Cycle-based coaching

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.

1 - 20 . (canceled) 
     
     
         21 . A computer program product for suggesting adjustments to a sleep regimen based on a circadian 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 circadian cycle for the user based on the heart rate data;   identifying a current sleep cycle for the user based on a prior sleep history for the user determined from the heart rate data; and   generating a recommendation for the user for adjusting a timing of the circadian cycle based on the current sleep cycle and the circadian cycle.   
     
     
         22 . 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 circadian cycle for the user based on the heart rate data, to identify a current sleep cycle for the user based on a prior sleep history for the user, and to generate a recommendation for the user for adjusting a timing of the circadian cycle based on the current sleep cycle and the circadian cycle; and   a user interface configured to present the recommendation to the user.   
     
     
         23 . The system of  claim 22 , wherein the recommendation is a recommended time to fall asleep or wake up. 
     
     
         24 . A method comprising:
 acquiring heart rate data for a user from a wearable physiological monitoring device;   identifying a circadian cycle for the user;   identifying a current sleep cycle for the user based on one or both of a prior sleep history for the user or the heart rate data; and   generating a recommendation for the user for adjusting a timing of the circadian cycle based on the current sleep cycle and the circadian cycle.   
     
     
         25 . The method of  claim 24 , wherein identifying the circadian cycle includes identifying the circadian cycle based on the heart rate data. 
     
     
         26 . The method of  claim 25 , wherein identifying the circadian cycle includes identifying the circadian cycle based on a pattern of change in a heart rate variability for the user over a period of the circadian cycle. 
     
     
         27 . The method of  claim 24 , wherein identifying the circadian cycle includes determining a respiratory rate for the user based on a heart rate variability for the user and identifying the circadian cycle based on a pattern of change in the respiratory rate. 
     
     
         28 . The method of  claim 24 , wherein identifying the circadian cycle includes training a machine learning model to detect a phase within the circadian cycle based on one or more of a respiratory rate and a resting heart rate for the user. 
     
     
         29 . The method of  claim 24 , wherein identifying the circadian cycle includes identifying the circadian cycle based on user input. 
     
     
         30 . The method of  claim 24 , wherein identifying the circadian cycle includes identifying the circadian cycle based on a skin temperature measured for the user with the wearable physiological monitoring device. 
     
     
         31 . The method of  claim 24 , wherein the sleep cycle for the user includes information related to a duration of sleep for a prior sleep event. 
     
     
         32 . The method of  claim 24 , wherein the heart rate data spans at least 24 hours for the user. 
     
     
         33 . The method of  claim 24 , wherein the heart rate data is captured substantially continuously by the wearable physiological monitoring device. 
     
     
         34 . The method of  claim 24 , further comprising presenting the recommendation to the user in a user interface. 
     
     
         35 . The method of  claim 24 , further comprising transmitting the heart rate data to a server for remote processing and transmitting the recommendation from the server to a local device for viewing by the user. 
     
     
         36 . A method comprising:
 acquiring heart rate data for a user from a wearable physiological monitoring device;   identifying a circadian cycle for the user based on the heart rate data;   identifying information related to a sleep cycle for the user based on the heart rate data; and   generating a recommendation for the user for adjusting a timing of the circadian cycle.   
     
     
         37 . The method of  claim 36 , wherein the recommendation is for the user to fall asleep earlier or later than a prior time associated with the sleep cycle when the user fell asleep. 
     
     
         38 . The method of  claim 36 , wherein the recommendation is for the user to wake up earlier or later than a prior time associated with the sleep cycle when the user woke up. 
     
     
         39 . The method of  claim 36 , wherein the recommendation is for the user to be exposed to light within a particular amount of time after waking. 
     
     
         40 . The method of  claim 36 , wherein the recommendation is for the user to consume caffeine only within a particular amount of time after waking.