Disclosed is a system for delivering a digital therapeutic, specific to both a user-selected emotional or mental state (EMS) and at least one user profile attribute comprising of: an EMS store; a message prescriber; a processor coupled to a memory element with instructions, the processor when executing the memory-stored instructions, configure the system to cause: the EMS store comprising a plurality of EMS to allow a user to select at least one EMS, said selected EMS indicating at least one of an emotion, mental, and/or physical state of the user. The message prescriber then pushing to a user device at least one stored digital therapeutic coupled to the selected EMS from the EMS store, wherein the digital therapeutic is at least one of a message or content with a behavioral and/or physical (b/p) recommendation of varying severity and/or varying design cues specific to both the user-selected EMS and at least one user profile attribute. In other words, two users may independently input identical EMS's and receive completely different digital therapeutics—both in terms of messaging and/or design—based on at least one varying profile attribute (age, gender, race, etc.) between user 1 and user 2.

Disclosed is a system for delivering a digital therapeutic, specific to both a user-selected emotional or mental state (EMS) and at least one user profile attribute comprising of: an EMS store; a message prescriber; a processor coupled to a memory element with instructions, the processor when executing the memory-stored instructions, configure the system to cause: the EMS store comprising a plurality of EMS to allow a user to select at least one EMS, said selected EMS indicating at least one of an emotion, mental, and/or physical state of the user. The message prescriber then pushing to a user device at least one stored digital therapeutic coupled to the selected EMS from the EMS store, wherein the digital therapeutic is at least one of a message or content with a behavioral and/or physical (b/p) recommendation of varying severity and/or varying design cues specific to both the user-selected EMS and at least one user profile attribute. In other words, two users may independently input identical EMS's and receive completely different digital therapeutics—both in terms of messaging and/or design—based on at least one varying profile attribute (age, gender, race, etc.) between user 1 and user 2.

A cleanup support system that supports a cleanup behavior includes: a first obtaining unit configured to obtain first information indicating a level of interest of a target person in cleanup; a second obtaining unit configured to obtain second information indicating a level of achievement of the cleanup performed by the target person; a determination unit configured to determine a content of control corresponding to the first information obtained and the second information obtained, with reference to a rule which associates the level of interest in the cleanup and the level of achievement of the cleanup with a content of control performed on a device; and a control unit configured to control the device according to the content of control determined.

A method includes receiving first physiological data associated with a user during a first sleep session. The method also includes receiving second physiological data associated with the user subsequent to the first sleep session and prior to a second sleep session. The method also includes determining a recommended bedtime for the user for the second sleep session based at least in part on the first physiological data, the second physiological data, or both. The method also includes causing an indication of the recommended bedtime for the second sleep session to be communicated to the user via a user device before the recommended bedtime.

A method includes receiving first physiological data associated with a user during a first sleep session. The method also includes receiving second physiological data associated with the user subsequent to the first sleep session and prior to a second sleep session. The method also includes determining a recommended bedtime for the user for the second sleep session based at least in part on the first physiological data, the second physiological data, or both. The method also includes causing an indication of the recommended bedtime for the second sleep session to be communicated to the user via a user device before the recommended bedtime.

Systems and associated methods are provided for monitoring a user while operating a computing device and providing active feedback to said user regarding health and safety best practices associated with operating said computing device. The methods comprise obtaining user biometric data; converting said biometric data into actionable instances of health and safety user device operation use cases; and interacting with the user based on said actionable instances in order to improve or remedy any deviations from recommended health and safety user device operation practices.

A first value for each of a plurality of parameters is received, each of the first values being associated with a user and a first day. A second value for each of the plurality of parameters is received, each of the second values being associated with the user and a second day that is subsequent to the first day. For each of the plurality of parameters, a trend indication is determined, the trend indication being based on the first values, the second values, and a first time period. A base weight value for each of the plurality of parameters is determined, the base weight value being based on the first time period and the determined trend indication associated with the one of the plurality of parameters. A mood score is determined, based on the base weight value for each of the plurality of parameters.

A first value for each of a plurality of parameters is received, each of the first values being associated with a user and a first day. A second value for each of the plurality of parameters is received, each of the second values being associated with the user and a second day that is subsequent to the first day. For each of the plurality of parameters, a trend indication is determined, the trend indication being based on the first values, the second values, and a first time period. A base weight value for each of the plurality of parameters is determined, the base weight value being based on the first time period and the determined trend indication associated with the one of the plurality of parameters. A mood score is determined, based on the base weight value for each of the plurality of parameters.

A predictive patient health machine learning model is trained based on baseline health data configured as directed graphs. Patient-healthcare system encounter data formed at least in part by electronic medical records (EMRs) is gathered. The patient-healthcare system encounter data is configured as directed graphs to generate graphed health data and the predictive patient health machine learning model is trained on that graphed health data.

The present disclosure relates to a computer-implemented process for generating and providing simulated user absorption information pertaining to users and based on target profiles and target situations, thereby providing user targeted and situationally targeted content recommendations. It is an object of the present disclosure to provide a technological solution to the long felt need in small scale content recommendation systems caused by the technical problem of generating situationally targeted and user profile targeted content recommendations for users of an interactive electronic system.