Optimized individual sleep patterns

Abstract

Embodiments of the invention are directed to a computer-implemented method for generating a sleep optimization plan. A non-limiting example of the computer-implemented method includes receiving, by a processor, genetic data for a user. The method also includes receiving, by the processor, Internet of Things (IoT) device data for the user. The method also includes generating, by the processor, a sleep duration measurement for the user based at last in part upon the IoT device data. The method also includes generating, by the processor, a sleep optimization plan for the user based at least in part upon the genetic data.

What is claimed is: 
     
         1 . A computer-implemented method for generating a sleep optimization plan, the method comprising:
 receiving, by a processor, caffeine-metabolism genetic data for a user, the caffeine-metabolism genetic data comprising a presence of a gene polymorphism in one or both of a cytochrome P450 1A2 (CYP1A2) gene and an aryl hydrocarbon receptor (AHR) gene;   receiving, by the processor, caffeine intake data for the user; and   generating, by the processor, a sleep optimization plan for the user based at least in part upon the caffeine-metabolism genetic data.   
     
     
         2 . The computer-implemented method of  claim 1  further comprising receiving, by the processor, Internet of Things (IoT) device data for the user. 
     
     
         3 . The computer-implemented method of  claim 2  further comprising generating, by the processor, a sleep duration measurement for the user based at last in part upon the IoT device data. 
     
     
         4 . The computer-implemented method of  claim 3 , wherein generating the sleep optimization plan for the user is further based on the sleep duration measurement. 
     
     
         5 . The computer-implemented method of  claim 1  further comprising receiving, by the processor, sleep-quality genetic data for the user, the sleep-quality genetic data comprising a presence of a gene polymorphism in BTBD9, TOX3, BC034767, MEIS1, MAP2K/SKOR1, or PTPRD. 
     
     
         6 . The computer-implemented method of  claim 5  further comprising correcting the sleep duration measurement based on the presence of the gene polymorphism in the sleep-quality genetic data. 
     
     
         7 . The computer-implemented method of  claim 5 , wherein the sleep-quality genetic data comprises a determination of a presence or an absence of a polymorphism in adenosine deaminase. 
     
     
         8 . The computer-implemented method of  claim 2 , wherein the IoT device data comprises physical exertion data for the user. 
     
     
         9 . The computer-implemented method of  claim 8 , wherein the physical exertion data comprises data from the group consisting of heart rate data, accelerometer data, gyroscope data, altimeter data, temperature sensor data, bioimpedance data, and combinations thereof. 
     
     
         10 . The computer-implemented method of  claim 8 , wherein the IoT device data comprises light exposure data. 
     
     
         11 . A computer program product for generating a sleep optimization plan, the computer program product comprising a computer readable storage medium readable by a processing circuit and storing program instructions for execution by the processing circuit for performing a method comprising:
 receiving caffeine-metabolism genetic data for a user, the caffeine-metabolism genetic data comprising a presence of a gene polymorphism in one or both of a cytochrome P450 1A2 (CYP1A2) gene and an aryl hydrocarbon receptor (AHR) gene;   receiving caffeine intake data for the user; and   generating a sleep optimization plan for the user based at least in part upon the caffeine-metabolism genetic data.   
     
     
         12 . The computer program product of  claim 11  further comprising receiving Internet of Things (IoT) device data for the user. 
     
     
         13 . The computer program product of  claim 12  further comprising generating a sleep duration measurement for the user based at last in part upon the IoT device data. 
     
     
         14 . The computer program product of  claim 13 , wherein generating the sleep optimization plan for the user is further based on the sleep duration measurement. 
     
     
         15 . The computer program product of  claim 11  further comprising receiving sleep-quality genetic data for the user, the sleep-quality genetic data comprising a presence of a gene polymorphism in BTBD9, TOX3, BC034767, MEIS1, MAP2K/SKOR1, or PTPRD. 
     
     
         16 . The computer program product of  claim 15  further comprising correcting the sleep duration measurement based on the presence of the gene polymorphism in the sleep-quality genetic data. 
     
     
         17 . A processing system for generating a sleep optimization plan, the processing system comprising a processor in communication with one or more types of memory, the processor configured to:
 receive caffeine-metabolism genetic data for a user, the caffeine-metabolism genetic data comprising a presence of a gene polymorphism in one or both of a cytochrome P450 1A2 (CYP1A2) gene and an aryl hydrocarbon receptor (AHR) gene;   receive caffeine intake data for the user; and   generate a sleep optimization plan for the user based at least in part upon the caffeine-metabolism genetic data.   
     
     
         18 . The processing system according to  claim 17  further comprising receiving Internet of Things (IoT) device data for the user. 
     
     
         19 . The computer program product of  claim 18  further comprising generating a sleep duration measurement for the user based at last in part upon the IoT device data. 
     
     
         20 . The computer program product of  claim 19 , wherein generating the sleep optimization plan for the user is further based on the sleep duration measurement.