Systems and methods for obtaining consent for data

Abstract

A method for analyzing data related to a user's use of a respiratory therapy system includes receiving a first type of data, determining a first value of a first parameter based at least in part on the first type of data, identifying desired second type of data, transmitting a request for consent to receive the second type of data, and determining a second value of the first parameter, a value of the second parameter, or both, based at least in part on the second type of data. The first type of data and the first parameter relate to the user's use of the respiratory therapy system. The identification of the second type of data is based at least in part on the first type of data, the first value of the first parameter, an accuracy of the first value of the first parameter, or any combination thereof.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims the benefit and priority of U.S. Provisional Patent Application No. 62 / 968,777, filed on 31 January 2020, which is incorporated herein by reference in its entirety.

[0002] Technical field This disclosure generally pertains to systems and methods for analyzing data relating to users of respiratory therapy systems, and more specifically, to systems and methods for obtaining consent to receive and analyze data relating to users of respiratory therapy systems. [Background technology]

[0003] Many individuals suffer from sleep-related and / or respiratory-related disorders, such as insomnia (e.g., difficulty initiating sleep, frequent or prolonged awakenings after initial sleep onset, and early morning awakenings from which they cannot return to sleep), periodic limb movement disorder (PLMD), obstructive sleep apnea (OSA), Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), and neuromuscular disorders (NMD). For many of these disorders, receiving and analyzing specific data about the individual can enhance the effectiveness of treatment or management. Therefore, efficiently obtaining consent to receive and analyze personal data would be beneficial. This disclosure aims to address these issues. [Overview of the Initiative]

[0004] According to some implementations of this disclosure, a method for analyzing data relating to a user's use of a respiratory therapy system during a sleep session includes: receiving a first type of data relating to a user's use of a respiratory therapy system during a sleep session; determining a first value of a first parameter relating to the user's use of a respiratory therapy system, at least in part on the first type of data; identifying a desired second type of data; sending a request to the user for consent to receive the second type of data; receiving the second type of data upon receiving consent from the user; and determining, at least in part on the second type of data, (i) a second value of the first parameter, (ii) a value of the second parameter, or (iii) both (i) and (ii).

[0005] According to some implementations of this disclosure, a method for analyzing data relating to a user's use of a respiratory therapy system during a sleep session includes: receiving (i) a first type of data relating to the user during the sleep session; and (ii) consent to analyze the first type of data to determine the value of a first parameter relating to the user; determining the value of the first parameter relating to the user based on at least the first type of data; identifying a desired second parameter; sending a request to the user for consent to analyze the first type of data to determine the value of the second parameter relating to the user; and, upon receiving consent from the user, determining the value of the second parameter relating to the user based on at least the first type of data.

[0006] According to some implementations of the present disclosure, a method for analyzing data associated with the use of a plurality of respiratory therapy systems by a plurality of users includes sending a plurality of requests to each of the plurality of users, the plurality of requests being requests for consent to receive data associated with each use of one of the plurality of respiratory therapy systems by each user, the plurality of requests being sent to each user in a respective order; receiving data from two or more of the plurality of users upon receiving consent; and analyzing the data received from each of the two or more users of the plurality of users to determine an optimal order for sending the plurality of requests for consent to receive the data.

[0007] According to some implementations of the present disclosure, a method for analyzing data related to the use of a respiratory therapy system by a user during a current sleep session includes storing a plurality of historical values of a first parameter related to the user; receiving a first type of data related to the user during the current sleep session; determining a current value of the first parameter based at least in part on the first type of data; comparing the current value of the first parameter with the plurality of historical values of the first parameter; identifying a desired second type of data in response to the comparison between the current value of the first parameter and the plurality of historical values of the first parameter satisfying a threshold; and sending a request to the user for consent to receive the second type of data.

[0008] The above summary is not intended to represent each implementation or every aspect of the present disclosure. Further features and advantages of the present disclosure will become apparent from the following detailed description and the figures. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] [Figure 1] FIG. is a functional block diagram of a system for analyzing data related to a user using a respiratory therapy system, according to some implementations of the present disclosure. [Figure 2]A perspective view of the system of FIG. 1, a user of the system, and a roommate of the user, according to some implementations of the present disclosure. [Figure 3] An exemplary time series of a sleep session is shown, according to some implementations of the present disclosure. [Figure 4] An exemplary sleep progression diagram associated with the sleep session of FIG. 3 is shown, according to some implementations of the present disclosure. [Figure 5] A process flow diagram of a first method for analyzing data related to the use of a respiratory therapy system, according to some implementations of the present disclosure. [Figure 6] A process flow diagram of a second method for analyzing data related to the use of a respiratory therapy system, according to some implementations of the present disclosure. [Figure 7] A process flow diagram of a method for determining an optimal order for sending multiple requests for consent to receive and analyze data, according to some implementations of the present disclosure. [Figure 8] A process flow diagram of a method for analyzing data related to the use of a respiratory therapy system to determine a change in a parameter related to a user, according to some implementations of the present disclosure.

[0010] Although various modifications and alternative forms are possible, specific implementations and embodiments of the present disclosure are shown by way of example in the drawings and are detailed herein. However, it is not intended to limit the present disclosure to the specific forms disclosed, and it should be understood that the present disclosure covers all modifications, equivalents, and alternatives that fall within the spirit and scope of the present disclosure as defined by the appended claims.

Mode for Carrying Out the Invention

[0011] Many people suffer from sleep-related disorders and / or respiratory disorders. Examples of sleep-related disorders and / or respiratory disorders include periodic limb movement disorder (PLMD), restless leg syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), central sleep apnea (CSA), other types of apnea, Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), and chest wall disorders.

[0012] Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events such as obstruction or blockage of the upper airway during sleep, resulting from a combination of an abnormally small upper airway and a normal loss of muscle tone in the tongue, soft palate, and posterior oropharynx. Central sleep apnea (CSA) is another form of SDB that occurs when the brain temporarily stops transmitting signals to the muscles that control breathing. More generally, apnea is generally a cessation of breathing or cessation of respiratory function caused by air obstruction. In cases of obstructive sleep apnea, breathing typically stops for about 15 to 30 seconds.

[0013] Other types of apnea include hypopnea, hyperpnea, and hypercapnia. Hypopnea is generally characterized by slow or shallow breathing resulting from narrowing of the airways rather than airway obstruction. Hyperpnea is generally characterized by an increase in the depth and / or rate of breathing. Hypercapnia is generally characterized by a sudden or excessive increase in the amount of carbon dioxide in the bloodstream and is typically caused by insufficient breathing.

[0014] Cheyne-Stokes respiration (CSR) is another form of sustained lung disease (SDB). CSR is a disorder of the patient's respiratory regulator, characterized by alternating and cyclical increases and decreases in ventilation known as the CSR cycle. CSR is characterized by repeated deoxygenation and re-aeration of arterial blood.

[0015] Obesity hyperventilation syndrome (OHS) is defined as a combination of severe obesity and chronic hypercapnia while awake, in the absence of other clearly identifiable causes of hypoventilation. Symptoms include shortness of breath, morning headache, and excessive daytime sleepiness.

[0016] Chronic obstructive pulmonary disease (COPD) encompasses a group of lower respiratory tract diseases that share certain characteristics, such as increased resistance to air movement, prolonged expiratory phase of respiration, and loss of normal lung elasticity.

[0017] Neuromuscular diseases (NMDs) encompass a large number of illnesses and diseases that impair muscle function, either directly through intrinsic muscle pathology or indirectly through neuropathology. Chest wall disorders are a group of thoracic deformities that result in a failure of the connection between the respiratory muscles and the rib cage.

[0018] These and other disorders are characterized by specific events that occur during sleep (e.g., snoring, apnea, hypopnea, inability to keep the legs still, sleep disturbances, suffocation, increased heart rate, dyspnea, asthma attacks, epileptic interstitial movements, seizures, or any combination thereof).

[0019] The Apnea-Hypopnea Index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the user during the sleep session by the total sleep duration in that session. These events can include, for example, pauses in breathing lasting at least 10 seconds. An AHI of less than 5 is considered normal. An AHI of 5 to less than 15 is considered mild sleep apnea. An AHI of 15 to less than 30 is considered moderate sleep apnea. An AHI of 30 or more is considered severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. Sleep apnea can be considered "controlled" when the AHI is normal, or when the AHI is normal or mild. The AHI can also be used in combination with oxygen saturation to indicate the severity of obstructive sleep apnea.

[0020] Various types of data can be used to monitor the health of individuals with any of the above types of sleep-related disorders and / or respiratory disorders (or other disorders). However, these individuals generally do not initially consent, or automatically consent, to providing large amounts of data that can actually be used to monitor their health, and often initially only consent to providing limited data on their breathing during sleep. Therefore, it is beneficial to explain to users why additional data is needed and how it will be used in order to obtain appropriate informed consent for acquiring and analyzing that additional data.

[0021] Referring to Figure 1, System 100 is shown in several implementations of the present disclosure. System 100 is intended, in particular, to provide various different sensors related to a user's use of a respiratory therapy system. System 100 includes a control system 110, a memory device 114, an electronic interface 119, one or more sensors 130, and one or more external devices 170. In some implementations, System 100 further includes a respiratory therapy system 120 (including a respiratory therapy device 122), a blood pressure device 180, an activity tracker 190, or any combination thereof. System 100 can be used to analyze various different types of data related to a user's use of the respiratory therapy system 120.

[0022] The control system 110 includes one or more processors 112 (hereinafter, processor 112). The control system 110 is generally used to control (e.g., operate) various components of system 100 and / or to analyze data acquired and / or generated by the components of system 100. The processors 112 may be general-purpose or special-purpose processors or microprocessors. Although one processor 112 is shown in Figure 1, the control system 110 may include any appropriate number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) which may reside in a single housing or be located apart from one another. The control system 110 (or any other control system) or any part of the control system 110, such as a processor 112 (or any other processor(s) or any other part(s) of any other control system(s)), may be used to perform one or more steps of any of the methods described and / or claimed herein. The control system 110 can be connected to, for example, the housing of an external device 170 and / or one or more housings of the sensor 130, and / or located inside them. The control system 110 can be centralized (in one such housing) or distributed (in two or more physically separate housings). In such an implementation configuration that includes two or more housings housing the control system 110, such housings can be located close to and / or far apart from each other.

[0023] The memory device 114 stores machine-readable instructions that can be executed by the processor 112 of the control system 110. The memory device 114 can be any suitable computer-readable storage device or media, such as a random or serial access memory device, a hard drive, a solid-state drive, or a flash memory device. Although one memory device 114 is shown in Figure 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 can be coupled to and / or located inside one or more housings of the sensor 130. Similar to the control system 110, the memory device 114 can be centralized (within one such housing) or distributed (within two or more physically separate such housings).

[0024] In some implementations, the memory device 114 (Figure 1) stores a user profile associated with the user. The user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. Demographic information may include, for example, information indicating the user's age, social gender, race, family history, employment status, education level, socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions associated with the user, the user's medication use, or both. Medical information data may further include the results or scores of the Multiple Sleep Latency Test (MSLT) and / or the scores or values ​​of the Pittsburgh Sleep Quality Index (PSQI). Self-reported user feedback may include information indicating a self-reported subjective sleep score (e.g., poor, average, good), a self-reported subjective stress level, a self-reported subjective fatigue level, a self-reported subjective health status, recent life events experienced by the user, or any combination thereof.

[0025] The electronic interface 119 is configured to receive data (e.g., physiological data and / or acoustic data) from one or more sensors 130, which can be stored in a memory device 114 and / or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with one or more sensors 130 using wired or wireless connections (e.g., using / via RF communication protocols, Wi-Fi communication protocols, Bluetooth communication protocols, IR communication protocols, cellular networks, or any other optical communication protocols). 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 also include another processor and / or another memory device which are identical or similar to the processor 112 and memory device 114 described herein. In some implementations, the electronic interface 119 is connected to or integrated with an external device 170. In other implementations, the electronic interface 119 is connected to or integrated with the control system 110 and / or the memory device 114 (for example, within the housing).

[0026] As described above, in some implementations, system 100 optionally includes a respiratory therapy system 120 (also referred to as a respiratory pressure therapy system). The respiratory therapy system 120 may include a respiratory therapy device 122 (also referred to as a respiratory pressure therapy device), a user interface 124, a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidifier tank 129, or a combination thereof. In some implementations, one or more of the control system 110, memory device 114, display device 128, sensor 130, and humidifier tank 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to applying an air supply to the inlet of the user's airway at a controlled target pressure that is nominally positive to the atmosphere throughout the user's entire respiratory cycle (unlike negative pressure therapy such as tank ventilators or positive / negative pressure external ventilators (cuirass), for example). The respiratory therapy system 120 is typically used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea), other respiratory disorders such as COPD, or other disorders that can lead to respiratory failure and may occur during sleep or wakefulness.

[0027] The respiratory therapy device 122 is generally used to generate pressurized air to be delivered to a user (for example, using one or more motors that drive one or more compressors). In some implementations, the respiratory therapy device 122 generates a continuous, constant air pressure to be delivered to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (for example, a first predetermined air pressure and a second predetermined air pressure). In yet another implementation, the respiratory therapy device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the respiratory therapy device 122 can deliver at least about 6 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, from about 6 cmH2O to about 10 cmH2O, from about 7 cmH2O to about 12 cmH2O, and so on. The respiratory therapy device 122 can also deliver pressurized air at a predetermined flow rate between, for example, about -20 L / min and about 150 L / min while maintaining positive pressure (relative to ambient pressure). In some implementations, the control system 110, the memory device 114, the electronic interface 119, or any combination thereof, can be connected to and / or located inside the housing of the respiratory therapy device 122.

[0028] The user interface 124 engages with a portion of the user's face and delivers pressurized air from the respiratory therapy device 122 to the user's airway to help prevent airway narrowing and / or obstruction during sleep. This may also increase the user's oxygen intake during sleep. Depending on the therapy applied, the user interface 124 may form a tight seal with, for example, a region or portion of the user's face, thereby facilitating gas delivery at a pressure sufficiently different from the ambient pressure to produce a therapeutic effect, such as a positive pressure of approximately 10 cmH2O relative to the ambient pressure. In other forms of therapy, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate gas delivery to the airway at a positive pressure of approximately 10 cmH2O.

[0029] In some implementations, the user interface 124 is or includes a face mask that covers the user's nose and mouth (for example, as shown in Figure 2). Alternatively, the user interface 124 is or includes a nasal mask that provides air to the user's nose, or a nasal pillow mask that delivers air directly to the user's nostrils. The user interface 124 may include a strap assembly having multiple straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing the user interface 124 on a portion of the user interface 124 at a desired location on the user (e.g., the face), and a shape-conforming cushion (e.g., silicone, plastic, foam, etc.) that helps provide an airtight seal between the user interface 124 and the user. The user interface 124 may also include one or more vents to allow carbon dioxide and other gases exhaled by the user to escape. In other implementations, the user interface 124 includes a mouthpiece (e.g., a night guard mouthpiece molded to fit the user's teeth, a mandibular repositioning device, etc.).

[0030] The conduit 126 allows air to flow between two components of the respiratory therapy system 120, such as the respiratory therapy device 122 and the user interface 124. In some implementations, this conduit may have separate branches for inspiration and expiration. In other implementations, a single branch conduit is used for both inspiration and expiration. The respiratory therapy system 120 typically forms an air passage extending between the motor of the respiratory therapy device 122 and the user and / or the user's airway. Thus, this air passage generally includes at least the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126.

[0031] One or more of the respiratory therapy device 122, user interface 124, conduit 126, display device 128, and humidification tank 129 may include one or more sensors (e.g., pressure sensors, flow sensors, or more generally, any of the other sensors 130 described herein). These one or more sensors can be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the respiratory therapy device 122.

[0032] The display device 128 is generally used to display images (one or more) including still images, moving images, or both, and / or information related to the respiratory therapy device 122. For example, the display device 128 can provide information about the status of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on or off, the pressure of the air delivered by the respiratory therapy device 122, the temperature of the air delivered by the respiratory therapy device 122, etc.) and / or other information (also referred to as the myAir® score, as described in International Publication No. 2016 / 061629, which is incorporated herein by reference in its entirety, sleep score or therapy score, current date / time, user's personal information, 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 (one or more) as an input interface. The display device 128 may be an LED display, an organic EL display, a liquid crystal display, etc. The input interface may be, for example, a touchscreen or contact sensing board, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the respiratory therapy device 122.

[0033] The humidifying tank 129 is connected to or integrated with the respiratory therapy device 122 and includes a water reservoir that can be used to humidify the pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 122 may include a heater that heats the water in the humidifying tank 129 to humidify the pressurized air provided to the user. In addition, in some implementations, the conduit 126 may also include a heating element (e.g., connected to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user. In other implementations, the respiratory therapy device 122 or the conduit 126 may include an anhydrous humidifier. The anhydrous humidifier may incorporate a sensor that interfaces with other sensors located elsewhere in the system 100.

[0034] The respiratory therapy system 120 can be used as a ventilator or positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automated positive airway pressure (APAP) system, a biphasic or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system delivers a predetermined air pressure to the user (determined, for example, by a sleep physician). An APAP system automatically changes the air pressure delivered to the user, for example, based at least in part on respiratory data associated with the 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 lower than the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0035] Referring to Figure 2, parts of system 100 (Figure 1) relating to several implementation configurations are shown. The user 210 and co-sleep partner 220 of the respiratory therapy system 120 are in bed 230 and lying on mattress 232. A user interface 124 (e.g., a full-face mask) may be worn by the user 210 during a sleep session. The user interface 124 is fluidically connected to and / or connected to a respiratory therapy device 122 via a conduit 126. The respiratory therapy device 122 delivers pressurized air to the user 210 via the conduit 126 and user interface 124 to increase air pressure in the user 210's throat, helping to prevent airway obstruction and / or narrowing during sleep. The respiratory therapy device 122 can be positioned on a nightstand 240 directly adjacent to the bed 230, or more generally, on any surface or structure substantially adjacent to the bed 230 and / or the user 210, as shown in Figure 2.

[0036] Referring again to Figure 1, one or more sensors 130 of system 100 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 (IR) sensor 152, a photoelectric (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 moisture sensor 176, a light detection and ranging (LiDAR) sensor 178, or any combination thereof. Generally, one or each of the sensors 130 is configured to output sensor data received and stored in a memory device 114 or one or more other memory devices. Sensor 130 may include an electrooculography (EOG) sensor, a peripheral oxygen saturation (SpO2) sensor, a galvanic skin response (GSR) sensor, a carbon dioxide (CO2) sensor, or any combination thereof.

[0037] One or more sensors 130 are illustrated and described as including each of the following: pressure sensor 132, flow sensor 134, temperature sensor 136, motion sensor 138, microphone 140, speaker 142, RF receiver 146, RF transmitter 148, camera 150, IR sensor 152, PPG sensor 154, ECG sensor 156, EEG sensor 158, capacitance sensor 160, force sensor 162, strain gauge sensor 164, EMG sensor 166, oxygen sensor 168, analyte sensor 174, moisture sensor 176, and LiDAR sensor 178, but it is more common for one or more sensors 130 to include any combination and any number of each of the sensors described and / or illustrated herein.

[0038] One or more sensors 130 can be used to generate physiological data, acoustic data, or both, associated with, for example, a user of the respiratory therapy system 120 (such as user 210 in Figure 2), the respiratory therapy system 120, both the user and the respiratory therapy system 120, or other entities, objects, activities, etc. The physiological data generated by one or more of the sensors 130 can be used by the control system 110 to determine sleep-wake signals and one or more sleep-related parameters associated with the user during a sleep session. The sleep-wake signals can indicate one or more sleep stages and / or sleep states, including sleep, wakefulness, relaxed wakefulness, micro-wakefulness, or distinct sleep stages, such as the rapid eye movement (REM) stage, the first non-REM stage (often referred to as "N1"), the second non-REM stage (often referred to as "N2"), the third non-REM stage (often referred to as "N3"), or any combination thereof. Methods for determining sleep stages and / or sleep states from physiological data generated by one or more sensors, such as sensor 130, are described, for example, in International Publication 2014 / 047310, U.S. Patent Application Publication 2014 / 0088373, International Publication 2017 / 132726, International Publication 2019 / 122413, and International Publication 2019 / 122414, all of which are incorporated herein by reference in their entirety.

[0039] The sleep-wake signal can also be timestamped to indicate the user's bedtime, wake-up time, and sleep-onset attempt time. The sleep-wake signal can be measured during a sleep session by one or more of the sensors 130 at a predetermined sampling rate, such as one sample per second, one sample per 30 seconds, or one sample per minute. Examples of one or more sleep-related parameters that can be determined about a user during a sleep session based at least partially on the sleep-wake signal include total bedtime, total sleep time, total wake time, sleep latency, post-sleep wakefulness parameters, sleep efficiency, fragmentation index, time to fall asleep, respiratory rate consistency, sleep onset time, wake time, sleep disturbance rate, number of sleep shifts, or any combination thereof.

[0040] Physiological and / or acoustic data generated by one or more sensors 130 can also be used to determine respiratory signals associated with the user during a sleep session. Respiratory signals generally indicate the user's breathing or breath during a sleep session. Respiratory signals may indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory amplitude ratio, inspiratory-to-expiratory duration ratio, number of events per hour, event patterns, pressure settings of the respiratory therapy device 122, or any combination thereof. These events (one or more) may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leakage (e.g., from the user interface 124), lower limb restlessness, sleep disturbance, suffocation, increased heart rate, heart rate variability, dyspnea, asthma attack, epileptic interstitial, seizure, fever, cough, sneeze, snoring, shortness of breath, the presence of an illness such as a cold or influenza, elevated stress levels, etc.

[0041] The pressure sensor 132 outputs pressure data that can 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., atmospheric pressure sensor) that generates sensor data indicating the user's breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of the respiratory therapy system 120. In such implementations, the pressure sensor 132 can be connected to or integrated with the respiratory therapy device 122. The pressure sensor 132 may be, for example, a capacitive sensor, an electromagnetic sensor, an inductive sensor, a resistive sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof. In one example, the pressure sensor 132 can be used to determine the user's blood pressure.

[0042] The flow sensor 134 outputs flow data that can 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 therapy device 122, the airflow rate through the conduit 126, the airflow rate through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be connected to or integrated with the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow sensor 134 may be 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, an eddy current sensor, a membrane sensor, or any combination thereof.

[0043] The temperature sensor 136 outputs temperature data that can 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 indicating the user's core body temperature, the user's skin temperature, the temperature of the air flowing from the respiratory therapy 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 bandgap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0044] The motion sensor 138 outputs motion data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 can be used to detect user movement during a sleep session and / or to detect movement of any component of the respiratory therapy system 120, such as the respiratory therapy device 122, the user interface 124, or the conduit 126. The motion sensor 138 may include one or more inertial sensors, such as an accelerometer, gyroscope, and magnetometer. The motion sensor 138 can be used to detect movement or acceleration associated with an arterial pulse, such as a pulse in or around the user's face and proximal to the user interface 124, and can be configured to detect pulse shape, velocity, amplitude, or ventilation volume features.

[0045] The microphone 140 outputs acoustic data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The acoustic data generated by the microphone 140 can be reproduced as one or more sounds (one or more) during a sleep session (e.g., sounds from the user) to determine one or more sleep-related parameters (e.g., using the control system 110), as will be further detailed herein. The acoustic data from the microphone 140 can also be used to identify events experienced by the user during a sleep session (e.g., using the control system 110), as will be further detailed herein. In other implementations, the acoustic data from the microphone 140 represents noise associated with the respiratory therapy system 120. The microphone 140 can generally be connected to or integrated with the respiratory therapy system 120 (or system 100) in any configuration. For example, the microphone 140 can be placed inside the respiratory therapy device 122, the user interface 124, the conduit 126, or other components. The microphone 140 can also be positioned adjacent to or connected to the outside of the respiratory therapy device 122, the outside of the user interface 124, the outside of the conduit 126, or any other component. The microphone 140 may also be a component of an external device 170 (for example, the microphone 140 is the microphone of a smartphone). The microphone 140 can be integrated into the user interface 124, the conduit 126, the respiratory therapy device 122, or any combination thereof. In general, the microphone 140 may be located within or adjacent to the air passage of the respiratory therapy system 120, which includes at least the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126. This air passage may therefore also be referred to as an acoustic passage.

[0046] Speaker 142 outputs sound waves audible to the user. Speaker 142 can be used, for example, as an alarm clock, or to play an alert or message to the user (for example, in response to an event). In some implementations, speaker 142 can be used to transmit acoustic data generated by microphone 140 to the user. Speaker 142 can be connected to or integrated with the respiratory therapy device 122, user interface 124, conduit 126, or external device 170.

[0047] The microphone 140 and speaker 142 can be used as separate devices. In some implementations, the microphone 140 and speaker 142 can be incorporated into an acoustic sensor 141 (e.g., a SONAR sensor), for example, as described in WO2018 / 050913 and WO2020 / 104465, which are incorporated herein by reference in their entirety. In such implementations, the speaker 142 generates or emits sound waves at predetermined intervals and / or frequencies, and the microphone 140 detects reflections of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have frequencies inaudible to the human ear (e.g., less than 20 Hz or greater than about 18 kHz) so as not to disturb the sleep of the user or the user's bedmate (e.g., bedmate 220 in Figure 2). The control system 110 can determine, at least in part, the user's position and / or one or more of the sleep-related parameters described herein, such as respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per unit time, event pattern, sleep stage, pressure setting of the respiratory therapy device 122, or any combination thereof, based at least in part on data from the microphone 140 and / or speaker 142. In this context, the SONAR sensor may be understood to involve active acoustic sensing, such as generating / transmitting an ultrasonic or low-frequency ultrasonic sensing signal (e.g., within a frequency range of approximately 17–23 kHz, 18–22 kHz, or 17–18 kHz) in the air. Such a system may be considered in reference to the above-mentioned International Publication Nos. 2018 / 050913 and International Publication Nos. 2020 / 104465. In some implementations, speaker 142 is a bone conduction speaker. In some implementations, one or more sensors 130 include (i) a first microphone which is identical or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone which is identical or similar to microphone 140 but is separate and distinct from the first microphone integrated into acoustic sensor 141.

[0048] The RF transmitter 148 generates and / or emits radio waves having a predetermined frequency and / or amplitude (e.g., within the high frequency band, within the low frequency band, long wave signal, short wave signal, etc.). The RF receiver 146 detects the reflection of the radio waves emitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the user's location and / or one or more of the sleep-related parameters described herein. The RF receiver (either the RF receiver 146 and the RF transmitter 148, or another RF pair) can also be used for wireless communication between the control system 110, the respiratory therapy device 122, one or more sensors 130, an external device 170, or any combination thereof. Although the RF receiver 146 and the RF transmitter 148 are shown as separate and distinct elements in Figure 1, in some implementations the RF receiver 146 and the RF transmitter 148 are combined as part of an RF sensor 147 (e.g., a RADAR sensor). In some such implementations, the RF sensor 147 includes a control circuit. Specific forms of RF communication can include Wi-Fi and Bluetooth.

[0049] In some implementations, the RF sensor 147 is part of a mesh system. An example of a mesh system is a WiFi mesh system, which may include mesh nodes, mesh routers (one or more), and mesh gateways (one or more), each of which may be mobile / movable or fixed. In such an implementation, the WiFi mesh system includes WiFi routers and / or WiFi controllers, as well as 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 routers and satellites communicate with each other in constant communication using WiFi signals. The WiFi mesh system can be used to generate motion data at least partially based on changes in the WiFi signal between the routers and satellites (one or more) caused by the movement of objects or people partially interfering with the signal (e.g., differences in received signal strength). This motion data may represent motion, breathing, heart rate, walking, falls, behavior, or any combination thereof.

[0050] Camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, videos, thermal images, or a combination thereof) that can be stored in the memory device 114. The image data from camera 150 can be used by the control system 110 to determine one or more of the sleep-related parameters described herein. For example, the image data from camera 150 can be used to locate the user, determine the time when the user enters their bed (e.g., bed 230 in Figure 2), and determine the time when the user leaves bed 230. Camera 150 can also be used to track eye movements, pupil dilation (if one or both of the user's eyes are open), blink rate, or any changes during REM sleep. Camera 150 can also be used to track the user's posture, which may affect the duration and / or severity of apnea episodes in users with postural obstructive sleep apnea.

[0051] The IR sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, videos, or both) that can be stored in the memory device 114. The infrared data from the IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the user's temperature and / or movement. The IR sensor 152 can also be used in combination with the camera 150 to measure the user's presence, location, and / or movement. The IR sensor 152 can detect infrared light having wavelengths between approximately 700 nm and 1 mm, for example, while the camera 150 can detect visible light having wavelengths between approximately 380 nm and 740 nm.

[0052] The PPG sensor 154 outputs user-associated physiological data that can be used to determine one or more sleep-related parameters, such as heart rate, heart rate pattern, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, estimated blood pressure parameters (one or more), or any combination thereof. The PPG sensor 154 can be worn by the user, embedded in the clothing and / or fabric worn by the user, embedded in and / or connected to the user interface 124 and / or its associated headgear (e.g., a strap).

[0053] The ECG sensor 156 outputs physiological data associated with the user's cardiac electrical activity. In some implementations, the ECG sensor 156 includes one or more electrodes positioned on or around a portion of the user during a sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more of the sleep-related parameters described herein.

[0054] The EEG sensor 158 outputs physiological data associated with the electrical activity of the user's brain. In some implementations, the EEG sensor 158 includes one or more electrodes positioned on or around the user's scalp during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the user's sleep stage and / or sleep state at any given time during a sleep session. In some implementations, the EEG sensor 158 can be integrated into the user interface 124 and / or associated headgear (e.g., a strap).

[0055] The capacitance sensor 160, force sensor 162, and strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data associated with electrical activity generated by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating 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 electro-oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, one or more sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiratory sensor, a pulse sensor, a blood pressure sensor, an oximetry sensor, or any combination thereof.

[0056] The analyte sensor 174 can be used to detect the presence of analytes in the user's exhaled breath. The data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analyte contained in the user's breath. In some implementations, the analyte sensor 174 is positioned near the user's mouth to detect analytes contained in the breath exhaled from the user's mouth. For example, if the user interface 124 is a face mask that covers the user's nose and mouth, the analyte sensor 174 may be positioned inside the face mask to monitor the user's mouth breathing. In other implementations, such as when the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 may be positioned near the user's nose to detect analytes contained in the breath exhaled through the user's nose. In yet another implementation, when the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 may be positioned near the user's mouth. In this implementation, the analyte sensor 174 can be used to detect whether air is inadvertently leaking from the user's mouth. 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 such as carbon dioxide. In some implementations, the analyte sensor 174 can also be used to detect whether the user is breathing through their nose or mouth. For example, if the presence of an analyte is detected by data output from the analyte sensor 174 positioned near the user's mouth or (in implementations where the user interface 124 is a face mask) inside the face mask, the control system 110 can use this data as an indication that the user is breathing through their mouth.

[0057] The moisture sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or user interface 124, near the user's face, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the respiratory therapy device 122, etc.). Therefore, in some implementations, the moisture sensor 176 can be connected to or integrated with the user interface 124 or integrated with the conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other implementations, the moisture sensor 176 is placed near any area where the moisture level needs to be monitored. The moisture sensor 176 can also be used to monitor the humidity of the surrounding environment around the user, such as the air in the user's bedroom. The moisture sensor 176 can also be used to track the user's biological response to environmental changes.

[0058] One or more LiDAR sensors 178 can be used for depth sensing. This type of optical sensor (e.g., laser sensor) can be used to detect objects and create a three-dimensional (3D) map of the surrounding environment, such as a living space. LiDAR generally uses pulsed lasers to measure time of flight. LiDAR is also called 3D laser scanning. In one use case of such a sensor, a stationary or mobile device (such as a smartphone) having a LiDAR sensor 178 can measure and map an area more than 5 meters away from the sensor. LiDAR data can be fused with point cloud data estimated by, for example, an electromagnetic RADAR sensor. The LiDAR sensor 178 can also use artificial intelligence (AI) to automatically create a geofence for a RADAR system by detecting and classifying features in space that may pose problems for the RADAR system, such as glass windows (which may be highly reflective to RADAR). 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 down, etc. LiDAR can be used to form a 3D mesh representation of the environment. In further applications, LiDAR can reflect off solid surfaces through which radio waves pass (e.g., radio wave-transparent materials), enabling the classification of different types of obstacles.

[0059] Although shown separately in Figure 1, any combination of one or more sensors 130 can be integrated and / or coupled to any one or more components of system 100, including the respiratory therapy device 122, user interface 124, conduit 126, humidifier tank 129, control system 110, external device 170, or any combination thereof. For example, the acoustic sensor 141 and / or RF sensor 147 can be integrated and / or coupled to the external device 170. In such an implementation, the external device 170 can be considered a secondary device that generates additional or secondary data used by system 100 (e.g., the control system 110) according to some aspects of the present disclosure. In some implementations, the pressure sensor 132 and / or flow sensor 134 are integrated and / or coupled to the respiratory therapy device 122. In some implementations, at least one of the one or more sensors 130 is not connected to the respiratory therapy device 122, the control system 110, or the external device 170, and is positioned generally adjacent to the user during a sleep session (e.g., positioned on or in contact with a part of the user, worn by the user, connected to or positioned on a nightstand, connected to a mattress, connected to the ceiling, etc.). More commonly, the one or more sensors 130 are positioned at any appropriate location relative to the user so that the one or more sensors 130 can generate physiological data associated with the user and / or co-sleep companions 220 during one or more sleep sessions.

[0060] By analyzing data from one or more sensors 130, one or more sleep-related parameters can be determined, which may include respiratory signals, respiratory rate, respiratory pattern, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, occurrence of one or more events, number of events per hour, event pattern, mean duration of events, range of event durations, ratio of different numbers of events, sleep stage, apnea-hypopnea index (AHI), or any combination thereof. These one or more events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, intentional user interface leak, accidental user interface leak, mouth leak, cough, inability to rest the lower extremities, sleep disturbance, suffocation, increased heart rate, dyspnea, asthma attack, epileptic interstitial, seizure, increased blood pressure, or any combination thereof. While many of these sleep-related parameters are physiological, some can be considered non-physiological. Other types of physiological and non-physiological parameters can be determined from either data from one or more sensors 130 or other types of data.

[0061] The external device 170 includes a display device 172. The external device 170 may be, for example, a mobile device such as a smartphone, tablet, or laptop computer. Alternatively, the external device 170 may be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home, Amazon Echo, or Alexa). In some implementations, this user device is a wearable device (e.g., a smartwatch). The display device 172 is generally used to display images (one or more) including still images, videos, or both. In some implementations, the display device 172 functions as a human-machine interface (HMI) including a graphic user interface (GUI) configured to display images (one or more) and an input interface. The display device 172 may be an LED display, an OLED display, a liquid crystal display, etc. The input interface may be, for example, a touchscreen or contact sensing substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the external device 170. In some implementations, one or more user devices may be used by and / or included in the system 100.

[0062] The blood pressure device 180 is typically used to help generate physiological data for determining one or more blood pressure measurements associated with the user. The blood pressure device 180 may include, for example, at least one of one or more sensors 130 for measuring systolic and / or diastolic blood pressure components.

[0063] In some implementations, the blood pressure device 180 is a blood pressure monitor comprising a user-wearable inflatable cuff and a pressure sensor (e.g., the pressure sensor 132 described herein). For example, as shown in the example in Figure 2, the blood pressure device 180 can be worn on the user's upper arm. In such implementations where the blood pressure device 180 is a blood pressure monitor, the blood pressure device 180 also includes a pump (e.g., a manually operated valve) for inflating the cuff. In some implementations, the blood pressure device 180 is connected to a respiratory therapy device 122 of a respiratory therapy system 120, which delivers pressurized air to inflate the cuff. More commonly, the blood pressure device 180 can be communicatively connected to and / or physically integrated (e.g., within a housing) with a control system 110, a memory device 114, a respiratory therapy system 120, an external device 170, and / or an activity tracker 190.

[0064] Activity trackers 190 are typically used to assist in generating physiological data for determining activity metrics associated with the user. These activity metrics may include, for example, steps taken, distance traveled, steps taken uphill, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, mean respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, mean heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, skin electrical activity (also referred to as skin conductance or galvanic skin response) or any combination thereof. Activity trackers 190 include, for example, one or more of the sensors 130 described herein, such as motion sensors 138 (e.g., one or more accelerometers and / or gyroscopes), PPG sensors 154, and / or ECG sensors 156.

[0065] In some implementations, the activity tracker 190 is a wearable device that the user can wear, such as a smartwatch, wristband, ring, or patch. For example, as shown in Figure 2, the activity tracker 190 is worn on the user's wrist. The activity tracker 190 can also be linked to or integrated with clothing or garments worn by the user. As a further alternative, the activity tracker 190 can also be linked to or integrated with an external device 170 (e.g., within the same housing). More commonly, the activity tracker 190 can be communicatively linked to or physically integrated (e.g., within the housing) with the control system 110, memory device 114, respiratory therapy system 120, external device 170, and / or blood pressure device 180.

[0066] Although the control system 110 and the memory device 114 are shown and illustrated in Figure 1 as separate and distinct components of system 100, in some implementations the control system 110 and / or the memory device 114 are integrated into the external device 170 and / or the respiratory therapy device 122. Alternatively, in some implementations the control system 110 or a part thereof (e.g., the processor 112) may reside in the cloud (e.g., integrated into a server, integrated into an Internet of Things (IoT) device, connected to the cloud, and subject to edge cloud processing), or may reside on one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0067] Although System 100 is shown as including all of the above components, depending on the various implementations of this disclosure, there may be more or fewer components that can be included in the system to cancel noise during use of the respiratory therapy system 120. For example, a first alternative system includes a control system 110, a memory device 114, and at least one of one or more sensors 130. Another example is a second alternative system including a control system 110, a memory device 114, at least one of one or more sensors 130, and an external device 170. A further alternative is a third alternative system including a control system 110, a memory device 114, a respiratory therapy system 120, at least one of one or more sensors 130, and an external device 170. As a further alternative example, a fourth alternative system includes a control system 110, a memory device 114, a respiratory therapy system 120, at least one of one or more sensors 130, an external device 170, a blood pressure device 180 and / or an activity tracker 190. Thus, any(s) of the components shown and described herein can be used and / or combined with one or more other components to form a variety of systems for analyzing data on the user's use of the respiratory therapy system 120.

[0068] As used herein, a sleep session can be defined in several ways, for example, based at least in part on the initial start and end times. In some implementations, a sleep session is the duration of time a user is asleep, i.e., a sleep session has a start time and an end time, and the user remains awake until the end time during the sleep session. In other words, time the user is awake is not included in the sleep session. From the first definition of a sleep session, if a user wakes up and falls asleep multiple times during the night, each sleep period separated by those periods of wakefulness constitutes a sleep session.

[0069] Alternatively, in some implementations, a sleep session has a start time and an end time, and during that sleep session, the user can remain awake without the sleep session ending, as long as the continuous time the user is awake is less than the wakefulness duration threshold. The wakefulness duration threshold can be defined as a percentage of the sleep session. The wakefulness duration threshold could be, for example, about 20 percent of the sleep session, about 15 percent of the sleep session duration, about 10 percent of the sleep session duration, about 5 percent of the sleep session duration, about 2 percent of the sleep session duration, etc., or any other arbitrary threshold percentage. In some implementations, the wakefulness duration threshold is defined as, for example, about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, etc., or any other arbitrary amount of time.

[0070] In some implementations, a sleep session is defined as the total time from the time the user first goes to bed at night until the time the user last wakes up the following morning. In other words, a sleep session can be defined as the time that begins at a first time (e.g., 10:00 p.m.) on a first date that can be called the current night (e.g., Monday, January 6, 2020) when the user first goes to bed with the intention of sleeping (not if the user first intends to watch TV or use their smartphone before going to sleep), and ends at a second time (e.g., 7:00 a.m.) on a second date that can be called the following morning (e.g., Tuesday, January 7, 2020) when the user first wakes up with the intention of not going to sleep again the following morning.

[0071] In some implementations, users can manually define the start of a sleep session and / or manually end a sleep session. For example, a user can manually start or end a sleep session by selecting one or more user-selectable elements displayed on the display device 172 of the external device 170 (Figure 1) (for example, by clicking or tapping).

[0072] Referring to FIG. 3, an exemplary time series 300 of a sleep session is shown. The time series 300 includes a bedtime (t bed ), a time to fall asleep (t GTS ), a first sleep time (t wake ), a first micro - arousal MA1 and a second micro - arousal MA2, a wake - up A, a wake - up time (t wake ), and a wake - up time (t rise ).

[0073] The bedtime t bed is associated with the time when the user first gets into bed (e.g., the bed 230 in FIG. 2) before falling asleep (e.g., the user lies down or sits on the bed). The bedtime t bed can be specified based at least in part on a bedtime threshold duration to distinguish between the time when the user gets into bed to sleep and the time when the user gets into bed for other reasons (e.g., to watch TV). For example, the bedtime 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. In this specification, the bedtime t bed is described with reference to the bed, but more generally, the bedtime t bed can represent the time when the user first gets into a place (e.g., a sofa, a chair, a sleeping bag, etc.) to sleep.

[0074] The time to fall asleep (GTS) is associated with the time (t bed ) when the user first attempts to fall asleep after getting into bed. For example, after getting into bed and before trying to sleep, the user can engage in one or more activities (e.g., reading, watching TV, listening to music, using the external device 170, etc.) to relax. The first sleep time (t) wake ) is the time when the user first falls asleep. For example, the first sleep time (t wake ) can be the time when the user first enters the non - REM sleep stage.

[0075] The wake - up time t wakeThis is the time associated with when the user became awake without falling back asleep (for example, instead of waking up in the middle of the night and falling back asleep). After initially falling asleep, the user may experience one of several more unconscious micro-awakenings (e.g., micro-awakenings MA1 and MA2) with short durations (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.). The user's wake time t wake Instead, the user goes through micro-awakenings MA1 and MA2 respectively, and then falls back asleep. Similarly, after initially falling asleep, the user may have one or more conscious awakenings (e.g., awakening A) (e.g., getting up to go to the toilet, taking care of a child or pet, sleepwalking, etc.). However, the user returns to sleep after awakening A. Therefore, the awakening time t wake This can be defined, for example, based at least in part on the arousal threshold duration (for example, the duration for which the user is awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0076] Similarly, wake-up time t rise This is associated with the time when the user leaves bed with the intention of ending the sleep session (not, for example, going to the toilet in the middle of the night, taking care of children or pets, or wandering around). In other words, the wake-up time t rise This is the time when the user last left bed without returning to bed until the next sleep session (e.g., the following night). Therefore, the wake-up time t rise This can be defined, for example, based at least in part on the wake-up threshold duration (e.g., the user is out of bed for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). The bedtime t of the second subsequent sleep session bed It can also be defined at least partially based on the wake-up threshold duration (for example, when the user is out of bed for at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.).

[0077] As stated above, the user's first t bed from the final trise The last awakening time t is determined at least partially based on a predetermined threshold duration after an event (e.g., falling asleep or getting out of bed). wake and / or last wake-up time t rise The threshold duration can be customized to suit the user. For a typical user who goes to bed at night and wakes up and gets up in the morning, any time between approximately 12 and 18 hours (when the user is awake (t wake ) or wake up (t rise ) then go to bed (t bed ), falling asleep (t GTS ) or sleep (t wake A threshold time of (until) can be used. For users who spend a long time in bed, a shorter threshold time (e.g., approximately 8 to 14 hours) may be used. This threshold time may be initially selected and / or adjusted later, at least in part, based on a system that monitors the user's sleep behavior.

[0078] Total time in bed (TIB) is the time in bed t bed From wake time rise This is the duration up to the time of first sleep. Total sleep time (TST) is the duration from the time of first sleep to the time of wakefulness, excluding conscious and unconscious wakefulness and / or minute wakefulness during that time. Total sleep time (TST) is generally shorter than total time in bed (TIB) (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to time series 300 in Figure 3, total sleep time (TST) is from the time of first sleep to t wake From awakening time t wake However, the durations of the first micro-awakening MA1, the second micro-awakening MA2, and awakening A are excluded. As shown in the figure, in this example, total sleep time (TST) is shorter than total time in bed (TIB).

[0079] In some implementations, total sleep time (TST) can be defined as total continuous sleep time (PTST). In such implementations, total continuous sleep time excludes a predetermined initial portion or duration of the first non-REM stage (e.g., light sleep stage). For example, this predetermined initial portion may be approximately 30 seconds to 20 minutes, approximately 1 minute to 10 minutes, or approximately 3 minutes to 5 minutes. Total continuous sleep time is a measure of continuous sleep and smooths the sleep-wake sleep progression diagram. For example, upon a user's initial sleep onset, the user may enter a first non-REM stage for a very short period (e.g., approximately 30 seconds), return to a short period (e.g., 1 minute) of wakefulness, and then return to the first non-REM stage. In this example, total continuous sleep time excludes the first instance of the first non-REM stage (e.g., approximately 30 seconds).

[0080] In some implementations, the sleep session is defined by the time of going to bed (t bed It starts with ) and wake-up time (t rise The time that ends at the first sleep time (t) is defined as total bedtime (TIB). In some implementations, a sleep session is defined as the time that ends at the first sleep time (t). wake ) begins, and the wake time (t wake It is defined as ending at t GTS ) begins, and the wake time (t wake It is defined as ending at the time of sleep onset (t). In some implementations, a sleep session is defined as ending at the time of sleep onset (t). GTS It starts with ) and wake-up time (t rise It is defined as ending at bedtime (t). In some implementations, a sleep session is defined as ending at bedtime (t). bed ) begins, and the wake time (t wake It is defined as ending at the time of the first sleep (t). In some implementations, a sleep session is defined as ending at the time of the first sleep (t). wake It starts with ) and wake-up time (t rise It is defined as something that ends in ).

[0081] Referring to Figure 4, exemplary sleep progression diagrams 400 corresponding to time series 300 (Figure 3) are shown for several implementation configurations. As illustrated, the sleep progression diagram 400 includes a sleep-wake signal 401, an arousal stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. The intersection of the sleep-wake signal 401 and one of the axes 410-440 indicates the sleep stage at any given time during the sleep session.

[0082] The sleep-wake signal 401 can be generated at least in part based on physiological data associated with the user (e.g., generated by one or more of the sensors 130 described herein). The sleep-wake signal may indicate one or more sleep stages, including wakefulness, relaxed wakefulness, micro-wakefulness, 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 the first non-REM stage, and a deep sleep stage may include the second and third non-REM stages. In Figure 4, the sleep progression diagram 400 is shown to include a light sleep stage axis 430 and a deep sleep stage axis 440, but in some implementations, the sleep progression diagram 400 may include axes representing the first non-REM stage, the second non-REM stage, and the third non-REM stage, respectively. In other implementations, the sleep-wake signal may also show respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory amplitude ratio, inspiratory-to-expiratory duration ratio, number of events per unit time, event pattern, or any combination thereof. Information describing the sleep-wake signal can be stored in the memory device 114.

[0083] The sleep progression diagram 400 can be used to determine one or more sleep-related parameters, such as sleep latency (SOL), wakefulness after sleep onset (WASO), sleep efficiency (SE), sleep fragmentation index, sleep block, or any combination thereof.

[0084] Sleep latency (SOL) is the time it takes to fall asleep (t GTS ) and first sleep time (t wake It is defined as the time between the time the user first attempts to fall asleep and the time the user actually falls asleep. In some implementations, sleep latency is defined as persistent sleep onset latency (PSOL). Persistent sleep latency differs from sleep latency in that it is defined as the duration from the time the user falls asleep to a given amount of sustained sleep. In some implementations, a given amount of sustained sleep may include, for example, at least 10 minutes of sleep within a second non-REM phase, a third non-REM phase, and / or a wake of 2 minutes or less, a first non-REM phase, and / or transitions between them within a REM phase. In other words, persistent sleep latency requires, for example, at least 8 minutes of sustained sleep within a second non-REM phase, a third non-REM phase, and / or within a REM phase. In other implementations, a predetermined amount of sustained sleep may include at least 10 minutes of sleep within the first non-REM phase, the second non-REM phase, the third non-REM phase, and / or within the REM phase after the initial sleep time. In such implementations, the predetermined amount of sustained sleep may exclude all minute awakenings (for example, a 10-second minute awakening will not be followed by a resumption of sleep for 10 minutes).

[0085] Post-sleep-onset wakefulness (WASO) is associated with the total duration of wakefulness a user experiences from the time of initial sleep to the time of wakefulness. Therefore, WASO includes short-term and minute wakefulnesses during a sleep session, whether conscious or unconscious (e.g., minute wakefulnesses MA1 and MA2 shown in Figure 4). In some implementations, WASO is defined as persistent post-sleep-onset wakefulness (PWASO), which includes only the total wakefulness duration having a predetermined length (e.g., more than 10 seconds, more than 30 seconds, more than 60 seconds, more than approximately 5 minutes, more than approximately 10 minutes, etc.).

[0086] Sleep efficiency (SE) is determined as the ratio of total time in bed (TIB) to total sleep time (TST). For example, if total time in bed is 8 hours and total sleep time is 7.5 hours, the sleep efficiency for that sleep session is 93.75%. Sleep efficiency represents the user's sleep hygiene. For example, if a user goes to bed and spends time on other activities (e.g., watching television) before falling asleep, sleep efficiency decreases (e.g., the user may be penalized). In some implementations, sleep efficiency (SE) can be calculated based at least partially on total time in bed (TIB) and the total time the user attempts to fall asleep. In such implementations, the total duration the user attempts to fall asleep is defined as the time from the time of falling asleep (GTS) to the time of waking up as described herein. For example, if total sleep time is 8 hours (e.g., from 11 p.m. to 7 a.m.), the time of falling asleep is 10:45 p.m., and the time of waking up is 7:15 a.m., then in such an implementation, the sleep efficiency parameter is calculated to be approximately 94%.

[0087] The fragmentation index is determined at least partially based on the number of awakenings during a sleep session. For example, if a user had two minor awakenings (e.g., minor awakenings MA1 and MA2 shown in Figure 4), the fragmentation index could be represented as 2. In some implementations, this fragmentation index is scaled within a predetermined range of integers (e.g., between 0 and 10).

[0088] A sleep block is associated with a transition between any sleep stage (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and the wakefulness stage. Sleep blocks can be calculated, for example, with a resolution of 30 seconds.

[0089] In some implementations, the system and method described herein are used for bedtime (t bed ), sleep onset time (t GTS ), first sleep time (t wake ), one or more first minute awakenings (e.g., MA1 and MA2), awakening time (t wake ), wake-up time (t riseThis may include generating or analyzing a sleep / wake chart that includes sleep / wake signals in order to determine or identify, or any combination thereof, based at least in part on the sleep / wake signals of the sleep / wake chart.

[0090] In other implementations, one or more of the sensors 130 are used to determine the time of going to bed (t bed ), sleep onset time (t GTS ), first sleep time (t wake ), one or more first minute awakenings (e.g., MA1 and MA2), awakening time (t wake ), wake-up time (t rise ), or any combination thereof, can be determined or identified, and by extension, a sleep session can be defined. For example, bedtime t bed The time of falling asleep can be determined at least in part on data generated by, for example, the motion sensor 138, the microphone 140, the camera 150, or any combination thereof. The time of falling asleep can be determined at least in part on, for example, data from the motion sensor 138 (e.g., data indicating no user movement), data from the camera 150 (e.g., data indicating no user movement and / or the user has turned off the lights), data from the microphone 140 (e.g., data indicating the user has turned off the TV), data from the external device 170 (e.g., data indicating the user is no longer using the external device 170), data from the pressure sensor 132 and / or the flow sensor 134 (e.g., data indicating the user has turned on the respiratory therapy device 122, data indicating the user has put on the user interface 124, etc.), or any combination thereof.

[0091] When a user uses the respiratory therapy system 120 during a sleep session, a large amount of data about the user can be generated during that sleep session. One or more sensors 130 are configured to generate not only physiological data about the user during the sleep session, but also non-physiological data (such as data related to the operation of the respiratory system). However, only basic data about the user's use of the respiratory therapy system 120 (such as flow data and pressure data) is initially provided to the control system 110 and / or memory device 114 and is often used to determine parameters or metrics about the user. Additional data about (i) the user's use of the respiratory therapy system 120, (ii) the respiratory therapy system 120 itself, (iii) aspects or characteristics of the user other than the respiratory therapy system 120, or (iv) other general data may be useful in determining more accurate parameter values ​​or determining new parameter values. However, no additional data can be obtained and used without the user's consent. Various methods or techniques can be used to obtain the user's consent to receive and analyze additional data other than the initial data provided to the control system 110 and / or memory device 114. One or more steps of any of the following methods or techniques can generally be carried out using any element or embodiment of System 100 (Figures 1-2) described herein.

[0092] Referring next to Figure 5, a method 500 is shown for analyzing data relating to a user's (e.g., user 210) use of a respiratory therapy system (e.g., respiratory therapy system 120) during a sleep session. The respiratory therapy system may include a respiratory therapy device (e.g., respiratory therapy device 122), a user interface (e.g., user interface 124), and a conduit (e.g., conduit 126). Method 500 can typically be implemented using a system (e.g., system 100) that includes a control system (e.g., control system 110). The control system or a portion of a control system (e.g., one or more processors 112) can be configured to perform various steps of Method 500. A memory device (e.g., memory device 114) can be used to store any type of data used in the steps of Method 500 (or other methods disclosed herein).

[0093] Step 502 of Method 500 includes receiving a first type of data relating to the user during a sleep session. The first type of data may include any type of data relating to the user's use of the respiratory therapy system. In some implementations, the first type of data is physiological data associated with the user during the sleep session. The first type of data may be, for example, flow data and / or pressure data relating to the user's breathing.

[0094] Step 504 of Method 500 includes determining a first value for a first parameter relating to the user. The first value for the first parameter is at least partially based on a first type of data. In some implementations, the first parameter is a sleep-related parameter during the user's sleep session that can be determined by analyzing physiological data. For example, if the first type of data is flow data and / or pressure data (e.g., respiratory data relating to the user's breathing), then the number of breaths during the user's sleep session can be determined. The first parameter can commonly be any sleep-related parameter, any physiological parameter, or any other parameter.

[0095] Step 506 of Method 500 includes identifying a desired second type of data. The second type of data is typically any data to which the user has not yet given consent to be collected and / or analyzed. The second type of data may include, for example, physiological data (such as additional respiratory data), non-physiological data about the user, and non-physiological data about the respiratory system. The second type of data may relate to the user's use of the respiratory therapy system. The second type of data may relate to activities, events, information, etc., that are unrelated to or occur outside of the user's sleep sessions or the user's use of the respiratory therapy system.

[0096] Step 508 of Method 500 includes sending a request to the user for consent to receive second type data. The control system sends a request to the user for consent because the user has not given consent to acquire and / or analyze second type data. Step 510 of Method 500 includes receiving second type data after receiving consent from the user to receive second type data. The user may respond to the request for consent in a variety of different ways, such as via voice commands (e.g., speaking to a smart speaker or smart device), via biometric indicators (e.g., fingerprint or facial scan), via gestures in front of any type of sensor, via physical input mechanisms (e.g., pressing a touchscreen, activating a button, typing on a keyboard, clicking a button with a mouse), or via any combination of these input methods or other methods. Other types of sounds or utterances may also be used to provide consent. This request for consent can be met by using any of the system components, which may include a user device (such as user device 170), a microphone (such as microphone 140), or one or more sensors (such as sensor 130). In some implementations, the consent referred to herein may be sought from a third party, such as a family member, physician, or healthcare provider. This third-party consent may be sought if the user is physically or mentally incapacitated and / or unable to respond to the request and / or provide consent.

[0097] In some implementations, the control system may activate a specific sensor to begin receiving a second type of data. In other implementations, the control system may begin receiving a second type of data from another source, such as a wired or wireless connection to the internet. In yet another implementation, a user or another person or system may actively transmit a second type of data to the control system.

[0098] Finally, step 512 of method 500 involves determining a second value for the first parameter, a value for the second parameter, or both. In any of these determinations, it is common to consider a second type of data. Therefore, the second type of data is used to determine an additional value for a previously determined parameter, or a completely new parameter value. In many of these implementations, the second type of data is used to determine the value of the first parameter more accurately, since the second value of the first parameter is more accurate than the first value of the first parameter. In some implementations, the first value of the first parameter is determined using a first confidence interval or probability level. For example, a control system may determine a first value of the first parameter plus or minus X%, and a second value of the first parameter plus or minus Y%, where Y is less than X (e.g., the range of possible values ​​for the second value of the first parameter is smaller than the range for the first value of the first parameter). In another example, the control system can determine a first value of the first parameter using a confidence interval of X%, and a second value of the first parameter using a confidence level of Z%, where Z is greater than X.

[0099] A second, and more accurate, value for the first parameter can be based on a new determination or a modification of the first value of the first parameter. Therefore, the second value of the first parameter can be based on any combination of data of the first type, the first value of the first parameter, and data of the second type. Similarly, the first value of the second parameter (e.g., a new parameter) can be based on any combination of data of the first type, the first value of the first parameter, and data of the second type.

[0100] In some implementations, the first type of data is received during or after the first sleep session, and the second type of data is received during or after the second sleep session. For example, in one implementation, the value of the first parameter is determined while the user is asleep during the first sleep session, and the user can then consent to the control system receiving the second type of data during the second sleep session the following day while awake, and after the user falls asleep that night. This example can be used when the second type of data is generated during the sleep session. A request for consent to receive the second type of data can be sent during the sleep session (e.g., while the user is still asleep) or after the sleep session (e.g., after the user wakes up and gets out of bed). The second type of data can generally be received after the first sleep session, and this timing includes both when the user is awake after the first sleep session and after the second sleep session has started. In yet another implementation, both the first and second types of data can be received during the first sleep session. For example, a user might respond positively to a request to consent to receiving a second type of data while lying in bed during a first sleep session but not yet asleep.

[0101] The identification of a desired second type of data in step 506 of Method 500 can be based on a variety of different factors. In some implementations, the desired second type of data is based on the judgment value of the first parameter. For example, since the value of the first parameter may reveal that the user potentially has a particular medical condition or pain, the control system can identify second type of data that can provide more insight into whether the user has a medical condition or pain. In other implementations, the identification of second type of data may be based solely on the definition of the first type of data. For example, the control system may identify additional data that may be useful when analyzed in conjunction with first type data already provided to the control system. In further implementations, the identification of second type of data is based on the accuracy of the value of the first parameter if judged solely from the first type of data. If the accuracy does not meet some threshold accuracy, the control system can identify second type of data as data that can be used to obtain a more accurate value.

[0102] In some implementations, step 508 may optionally or additionally include sending a request for consent to analyze the second type of data. In some implementations, the control system may already have access to the second type of data. For example, the control system may have consent to store the second type of data in a memory device. However, the control system may not have consent from the user to analyze the second type of data. In these implementations, instead of sending a request for consent to receive the second type of data, the control system sends a request to the user for consent to analyze the second type of data. In implementations where the control system and / or memory device do not have access to the second type of data, the control system may send a request for consent to analyze the second type of data in addition to sending a request for consent to receive the second type of data. These implementations can be used when separate consents are required for both receiving and analyzing the second type of data.

[0103] In some implementations, step 508 may optionally include a request for consent to activate one or more sensors to generate and receive a second type of data. In many implementations, one or more sensors are present during a sleep session (e.g., as part of a respiratory therapy system) but do not actively generate data. Therefore, the control system may send a request for consent to activate a given sensor to generate and receive a second type of data. In one implementation, the first type of data is respiratory data generated by a pressure sensor or flow sensor, and after analyzing that respiratory data, a request is sent to the user to activate an acoustic sensor and receive audio data.

[0104] In some implementations, Method 500 further includes a step of sending a request to the user seeking consent to transmit the first and / or second type of data to a third party. This third party may be a healthcare provider (e.g., the user's doctor), family, friends, or caregivers. The consent request may also be accompanied by an explanation of why the first and / or second type of data should be transmitted to the third party. For example, the control system may provide an explanation that the user's healthcare provider can use the first and / or second type of data to improve the user's treatment at future appointments or to better track any illnesses or diseases the user may have.

[0105] In some implementations, the second type of data includes a portion of the user's medical history. For example, the second type of data may include past illnesses or pains the user has experienced, or any ongoing medical problems that the control system is not yet aware of. This medical history may also include information about the user's family history, such as illnesses, diseases, pains, or problems suffered by any of the user's relatives. This medical history may be obtained directly from the user, or from an external source separate from the user, such as the user's healthcare provider, the user's electronic medical records, or the internet. The control system can then use the user's medical history to take various different actions. For example, the control system may be able to more accurately determine the value of the first parameter based on the information obtained from the user's medical history.

[0106] In some implementations, Method 500 further includes a step of sending a request to the user seeking consent to analyze a second type of data to determine whether the user is asleep. This system typically monitors the user during a sleep session to determine the number of respiratory events the user experiences per hour. However, the accuracy of the determination of the number of events per hour may be affected by whether the user is asleep or not. For example, data analyzed by the control system may show that the user is experiencing a certain number of events even when the user is awake. By determining whether the user is asleep or not, the control system can more accurately determine when the actual events occur.

[0107] In some implementations, the second type of data includes motion data indicating the user's movements during a sleep session. This motion data may indicate that the user is moving frequently, thereby indicating that the user has not yet fallen asleep. This motion data may also indicate that the user is not moving or is moving infrequently, thereby indicating that the user is asleep. In other implementations, the second type of data includes motion data indicating components of the respiratory therapy system, such as the user interface and conduits. These components (or other components) may move as the user moves during a sleep session. Therefore, any movement of these components (or other components) can be used to help determine whether the user is asleep. This motion data may also indicate vibrations of various components of the respiratory therapy system, which can be used to determine whether the user is asleep, as it may indicate that the respiratory therapy device is currently operating and circulating air.

[0108] In other implementations, the second type of data may include audio data indicating noise generated by the user and / or the respiratory therapy system during use. This audio data may be generated by a microphone. For example, this audio data may reveal that the user is sleeping by indicating that the user is snoring. This audio data may reveal that the user is speaking, thereby generally indicating that the user is awake. This audio data may reveal that the respiratory therapy system is emitting noise, for example, due to the operation of a motor in the respiratory therapy device or pressurized air flowing through the respiratory therapy system. This noise from the respiratory therapy system may indicate that the respiratory therapy system is in use and help determine that the user is asleep.

[0109] In any of these implementations, the control system analyzes the second type of data to determine whether the user is asleep. Once this determination is made, the control system can more accurately determine the number of events per hour the user experiences compared to when it was unaware of whether the user was asleep. In any of these implementations, when the control system sends a request to the user seeking consent to receive and analyze the second type of data to determine whether the user is asleep, it may also send the user an explanation of the benefits of receiving and analyzing the second type of data.

[0110] This audio data can also be used to determine whether air is leaking from the user's mouth. When the user interface is a nasal mask or nasal pillow mask, air may leak from the user's mouth, especially if the user tends to breathe through their mouth while sleeping without a respiratory therapy system. Since the leaking air is typically pressurized air from a respiratory therapy device, it escapes from the user's mouth without being delivered to the user's airway. Therefore, in some implementations, the control system may send a request seeking consent to receive and analyze the audio data to determine whether air is leaking from the user's mouth. This may be based on the value of a first parameter. For example, the first type of data could be respiratory data, and the value of the first parameter could indicate some type of problem with the user's breathing. The control system can then first check whether the air leaking from the user's mouth is causing this problem.

[0111] In some implementations, the type of respiratory therapy system used by the user may influence the value of the first parameter. For example, the value of any determined sleep-related parameter may differ depending on the type of interface used by the user, the type of conduit used by the user, etc. By determining various characteristics of the respiratory therapy system, more accurate parameters can be determined. Therefore, in some implementations, a second type of data is analyzed to determine any desired characteristics of the respiratory therapy system, such as the characteristics of the conduit or interface. The control system can then determine a more accurate value for the first parameter based on both the first type of data and the characteristics of the components of the respiratory therapy system.

[0112] The health of the motor of the respiratory therapy device may also affect the value of the first parameter. Therefore, in some implementations, Method 500 may include a step of sending a request to the user asking for consent to receive and analyze audio data to determine the health of various components of the respiratory therapy system, such as the motor of the respiratory therapy system or the respiratory therapy device. Once the health of the motor is determined, the value of any parameter (such as the first parameter) can be determined more accurately. For example, if the motor of a respiratory therapy device is malfunctioning, the control system may determine that the number of events the user is experiencing per unit of time differs significantly from the events actually occurring. Therefore, by determining the health of the motor, the control system can determine the number of events the user is experiencing per unit of time more accurately. Other physiological and non-physiological parameters can also be determined more accurately in this way.

[0113] In some implementations, the first parameter may be a parameter indicating the quality of the user's sleep. For example, the first parameter may be a sleep score that takes into account, for example, the length of time the user slept, the amount of time the user spent in different stages of the sleep cycle (e.g., REM sleep, non-REM sleep), and the number of events the user experienced per unit of time. In these implementations, method 500 may further include a step of sending the user suggestions for improving the quality of their sleep based on at least the value of the first parameter and the second type of data.

[0114] In some implementations, it may be desirable to analyze the first type of data to determine parameters other than the first parameter. Therefore, Method 500 may further include a step of sending a request to the user seeking consent to analyze the first type of data to determine the values ​​of parameters other than the first type of parameter. This consent request may also include an explanation of the benefits of determining the additional parameters, thereby motivating the user to give consent. Upon receiving the user's consent, the control system can analyze the first type of data (which it already has access to) to determine the values ​​of the additional parameters. In some of these implementations, the first data is physiological data, and the additional parameters are physiological parameters.

[0115] In one implementation, Method 500 also includes the step of sending the user a description of the potential uses of a second type of data. In some circumstances, the user may hesitate to provide more data to the control system. By describing the possible uses of the second type of data, the user can be motivated to respond positively to this consent request and provide access to the second type of data. The description of the potential uses of the second type of data may include an explicit statement that the second type of data will enable a more accurate determination of the value of the first parameter than the first value (e.g., an improvement in the confidence level or a reduction in the range of possible values).

[0116] In some of these implementations, the value of a first parameter may correlate with a user having a certain medical condition (for example, heart rate data may indicate a possibility of heart disease). The explanation of the potential uses of the second type of data may include explicit indication of the correlation between the value of the first parameter and the medical condition, thereby motivating the user to consent to the control system receiving the second type of data. In these implementations, the control system can estimate the percentage likelihood that the user has the medical condition based on the second type of data. If this percentage likelihood meets a predetermined threshold, the control system may take several actions, such as (i) sending a notification to the user or a third party, (ii) sending a suggested treatment routine (such as a suggestion of medications to take) to the user or a third party, or (iii) suggesting an appointment with a healthcare provider. This third party may include a healthcare provider, the user's friends, the user's family, any other desired third party, or any combination of third parties.

[0117] In other implementations, the second type of data may include some or all of the user's medical records (which may be electronic medical records). A description of the potential use of the user's medical records may include an explicit statement that access to the user's medical records will allow for the identification of any desired additional parameters. For example, a certain value of the first parameter may not, in itself, indicate that it would be useful if any other parameters were known. However, if the control system has access to the user's medical records, it can determine whether the user has any existing illnesses or diseases for which additional parameters would be useful when considered in combination with the values ​​of the first parameter. For example, the first type of data may reveal specific respiratory or cardiac characteristics (e.g., respiratory rate or respiratory variability, heart rate or heart rate variability) that do not, in themselves, indicate any type of health problem. However, if the control system accesses the user's medical records and determines that the user has an existing illness or disease, those same respiratory or cardiac characteristics may indicate a health problem for which additional parameters need to be determined. In some implementations, the control system determines the values ​​of any additional parameters based on the first type of data after receiving consent from the user. In other implementations, after receiving consent from the user, the control system activates one of the respiratory therapy system's sensors to receive additional data, and determines the value of an additional parameter based on this additional data.

[0118] As described above, the first type of data or the second type of data includes audio data generated by the microphone of the respiratory therapy system. This audio data can be associated with the user's movements during a sleep session, the movements of one or more components of the respiratory therapy system (such as the user interface or conduit) during a sleep session, air leaks from any component of the respiratory therapy system (e.g., the user interface), air leaks from the user's mouth when the user interface is a nasal mask or nasal pillow mask (indicating that a portion of the pressurized air supplied to the user is leaking from the user's mouth), or any combination thereof. The first type of data or the second type of data may also include motion data indicating the user's movements during a sleep session, the movements of components of the respiratory therapy system during a sleep session, or both. This audio data and motion data can be used in any manner according to the various techniques disclosed herein.

[0119] In some implementations, requests for consent to receive the second type of data are based at least partially on the user's location. For example, different jurisdictions (e.g., different states or countries) may have different laws and regulations regarding privacy and data collection. Therefore, any consent requests sent to a user may differ depending on local laws and regulations. In these implementations, the control system is configured to determine the user's location before sending a request for consent to receive the second type of data. In some of these implementations, the control system also asks the user for consent to determine their location before requesting consent to receive the second type of data. The user's location can be determined with varying levels of specificity, for example, by determining which continent, country, state, province, city, or region the user is in. The control system can also determine the user's location relative to a reference location, for example, by determining whether the user is at home or somewhere other than home. The control system can also determine the user's location based on coordinates, such as latitude and longitude.

[0120] In some implementations, Method 500 can be used to analyze a user's respiration to determine whether the user has a medical condition. In these implementations, the first data is respiratory data associated with the user, which can be generated by pressure sensors and / or flow sensors (e.g., pressure sensor 132 and flow sensor 134). The control system sends a request seeking consent to analyze the respiratory data to determine the user's respiratory parameter(s) during a sleep session, and then analyzes that respiratory data to determine the respiratory parameter(s). Based at least partially on the values ​​of the respiratory parameter(s), the control system estimates the percentage likelihood that the user has a particular medical condition, and then sends a notification to the user or a third party (e.g., healthcare provider, friend, family) indicating the percentage likelihood that the user has that condition. In some implementations, this respiratory parameter is the inspiratory / expiratory ratio, which may indicate whether the user has chronic obstructive pulmonary disease (COPD), bronchitis, emphysema, etc.

[0121] In some implementations, the control system can, as an addition or alternative, analyze audio data to detect user breathing problems. For example, the control system may send a request to ask for consent to analyze audio data generated by a microphone. If the user consents to the request, the control system can analyze the audio data to detect whether the user is breathing irregularly, coughing, wheezing, choking, snoring, etc., during a sleep session, thereby helping to estimate the percentage likelihood that the user has a particular medical condition or breathing problem.

[0122] In some implementations, the second type of data is personal data associated with the user, such as (i) the user's age, (ii) the user's physical sex, (iii) the user's social sex, (iv) the user's geographical location, (v) the user's height, (vi) the user's weight, (vii) medical information associated with the user, (viii) the user's smoking status, (ix) the user's occupation, (x) the user's education level, (xi) the user's income level, (xii) the frequency and duration of any movement the user makes, or (xiii) any combination of (i) to (xii). Medical information associated with the user may include any medical conditions, diseases, or ailments the user may have, such as hypertension, drug-resistant hypertension, diabetes mellitus, chronic obstructive pulmonary disease (COPD), asthma, obesity, depression, gastroesophageal reflux disease (GERD), hypercholesterolemia, diabetes mellitus, stroke, heart attack, heart failure, or any combination thereof. This medical information can be analyzed to determine the user's various comorbidities.

[0123] In these implementations, the control system is configured to send a request to the user seeking consent to analyze the user's personal data and classify the user into one or more populations. These populations may include age-based populations (e.g., teenagers, 18-30, 31-50, 50-65, 65 and over), sex-based or social sex-based populations, health-based populations (e.g., smokers and non-smokers, normal weight and overweight), location-based populations (e.g., residents of a region, state, or country), or any other appropriate population that can be formed from personal data. These are sample populations into which the user can be classified. It is common to use any appropriate population based on any of the personal data (or other data).

[0124] In some of these implementations, the first value of the first parameter can be modified based on an arbitrary population into which the user is classified, thereby determining a more accurate second value of the first parameter. For example, an analysis of the first type of data may reveal a certain value for a parameter, but if the user is overweight and a smoker, the control system can adjust the value of that parameter to more accurately determine its true value. Thus, the second value of the first parameter can be based at least partially on the first value of the first parameter and an arbitrary population into which the user is classified.

[0125] In other implementations of these methods, desired additional parameters can be identified at least partially based on the value of the first parameter and the population to which the user belongs. For example, a specific value for a parameter (e.g., heart rate, inspiratory / expiratory ratio) may be considered normal for a 25-year-old non-smoker with a normal weight. However, if the user is older, a smoker, overweight, or obese, the same value for that parameter may indicate a potential medical problem or disease. The control system can then identify any additional parameters to better determine whether the user has a potential medical problem or disease.

[0126] The control system can also generate alerts based on at least the value of a first parameter and the population to which the user belongs. These alerts can be stored and / or sent to the user or any desired third party, such as a healthcare provider, friend, or family member.

[0127] In some implementations, users can withdraw previously granted consent. In these implementations, the control system can actively stop receiving data that the user has withdrawn consent for, even though it was supposed to receive it. The control system can also actively stop analyzing data that the user has withdrawn consent for, even though it was supposed to analyze it. Generally, users can withdraw their consent using any appropriate method, such as via voice commands, biometric indicators, gestures in front of some type of sensor, physical input mechanisms, or any combination of these input methods or others. In some implementations, the control system is configured to periodically send the user a message indicating that the user can withdraw previously granted consent. Furthermore, upon the user's explicit request to withdraw consent, the control system in some implementations can provide the user with information about features that will become inaccessible if the user does not have the data they wish to withdraw consent for.

[0128] Referring next to Figure 6, a method 600 is shown for analyzing data regarding the use of a respiratory therapy system (e.g., respiratory therapy system 120) by a user (e.g., user 210) during a sleep session. The respiratory therapy system may include a respiratory therapy device (e.g., respiratory therapy device 122), a user interface (e.g., user interface 124), and a conduit (e.g., conduit 126). Method 600 can typically be implemented using a system (e.g., system 100) that includes a control system (e.g., control system 110). The control system or a part of a control system (e.g., one or more processors 112) can be configured to perform various steps of Method 500. A memory device (e.g., memory device 114) can be used to store any type of data used in the steps of Method 600 (or other methods disclosed herein).

[0129] Step 602 of Method 600 is similar to step 502 of Method 500 and includes receiving a first type of data about the user during a sleep session and agreeing to analyze the first type of data to determine the value of a first parameter about the user. Generally, the first type of data may include any type of data about the user's use of the respiratory therapy system and may be physiological or non-physiological data. Step 604 of Method 600 is similar to step 504 of Method 500 and includes determining the value of a first parameter based on at least the first type of data. The first parameter may be a sleep-related parameter or may be another physiological or non-physiological parameter.

[0130] Step 606 of Method 600 is similar to step 506 of Method 500 and includes identifying a desired second parameter. In some implementations, the identification of the desired second parameter is at least partially based on the value of the first parameter. For example, elevated or low respiratory rate or heart rate may indicate other types of parameters to check to determine whether the condition is problematic. The identification of the desired second parameter can be at least partially based on the identification of the first parameter, regardless of the value of the first parameter.

[0131] Step 608 of Method 600 includes sending a request to the user seeking consent to analyze a first type of data to determine the value of a second parameter. In some circumstances, the user may have already consented to the control system analyzing the first type of data for a specific purpose, such as determining the value of a first parameter. However, since this consent is often limited to this purpose, the control system requires specific consent to analyze the first type of data for any other purpose, such as determining the value of a second parameter. For example, the first type of data could be audio data relating to the operation of the motor of a respiratory therapy system, and the first parameter could indicate the health of the motor. If the motor is malfunctioning, the control system may need to measure respiratory-related parameters to ensure that the respiratory therapy system is still providing the user with a sufficient amount of pressurized air. If the user consents, step 610 of Method 600 includes determining the value of a second parameter based on at least the first type of data.

[0132] In some implementations, method 600 additionally includes the step of identifying a desired third parameter based at least partially on (i) the value of a first parameter, (ii) the value of a second parameter, or (iii) both (i) and (ii).

[0133] Referring next to Figure 7, a method 700 is shown for determining the optimal order in which to send multiple requests seeking consent to receive and analyze data on the use of a respiratory therapy system (such as respiratory therapy system 120) by one or more users (which may include user 210). The respiratory therapy system may include a respiratory therapy device (such as respiratory therapy device 122), a user interface (such as user interface 124), and a conduit (such as conduit 126). It may be desirable to determine the optimal order in which to send requests to a user seeking consent to receive various different types of data. For example, a user may be more likely to consent to the transmission of a certain type of data if they are asked for consent to transmit that type of data before requests seeking consent for other types of data. The optimal order can be determined by comparing data collected from multiple different users. Method 700 can typically be implemented using a system (such as system 100) that includes a control system (such as control system 110). The control system or a part of the control system (such as one or more processors 112) can be configured to perform various steps of Method 500. A memory device (such as memory device 114) can be used to store any type of data used in a step of method 700 (or any other method disclosed herein).

[0134] Step 702 of Method 700 involves sending multiple requests to multiple users seeking their consent to receive data. The requested data is typically associated with each user's use of the respiratory therapy system. However, some requests may also include other data. For each user, multiple requests are typically sent in their respective order.

[0135] Step 704 of Method 700 involves receiving data from two or more of the users, after obtaining consent from each user to receive the data. To determine the optimal order, data must be collected from at least two users and the received data must be compared. However, data can be collected from any number of users, as long as data is collected from at least two users.

[0136] Step 706 of Method 700 includes analyzing all received data to determine the optimal order for sending requests seeking consent to receive data. The optimal order can be determined in various different ways. In some implementations, the optimal order for sending requests is the order in which the maximum amount of data received from a user is achieved. Thus, the order of requests to a user who has consented to transmitting the maximum amount of data can be used in the future for that user or other users to collect the maximum amount of data. In other implementations, the optimal order is the order in which the time between the start of a request sent to a user and the reception of some threshold of data is minimized. In some situations, there may be an order in which the maximum amount of data received is achieved. However, this order is impractical to use because it may require a much longer time for the user to consent to transmitting that amount of data. Instead, one may identify a minimum amount of data, and the optimal request order can be considered to be the order in which that amount (or more) of data is obtained in the minimum amount of time.

[0137] In some implementations, method 700 further includes a step of determining the optimal time to send multiple requests. For example, a user may be more receptive to data requests in the afternoon or evening compared to the morning. The optimal time can be determined by analyzing all the data received from the user.

[0138] In one implementation, the received data may include, but not limited to, personal data such as (i) the user's age, (ii) the user's physical sex, (iii) the user's social sex, (iv) the user's geographical location, (v) the user's height, (vi) the user's weight, (vii) medical information associated with the user, (viii) the user's smoking status, (ix) the user's occupation, (x) the user's education level, (xi) the user's income level, (xii) the frequency and duration of any movement of the user, or any combination of (xiii)(i) to (xii). This personal data can be analyzed to classify each user into one or more populations. Then, the optimal order for each user population can be identified. For example, different age ranges of users may respond differently to the same order of consent requests. To collect more data, the optimal order for sending consent requests for each age range can be determined.

[0139] The data can be analyzed to determine how consent was received from the user, and this method can be used to help determine the optimal order in which requests for consent to receive data are sent. As detailed herein, users can respond to requests for consent in a variety of different ways, such as via voice commands (e.g., speaking into a smart speaker or smart device), via biometric indicators (e.g., fingerprint or facial scan), via gestures in front of some type of sensor, via physical input mechanisms (e.g., pressing a touchscreen, activating a button, typing on a keyboard, clicking a button with a mouse), or via any combination of these input methods or other methods. Users who respond to requests for consent using different methods may be better able to respond to receiving requests for consent in different orders. Therefore, the optimal order for sending multiple requests for consent to receive data can be based at least in part on the way the user responds to these requests for consent.

[0140] Referring next to Figure 8, a method 800 is shown for analyzing data on a user's (e.g., user 210) use of a respiratory therapy system (e.g., respiratory therapy system 120) to determine changes in user parameters during a sleep session. The respiratory therapy system may include a respiratory therapy device (e.g., respiratory therapy device 122), a user interface (e.g., user interface 124), and a conduit (e.g., conduit 126). Method 800 can typically be implemented using a system (e.g., system 100) that includes a control system (e.g., control system 110). The control system or a part of a control system (e.g., one or more processors 112) can be configured to perform various steps of Method 500. A memory device (e.g., memory device 114) can be used to store any type of data used in the steps of Method 600 (or other methods disclosed herein).

[0141] Step 802 of Method 800 includes storing a number of historical values ​​of a first parameter. These historical values ​​may be previous values ​​of the first parameter from the current sleep session, previous values ​​of the first parameter from one or more previous sleep sessions, or both. Step 804 of Method 800 includes receiving a first type of data about the user during the sleep session. Step 806 of Method 800 includes determining the current value of the first parameter based at least in part on the received first type of data.

[0142] Step 808 of Method 800 includes comparing a set of historical values ​​of a first parameter with the current value of the first parameter. This comparison can be performed in any number of ways. In some implementations, a statistical parameter is determined based on the historical values, and then that statistical parameter is compared with the current value. This statistical parameter may be, for example, the mean of the set of historical values ​​of the first parameter, the median of the set of historical values ​​of the first parameter, the moving mean of the set of historical values ​​of the first parameter, the moving median of the set of historical values ​​of the first parameter, or any other suitable statistical parameter. This statistical parameter can then be compared with the current value of the first parameter.

[0143] In other implementations, this comparison involves performing statistical operations on the current value and historical value of the first parameter. These statistical operations may include change point analysis, t-tests, morphological comparisons or analyses, or any other appropriate statistical operations. Generally, change point analysis attempts to identify when the probability distribution of the first parameter's (historical and current) values ​​changes. A t-test attempts to determine whether the current value of the first parameter differs significantly from the mean of several historical values.

[0144] In step 810 of Method 800, a desired second type of data is identified if a comparison between the historical value of the first parameter and the current value of the first parameter meets a predetermined threshold (for example, if the current value of the first parameter is too high, too low, indicates a potential medical problem, or indicates a potential problem in the respiratory therapy system). The second type of data may be any type of data that can help explain why the current value of the first parameter met that threshold. In step 812 of Method 800, the control system may send a request to the user seeking consent to receive the second type of data. In some implementations, step 812 includes sending a description of the potential uses of the second type of data and / or sending a request seeking consent to analyze the second type of data to determine why the comparison between the current and historical values ​​of the first parameter met the threshold.

[0145] Therefore, Method 800 can be used to monitor a user in real time during a sleep session to determine whether any parameters (such as sleep-related parameters or other physiological and non-physiological parameters) deviate from normal or expected values ​​or ranges during the sleep session. Method 800 can be used, for example, to attempt to determine why a user's heart rate or respiratory rate suddenly increases or decreases during a sleep session. In another example, if a user's heart rate variability suddenly changes from the expected range, the control system can attempt to identify this problem and determine the reason. Method 800 also includes sending notifications about any information discovered to the user or to any desired third party, such as a healthcare provider, family, or friend.

[0146] In some implementations, the various methods discussed herein can be used as part of a feature called "cascading consent," where analyzing one type of data continuously requires consent to receive and analyze different types of data. For example, a control system may analyze respiratory data about a user's breathing during a sleep session to determine parameters. Based on this analysis, the control system may request consent to receive and analyze audio data to determine the characteristics of the respiratory therapy system (such as the type of user interface or conduit). The parameters related to the user's breathing may be modified, and the control system may then request consent to analyze the audio data to determine the health of the motor of the respiratory therapy device, and this consent may be used to modify the determined parameters. In other implementations, the control system may request consent to monitor flow or pressure data to determine the user's heart rate or respiratory rate, request consent to share that data and the determined heart rate or respiratory rate with a third party, and then request consent to analyze that flow or pressure data (or receive new data) to determine whether the user interface is properly fitted to the user's face. Control systems can typically continuously request consent to receive and analyze various different types of data based on the data they currently have access to, in order to provide users with comprehensive care.

[0147] One or more further implementations and / or claims of the present disclosure can be formed by combining one or more elements, aspects, steps, or parts thereof from one or more of any one or more of the following claims 1 to 93 with one or more elements, aspects, steps, or parts thereof from one or more of the other claims 1 to 93 or any combination thereof.

[0148] While this disclosure has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that numerous modifications are possible without departing from the intent and scope of this disclosure. Each of these implementations and its clearest modifications is intended to fall within the intent and scope of this disclosure. Furthermore, it is intended that further implementations in various aspects of this disclosure may combine any number of features from any of the implementations described herein. The following are additional notes to this disclosure. (Additional note 1) A method for analyzing data on the use of a respiratory therapy system by a user during a sleep session, Receiving first type of data regarding the user's use of the respiratory therapy system during the sleep session, Determining a first value of a first parameter relating to the user's use of the respiratory therapy system, based at least in part on the data of the first type, Identifying the desired second type of data, Sending a request to the user to ask for consent to receive the second type of data described above, Upon receiving consent from the aforementioned user, the recipient receives the second type of data, A method comprising determining, at least in part, a second value of the first parameter, (ii) a value of the second parameter, or (iii) both (i) and (ii), based on data of the second type. (Additional note 2) The method according to Appendix 1, wherein the desired second type of data is at least partially based on the determined first value of the first parameter, the first type of data, the accuracy of the determined first value of the first parameter, or any combination thereof. (Additional note 3) The method described in Appendix 1 or Appendix 2, further including a request to seek consent to analyze the second type of data described above. (Additional note 4) The method described in any one of the appendices 1 to 3, further comprising sending a request to the user seeking consent to transmit the first type of data, the second type of data, or both to a third party. (Additional note 5) The method described in Appendix 4, wherein the aforementioned third party is the user's healthcare provider. (Additional note 6) The method according to any one of appendices 1 to 5, wherein the first type of data is physiological data associated with the user during the sleep session, and the first parameter is a sleep-related parameter of the user during the sleep session. (Additional note 7) The method according to Appendix 6, wherein the second type of data is personal data associated with the user, and the method further comprises sending a request to the user seeking consent to analyze the personal data in order to classify the user into one or more user populations. (Additional note 8) The method according to Appendix 7, wherein the determined value of the sleep-related parameter is modified based on the one or more user populations into which the user is classified in order to determine the second value of the sleep-related parameter. (Additional note 9) The method according to Appendix 8, wherein the second value of the first parameter is more precise than the first value of the first parameter. (Additional note 10) The method according to any one of the appendices 7 to 9, wherein the second value of the first parameter is at least partially based on the first value of the first parameter and the one or more user populations into which the user is classified. (Additional note 11) The method according to any one of the appendices 7 to 10, wherein the personal data includes (i) the user's age, (ii) the user's physical sex, (iii) the user's social sex, (iv) the user's geographical location, (v) the user's height, (vi) the user's weight, (vii) medical information associated with the user, (viii) the user's smoking status, (ix) the user's occupation, (x) the user's education level, (xi) the user's income level, (xii) the frequency and duration of any movement of the user, or any combination of (xiii)(i) to (xii). (Additional note 12) The method according to any one of the appendices 7 to 11, further comprising (i) the value of the first parameter, (ii) the one or more user populations into which the user is classified, or (iii) identifying a desired additional parameter relating to the user based at least in part on both (i) and (ii). (Additional note 13) The method according to any one of the appendices 7 to 12, further comprising generating an alert about the user based at least partially on (i) the value of the first parameter, (ii) the one or more user populations into which the user is classified, or (iii) both of (i) and (ii). (Additional note 14) The method according to Appendix 13, further comprising sending the generated alert to the user, healthcare provider, the user's friends, the user's family, or any combination thereof. (Additional note 15) The method according to any one of the appendices 1 to 14, wherein the second value of the first parameter is more accurate than the first value of the first parameter, and the second value is at least partially based on (i) data of the first type, (ii) the first value of the first parameter, or (iii) both (i) and (ii). (Additional note 16) The method according to any one of the appendices 1 to 15, wherein the value of the second parameter is further based at least partially on (i) the data of the first type, (ii) the first value of the first parameter, or (iii) both (i) and (ii). (Additional note 17) The method according to any one of the appendices 1 to 16, wherein the second type of data relating to the user includes at least a portion of the user's medical history. (Additional note 18) The method described in Appendix 17, wherein the user's medical history is obtained from the user. (Additional note 19) The method according to Appendix 17, wherein the user's medical history is obtained from an external source separate from the user. (Additional note 20) The method according to any one of the appendices 1 to 19, wherein the first value of the first parameter is determined by a first confidence interval, and the second value of the first parameter is determined by a second confidence interval that is wider than the first confidence interval. (Additional note 21) The method according to Appendix 20, further comprising sending the user a description of the potential uses of the second type of data. (Additional note 22) The method described in Appendix 21, wherein the description of the potential use of the second type of data includes an explicit statement that the second type of data enables the determination of the second value of the first parameter within the second confidence interval. (Additional note 23) The method according to any one of the appendices 1 to 22, further comprising transmitting a description of the potential uses of the second type of data described above. (Additional note 24) The method according to Appendix 23, wherein the description of the potential use of the second type of data includes an explicit statement of the correlation between the first value of the first parameter and the potential medical condition. (Additional note 25) The method according to Appendix 24, further comprising estimating the percentage likelihood of the user having the medical condition, based at least in part on the second type of data. (Additional note 26) The method according to Appendix 25, further comprising sending a notification associated with the medical condition, a suggested treatment routine, an appointment with a suggested healthcare provider, or any combination thereof, upon confirmation that the estimated percentage likelihood meets a threshold. (Additional note 27) The method described in Appendix 26, wherein the notice is sent to the user, the healthcare provider, the user's friends, the user's family, or any combination thereof. (Additional note 28) The method described in Appendix 26, wherein the proposed treatment routine includes the proposed drug. (Additional note 29) The method according to any one of the appendices 23 to 28, wherein the second data includes the user's electronic medical record, and the description of the potential use of the user's electronic medical record includes an explicit statement that the user's electronic medical record will enable the identification of desired additional parameters based on the first data. (Additional note 30) Analyzing the user's electronic medical records to identify the desired additional parameters, The method according to Appendix 29, further comprising determining the value of the desired additional parameter based on at least the first data. (Additional note 31) To identify the desired additional parameters, the user's electronic medical records are analyzed. Activating the sensor of the respiratory therapy system, Receiving additional data from the activated sensor, The method according to Appendix 29, further comprising determining the value of the desired additional parameter based on the additional data from at least the activated sensor. (Additional note 32) The method according to any one of the appendices 1 to 31, further comprising analyzing the second type of data described above and sending a request to the user asking for their consent to determine whether or not the user is asleep. (Additional note 33) The method according to Appendix 32, further comprising determining the number of respiratory events per hour experienced by the user based on the first data and a determination of whether the user is asleep. (Additional note 34) The method according to Appendix 33, wherein the determination of the number of respiratory events per hour based on both the first data and the determination of whether the user is asleep is more accurate than the determination of the number of respiratory events per hour based on the first data alone. (Additional note 35) The second type of data includes (i) the user's movements during the sleep session, (i i) the movement of the components of the respiratory therapy system during the sleep session, or (iii) the method according to any one of the appendices 32 to 34, including motion data showing both (i) and (ii). (Additional note 36) The method according to any one of the appendices 32 to 35, wherein the second type of data includes (i) noise generated by the user during the sleep session, (ii) noise generated by the respiratory therapy system during the sleep session, or (iii) audio data indicating both (i) and (ii). (Additional note 37) The method according to any one of Appendix 1 to 36, wherein the respiratory therapy system includes a respiratory therapy device, a conduit, and an interface, and the user is connected to the respiratory therapy device via the conduit and the interface. (Additional note 38) The method according to Appendix 37, wherein the second type of data indicates (i) one or more characteristics of the conduit, (ii) one or more characteristics of the interface, or (iii) both (i) and (ii). (Additional note 39) The method according to Appendix 38, wherein the second value of the first parameter is based on at least (i) one or more characteristics of the conduit, (ii) one or more characteristics of the interface, or (iii) both (i) and (ii), and the second value of the first parameter is more precise than the first value of the first parameter. (Additional note 40) The method according to any one of Appendix 1 to 39, wherein the second type of data is audio data associated with the respiratory therapy system, and the method further comprises sending a request to the user for consent to analyze the second type of data to determine the health of the motor of the respiratory therapy system. (Additional note 41) The method according to Appendix 40, wherein the second value of the first parameter is based at least on the determined health of the motor of the respiratory therapy system, and the second value of the first parameter is more accurate than the first value of the first parameter. (Additional note 42) The method according to any one of Appendix 1 to 41, wherein the first parameter indicates the quality of the user's sleep during the sleep session, and the method, upon receiving the second type of data, further comprises sending to the user suggestions for improving the quality of the user's sleep during the sleep session. (Additional note 43) The method according to any one of the appendices 1 to 42, wherein the first type of data or the second type of data includes respiratory data associated with the user during the sleep session. (Additional note 44) The method according to any one of the appendices 1 to 43, wherein the respiratory therapy system includes a microphone, and the first type of data or the second type of data includes audio data generated by the microphone. (Additional note 45) The method according to Appendix 44, wherein the audio data is associated with (i) the user's movements during the sleep session, (ii) the movements of one or more components of the respiratory therapy system during the sleep session, (iii) air leaks from one or more components of the respiratory therapy system during the sleep session, or (iv) any combination of (i) to (iii). (Additional note 46) The method according to any one of the appendices 1 to 45, wherein the first type of data or the second type of data includes (i) the user's movements during the sleep session, (ii) the movements of the components of the respiratory therapy system during the sleep session, or (iii) motion data indicating both (i) and (ii). (Additional note 47) The method according to any one of Appendix 1 to 46, wherein the respiratory therapy system includes one or more sensors, and the first type of data is physiological data generated by the one or more sensors during the user's use of the respiratory therapy system during the sleep session. (Additional note 48) (i) a request to the user to consent to analyze the first type of data to identify a desired third parameter different from the first parameter, and (ii) an explanation of the benefits of determining the third parameter, The method of Appendix 47, further comprising, upon receiving consent to analyze the first type of data, identifying the desired third parameter and analyzing the first type of data to determine the value of the desired third parameter. (Additional note 49) The method according to any one of the appendices 1 to 48, wherein the first type of data is received along with consent to analyze the first data to determine the first parameter, and the method further includes sending a request to the user for consent to analyze the first data to determine the values ​​of an additional parameter. (Additional note 50) The first data is respiratory data associated with the user of the respiratory therapy system during the sleep session, and the method is Sending a request to the user to obtain their consent to analyze the respiratory data and determine the user's inspiratory / expiratory ratio during the sleep session, Upon receiving consent from the user, the system analyzes the user's breathing to determine the user's inspiratory / expiratory ratio during the sleep session, To estimate the percentage likelihood that the user has a medical condition, based at least in part on the user's determined inspiratory / expiratory ratio, The method according to any one of Appendix 1 to 49, further comprising sending a notification indicating the percentage likelihood that the user has the condition to (i) the user, (ii) the healthcare provider, or (iii) both (i) and (ii). (Additional note 51) The method according to Appendix 50, wherein the determined inspiratory / expiratory ratio of the user indicates the type of chronic obstructive pulmonary disease (COPD) present in the user. (Additional note 52) The respiratory therapy system includes a microphone, and the method is Sending a request to the user to ask for consent to receive audio data from the microphone, Upon receiving consent from the user, the system receives the audio data from the microphone, The method according to Appendix 50 or Appendix 51, further comprising analyzing the audio data to detect a cough or wheeze from the user, wherein the detected cough or wheeze helps estimate the percentage likelihood that the user has the condition. (Additional note 53) The method according to any one of appendices 1 to 52, wherein the respiratory therapy system includes a first sensor configured to generate the first type of data and a second sensor configured to generate the second type of data. (Additional note 54) The method according to Appendix 53, further comprising sending a request to the user for consent to activate the second sensor and generate the second type of data. (Additional note 55) The method according to Appendix 53 or Appendix 54, wherein the first sensor is a pressure sensor or a flow sensor, and the second sensor is an acoustic sensor. (Additional note 56) The method according to any one of the appendices 1 to 55, wherein the identification of the desired second type of data is based at least partially on (i) the first type of data, (ii) the value of the first parameter, or (iii) both (i) and (ii). (Additional note 57) The method according to any one of the appendices 1 to 56, wherein the second type of data relates to the user's use of the respiratory therapy system. (Additional note 58) The method according to any one of the appendices 1 to 57, wherein the second type of data relates to the user's activities taking place outside of the sleep session. (Additional note 59) The method according to any one of the appendices 1 to 58, wherein the first type of data is received during or after a first sleep session, and the second type of data is received during or after a second sleep session that follows the first sleep session. (Additional note 60) The method according to Appendix 59, wherein the request seeking consent to receive the second type of data is sent during or after the first sleep session. (Additional note 61) The method according to any one of the appendices 1 to 60, wherein the first type of data is received during a first sleep session, and the second type of data is received after the first sleep session. (Additional note 62) The method according to any one of the appendices 1 to 61, further comprising determining the location of the user. (Additional note 63) The method described in Appendix 62, wherein the request seeking consent to receive the second type of data is at least partially based on the determined location of the user. (Additional note 64) The method according to Appendix 62 or Appendix 63, wherein the determined location of the user is the user's country, the user's state, the user's town, or the user's latitude and longitude. (Additional note 65) A method for analyzing data on the use of a respiratory therapy system by a user during a sleep session, (i) receiving first type data relating to the user during the sleep session, and (ii) consent to analyze the first type data to determine the value of a first parameter relating to the user. Determining the value of the first parameter relating to the user based on at least the first type of data, Identifying the desired second parameter, Sending a request to the user asking for consent to analyze the first type of data to determine the value of the second parameter relating to the user, Upon receiving consent from the user, the second parameter relating to the user A method comprising determining the value of ta based on at least the first type of data. (Additional note 66) The method according to Appendix 65, wherein the identification of the second parameter is based at least partially on (i) the data of the first type, (ii) the value of the first parameter, or (iii) both (i) and (ii). (Additional note 67) The method according to Appendix 65 or Appendix 66, wherein the identification of the second parameter is at least partially based on the value of the first parameter, and the method further comprises identifying a desired third parameter at least partially based on (i) the value of the first parameter, (ii) the value of the second parameter, or (iii) both (i) and (ii). (Additional note 68) The method according to any one of appendices 65 to 67, wherein the first type of data is audio data and the first parameter indicates the health of the motor of the respiratory therapy system. (Additional note 69) A method for analyzing data associated with the use of multiple respiratory therapy systems by multiple users, Sending a plurality of requests to each of the plurality of users, each request being sent to each user in the respective order, to request consent to receive data associated with each user's use of one of the plurality of respiratory therapy systems, Upon receiving consent, we will receive data from two or more of the aforementioned users, A method comprising: analyzing the data received from each of the two or more users among the plurality of users to determine the optimal order for sending the plurality of requests seeking consent to receive the data. (Additional note 70) The method according to Appendix 69, wherein the optimal order for sending the plurality of requests is the order that maximizes the amount of requested data received compared to each of the other orders. (Additional note 71) The method according to Appendix 69, wherein the optimal order for sending the plurality of requests is the order that minimizes the amount of time between sending the plurality of requests and receiving at least a portion of the requested data, compared to each of the other orders for sending the plurality of requests. (Additional note 72) The method according to any one of the appendices 69 to 71, further comprising analyzing the data received from each of the two or more users among the plurality of users, and determining the optimal time to send the plurality of requests based at least in part on the analyzed data. (Additional note 73) The requested data includes personal data, and the method is Classifying each of the aforementioned users into one or more user populations based at least partially on the personal data received from each of the aforementioned users, The method according to any one of the appendix 69 to 72, further comprising determining the optimal order for sending the multiple requests for the multiple types of data for each of the one or more user populations. (Additional note 74) The method according to any one of the appendices 69 to 73, further comprising determining how consent was received from each of the two or more of the aforementioned users. (Additional note 75) The method according to Appendix 74, wherein the consent from each of the two or more of the aforementioned users is received via (i) a voice command, (ii) a biometric indicator, (iii) a gesture, (iv) a physical input mechanism, or (v) any combination of (i) to (iv). (Additional note 76) The method according to Appendix 75, wherein the biometric indicator is an image of the user's face or the user's fingerprint. (Additional note 77) The method according to any one of the appendices 74 to 76, wherein the optimal order for sending the multiple requests is at least in part based on the determined method in which consent was received from each of the two or more of the multiple users. (Additional note 78) A method for analyzing data on the use of a respiratory therapy system by a user during a current sleep session, The system stores multiple historical values ​​of the first parameter relating to the user, Receiving a first type of data relating to the user during the current sleep session, Determining the current value of the first parameter based at least partially on the first type of data, Comparing the current value of the first parameter with the plurality of historical values ​​of the first parameter, Upon confirming that the comparison between the current value of the first parameter and the plurality of historical values ​​of the first parameter satisfies a threshold, a desired second type of data is identified. A method comprising sending a request to the user seeking their consent to receive the second type of data. (Additional note 79) The method according to Appendix 78, further comprising sending the user a description of the potential uses of the second type of data. (Additional note 80) The method according to Appendix 78 or Appendix 79, wherein the request for consent to receive the second type of data includes a request for consent to analyze the second type of data to determine why the comparison between the current value of the first parameter and the plurality of historical values ​​of the first parameter satisfies the threshold. (Additional note 81) The method according to any one of the appendices 78 to 80, further comprising determining a statistical parameter based on the plurality of historical values ​​of the first parameter. (Additional note 82) The method according to Appendix 81, wherein the statistical parameter is the mean of the plurality of historical values ​​of the first parameter, the median of the plurality of historical values ​​of the first parameter, the moving average of the plurality of historical values ​​of the first parameter, or the moving median of the plurality of historical values ​​of the first parameter. (Additional note 83) The method according to Appendix 81 or Appendix 82, wherein the current value of the first parameter is compared with (i) the historical value of the first parameter, or (ii) the statistical parameter based on a plurality of historical values ​​of the first parameter. (Additional note 84) The comparison is based on the multiple historical values ​​of the first parameter, and the first parameter The method according to Appendix 83, comprising performing statistical calculations on the current value of and the statistical parameters of . (Additional note 85) The method according to Appendix 84, wherein the statistical operation is change point analysis, t-test, or morphological comparison. (Additional note 86) A system for analyzing data on the use of a respiratory therapy system by users during sleep sessions, A control system including one or more processors, It comprises a memory that stores machine-readable instructions, A system in which the method described in any one of appendices 1 to 85 is performed when the control system is connected to the memory and the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system. (Additional note 87) A system for analyzing data relating to a user's use of a respiratory therapy system during a sleep session, comprising a control system configured to perform the method described in any one of the appendices 1 to 85. (Additional note 88) A computer program product that, when executed by a computer, includes instructions causing the computer to perform any of the procedures described in any one of the appendices 1 through 85. (Additional note 89) The computer program product described in Appendix 88, wherein the computer program product is a non-temporary computer-readable medium. (Additional note 90) A respiratory therapy system comprising a respiratory therapy device and a conduit, wherein the respiratory therapy system is configured to supply pressurized air to the user's airway via a user interface connected to the respiratory therapy device through the conduit, Memory for storing machine-readable instructions, To receive first type data regarding the user's use of the respiratory therapy system during a sleep session, Based at least in part on the data of the first type, in order to determine a first value of a first parameter relating to the user's use of the respiratory therapy system, To identify the desired second type of data, To send a request to the user seeking consent to receive the second type of data, In order to receive the second type of data, having received consent from the user, Based at least in part on the second type of data, in order to determine (i) the second value of the first parameter, (ii) the value of the second parameter, or (iii) both (i) and (ii) A system comprising: a control system including one or more processors configured to execute the machine-readable instructions; (Additional note 91) A respiratory therapy system comprising a respiratory therapy device and a conduit, wherein the respiratory therapy system is configured to supply pressurized air to the user's airway via a user interface connected to the respiratory therapy device through the conduit, Memory for storing machine-readable instructions, (i) first type of data relating to the user during a sleep session, and (ii) consent to analyze the first type of data to determine the value of a first parameter relating to the user, Based on at least the first type of data, in order to determine the value of the first parameter relating to the user, To identify the desired second parameter, To send a request to the user asking for consent to analyze the first type of data to determine the value of the second parameter relating to the user, and Having received consent from the user, in order to determine the value of the second parameter relating to the user based on at least the first type of data A system comprising: a control system including one or more processors configured to execute the machine-readable instructions; (Additional note 92) A respiratory therapy system comprising a respiratory therapy device and a conduit, configured to supply pressurized air to the user's airway via a user interface connected to the respiratory therapy device through the conduit, Memory for storing machine-readable instructions, A set of requests to each of multiple users to obtain consent from each user to receive data associated with each user's use of one of the multiple respiratory therapy systems, and to send a set of requests to each user in the order in which they are sent. Upon receiving consent, in order to receive data from two or more of the aforementioned users, In order to analyze the data received from each of the two or more users among the aforementioned multiple users and to determine the optimal order for sending the multiple requests seeking consent to receive the data A system comprising: a control system including one or more processors configured to execute the machine-readable instructions; (Additional note 93) A respiratory therapy system comprising a respiratory therapy device and a conduit, wherein the respiratory therapy system is configured to supply pressurized air to the user's airway via a user interface connected to the respiratory therapy device through the conduit, Memory for storing machine-readable instructions, In order to store multiple historical values ​​of the first parameter relating to the user, To receive a first type of data concerning the user during the current sleep session, In order to determine the current value of the first parameter based at least in part on the data of the first type, In order to compare the current value of the first parameter with the plurality of historical values ​​of the first parameter, In order to identify a desired second type of data, based on the fact that the comparison between the current value of the first parameter and the plurality of historical values ​​of the first parameter satisfies a threshold, and To send a request to the user asking for consent to receive the second type of data described above. A system comprising: a control system including one or more processors configured to execute the machine-readable instructions;

Claims

1. A system for analyzing data on the use of a respiratory therapy system by users during sleep sessions, A control system including one or more processors, It comprises a memory in which machine-readable instructions are stored, The control system is connected to the memory, and at least one of the one or more processors of the control system is configured to execute the machine-readable instruction, wherein the machine-readable instruction is: Receiving a first type of data, which is physiological data associated with the user during the sleep session, relating to the user's use of the respiratory therapy system during the sleep session, Determining a first value of a first parameter relating to the user's use of the respiratory therapy system, based at least in part on the first type of data, Identifying a desired second type of data based at least in part on the first value of the first parameter, Sending a request to the user to ask for consent to receive the second type of data, Upon receiving consent from the user, the second type of data is received, A system comprising determining, at least in part, the second value of the first parameter, (ii) the value of the second parameter, or (iii) both (i) and (ii), based on the second type of data.

2. The system according to claim 1, wherein identifying the desired second type of data is further based at least in part on the first type of data, the accuracy of the determined first value of the first parameter, or any combination thereof.

3. A machine-readable instruction executed by at least one of the one or more processors of the control system is (i) a request to consent to analyze the second type of data. The system according to claim 1 or 2, further comprising (ii) a request for consent to transmit the first type of data, the second type of data, or both to a third party, or (iii) both (i) and (ii) to the user.

4. The system according to any one of claims 1 to 3, wherein the first parameter is a sleep-related parameter of the user during the sleep session.

5. The system according to claim 4, wherein the second type of data is personal data associated with the user, and the machine-readable instruction executed by at least one of the one or more processors of the control system further includes sending a request to the user asking for consent to analyze the personal data in order to classify the user into one or more user populations, and the determined value of the sleep-related parameter is modified based on the one or more user populations into which the user is classified in order to determine the second value of the sleep-related parameter.

6. The system according to claim 5, wherein the second value of the first parameter is at least partially based on the first value of the first parameter and the one or more user populations into which the user is classified.

7. The system according to claim 5 or 6, wherein the machine-readable instruction executed by at least one of the one or more processors of the control system further includes specifying a desired additional parameter relating to the user based at least partially on (i) the value of the first parameter, (ii) the one or more user populations into which the user is classified, or (iii) both (i) and (ii).

8. The system according to any one of claims 1 to 7, wherein the second value of the first parameter is more accurate than the first value of the first parameter, and the second value is at least partially based on (i) data of the first type, (ii) the first value of the first parameter, or (iii) both (i) and (ii).

9. The system according to any one of claims 1 to 8, wherein the first value of the first parameter is determined by a first confidence interval, and the second value of the first parameter is determined by a second confidence interval that is wider than the first confidence interval.

10. The system according to claim 9, wherein a machine-readable instruction executed by at least one of the one or more processors of the control system further includes transmitting a description of a potential use of the second type of data to the user, the description including an explicit statement that the second type of data enables the determination of the second value of the first parameter within a second confidence interval.

11. The system according to any one of claims 1 to 10, wherein a machine-readable instruction executed by at least one of the one or more processors of the control system further includes transmitting a description of a potential use for the second type of data, the description including an explicit representation of a correlation between a first value of the first parameter and a potential medical condition.

12. The machine-readable instruction executed by at least one of the one or more processors of the control system further includes estimating the percentage likelihood of the user having the medical condition based at least in part on the second type of data, and upon confirmation that the estimated percentage likelihood satisfies a threshold, a notification, suggestion, associated with the medical condition is issued. The system according to claim 11, further comprising sending a treatment routine, an appointment with a proposed healthcare provider, or any combination thereof.

13. The system according to claim 11 or 12, wherein the second type of data includes the user's electronic medical record, and the description of the potential use of the user's electronic medical record includes an explicit statement that the user's electronic medical record allows for the identification of desired additional parameters based on the first type of data.

14. The machine-readable instruction executed by at least one of the one or more processors of the control system is To identify the desired additional parameters, the user's electronic medical records are analyzed. The system according to claim 13, further comprising determining the value of the desired additional parameter based on at least the first type of data.

15. The machine-readable instruction executed by at least one of the one or more processors of the control system is To identify the desired additional parameters, the user's electronic medical records are analyzed. Activating the sensor of the respiratory therapy system, Receiving additional data from the activated sensor, The system according to claim 13, further comprising determining the value of the desired additional parameter based on the additional data from at least the activated sensor.

16. The machine-readable instruction executed by at least one of the one or more processors of the control system is The process involves analyzing the second type of data described above and sending a request to the user asking for their consent to determine whether or not the user is asleep. The system according to any one of claims 1 to 15, further comprising determining the number of respiratory events per hour experienced by the user based on the first type of data and a determination of whether the user is asleep, wherein the determination based on both the first type of data and whether the user is asleep is more accurate than the determination of the number of respiratory events per hour based on the first type of data alone.

17. The system according to any one of claims 1 to 16, wherein the second type of data includes (i) motion data indicating the user's movements during the sleep session, (ii) motion data indicating the movements of the components of the respiratory therapy system during the sleep session, (iii) audio data indicating noise generated by the user during the sleep session, (iv) audio data indicating noise generated by the respiratory therapy system during the sleep session, (v) audio data indicating air leaks from one or more components of the respiratory therapy system during the sleep session, or (vi) any combination of (i) to (v).

18. The system according to any one of claims 1 to 17, wherein the second type of data indicates (i) one or more characteristics of the conduit of the respiratory therapy system, (ii) one or more characteristics of the interface of the respiratory therapy system connected to the respiratory therapy device of the respiratory therapy system by the conduit, or (iii)(i) and (ii), and the second value of the first parameter is more accurate than the first value of the first parameter and is based on at least (i) one or more characteristics of the conduit, (ii) one or more characteristics of the interface, or (iii)(i) and (ii).

19. The system according to any one of claims 1 to 18, wherein the second type of data is audio data associated with the respiratory therapy system, and the system further includes sending a request to the user for consent to analyze the second type of data to determine the health of the motor of the respiratory therapy system, wherein the second value of the first parameter is more accurate than the first value of the first parameter and is at least partially based on the determined health of the motor of the respiratory therapy system.

20. The system according to any one of claims 1 to 19, further comprising the first parameter indicating the quality of the user's sleep during the sleep session, and the system, upon receiving the second type of data, sending the user suggestions for improving the quality of the user's sleep during the sleep session.

21. The respiratory therapy system according to any one of claims 1 to 20, wherein the respiratory therapy system includes one or more sensors, and the first type of data is physiological data generated by the one or more sensors during the user's use of the respiratory therapy system during the sleep session.

22. The machine-readable instruction executed by at least one of the one or more processors of the control system is (i) a request to the user to consent to analyze the first type of data to identify a desired third parameter different from the first parameter, and (ii) an explanation of the benefits of determining the third parameter, The system according to claim 21, further comprising: receiving consent to analyze the first type of data; identifying the desired third parameter; and analyzing the first type of data to determine the value of the desired third parameter.

23. The first type of data is received along with consent to analyze the first type of data to determine the first parameter. The system according to any one of claims 1 to 22, wherein the machine-readable instruction executed by at least one of the one or more processors of the control system further includes sending a request to the user for consent to analyze the first type of data in order to determine the value of an additional parameter.

24. The first type of data is respiratory data associated with the user of the respiratory therapy system by the user during the sleep session, and the machine-readable instruction executed by at least one of the one or more processors of the control system is Sending a request to the user to obtain consent to analyze the respiratory data and determine the user's inspiratory / expiratory ratio during the sleep session, Upon receiving consent from the user, the system analyzes the user's respiration to determine the user's inspiratory / expiratory ratio during the sleep session, To estimate the percentage likelihood that the user has a medical condition, based at least in part on the user's determined inspiratory / expiratory ratio, The system according to any one of claims 1 to 23, further comprising sending a notification indicating the percentage likelihood that the user has the condition to (i) the user, (ii) the healthcare provider, or (iii) both (i) and (ii).

25. The respiratory therapy system includes a microphone, and at least one of the one or more processors of the control system executes the machine-readable instruction, Sending a request to the user to ask for consent to receive audio data from the microphone, Upon receiving consent from the user, the system receives the audio data from the microphone, The system according to claim 24, further comprising analyzing the audio data to detect a cough or wheeze from the user, wherein the detected cough or wheeze helps estimate the percentage likelihood that the user has the medical condition.

26. The respiratory therapy system includes a first sensor configured to generate the first type of data and a second sensor configured to generate the second type of data. The system according to any one of claims 1 to 25, further comprising the machine-readable instruction executed by at least one of the one or more processors of the control system sending a request to the user for consent to generate the second type of data.

27. The system according to any one of claims 1 to 26, wherein the identification of the desired second type of data is at least partially based on (i) the first type of data, (ii) the value of the first parameter, or (iii) both (i) and (ii).

28. The system according to any one of claims 1 to 27, wherein the second type of data relates to the user's use of the respiratory therapy system, or to the user's activities outside of the sleep session, or both.

29. The system according to any one of claims 1 to 28, wherein the first type of data is received during or after a first sleep session, and the second type of data is received during or after a second sleep session that follows the first sleep session.

30. The system according to any one of claims 1 to 29, wherein the machine-readable instruction executed by at least one of the one or more processors of the control system further includes determining the location of the user, and the request for consent to receive the second type of data is at least partially based on the determined location of the user.

31. A method for analyzing data on the use of a respiratory therapy system by a user during a sleep session, A control system including one or more processors configured to execute machine-readable instructions stored in memory receives a first type of data, which is physiological data associated with the user during the sleep session, relating to the user's use of the respiratory therapy system during the sleep session. The control system determines, at least in part, a first value of a first parameter relating to the user's use of the respiratory therapy system, based on the first type of data. The control system identifies a desired second type of data based at least partially on the first value of the first parameter, The control system sends a request to the user asking for consent to receive the second type of data, Upon receiving consent from the user, the control system receives the second type of data, A method comprising determining, at least in part, based on the second type of data, (i) a second value of the first parameter, (ii) a value of the second parameter, or (iii) both (i) and (ii).

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