Contextualized personalized insomnia therapy regimen, system, and method

Abstract

A system and method for providing a recommendation of an insomnia therapy leverage various metrics including personal health profile, 24/7 biometrics, behavioral information, and environmental stressors to predict insomnia severity, to build a personalized severity and type insomnia therapy map ranked by historic therapy efficacy, to provide a contextualized personalized therapy recommendation, and to optimize the insomnia therapy.

What is claimed is: 
     
         1 . A method of providing a recommendation of an insomnia therapy to a patient, comprising:
 for each of a plurality of periods of attempted sleep by the patient:
 determining a predicted insomnia severity that is based at least in part upon an insomnia severity prediction model; 
 outputting a recommendation of an insomnia therapy from among a plurality of insomnia therapies based at least in part upon the predicted insomnia severity and an insomnia therapy map of the patient, the insomnia therapy map including a corresponding efficacy for each of the plurality of insomnia therapies; 
 determining an actual insomnia severity that is based at least in part upon a number of insomnia symptoms in the patient; 
 determining an efficacy of the insomnia therapy based at least in part upon the predicted insomnia severity and the actual insomnia severity; and 
 updating the insomnia therapy map to reflect the efficacy. 
   
     
     
         2 . The method of  claim 1 , further comprising:
 determining an insomnia type that is based at least in part upon at least one of an insomnia type trend in the patient and the number of insomnia symptoms; and   outputting the recommendation of the insomnia therapy further based at least in part upon the insomnia type.   
     
     
         3 . The method of  claim 2  wherein the insomnia therapy map comprises a table having a plurality of insomnia severities, a plurality of insomnia types, the plurality of insomnia therapies, and the corresponding efficacies, the insomnia therapy map further comprising, for a given insomnia severity of the plurality of insomnia severities and for a given insomnia type plurality of insomnia types, a plural quantity of insomnia therapies and a plural quantity of corresponding efficacies, and further comprising:
 for a given period of attempted sleep:
 determining that the actual insomnia severity is the given insomnia severity; 
 determining that the insomnia type is the given insomnia type; and 
 outputting as the recommendation the insomnia therapy from among the plural quantity of insomnia therapies whose corresponding efficacy is the greatest. 
 
 
     
     
         4 . The method of  claim 3  further comprising, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation an insomnia therapy from among the plural quantity of insomnia therapies whose corresponding efficacy is other than the greatest. 
     
     
         5 . The method of  claim 1 , further comprising:
 detecting as a set of insomnia indication data one or more of:
 a number of sleep metrics and/or a sleep debt via a sleep metrics sensing module, 
 a number of caffeine intake amounts and timings via a related behaviors sensing module, 
 an exercise intensity and timing via an activity metrics sensing module, 
 a room temperature and/or a stress level via a personal and environmental stressor sensing module, 
 an alertness via an alertness sensing module, and 
 a number of chronic conditions via a personal profile; 
   in a training phase of the insomnia severity prediction model, generating the insomnia severity prediction model by building a machine learning model based at least in part upon at least a portion of the set of insomnia indication data; and   deploying the insomnia severity prediction model.   
     
     
         6 . The method of  claim 2  further comprising, for a given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of an insomnia therapy from among the plurality of insomnia therapies that includes a pharmacological therapy from among a plurality of pharmacological therapies. 
     
     
         7 . The method of  claim 6  further comprising, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of another insomnia therapy from among the plurality of insomnia therapies other than the pharmacological therapy. 
     
     
         8 . The method of  claim 6  further comprising, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of another insomnia therapy from among the plurality of insomnia therapies that includes another pharmacological therapy from among a plurality of pharmacological therapies other than the pharmacological therapy. 
     
     
         9 . The method of  claim 6 , further comprising detecting in the patient a potential tolerance for the pharmacological therapy and, responsive thereto, outputting as the recommendation of the insomnia therapy a recommendation of other than the pharmacological therapy for a predetermined period of time. 
     
     
         10 . The method of  claim 1 , further comprising detecting as the number of insomnia symptoms at least one of a number of sleep metrics and a number of next day alertness metrics, the number of sleep metrics comprising one or more of a Sleep Efficiency (SE), a Wake After Sleep Onset (WASO), a number of awakenings, a Sleep Onset Latency (SOL), a deep sleep percentage, and a Total Sleep Time (TST), and the number of next day alertness metrics comprising one or more of a PERcentage of eyelid CLOSure over the time (PERCLOS), a number of naps, and a duration of naps. 
     
     
         11 . A system structured and configured to provide a recommendation of an insomnia therapy to a patient, comprising:
 a processor apparatus comprising a processor and a storage;   an input apparatus structured to provide input signals to the processor apparatus and comprising one or more of a sleep metrics sensing module comprising a photoplethysmogram (PPG), an alertness sensing module, an activity metrics sensing module comprising at least one of a step counter and a Global Positioning System (GPS) sensor, a personal and environmental stressor sensing module comprising at least one of a Galvanic Skin Response (GSR) sensor and a room temperature sensor, a related behaviors sensing module, and a personal profile;   an output apparatus structured to receive output signals from the processor apparatus and to generate outputs;   the storage having stored therein a number of routines which, when executed on the processor, cause the system to perform operations comprising:
 for each of a plurality of periods of attempted sleep by the patient:
 determining a predicted insomnia severity that is based at least in part upon an insomnia severity prediction model; 
 outputting a recommendation of an insomnia therapy from among a plurality of insomnia therapies based at least in part upon the predicted insomnia severity and an insomnia therapy map of the patient, the insomnia therapy map including a corresponding efficacy for each of the plurality of insomnia therapies; 
 determining an actual insomnia severity that is based at least in part upon a number of insomnia symptoms in the patient; 
 determining an efficacy of the insomnia therapy based at least in part upon the predicted insomnia severity and the actual insomnia severity; and 
 updating the insomnia therapy map to reflect the efficacy. 
 
   
     
     
         12 . The system of  claim 11  wherein the operations further comprise:
 determining an insomnia type that is based at least in part upon at least one of an insomnia type trend in the patient and the number of insomnia symptoms; and 
 outputting the recommendation of the insomnia therapy further based at least in part upon the insomnia type. 
 
     
     
         13 . The system of  claim 12  wherein the insomnia therapy map comprises a table having a plurality of insomnia severities, a plurality of insomnia types, the plurality of insomnia therapies, and the corresponding efficacies, the insomnia therapy map further comprising, for a given insomnia severity of the plurality of insomnia severities and for a given insomnia type plurality of insomnia types, a plural quantity of insomnia therapies and a plural quantity of corresponding efficacies, and wherein the operations further comprise:
 for a given period of attempted sleep:
 determining that the actual insomnia severity is the given insomnia severity; 
 determining that the insomnia type is the given insomnia type; and 
 outputting as the recommendation the insomnia therapy from among the plural quantity of insomnia therapies whose corresponding efficacy is the greatest. 
 
 
     
     
         14 . The system of  claim 13  wherein the operations further comprise, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation an insomnia therapy from among the plural quantity of insomnia therapies whose corresponding efficacy is other than the greatest. 
     
     
         15 . The system of  claim 14  wherein the operations further comprise:
 detecting as a set of insomnia indication data one or more of:
 a number of sleep metrics and/or a sleep debt via the sleep metrics sensing module, 
 a number of caffeine intake amounts and timings via the related behaviors sensing module, 
 an exercise intensity and timing via the activity metrics sensing module, 
 a room temperature and/or a stress level via the personal and environmental stressor sensing module, 
 an alertness via the alertness sensing module, and 
 a number of chronic conditions via the personal profile; 
 
 in a training phase of the insomnia severity prediction model, generating the insomnia severity prediction model by building a machine learning model based at least in part upon at least a portion of the set of insomnia indication data; and 
 deploying the insomnia severity prediction model. 
 
     
     
         16 . The system of  claim 12  wherein the operations further comprise, for a given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of an insomnia therapy from among the plurality of insomnia therapies that includes a pharmacological therapy from among a plurality of pharmacological therapies. 
     
     
         17 . The system of  claim 16  wherein the operations further comprise, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of another insomnia therapy from among the plurality of insomnia therapies other than the pharmacological therapy. 
     
     
         18 . The system of  claim 16  wherein the operations further comprise, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of another insomnia therapy from among the plurality of insomnia therapies that includes another pharmacological therapy from among a plurality of pharmacological therapies other than the pharmacological therapy. 
     
     
         19 . The system of  claim 16  wherein the operations further comprise detecting in the patient a potential tolerance for the pharmacological therapy and, responsive thereto, outputting as the recommendation of the insomnia therapy a recommendation of other than the pharmacological therapy for a predetermined period of time. 
     
     
         20 . The system of  claim 11  wherein the operations further comprise detecting as the number of insomnia symptoms at least one of a number of sleep metrics and a number of next day alertness metrics, the number of sleep metrics comprising one or more of a Sleep Efficiency (SE), a Wake After Sleep Onset (WASO), a number of awakenings, a Sleep Onset Latency (SOL), a deep sleep percentage, and a Total Sleep Time (TST), and the number of next day alertness metrics comprising one or more of a PERcentage of eyelid CLOSure over the time (PERCLOS), a number of naps, and a duration of naps.