# US 2022/0273232 A1 · Cycle-based sleep coaching

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- Number: US 2022/0273232 A1
- Document ID: US20220273232A1
- Status: Published application
- Kind: A1
- Application number: 17/210,653
- Filed: 2021-03-24
- Published: 2022-09-01
- Earliest claimed: 2021-03-01
- Assignees: Whoop, Inc.
- Applicants: Whoop, Inc.
- Inventors: Emily Rachel Capodilupo; Laura Ware
- Web page: https://patentgrep.com/patent/US20220273232A1

## CPC

- A61B 10/0012: {Ovulation-period determination (for animals A61D17/002)}
- A61B 5/0004: {characterised by the type of physiological signal transmitted}
- A61B 5/0024: {for multiple sensor units attached to the patient, e.g. using a body or personal area network}
- A61B 5/02055: {Simultaneously evaluating both cardiovascular condition and temperature}
- A61B 5/4812: {Detecting sleep stages or cycles}
- A61B 5/486: {Biofeedback (using electroencephalography [EEG] A61B5/375)}
- A61B 5/6801: {specially adapted to be attached to or worn on the body surface}
- A61B 5/681: {Wristwatch-type devices}
- A61B 5/7267: {involving training the classification device}
- A61B 5/7278: {Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals}
- A61B 5/7475: {User input or interface means, e.g. keyboard, pointing device, joystick}
- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61B 5/02438: {with portable devices, e.g. worn by the patient}
- A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)}
- A61B 5/375: using biofeedback
- A61B 5/4806: {Sleep evaluation (A61B5/4821 takes precedence; devices for inducing sleep A61M21/02)}
- A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)}
- A61B 2010/0019: {based on measurement of temperature}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Physiological parameters such as respiratory rate and/or heart rate variability can be measured over time for a user and correlated to physiological and/or hormonal cycles such as the menstrual cycle. By determining the phase of such a cycle in this manner, an automatic coach for the user can recommend phase-specific adjustments to activities such as sleep and exercise.

## Claims

### Claim 1

A computer program product for suggesting adjustments to a sleep regimen based on a menstrual cycle, the computer program product comprising non-transitory computer executable code embodied in a computer readable medium that, when executing on one or more computing devices, performs the steps of:
acquiring heart rate data for a user from a wearable physiological monitoring device;
identifying a hormonal cycle and a phase within the hormonal cycle for the user based on at least one of a resting heart rate or a respiratory rate of the user calculated using a heart rate variability of the heart rate data;
determining a current sleep need for the user based on a prior sleep history for the user calculated from the heart rate data and a current daily strain for the user calculated from the heart rate data;
generating a recommended time in bed for the user based on the current sleep need; and
automatically adjusting the recommended time in bed for the user by decreasing the recommended time in bed during an early follicular phase of the menstrual cycle and increasing the recommended time in bed during a late luteal phase of the menstrual cycle.

### Claim 2

A system comprising:
a wearable physiological monitor including one or more sensors, a first processor configured to substantially continuously acquire heart rate data for a user based on a signal from the one or more sensors, and a communications interface for coupling with a remote resource;
a server coupled in a communicating relationship with the wearable physiological monitor, the server including a second processor configured by computer executable code to receive the heart rate data from the wearable physiological monitor, to identify a menstrual cycle and a phase within the menstrual cycle for the user based on the heart rate data, to determine a recommended time in bed for the user based on a prior sleep history for the user, and to adjust the recommended time in bed for the user according to the phase within the menstrual cycle; and
a user interface configured to present the recommended time in bed to the user.

### Claim 3 (depends on 2)

The system of claim 2, wherein the server adjusts the recommended time in bed by decreasing the recommended time in bed during an early follicular phase of the menstrual cycle and increasing the recommended time in bed during a late luteal phase of the menstrual cycle.

### Claim 4

A method comprising:
acquiring heart rate data for a user from a wearable physiological monitoring device;
identifying a hormonal cycle and a phase within the hormonal cycle for the user;
identifying a prior sleep history for the user based on the heart rate data;
determining a current sleep need for the user based on a prior sleep history for the user;
generating a first recommended time in bed for the user based on the current sleep need; and
determining a second recommended time in bed for the user by adjusting the first recommended time in bed according to the phase within the hormonal cycle.

### Claim 5 (depends on 4)

The method of claim 4, wherein identifying the hormonal cycle and the phase includes identifying the hormonal cycle and the phase based on heart rate data.

### Claim 6 (depends on 5)

The method of claim 5, wherein identifying the phase within the hormonal cycle includes identifying the phase based on a pattern of change in a heart rate variability for the user over a period of the hormonal cycle.

### Claim 7 (depends on 5)

The method of claim 5, wherein identifying the phase within the hormonal cycle includes determining a respiratory rate for the user based on a heart rate variability for the user and identifying the phase based on a pattern of change in the respiratory rate.

### Claim 8 (depends on 4)

The method of claim 4, wherein identifying the phase within the hormonal cycle includes training a machine learning model to detect the phase within the hormonal cycle based on one or more of a respiratory rate and a resting heart rate for the user.

### Claim 9 (depends on 4)

The method of claim 4, wherein identifying the hormonal cycle and the phase includes identifying the hormonal cycle and the phase based on user input.

### Claim 10 (depends on 4)

The method of claim 4, wherein identifying the hormonal cycle and the phase includes identifying the hormonal cycle and the phase based on a skin temperature measured for the user with the wearable physiological monitoring device.

### Claim 11 (depends on 4)

The method of claim 4, wherein the current sleep need is based on one or more of a current daily strain, a prior strain, a heart rate variability, a resting heart rate, and a respiratory rate for the user.

### Claim 12 (depends on 4)

The method of claim 4, wherein the prior sleep history for the user includes a duration of sleep for a prior sleep event.

### Claim 13 (depends on 4)

The method of claim 4, wherein the first recommended time in bed includes a time selected to satisfy the current sleep need based on an historic ratio of time in bed to time asleep for the user.

### Claim 14 (depends on 4)

The method of claim 4, wherein the hormonal cycle is a menstrual cycle.

### Claim 15 (depends on 14)

The method of claim 14, wherein adjusting the first recommended time in bed includes decreasing the first recommended time in bed during an early follicular phase of the menstrual cycle.

### Claim 16 (depends on 14)

The method of claim 14, wherein adjusting the first recommended time in bed includes increasing the first recommended time in bed during a late luteal phase of the menstrual cycle.

### Claim 17 (depends on 4)

The method of claim 4, wherein the heart rate data spans at least twenty-eight days for the user.

### Claim 18 (depends on 4)

The method of claim 4, wherein the heart rate data is captured substantially continuously by the wearable physiological monitoring device.

### Claim 19 (depends on 4)

The method of claim 4, further comprising presenting the second recommended time in bed to the user in a user interface.

### Claim 20 (depends on 4)

The method of claim 4, further comprising transmitting the heart rate data to a server for remote processing and transmitting the second recommended time in bed from the server to a local device for viewing by the user.