloud server) that may receive sensor data and / or sleep performance data from one or more user devices.

[0192] In some cases, an incentive may be provided at optional block 612. Providing the incentive may include determining that a sleep performance metric has reached a threshold, or determining that a cohort member or cohort has achieved a goal (e.g., goals may be set, tracked and / or reported according to process 700), or completing a threshold number of coaching suggestions (e.g., goals may be set, tracked and / or reported according to process 800). Providing the incentive may include initiating the transfer of an incentive (e.g., cash prize, gift card, gift, etc.) to the cohort member or cohort associated with the incentive. In some cases, providing the incentive at block 612 may include providing an individual incentive to a first cohort member and a second cohort member for i) coaching suggestions for meeting their threshold sleep performance metric and achieving their goals and / or number of achieved thresholds, ii) coaching suggestions for meeting their threshold sleep performance metric and achieving their goals and / or number of achieved thresholds, or iii) any combination of i or ii to another cohort member.

[0193] In some cases, in optional block 614, parameters of a therapeutic device (e.g., a respiratory therapeutic device) may be adjusted based on the sensor data from block 602. The therapeutic device may be used by a second cohort member. Thus, parameters of the therapeutic device may be adjusted based on sleep session data associated with the sleep session of the therapeutic adjacent individual. In some cases, adjusting the therapeutic parameters in block 614 may include dynamically adjusting parameters during the therapeutic user's sleep session, although this need not always be the case. In some cases, the therapeutic parameters are adjusted in block 614 after receiving the second sleep performance data in block 606. In some cases, the therapeutic parameters are adjusted in block 614 after generating sleep performance metrics in block 608.

[0194] 7 is a flow chart illustrating a process 700 for tracking goals of a sleep cohort according to some aspects of the disclosure. The process 700 may be performed by the system 100 of FIG. 1 or may be instances or components of the system 100 of FIG.

[0195] Goal information is received at block 702. The goal information may include information indicative of the goal and an association of the goal with a sleep session with a first cohort member, a sleep session with a second cohort member, a cohort sleep session, or any combination thereof.

[0196] In some cases, the goal information may include a target achievement date. In some cases, the target achievement date may be user-selected. In some cases, the target achievement date may be automatically determined based on any combination of one or more cohort member's sleep performance data (e.g., current or historical). The automatically determined target achievement date may be automatically set for the goal or may be presented to the cohort member and set upon confirmation or selection by the cohort member.

[0197] In some cases, the goal information may be received directly from a user input. In some cases, the goal information may be received based on one or more generated goals. At block 704, one or more suggested goals may be generated. At block 706, a goal selection may be received indicating one or more of the one or more suggested goals to be used as a goal.

[0198] The generation of the suggested goals in block 704 may be performed automatically in response to receiving responses to one or more prompts (e.g., a questionnaire) or in response to receiving sleep performance data and / or generated sleep performance metrics. These responses may be used to generate a set of suggested goals.

[0199] In some cases, generating suggested goals may include identifying factors that affect the historical sleep performance metrics of a cohort member or cohort, and then determining suggested actions that can be taken to improve future sleep performance metrics (e.g., future instances of the historical sleep performance metrics). A set of suggested goals can then be generated based on the suggested actions.

[0200] In some cases, generating the suggested goal may include receiving demographic information about the cohort member and then generating one or more suggested goals based on the demographic information. For example, if the received demographic information about the cohort member indicates that the cohort member may be suffering from any factors that may affect sleep performance metrics, one or more suggested goals may be generated based on these factors to improve the sleep performance metrics. In some cases, generating the one or more suggested goals based on the demographic information may include accessing a database that includes suggested goals associated with individuals who share demographic information with the cohort member.

[0201] In some cases, generating the suggested goal includes receiving historical therapy device use information (e.g., historical respiratory therapy device use) and generating the suggested goal using the received historical therapy device use information. Thus, the generated goal may be customized to the therapy user's historical therapy device use (e.g., as determined from the therapy device usage data, such as the component data used to generate a myAir® score as described herein).

[0202] In some cases, generating the suggested goal includes receiving subjective feedback associated with the historical sleep sessions and generating the suggested goal using the subjective feedback. Thus, the generated goal can be based on the cohort members' own subjective interpretation of their previous sleep time.

[0203] At block 708, a goal state update may be generated. The goal state update may include information regarding the cohort member's or cohort's progress toward achieving the goal. Generating the goal state update may include evaluating the goal using sleep performance data (e.g., the first sleep performance data and / or the second sleep performance data from process 600 of FIG. 6). In some cases, the goal evaluation may include using sensor data. Evaluating the goal using the sensor data may include determining sleep quality, determining sleep-related metrics (e.g., sleep performance metrics), or determining other information using the sensor data. In one example, sensor data indicative of the distance between cohort members (or the cohort members' user devices) may be used to evaluate a goal based on the distance between the cohort members while the cohort members are sleeping.

[0204] The goal state update may be output at block 710. Outputting the goal state update may include transmitting the state update to another computing device or presenting the goal state update on a display (e.g., a display of a user device). Other techniques may be used.

[0205] 8 is a flow chart illustrating a process 800 for generating coaching recommendations for a sleep cohort according to some aspects of the disclosure. Process 800 may be performed by system 100 of FIG. 1 or components thereof, or multiple instances or components of system 100 of FIG.

[0206] Coaching suggestions may be identified at block 802. Coaching suggestions may be identified to improve future sleep performance metrics of a cohort member or the cohort.

[0207] In some cases, the coaching suggestions may be received directly from user input (e.g., user input of other cohort members). In some cases, the coaching suggestions may be identified automatically in response to receiving responses (e.g., subjective feedback) to one or more prompts (e.g., questionnaires). These responses may be used to generate the suggestions.

[0208] In some cases, instructor suggestions may include identifying factors that affect the cohort member's or cohort's historical sleep performance metrics and then determining suggested actions that can be taken to improve future sleep performance metrics (e.g., future instances of historical sleep performance metrics). Coaching suggestions can then be generated based on the suggested actions.

[0209] At block 804, the coaching suggestions may be presented. Presenting the coaching suggestions may include transmitting the coaching suggestions to another computing device or presenting the coaching suggestions on a display (e.g., a display of the user device). Other techniques may be used.

[0210] 9 is a flow chart illustrating a process 900 for generating sounds that mimic a respiratory treatment device according to some aspects of the disclosure. The process 900 may be performed by the system 100 of FIG. 1 or a component thereof, or multiple instances or components of the system 100 of FIG.

[0211] In block 902, a simulated respiratory treatment device sound may be generated. Generating the simulated respiratory treatment device sound may include electronically generating the simulated respiratory treatment device sound or accessing a file containing a recording of the simulated respiratory treatment device sound. The simulated respiratory treatment device sound generated in block 902 may be based on a selected model of the respiratory treatment device, selected accessories (e.g., a user interface and / or conduits), and / or selected settings of the respiratory treatment device. In some cases, the simulated respiratory treatment device sound may be generated based on prescribed or actual settings of the prospective or treatment user's respiratory treatment device.

[0212] The simulated respiratory treatment device sound may be output at block 906. Outputting the simulated respiratory treatment device sound may include playing the simulated respiratory treatment device sound via a speaker. The simulated respiratory treatment device sound may be monitored at block 908. Monitoring the simulated respiratory treatment device sound may include monitoring the simulated respiratory treatment device sound using a microphone.

[0213] In block 910, the simulated respiratory therapy device sound may be adjusted based on the monitored simulated respiratory therapy device sound. Adjusting the simulated respiratory therapy device sound based on the monitored simulated respiratory therapy device sound may include adjusting a volume or other characteristics of the output simulated respiratory therapy device sound. In some cases, making the adjustment in block 910 may include applying one or more filters to the output simulated respiratory therapy device sound. Adjusting the simulated respiratory therapy device sound may be performed in block 910 to ensure that the monitored simulated respiratory therapy device sound matches a desired or expected respiratory therapy device sound.

[0214] At optional block 912, a respiratory treatment recommendation may be made. The respiratory treatment recommendation may include a recommendation for a particular respiratory treatment device model, a particular conduit model or type, a particular user interface model or type, one or more settings of the respiratory treatment device, or any combination thereof. In some cases, providing the respiratory treatment recommendation may include receiving an adjustment command to adjust a volume of a sound that simulates a respiratory treatment device. In this case, the respiratory treatment recommendation is based on the adjusted volume of the simulated respiratory treatment device sound.

[0215] In optional block 914, sleep performance data of the cohort members or cohorts may be monitored. The monitored sleep performance data may be used to determine sleep performance metrics of the cohort members or cohorts. In some cases, monitoring the sleep performance data in block 914 may include modifying simulated respiratory therapy device sounds using the sleep performance data in optional block 904. In some cases, monitoring the sleep performance data in block 914 may include using the sleep performance data to inform respiratory therapy recommendations in block 912.

[0216] The above description of the implementations, including the illustrated implementations, is presented for the purpose of illustration and description only, and is not intended to outline or limit the precise forms disclosed. Although the disclosed implementations can be modified in many ways in accordance with the disclosure herein without departing from the spirit or scope of the present disclosure, many modifications, variations and uses thereof will be apparent to those skilled in the art. Therefore, the breadth and scope of the present disclosure should not be limited by any of the above-described implementations.

[0217] Although certain aspects of the present disclosure have been illustrated and described with respect to one or more implementations, equivalent changes and modifications will occur or become known to those skilled in the art after reading and understanding this specification and drawings. Moreover, while a particular feature of one aspect of the present disclosure may be disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of other implementations that may be desirable and advantageous for any given or particular application.

[0218] One or more elements, aspects or steps, or any portion thereof, from any one or more of claims 1-87 below may be combined with one or more elements, aspects or steps, or any portion thereof, from any one or more of other claims 1-87 to form one or more additional implementations and / or claims of the present disclosure.

Claims

1. receiving sensor data from one or more sensors relating to a sleep session of an individual within the environment; determining first sleep performance data from the sensor data, the first sleep performance data including sleep stage information or sleep state information; receiving second sleep performance data associated with a sleep session of a user of a respiratory treatment device in the environment, the second sleep performance data including respiratory treatment device usage information; generating one or more sleep performance metrics using the first sleep performance data and the second sleep performance data; presenting the one or more sleep performance metrics.

2. The method of claim 1 , wherein the individual sleep session and the user sleep session overlap in time.

3. receiving goal information related to a first user and a second user, the goal information indicating goals related to i) the individual's sleep session, ii) the user's sleep session, or iii) a combination of i and ii; generating a goal state update, the goal state update comprising evaluating the goal information using i) the first sleep performance data, ii) the second sleep performance data, or iii) a combination of i and ii; The method of claim 1 or 2, further comprising the step of: outputting the target state update.

4. The step of receiving the target information includes: generating a set of one or more suggested goals; and accepting a selection of a goal selected from the set of suggested goals; The step of generating a set of one or more suggested goals comprises: (i) submitting a questionnaire including one or more questions, receiving response information in response to submission of the questionnaire, and using the received response information to generate the set of suggested goals; (ii) accessing historical sleep performance data associated with historical sleep performance metrics, identifying one or more factors as influencing the historical sleep performance metrics, determining a proposed action for each of the one or more factors that is predicted to improve future sleep performance metrics, and generating the set of proposed goals with the proposed action for each of the one or more factors; (iii) receiving demographic information associated with the individual or user and using the received demographic information to generate the set of suggested goals; (iv) receiving historical respiratory treatment device usage information associated with the user and using the received historical respiratory treatment device usage information to generate the set of suggested goals; (v) receiving historical respiratory treatment device usage information associated with the user and using the received historical respiratory treatment device usage information to generate the set of suggested goals; or (vi) any combination of steps (i) to (v) above; The method of claim 3 , comprising:

5. The method of claim 3 or 4, wherein evaluating the target further comprises using the sensor data.

6. The method of claim 5 , wherein evaluating the target using the sensor data comprises using the sensor data to estimate a distance between the individual and the user.

7. The method of any one of claims 3 to 6, wherein the goal information comprises a goal related to a distance between the individual and the user in a future sleep session.

8. The method of any one of claims 3 to 7, wherein the goal information includes goals related to future use of the respiratory treatment device.

9. The step of evaluating the goal information includes determining that the goal has been achieved, and the goal state update indicates that the goal has been achieved, the method comprising: In response to determining that the goal has been achieved, providing one or more recommendations based at least in part on: i) the achieved goal; ii) the time it took to achieve the achieved goal; iii) the sleep performance data; iv) the user's subjective data; or v) any combination of i-iv. determining a proposed follow-on goal; The method of any one of claims 3 to 8, further comprising the step of: presenting said one or more suggested follow-on goals.

10. receiving a follower goal selection in response to presentation of the one or more suggested follower goals, the follower goal selection indicating one of the one or more suggested follower goals; receiving follow-on target information associated with the follow-on target; generating a subsequent goal state update, the subsequent goal state update including evaluating the subsequent goal information; The method of claim 9 , further comprising: outputting the subsequent goal state update.

11. Generating the one or more sleep performance metrics may include generating a collaborative sleep performance score, the generating the collaborative sleep performance score comprising: generating a first sleep performance score using the first sleep performance data; generating a second sleep performance score using the second sleep performance data; and generating a collaborative sleep performance score using the first sleep performance score and the second sleep performance score.

12. identifying coaching suggestions for improving future sleep performance metrics; The method of any one of claims 1 to 11, further comprising the step of: providing said coaching suggestions after said first sleep session.

13. The step of identifying a coaching suggestion comprises: receiving subjective feedback relating to a plurality of historical sleep sessions; and using the subjective feedback to generate the coaching suggestions.

14. The step of identifying a coaching suggestion comprises: accessing historical sleep performance data associated with the historical sleep performance metrics; identifying one or more factors as influencing said historical sleep performance metrics; determining, for each of the one or more factors, a proposed action that is predicted to improve future sleep performance metrics; and generating the coaching suggestions with suggested changes for each of one or more factors.

15. The method of any one of claims 1 to 14, further comprising providing an incentive based on the first sleep performance data and the second sleep performance data.

16. 16. The method of claim 15, wherein providing the incentive is further based on a comparison between the one or more sleep performance metrics and the historical sleep performance metrics.

17. Generating the one or more sleep performance metrics may include generating a collaborative sleep performance score, the generating the collaborative sleep performance score comprising: generating a first sleep performance score using the first sleep performance data; generating a second sleep performance score using the second sleep performance data; and using the first sleep performance score and the second sleep performance score to generate a collaborative sleep performance score; 17. The method of claim 15 or 16, wherein the incentive is provided if the first sleep performance score exceeds a first threshold and the second sleep performance score exceeds a second threshold.

18. 20. The method of claim 17, wherein providing the incentive comprises providing a first personal incentive associated with the individual and providing a second personal incentive associated with the user.

19. providing a first individual incentive associated with the individual if the first sleep performance score exceeds the first threshold; 20. The method of claim 17, further comprising: if the second sleep performance score exceeds the second threshold, providing a second personal incentive associated with the user.

20. providing a first personalized incentive associated with the user if the first sleep performance score exceeds the first threshold; 20. The method of claim 17, further comprising: providing a second individual incentive associated with the individual if the second sleep performance score exceeds the second threshold.

21. 21. The method of any one of claims 1 to 20, further comprising the step of transmitting summary information based on the first sleep performance data, the summary information being usable when received by a user device associated with the user to generate an entry in a feed of historical summary information associated with the individual.

22. 22. The method of claim 21, further comprising receiving responsive feedback in response to generating the entry.

23. 23. The method of claim 21 or 22, wherein generating the one or more sleep performance metrics comprises generating a first sleep performance score using the first sleep performance data, and the summary information includes the first sleep performance score.

24. receiving summary information at a user device associated with the individual, the summary information being based on the second sleep performance data; A method according to any preceding claim, further comprising using the received summary information to generate an entry in a feed of historical summary information relating to the user.

25. The step of generating one or more sleep performance metrics includes:

25. The method of claim 24, comprising using performance data to generate a second sleep performance score, and wherein the summary information includes the second sleep performance score.

26. The method of any one of claims 1 to 25, wherein the second sleep performance data is determined using the sensor data, the sensor data further relating to a sleep session of a user in an environment.

27. 27. The method of any one of claims 1 to 26, wherein the second sleep performance data is determined using second sensor data from a second set of one or more sensors, the second sensor data relating to a sleep session of a user in an environment.

28. a memory storing machine-readable instructions; a memory storing machine-readable instructions; The control system is coupled to the memory, and when machine-executable instructions in the memory are executed by at least one of one or more processors of the control system, the method of any one of claims 1 to 27 is performed.

29. A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 27.

30. 30. The computer program product of claim 29, which is a non-transitory computer readable medium.

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