WO2021144652A1

WO2021144652A1 - Mental health management platform

Info

Publication number: WO2021144652A1
Application number: PCT/IB2021/000013
Authority: WO
Inventors: Daniel Leung; Shikib MEHRI
Original assignee: Livnao Technologies Corp.
Priority date: 2020-01-16
Filing date: 2021-01-15
Publication date: 2021-07-22

Abstract

The invention generally relates to health management, and, more particularly, to systems and methods for monitoring an individual's behavior, via passive collection of data associated with the individual, and determining a mental health status of the individual based on such behavior.

Description

MENTAL HEALTH MANAGEMENT PLATFORM

Field of the Invention

Background

Mental health includes one's emotional, psychological, and social well-being. It affects how an individual thinks, feels, and acts. As such, mental health plays a large role in determining how one makes choices, handles stress, relates to others, and the like. In particular, an individual's mental health is especially important with respect to their engagement and overall performance for any given activity, including school, work, and social settings. As a result, reduced mental health can be detrimental to one's performance in a given environment (i.e., school or work), as well as having a negative on their overall quality of life and well-being.

For example, employee-related costs incurred by employers and payers include both direct and indirect costs, and are directly correlated with the profitability of the employer. The direct costs include items such as insurance premiums, medical expenses, legal expenses, sick pay, disability income and administrative fees, while the indirect costs include items such as lost productivity, overtime, replacement worker expenses, investigation expenses and decreased product quality. In addition to employers, insurance carriers also benefit from a healthier population, as there is a direct correlation between the health of an individual and associated claim rates (i.e., claim rates tend to decrease with a healthier population while the claim rates tend to increase as the health declines). While poor mental health certainly contributes to mental health claims, it has been shown that mental health issues can also lead to physical health issues, thereby resulting in additional physical health claims.

It is estimated that companies lose an average of $500 billion in lost productivity every year. Contributing factors include poor work culture, lack of employee engagement, and high stress. As such, reduced mental health is understood to be a main contributing factor to lost productivity for any given business. For example, job burnout is a certain type of work-related stress in which an individual suffers from a state of physical or emotional exhaustion and may also exhibit a sense of reduced accomplishment and loss of personal identity. As such, one suffering from job burnout is less engaged and thus work productivity and quality of output suffers. In addition to job burnout, studies also show a direct correlation between deteriorating mental health and risk of workplace accidents, which can result in additional costs for businesses.

To improve profitability, businesses have traditionally opted to focus the majority of their efforts on improving functions or departments that generate revenues, often devoting little or no attention to the practice of health management. However, given the increasingly negative effect that employee health is having on the bottom line of many employers, greater efforts are now being directed to proactively managing employee health and engagement to reduce the costs associated therewith.

Although many businesses now realize the strategic importance of managing employee health, particularly an employee's mental health and engagement, the effective implementation of such management has been relatively difficult. For example, many businesses rely on wellness programs designed to support healthy behavior in the workplace and to improve health outcomes. Such workplace wellness programs are often comprised activities such as health education, medical screenings, weight management programs, on-site fitness programs or facilities. Thus far, due to certain drawbacks, wellness programs have not seen the return on investment that businesses had hoped for, which has led to high costs in employee turnover and reactive support. For example, wellness programs typically suffer from low participation rates by employees, as there is lack of interest and little incentive offered.

More recently, certain mobile-based platforms have been introduced, claiming to provide health management services for an individual with the aim to provide better insight to the individual's health while providing ease of use and improving participation. However, such platforms have drawbacks. For example, current platforms require direct and active participation from a user in order to collect data. Such direct and active participation may include, for example, the need for an individual to wear a specific device for the collection of data (i.e., a wearable, including a heart monitor or the like) and/or the need for an individual to provide direct input with an interface of the platform. For example, certain tests or assessments may present the user with a series of tailored questions used to determine a specific emotional or mental state of that user, which can be burdensome on the individual and may be biased, thus introducing the risk of an inaccurate assessment. Accordingly, such platforms may still suffer from lack of user participation and further introduce the potential for inaccurate health assessments as a result of requiring some form of active participation and input from the user, thereby resulting in potentially ineffective management of an individual's health.

Summary

The present invention recognizes the drawbacks of current health service systems, particularly within the workplace and healthcare environments, and provides a cloud-based health management platform to address such drawbacks.

In particular, aspects of the invention may be accomplished by using a health management platform providing health management services related to a user's mental health and well-being. For example, the health management services may be used by an entity associated with the user and having an interest in the user's health, such as an employer, an insurer, a healthcare provider, researcher, or, in some instances, family members or friends. It should further be noted that the entity associated with the user may be associated with a clinical or research setting in which the user's health status is of interest, such as a research facility that is monitoring clinical trial participants and their health status.

The platform is generally configured to monitor a user's behavior, via passive collection of data from a mobile device (i.e., smartphone, tablet, or other mobile computing device) that is associated with the user, and determine a mental health status of the user based on an analysis of such behavior.

The platform is configured to receive passive data captured from one or more devices associated with a mobile device of the user. In contrast to active data, the passive data is data gathered without the direct and active involvement of the user and, unlike active data, the passive data is completely objective. For example, the passive data may include, but is not limited to, a user's mobility, geolocation, and/or social behavior within a given environment, for example, wherein such passive data may be captured via one or more sensors of the user's mobile device. Additionally, or alternatively, the passive data may be collected from sources beyond a user's mobile device and are not necessarily tied specifically to the user's mobile device. For example, the passive data may be collected from an entity's internal metrics (e.g., an employer's metrics concerning employee absences, work performance, meeting attendance, etc.). Additionally, or alternatively, the passive data may be collected from social media sources in which the user is a member, wherein such data may include postings or other forms of communication and/or participation from the user. Additionally, or alternatively, the passive data may include other forms of external data collection, such as, for example, location coordinates provided via BLE beacons or the like.

The platform is then configured to assess a mental health status of the user based on analysis of the passive data relative to a set of baseline data and/or running an algorithm on the passive data.

For example, in some embodiments, the platform is configured to identify one or more changes in user behavior which may be indicative of one or more changes in the mental health status of the user as a result of a comparison of the passive data with a set of baseline data. The set of baseline data, for example, may represent a baseline behavior pattern for a user in the given environment over a period of time. Additionally, or alternatively, the platform is configured to run an algorithm on the passive data, wherein the algorithm has been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior.

In turn, in some embodiments, the platform is configured to generate and provide the user and/or the entity with actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user. For example, the actionable feedback may be provided directly to the user and include a suggestion of one or more actions to be carried out by the user aimed at addressing the one or more identified changes and having a positive impact on the user's mental health. The actionable feedback may be provided directly to the entity and include a suggestion of one or more resources and/or intervening actions effective in addressing the one or more identified changes and having a positive impact on the user's mental health. Additionally, or alternatively, in some embodiments, the platform is configured to provide a mental health assessment of the user based on running an algorithm on the passive data, wherein the mental health assessment may include a general wellness assessment concerning the user's mental health and/or a more detailed psychiatric assessment. For example, the psychiatric assessment may include a mental health diagnosis, including a specific determination of a mental health condition or mental illness, such as anxiety, depression, chronic stress, and post-traumatic stress disorder (PTSD).

Accordingly, the mental health management platform of the present disclosure addresses the drawbacks of current health service systems. In particular, the health management platform of the present invention leverages existing passive data available via mobile devices to track and identify early indicators of changes in mental health and further provides users and/or interested parties (i.e., employers, insurers, healthcare providers, researchers, family members or friends of the user, etc.) with such mental insights, including suggested minor interventions, before harmful symptoms manifest. Furthermore, by leveraging continuous streams of passive data, the platform is configured to provide early and accurate diagnosis, as well as constant monitoring, of users that may already be suffering from mental disorders. As such, the platform may help employers optimize productivity and reduce burnout through data-backed decisions, in that employers may better tailor resources to the needs of their teams and make smarter decisions on project management, to thereby maximize their team’s unique capabilities while ensuring employees remain healthy through data-driven preventative measures. Furthermore, because the platform runs on passive data, user participation improves as the platform does not require active user input and engage for the collection of data (i.e., the user is not burdened with daily, weekly, monthly questions). Additionally, the health management services provided by the platform are flexible and can be customized to fit any entity's culture and needs. For example, the health management services provided via the platform may be implemented via a cloud-based service, including, for example, a software as a service (SaaS) model, thereby providing an entity (i.e., an enterprise, insurer, healthcare provider, etc.) with on-demand mental-health insights using the existing passive data from the mobile device.

Certain aspects of the invention relate to systems and methods for providing health management services. In one embodiment, the system includes a processor configured to: receive, from a mobile device associated with a user, passive data captured from one or more devices associated with the mobile device; assess a mental health status of the user based entirely on analysis of the passive data relative to a set of baseline data to identify one or more changes in user behavior indicative of one or more changes in the mental health status of the user; and generate and provide actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user. In one embodiment, the method includes: receiving, from a mobile device associated with a user, passive data captured by one or more sensors of the mobile device; assessing a mental health status of the user based entirely on analysis of the passive data relative to a set of baseline data to identify one or more changes in user behavior indicative of one or more changes in the mental health status of the user; and generating and providing actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user.

In some embodiments, the processor is configured to provide the actionable feedback to at least one of the user and one or more secondary users associated with the user. In some embodiments, the actionable feedback provided to the user may include a suggestion of one or more actions to be carried out by the user aimed at addressing the one or more identified changes and having a positive impact on the user's mental health. In some embodiments, the actionable feedback provided to the one or more secondary users associated with the user may include a suggestion of one or more resources and/or intervening actions effective in addressing the one or more identified changes and having a positive impact on the user's mental health. The one or more secondary users may be associated with an employer of the user, a healthcare provider of the user, individual(s) associated with a research facility monitoring the user as a participant, and/or a family member of the user.

In some embodiments, the processor is configured to generate and provide the actionable feedback via a cloud-based service, wherein the cloud-based service comprises a software as a service (SaaS) model. In some embodiments, the processor is provided locally on the mobile device or provided on a server remote from the mobile device.

In some embodiments, the set of baseline data represents a baseline behavior pattern for a user in a given environment. For example, the mental health status of the user may be assessed over a period of time to identify one or more trending changes in the user's behavior relative to the baseline behavior pattern in the given environment. The given environment may include a place of work or employment, for example.

Other aspects of the invention relate to other embodiments of systems and methods for providing health management services. For example, the system may include a processor configured to: receive, from a mobile device associated with a user, passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; assess a mental health status of the user based entirely on a comparison of the passive data with a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the mental health status of the user; and in the event that one or more changes to the user's behavior are identified, generate and provide actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user. The method may include: receiving, from a mobile device associated with a user, passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; assessing a mental health status of the user based entirely on a comparison of the passive data with a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the mental health status of the user; and in the event that one or more changes to the user's behavior are identified, generating and providing actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user.

In some embodiments, the processor is configured to provide the actionable feedback to at least one of the user and one or more secondary users associated with the user. In one embodiment, the actionable feedback provided to the user may include a suggestion of one or more actions to be carried out by the user aimed at addressing the one or more identified changes and having a positive impact on the user's mental health and actionable feedback provided to the one or more secondary users associated with the user may include a suggestion of one or more resources and/or intervening actions effective in addressing the one or more identified changes and having a positive impact on the user's mental health. The one or more secondary users may be associated with an employer of the user, a healthcare provider of the user, researched s) associated with a research facility monitoring the user as a participant, and/or a family member of the user.

In some embodiments, the passive data may include data captured by at least one of a motion sensor and a global positioning system (GPS) sensor. The motion sensor may include at least one of an accelerometer, one or more gyroscopes, and a magnetometer for capturing motion of the mobile device and/or a direction of travel or orientation of the mobile device to thereby provide corresponding motion of the user.

In some embodiments, the passive data associated with social behavior may include data associated with a log of computing devices connected to, or otherwise associated with, a wireless network associated with the user's mobile device to thereby identify computing devices associated with individuals having a personal relationship with the user.

In some embodiments, the set of baseline data may represent a baseline behavior pattern for a user in the given environment, wherein the baseline data is associated with at least a user's mobility behavior, geolocation behavior, and social behavior in the given environment over a period of time. The mental health status of the user may be assessed over a period of time to identify one or more trending changes in the user's behavior relative to the baseline behavior pattern in the given environment. The given environment may include a place of work or employment.

Certain aspects of the invention relate to systems and methods for assessing wellness of a user. In one embodiment, the system includes a processor and memory coupled to the processor for storing instructions executable by the processor to cause the processor to: receive passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; and provide a wellness assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a wellness status is known with respect to associated user behavior. In one embodiment, the method includes receiving passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; and providing a wellness assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a wellness status is known with respect to associated user behavior.

In some embodiments, the wellness assessment includes a mental health and/or physical health context. The wellness assessment may include an assessment of a mental health status of the user. The wellness assessment may be provided to at least one of the user and one or more secondary users associated with the user. The processor, for example, may be configured to generate and provide actionable feedback to at least one of the user and the one or more secondary users associated with the user, wherein the actionable feedback may include suggested actions and/or resources having a positive impact on at least one of the user's mental health or physical health to thereby improve the wellness status of the user. The one or more secondary users may be associated with an employer of the user, a healthcare provider of the user, researcher(s) associated with a research facility monitoring the user as a participant, and/or a family member of the user.

In some embodiments, the algorithm may be run on data resulting from a comparison between the passive data and a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the wellness status of the user. The set of baseline data may represent, for example, a baseline behavior pattern for a user in the given environment, the baseline data associated with at least a user's mobility behavior, geolocation behavior, and social behavior in the given environment over a period of time.

In some embodiments, the processor may be configured to provide the wellness assessment via a cloud-based service, wherein the cloud-based service comprises a software as a service (SaaS) model.

In some embodiments, the database of constructed profiles of plurality of users may be obtained from academic research.

Other aspects of the invention relate to other embodiments of systems and methods for providing a psychiatric assessment of a user. For example, the system may include a processor and memory coupled to the processor for storing instructions executable by the processor to cause the processor to: receive passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; and provide a psychiatric assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior. The method may include: receiving passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; and providing a psychiatric assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior.

In some embodiments, the psychiatric assessment comprises a mental health diagnosis. The mental health diagnosis may include a determination of a mental health condition or mental illness associated with the user's mental health status. The mental health condition or mental illness may include, for example, at least one of anxiety, depression, chronic stress, and post- traumatic stress disorder (PTSD).

In some embodiments, the psychiatric assessment may be provided to at least one of the user and one or more secondary users associated with the user. The processor, for example, may be configured to generate and provide actionable feedback to at least one of the user and the one or more secondary users associated with the user. The actionable feedback may include suggested actions and/or resources having a positive impact on at least one of the user's mental health to thereby improve the mental health status of the user. The one or more secondary users may generally be associated with an employer of the user, a healthcare provider of the user, researcher(s) associated with a research facility monitoring the user as a participant, and/or a family member of the user.

In some embodiments, the algorithm may be run on data resulting from a comparison between the passive data and a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the mental health status of the user. The set of baseline data may represent a baseline behavior pattern for a user in the given environment, wherein the baseline data may be associated with at least a user's mobility behavior, geolocation behavior, and social behavior in the given environment over a period of time.

The database of constructed profiles of plurality of users may be obtained from academic research, for example.

Brief Description of the Drawings

FIG. l is a block diagram illustrating one embodiment of an exemplary system for providing a health management services.

FIG. 2 is a block diagram illustrating the health management platform of FIG. 1 in greater detail.

FIG. 3 is a block diagram illustrating the various databases in greater detail.

FIG. 4 is a block diagram illustrating at least one embodiment of a computing device (i.e., mobile device) for communicating with the health management platform, wherein the computing device is able to provide passive data therefrom and, in return, receive a subsequent health status assessment and/or actionable feedback. to FIG. 5 is a block diagram illustrating communication and exchange of data between a mobile device associated with a first user (employee/patient) and the health management platform as well as communication between at least a computing device associated with second user (employer, insurer, healthcare network, researcher(s), etc.) and the health management platform consistent with the present disclosure.

FIG. 6 is a block diagram illustrating collection of passive data captured from one or more devices associated with a mobile device of a user, including user mobility data captured by one or more motion sensors, user geolocation data captured by at least a GPS sensor, and user social behavior data provided via a data log.

FIGS. 7A-7E are screenshots of an interface on a mobile device associated with the health management services provided by the health management platform of the present disclosure. FIG. 7A illustrates an initial login and/or registration screen. FIG. 7B illustrates an exemplary dashboard or standard platform interface, including various features with which a user may interact to view certain metrics (FIGS. 7C and 7D) as well communications and/or notifications (FIG. 7E).

FIGS. 8A-8D are screenshots of an interface of a mobile device illustrating exemplary feedback provided to a user via a white-label software application provided on the mobile device (FIGS. 8A and 8C) and via direct API integration into an existing software applications on the mobile device (FIGS. 8B and 8D).

FIG. 9 is a flow diagram illustrating one embodiment of a method for providing mental health management services.

FIG. 10 is a flow diagram illustrating one embodiment of a method for assessing wellness of a user.

Detailed Description

The present invention is directed to a health management platform providing health management services related to a user's mental health and well-being. In particular, the health management platform leverages existing passive data available via mobile devices to track and identify early indicators of changes in mental health of users associated with such mobile devices and further provides the users and/or interested parties (i.e., employers, insurers, healthcare it providers, researchers, family members or friends of the user, etc.) with such mental insights, including suggested minor interventions, before harmful symptoms manifest.

The platform is generally configured to monitor a user's behavior, via passive collection of data from a mobile device (i.e., smartphone, tablet, or other mobile computing device) associated with the user, and determine a mental health status of the user based on such behavior. For example, the platform is configured to receive passive data captured from one or more devices associated with a mobile device of the user. The passive data may include, but is not limited to, a user's mobility, geolocation, and/or social behavior within a given environment, for example, wherein such passive data may be captured via one or more sensors of the user's mobile device. Additionally, or alternatively, the passive data may be collected from sources beyond a user's mobile device and are not necessarily tied specifically to the user's mobile device. For example, the passive data may be collected from an entity's internal metrics (e.g., an employer's metrics concerning employee absences, work performance, meeting attendance, etc.). Additionally, or alternatively, the passive data may be collected from social media sources in which the user is a member, wherein such data may include postings or other forms of communication and/or participation from the user. Additionally, or alternatively, the passive data may include other forms of external data

The platform is then configured to assess a mental health status of the user based on analysis of the passive data relative to a set of baseline data and/or running an algorithm on the passive data. For example, in some embodiments, the platform is configured to identify one or more changes in user behavior which may be indicative of one or more changes in the mental health status of the user as a result of a comparison of the passive data with a set of baseline data. The set of baseline data, for example, may represent a baseline behavior pattern for a user in the given environment over a period of time. Additionally, or alternatively, the platform is configured to run an algorithm on the passive data, wherein the algorithm has been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior.

Accordingly, the mental health management platform of the present disclosure addresses the drawbacks of current health service systems. In particular, the health management platform of the present invention leverages existing passive data available via mobile devices to track and identify early indicators of changes in mental health of users associated with such mobile devices and further provides the users and/or interested parties (i.e., employers, insurers, healthcare providers, researchers, family members or friends of the user, etc.) with such mental insights, including suggested minor interventions, before harmful symptoms manifest. Furthermore, by leveraging continuous streams of passive data, the platform is configured to provide early and accurate diagnosis, as well as constant monitoring, of users that may already be suffering from mental disorders. As such, the platform may help employers optimize productivity and reduce burnout through data-backed decisions, in that employers may better tailor resources to the needs of their teams and make smarter decisions on project management, to thereby maximize their team’s unique capabilities while ensuring employees remain healthy through data-driven preventative measures. Furthermore, because the platform runs on passive data, user participation improves as the platform does not require active user input and engage for the collection of data (i.e., the user is not burdened with daily, weekly, monthly questions). Additionally, the health management services provided by the platform are flexible and can be customized to fit any entity's culture and needs. For example, the health management services provided via the platform may be implemented via a cloud-based service, including, for example, a software as a service (SaaS) model, thereby providing an entity (i.e., an enterprise, insurer, healthcare provider, etc.) with on-demand mental-health insights using the existing passive data from the mobile device.

FIG. 1 illustrates one embodiment of an exemplary system 10 consistent with the present disclosure. As shown, system 10 includes a health management platform 12 embodied on an internet-based computing system/service. For example, as shown, the health management platform 12 may be embodied on a cloud-based service 14, for example. The health management platform 12 is configured to communicate and share data, specifically mental health-related data, with one or more users 15(a)-15(n) via computing devices 16(a)-16(n) over a network 18, for example. In the present context, at least some of the users include employees or patients (i.e., employee/patient 15a), while other users may include an entity 15(b)-15(n) (including members of that entity) having an interest in the mental health of the user 15a, such as an employer, an insurer, a healthcare provider, researcher(s), or the like, or, in some instances, family members or friends.

For example, the users associated with a company/employer may include an administrative staff member or management member of the company. The one or more users associated with a healthcare provider may include, but are not limited to, a physician, physician assistant, psychologist, psychiatrist, physical therapist, occupational therapist, social worker, therapist, counselor, and life coach.

The network 18 may represent, for example, a private or non-private local area network (LAN), personal area network (PAN), storage area network (SAN), backbone network, global area network (GAN), wide area network (WAN), or collection of any such computer networks such as an intranet, extranet or the Internet (i.e., a global system of interconnected network upon which various applications or service run including, for example, the World Wide Web). In alternative embodiments, the communication path between the computing devices 16 and/or between the computing devices 16 and the cloud-based service 14, may be, in whole or in part, a wired connection.

The network 18 may be any network that carries data. Non-limiting examples of suitable networks that may be used as network 18 include Wi-Fi wireless data communication technology, the internet, private networks, virtual private networks (VPN), public switch telephone networks (PSTN), integrated services digital networks (ISDN), digital subscriber link networks (DSL), various second generation (2G), third generation (3G), fourth generation (4G) cellular-based data communication technologies, Bluetooth radio, Near Field Communication (NFC), the most recently published versions of IEEE 802.11 transmission protocol standards, other networks capable of carrying data, and combinations thereof. In some embodiments, network 18 is chosen from the internet, at least one wireless network, at least one cellular telephone network, and combinations thereof. As such, the network 18 may include any number of additional devices, such as additional computers, routers, and switches, to facilitate communications. In some embodiments, the network 18 may be or include a single network, and in other embodiments the network 18 may be or include a collection of networks.

The health management platform 12 is configured to communicate and share data with the computing devices 16 associated with one or more users 15. Accordingly, the computing device 16 may be embodied as any type of device for communicating with the health management platform 12 and cloud-based service 14, and/or other user devices over the network 18. For example, at least one of the user devices may be embodied as, without limitation, a computer, a desktop computer, a personal computer (PC), a tablet computer, a laptop computer, a notebook computer, a mobile computing device, a smart phone, a cellular telephone, a handset, a messaging device, a work station, a distributed computing system, a multiprocessor system, a processor-based system, and/or any other computing device configured to store and access data, and/or to execute software and related applications consistent with the present disclosure.

In the embodiments described here, the device 16a associated with at least the employee/patient 15a is generally embodied as a smartphone or tablet and the devices 16b-16n associated with the other users (associated with the employer, insurer, healthcare network, researcher) may generally be embodied as a smartphone, as well as any one of the other computing devices previously listed herein.

The health management platform 12 provides health management services related to a user's mental health and well-being. In particular, the platform 12 is generally configured to monitor a user's behavior, via passive collection of data from the user's mobile device 16a (i.e., smartphone, tablet, or other mobile computing device) associated with the user, and determine a mental health status of the user 15a based on an analysis of such behavior. More specifically, the platform 12 is configured to receive passive data captured from one or more devices associated with a mobile device 16a. In contrast to active data, the passive data is data gathered without the direct and active involvement of the user 15a and, unlike active data, the passive data is completely objective. For example, the passive data may include, but is not limited to, a user's mobility, geolocation, and/or social behavior within a given environment. In the instance of an employee/employer scenario, in which the health management services are deployed as part of a company's wellness program, the environment includes the workplace and surrounding area.

The passive data may be captured via one or more sensors of the user's mobile device, for example. Additionally, or alternatively, the passive data may be collected from sources beyond a user's mobile device and are not necessarily tied specifically to the user's mobile device. For example, the passive data may be collected from an entity's internal metrics (e.g., an employer's metrics concerning employee absences, work performance, meeting attendance, etc.). Additionally, or alternatively, the passive data may be collected from social media sources in which the user is a member, wherein such data may include postings or other forms of communication and/or participation from the user. Additionally, or alternatively, the passive data may include other forms of external data

The platform 12 is then configured to assess a mental health status of the user 15a based on analysis of the passive data relative to a set of baseline data and/or running an algorithm on the passive data. For example, in some embodiments, the platform 12 is configured to identify one or more changes in user behavior which may be indicative of one or more changes in the mental health status of the user 15a as a result of a comparison of the passive data with a set of baseline data. The set of baseline data, for example, may represent a baseline behavior pattern for a user in the given environment over a period of time, which could include the user's pattern of mobility (i.e., movement) relative to the environment over a period of time, geolocation within the given environment over a period of time, as well as the user's social behavior in that given environment over a period of time. The pattern may be established after a predefined period of time sufficient to establish a baseline pattern, such as a set period of days (i.e., 30 days, 45 days, 60 days, etc), months, or years. The baseline behavior pattern represents the user's typical workday, including their behavior in that given workday, for example. Additionally, or alternatively, the platform 12 is configured to run an algorithm on the passive data, wherein the algorithm has been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior. The database of constructed profiles of plurality of users may be obtained from academic research, for example, in which specific mental health status with given behavior has been established.

In turn, the platform 12 is configured to generate and provide the user 15a and/or one or more secondary users 15b-15n associated with an entity having an interest in the user's mental health status (i.e., employers, insurer, healthcare provider, researcher, family member or friend, etc.) with actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user. The actionable feedback may generally be in the form of a communication (i.e., text message, email, phone call, push notification, or the like) that includes a suggestion of one or more actions to be carried out by the user or a suggestion of one or more resources and/or intervening actions effective in addressing the any potential negative signs to subsequently have a positive impact on the user's mental health. Additionally, or alternatively, in some embodiments, the platform 12 is configured to provide a mental health assessment of the user 15a based on running an algorithm on the passive data, wherein the mental health assessment may include a general wellness assessment concerning the user's mental health and/or a more detailed psychiatric assessment, such as a mental health diagnosis, including a specific determination of a mental health condition or mental illness, such as anxiety, depression, chronic stress, and post-traumatic stress disorder (PTSD), or any other mental health condition or illness.

It should be noted that embodiments of the system 10 of the present disclosure include computer systems, computer operated methods, computer products, systems including computer- readable memory, systems including a processor and a tangible, non-transitory memory configured to communicate with the processor, the tangible, non-transitory memory having stored instructions that, in response to execution by the processor, cause the system to perform steps in accordance with the disclosed principles, systems including non-transitory computer- readable storage medium configured to store instructions that when executed cause a processor to follow a process in accordance with the disclosed principles, etc.

It should be noted that the health management services provided by the platform 12 are flexible and can be customized to fit any entity's culture and needs. For example, the health management services provided via the platform 12 may be implemented via the cloud-based service, including, for example, a software as a service (SaaS) model, thereby providing an entity (i.e., an enterprise, insurer, healthcare provider, etc.) with on-demand mental-health insights using the existing passive data from the mobile devices of users.

FIG. 2 is a block diagram illustrating the health management platform 12 of FIG. 1 in greater detail. As shown, the health management platform 12 may include an interface 20, a data collection and management module 22, a mental health assessment module 24, a message creation and management module 26, and various databases 28 for storage of data.

FIG. 3 is a block diagram illustrating the various databases in greater detail. In particular, the various databases for storage of data include, but are not limited to, a user database 30 for storing profiles associated with at least the users 15a whose mental statuses are being monitored, as well as other users 15b-15n (i.e., the employer, insurer, healthcare network, researcher(s), friends, and/or family), a reference database 32 for storing reference data, including, but not limited to, baseline data representing baseline behavior patterns for given user's in a given environment, as well as one or more reference sets of data comprised of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior (i.e., obtained from academic research, including clinical studies and the like, upon which millions of data points have been received with regard to mental health status and user behavior), a mental health assessment database 34 for storing mental health assessments for users, an actionable feedback database 36 for storing actionable feedback which may include media ((i.e., an image file, a video file, an audio file, a document file, and a combination thereof) including a suggestion of one or more actions to be carried out by the user or one or more resources and/or intervening actions known to be effective in addressing potentially negative or concerning issues with the user's mental health status and have a positive impact on the user's mental health, and an incoming/outgoing message database 38 for storing communications to be delivered to the users 15. The data collection and management module 22 may be configured to communicate and exchange data with each of the databases.

The interface 20 may generally allow a user to gain access to one or more features of the health management services, including access to data on the health management platform 12, via a software application running on an associated computing device, or via a web-based portal.

For example, upon accessing a mobile software application, the interface 20 may be presented to the user via their device 16, in which the user may navigate a dashboard or standard platform interface so as to interact with one or more features provided by the health management services of the platform 12 and/or view data (stored in one or more of the databases). It should be noted, however, that, depending on the desired customization, certain data may have restricted access in place such that only those users that have been granted rights (e.g., role-based access) can access and view certain data that is considered confidential or sensitive. Accordingly, a user, upon registering or logging in to the health management service, via an interface 20, may only have access to certain features (i.e., viewing their profile, including basic identification details and preferences, as well as the ability to decide the manner in which actionable feedback is communicated, such as preferred communication via text messaging, email, and/or phone call). Furthermore, a user may provide certain data as part of their profile, which may include, but is not limited to, biological sex, blood type, date of birth, Fitzpatrick skin type, wheelchair use or any form of physical disability with regard to mobility, height, and body mass. The user may also provide clinical records data, including, but not limited to, allergy records, conditions records, immunization records, lab result records, medication records, procedure records, and vital sign records. Is should be noted that the previously described user inputted data may also be included in the baseline data sets and used as part of the analysis when assessing the user's mental health status.

In some instances, the user may have access to their mental health assessment. Certain users associated with the entity (i.e., employer, insurer, healthcare network, researcher, etc.) may have greater access to data and/or more features of the health management service, than the user.

As previously described, the platform 12 is generally configured to monitor a user's behavior, via passive collection of data from an associated mobile device (i.e., smartphone, tablet, or other mobile computing device), and determine a mental health status of the user based on such behavior. Upon receiving the passive data, the mental health assessment module 24 is configured to assess a mental health status of the user based on analysis of the passive data relative to a set of baseline data and/or running an algorithm on the passive data. For example, in some embodiments, the mental health assessment module 24 is configured to identify one or more changes in user behavior which may be indicative of one or more changes in the mental health status of the user as a result of a comparison of the passive data with a set of baseline data. For example, the mental health assessment module 24 may be configured to identify any particular trends in the user's behavior based on the passive data and, in turn, determine whether such trends fall outside of an acceptable range when compared to the baseline behavior pattern for that given user.

Additionally, or alternatively, the mental health assessment module 24 is configured to run an algorithm on the passive data, wherein the algorithm has been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior.

For example, the mental health assessment module 24 may include custom, proprietary, known and/or after-developed statistical analysis code (or instruction sets), hardware, and/or firmware that are generally well-defined and operable to receive two or more sets of data and identify, at least to a certain extent, a level of correlation and thereby associate the sets of data with one another based on the level of correlation. As such, the mental health assessment module 24 may analyze data sets from any one of the databases (user database 30, reference database 32, mental health assessment database 34, actionable feedback database 36, and message database 38) in order to assess a mental health status of the user and subsequently provide a mental health assessment of the user and/or generate and provide the user and/or the entity associated with the user (i.e., employer, insurer, healthcare network, researcher, etc.) with actionable feedback to address one or more identified issues or concerns and improve the mental health status of the user. As such, the message creation and management module 26 is configured to create and transmit one or more communication messages to at least one of the users 15, wherein the communication message may include a mental health assessment (i.e., a general wellness assessment or more detailed psychiatric assessment) and/or actionable feedback.

In certain embodiments, the systems and methods of the invention use an assessment predictor or classifier for determining a mental health status of a user. The assessment predictor can be based on any appropriate pattern recognition method that receives passive data related to a user's mobility, geolocation, and/or social behavior in a given environment and provides an output comprising an assessment of the user's mental health based on such behavior. The assessment predictor or classifier is trained with training data from a training population of users for whom mental health statuses are known with respect to specific user behaviors (including user movement and behavior). Various known statistical pattern recognition methods can be used in conjunction with the present invention. Suitable statistical methods include, without limitation, logic regression, ordinal logistic regression, linear or quadratic discriminant analysis, clustering, principal component analysis, nearest neighbor classifier analysis, and Cox proportional hazards regression. Non-limiting examples of implementing particular assessment predictors in conjunction are provided herein to demonstrate the implementation of statistical methods in conjunction with the training set.

In some embodiments, the assessment predictor is based on a regression model, preferably a logistic regression model. In such embodiments, the coefficients for the regression model are computed using, for example, a maximum likelihood approach.

Some embodiments of the present invention provide generalizations of the logistic regression model that handle multicategory (polychotomous) responses. Such embodiments can be used to discriminate an organism into one or three or more prognosis groups. Such regression models use multicategory logit models that simultaneously refer to all pairs of categories, and describe the odds of response in one category instead of another. Once the model specifies logits for a certain (J— 1) pairs of categories, the rest are redundant. See, for example, Agresti, An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc., 1996, New York, Chapter 8, which is hereby incorporated by reference. Linear discriminant analysis (LDA) attempts to classify a subject into one of two categories based on certain object properties. In other words, LDA tests whether object attributes measured in an experiment predict categorization of the objects. LDA typically requires continuous independent variables and a dichotomous categorical dependent variable.

Quadratic discriminant analysis (QDA) takes the same input parameters and returns the same results as LDA. QDA uses quadratic equations, rather than linear equations, to produce results. LDA and QDA are interchangeable, and which to use is a matter of preference and/or availability of software to support the analysis. Logistic regression takes the same input parameters and returns the same results as LDA and QDA.

In some embodiments of the present invention, decision trees are used. Decision tree algorithms belong to the class of supervised learning algorithms. The aim of a decision tree is to induce a classifier (a tree) from real-world example data. This tree can be used to classify unseen examples which have not been used to derive the decision tree. A decision tree is derived from training data. An example contains values for the different attributes and what class the example belongs. In one embodiment, the training data is data representative of a plurality of users for whom a mental health status is known with respect to associated user behaviors.

The following algorithm describes a decision tree derivation:

Tree(Examples, Class, Attributes)

Create a root node

If all Examples have the same Class value, give the root this label Else if Attributes is empty label the root according to the most common value Else begin

Calculate the information gain for each attribute

Select the attribute A with highest information gain and make this the root attribute

For each possible value, v, of this attribute

Add a new branch below the root, corresponding to A = v

Let Examples(v) be those examples with A = v

If Examples(v) is empty, make the new branch a leaf node labeled with the most common value among Examples

Else let the new branch be the tree created by

Tree(Examples(v), Class, Attributes - {A}) end

A more detailed description of the calculation of information gain is shown in the following. If the possible classes vi of the examples have probabilities P(vi) then the information content I of the actual answer is given by:

I( (vi),... ,E(vn))=nåi=l - (vi)log2 (vt)

The I-value shows how much information we need in order to be able to describe the outcome of a classification for the specific dataset used. Supposing that the dataset contains p positive (e.g., one or more changes in the user's behavior indicative of a negative change in the mental health status of the user) and n negative (e.g., no changes in the user's behavior indicative of a negative change in the mental health status of the user) examples (e.g. individuals), the information contained in a correct answer is:

\(p/p + n , nip + //) = - pip + n log2 pip + n - nip + n log2 n! p + n where log2 is the logarithm using base two. By testing single attributes the amount of information needed to make a correct classification can be reduced. The remainder for a specific attribute A (e.g. a trait) shows how much the information that is needed can be reduced.

Remainder! A)=V)M= 1 pi + n p + n I(pi//;i + m, n pi + m)

“v” is the number of unique attribute values for attribute A in a certain dataset, “i” is a certain attribute value, “pi” is the number of examples for attribute A where the classification is positive (e.g., negative change in mental health status), “m” is the number of examples for attribute A where the classification is negative (e.g., no negative change in mental health status). The information gain of a specific attribute A is calculated as the difference between the information content for the classes and the remainder of attribute A:

Gain(A) = I (pip + n , nip + n) - Remainder! A)

The information gain is used to evaluate how important the different attributes are for the classification (how well they split up the examples), and the attribute with the highest information.

In general there are a number of different decision tree algorithms, many of which are described in Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc. Decision tree algorithms often require consideration of feature processing, impurity measure, stopping criterion, and pruning. Specific decision tree algorithms include, cut are not limited to classification and regression trees (CART), multivariate decision trees, ID3, and C4.5.

In some embodiments, clustering may be used. Clustering is described on pages 211-256 of Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York. As described in Section 6.7 of Duda, the clustering problem is described as one of finding natural groupings in a dataset. To identify natural groupings, two issues are addressed. First, a way to measure similarity (or dissimilarity) between two samples is determined. This metric (similarity measure) is used to ensure that the samples in one cluster are more like one another than they are to samples in other clusters. Second, a mechanism for partitioning the data into clusters using the similarity measure is determined.

Similarity measures are discussed in Section 6.7 of Duda, where it is stated that one way to begin a clustering investigation is to define a distance function and to compute the matrix of distances between all pairs of samples in a dataset. If distance is a good measure of similarity, then the distance between samples in the same cluster will be significantly less than the distance between samples in different clusters. However, as stated on page 215 of Duda, clustering does not require the use of a distance metric. For example, a nonmetric similarity function s(x, x') can be used to compare two vectors x and x'. Conventionally, s(x, x') is a symmetric function whose value is large when x and x' are somehow “similar”. An example of a nonmetric similarity function s(x, x') is provided on page 216 of Duda.

Once a method for measuring “similarity” or “dissimilarity” between points in a dataset has been selected, clustering requires a criterion function that measures the clustering quality of any partition of the data. Partitions of the data set that extremize the criterion function are used to cluster the data. See page 217 of Duda. Criterion functions are discussed in Section 6.8 of Duda.

More recently, Duda et al., Pattern Classification, 2nd edition, John Wiley & Sons, Inc. New York, has been published. Pages 537-563 describe clustering in detail. More information on clustering techniques can be found in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, N.Y.; Everitt, 1993, Cluster analysis (3d ed.), Wiley, New York, N.Y.; and Backer, 1995, Computer- Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, N.J. Particular exemplary clustering techniques that can be used in the present invention include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering. Nearest neighbor classifiers are memory -based and require no model to be fit. Given a query point xo, the k training points x(r), r, . . . , k closest in distance to xo are identified and then the point xo is classified using the k nearest neighbors. Ties can be broken at random. In some embodiments, Euclidean distance in feature space is used to determine distance as: d(i)=||x(i)— xoll .

Typically, when the nearest neighbor algorithm is used, the expression data used to compute the linear discriminant is standardized to have mean zero and variance 1. In the present invention, the members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set. Profiles represent the feature space into which members of the test set are plotted. Next, the ability of the training set to correctly characterize the members of the test set is computed. In some embodiments, nearest neighbor computation is performed several times for a given combination of fertility-associated phenotypic traits. In each iteration of the computation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of traits is taken as the average of each such iteration of the nearest neighbor computation.

The nearest neighbor rule can be refined to deal with issues of unequal class priors, differential misclassification costs, and feature selection. Many of these refinements involve some form of weighted voting for the neighbors. For more information on nearest neighbor analysis, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York.

The pattern classification and statistical techniques described above are merely examples of the types of models that can be used to construct a model for classification. It is to be understood that any statistical method can be used in accordance with the invention. Moreover, combinations of these described above also can be used.

FIG. 4 is a block diagram illustrating at least one embodiment of a mobile device 16a associated with user 15a for communicating with the health management platform 12 and providing an interface upon which the user 15a can interact so as to participate with the health management services provided via the platform 12.

The mobile device 16 generally includes a computing system 100. As shown, the computing system 100 includes one or more processors, such as processor 102. Processor 102 is operably connected to communication infrastructure 304 (e.g., a communications bus, cross-over bar, or network). The processor 102 may be embodied as any type of processor capable of performing the functions described herein. For example, the processor may be embodied as a single or multi-core processor(s), digital signal processor, microcontroller, or other processor or processing/controlling circuit.

The computing system 100 further includes a display interface 106 that forwards graphics, text, sounds, and other data from communication infrastructure 104 (or from a frame buffer not shown) for display on display unit 108. The computing system further includes input devices 110. The input devices 110 may include one or more devices for interacting with the mobile device 16, such as a keypad, microphone, camera, as well as other input components, including motion sensors, and the like. In one embodiment, the display unit 108 may include a touch-sensitive display (also known as “touch screens” or “touchscreens”), in addition to, or as an alternative to, physical push-button keyboard or the like. The touch screen may generally display graphics and text, as well as provides a user interface (e.g., but not limited to graphical user interface (GUI)) through which a user may interact with the mobile device 16, such as accessing and interacting with applications executed on the device 16, including an app for providing direct user input with the health management service offered by the health management platform.

The computing system 100 further includes main memory 112, such as random access memory (RAM), and may also include secondary memory 114. The main memory 112 and secondary memory 114 may be embodied as any type of device or devices configured for short term or long-term storage of data such as, for example, memory devices and circuits, memory cards, hard disk drives, solid-state drives, or other data storage devices. Similarly, the memory 112, 114 may be embodied as any type of volatile or non-volatile memory or data storage capable of performing the functions described herein.

In the illustrative embodiment, the mobile device 16 may maintain one or more application programs, databases, media and/or other information in the main and/or secondary memory 112, 114. The secondary memory 114 may include, for example, a hard disk drive 116 and/or removable storage drive 118, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. Removable storage drive 318 reads from and/or writes to removable storage unit 120 in any known manner. The removable storage unit 120 may represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 118. As will be appreciated, removable storage unit 120 includes a computer usable storage medium having stored therein computer software and/or data.

In alternative embodiments, the secondary memory 114 may include other similar devices for allowing computer programs or other instructions to be loaded into the computing system 100. Such devices may include, for example, a removable storage unit 124 and interface 122. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an erasable programmable read only memory (EPROM), or programmable read only memory (PROM)) and associated socket, and other removable storage units 124 and interfaces 122, which allow software and data to be transferred from removable storage unit 124 to the computing system 100.

The computing system 100 further includes one or more application programs 126 directly stored thereon. The application program(s) 126 may include any number of different software application programs, each configured to execute a specific task.

The computing system 200 further includes a communications interface 128. The communications interface 128 may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications between the mobile device 16 external devices (other mobile devices 16, the cloud-based service 14, including the health management platform 12). The communications interface 128 may be configured to use any one or more communication technology and associated protocols, as described above, to effect such communication. For example, the communications interface 128 may be configured to communicate and exchange data with the health management platform 12, and/or one other mobile device 16, via a wireless transmission protocol including, but not limited to, Bluetooth communication, infrared communication, near field communication (NFC), radio-frequency identification (RFID) communication, cellular network communication, the most recently published versions of IEEE 802.11 transmission protocol standards, and a combination thereof. Examples of communications interface 128 may include a modem, a network interface (such as an Ethernet card), a communications port, a Personal Computer Memory Card International Association (PCMCIA) slot and card, wireless communication circuitry, etc.

Computer programs (also referred to as computer control logic) may be stored in main memory 112 and/or secondary memory 114 or a local database on the mobile device 16. Computer programs may also be received via communications interface 128. Such computer programs, when executed, enable the computing system 100 to perform the features of the present invention, as discussed herein. In particular, the computer programs, including application programs 126, when executed, enable processor 102 to perform the features of the present invention. Accordingly, such computer programs represent controllers of computer system 100.

In one embodiment where the invention is implemented using software, the software may be stored in a computer program product and loaded into the computing system 100 using removable storage drive 118, hard drive 116 or communications interface 128. The control logic (software), when executed by processor 102, causes processor 102 to perform the functions of the invention as described herein.

In another embodiment, the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using a combination of both hardware and software.

FIG. 5 is a block diagram illustrating communication and exchange of data between a mobile device 16a associated with a first user 15a (employee/patient) and the health management platform 12 as well as communication between at least a computing device 16b associated with second user 15b (employer, insurer, healthcare network, researcher, etc.) and the health management platform 12 consistent with the present disclosure. As shown, the platform 12 is configured to receive passive data captured from one or more devices associated with the mobile device 16a. As previously noted, the passive data is data gathered without the direct and active involvement of the user 15a and, unlike active data, the passive data is completely objective. For example, the passive data may include, but is not limited to, data related to a user's mobility, geolocation, and/or social behavior within a given environment. For example, the mobile device 16a may include various sensors 130 for capturing data related to user mobility (i.e., movement) and geolocation within a given environment. The mobile device 16a may further include data logs, for example, containing data related to the user's social behavior, as will be described in greater detail herein.

Upon receiving the passive data, the mental health assessment module 24 is configured to assess a mental health status of the user based on analysis of the passive data relative to a set of baseline data and/or running an algorithm on the passive data.

For example, in some embodiments, the mental health assessment module 24 is configured to assess a mental health status of the user based entirely on a comparison of the passive data with a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the mental health status of the user and, in the event that one or more changes to the user's behavior are identified, generate and provide actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user.

Yet still, in other embodiments, the mental health assessment module 24 is configured to provide a mental health assessment of the user based on running an algorithm on the passive data, wherein the mental health assessment may include a general wellness assessment concerning the user's mental health and/or a more detailed psychiatric assessment. For example, in one embodiment, the mental health assessment module 24 is configured to provide a wellness assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a wellness status is known with respect to associated user behavior. In another embodiment, the mental health assessment module 24 is configured to provide a psychiatric assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior.

In turn, in some embodiments, the mental health assessment module 24 is configured to generate and provide the user 15a and/or one or more users 15b-15n with actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user. For example, the actionable feedback may be provided directly to the user and include a suggestion of one or more actions to be carried out by the user aimed at addressing the one or more identified changes and having a positive impact on the user's mental health. The actionable feedback may be provided directly to the one or more users 15b-15n and include a suggestion of one or more resources and/or intervening actions effective in addressing the one or more identified changes and having a positive impact on the user's mental health. Additionally, or alternatively, in some embodiments, the mental health assessment module 24 is configured to provide a mental health assessment of the user based on running an algorithm on the passive data, wherein the mental health assessment may include a general wellness assessment concerning the user's mental health and/or a more detailed psychiatric assessment. For example, the psychiatric assessment may include a mental health diagnosis, including a specific determination of a mental health condition or mental illness, such as anxiety, depression, chronic stress, and post-traumatic stress disorder (PTSD).

FIG. 6 is a block diagram illustrating collection of passive data captured from one or more devices associated with a mobile device 16a of a user 15a, including user mobility data captured by one or more motion sensors 134, user geolocation data captured by at least a GPS sensor 136, and user social behavior data provided via a data log 132.

As shown, the mobile device 16a may include a variety of different sensors configured to capture data related to motion or position of the mobile device 16a, which is indicative of motion or position of the associated user 15a (it is generally assumed that the mobile device 16a is more often than not kept in the user's possession when they are moving within the environment). As shown, the sensors 130 may include one or more motion sensors 134 and a GPS sensor 136. It should be noted that, in some instances, the sensors 130 may further be configured to capture user input 138, such as touch input and the like.

It should be noted that FIG. 6 illustrates one embodiment of set of sensors included in a mobile device consistent with the present disclosure and by no means is meant to limit the kind and/or amount of sensors for use in a system and/or method consistent with the present disclosure. For example, a system and method consistent with the present disclosure may include more or less sensors than what is illustrated in FIG. 6.

The one or more motion sensors 134 may be embodied as any type of sensor configured to capture motion data and produce sensory signals from which the mobile device 16a and/or platform 12 may determine the user position and/or movement with the mobile device 16a. In particular, the motion sensor 134 may be configured to capture data corresponding to the movement of the mobile device 16a or lack thereof. The motion sensor 134 may include, for example, an accelerometer, an altimeter, one or more gyroscopes, or other motion or movement sensor to produce sensory signals corresponding to motion or movement of the device 16a and/or a magnetometer to produce sensory signals from which direction of travel or orientation can be determined. The one or more motion sensors 134 may further include, or are coupled to, an inertial measurement unit (IMU) for example.

Additionally, or alternatively, the passive data may be collected from sources beyond a user's mobile device and are not necessarily tied specifically to the user's mobile device. For example, the passive data may be collected from an entity's internal metrics (e.g., an employer's metrics concerning employee absences, work performance, meeting attendance, etc.). Additionally, or alternatively, the passive data may be collected from social media sources in which the user is a member, wherein such data may include postings or other forms of communication and/or participation from the user. Additionally, or alternatively, the passive data may include other forms of external data

It should further be noted that the passive data may further include data taken from health-related and/or behavioral-related mobile applications and/or features associated with the user's mobile device 16a, that may be provided via a third party. For example, the passive data may include data related to a user's vitals and body measurements, including, but not limited to, body fat percentage, heart rate, body temperature, blood pressure, blood pressure (Systolic), blood glucose, insulin delivery, respiratory rate, VO2 max, body mass index, lean body mass, and body fat percentage. The passive data may include data related to a user's physical activities, including, but not limited to, step count, distance walking and/or running, distance cycling, push count, distance in wheelchair (if the user is in a wheelchair), swimming stroke count, distance swimming, flights climbed, stand hours, and basal energy burned. The passive data may also include data related to a user's mindfulness and sleep, such as mindful session data and sleep analysis data. The passive data may further include data from existing productivity tracking software or tools, such that user productivity can be monitored and accounted for when assessing the user's mental health status. The passive data may also include other types of a data associated with a user's mobile device and/or surrounding environment and captured via one or more sensors of the mobile device. For example, the passive data may include display brightness, battery drainage, and/or ambient light of a surrounding environment, which such data may be used in determining a mental health status of the individual.

The passive data may further include social behavior that may be indicative of a user's mental health status. For example, the social behavior may include data associated with one or more data logs 132, which may include, for example a log of computing devices connected to, or otherwise associated with, a wireless network associated with the user's mobile device (logged data 136) to thereby identify computing devices associated with individuals having a personal relationship with the user.

FIGS. 7A-7E are screenshots of an interface on a mobile device associated with the health management services provided by the health management platform of the present disclosure. FIG. 7A is a screenshot of an interface on a mobile device illustrating an initial login and/or registration screen. For example, upon opening a white-label software application or a visiting a website associated with the health management services, a user may first be presented with a login screen. Upon providing the credentials (e.g., username or email and associated password), the user is then brought to a dashboard or home screen (shown in FIG. 7B). The dashboard may be configured to show a user's overall productivity score, for example. The overall productivity score may be based on a calculation of that user's perceived productivity for the given day, for example. The level of productivity may be based on a percentage scale from 0% to 100% (where 0% is considered least productive and 100% is considered most productive). It should be noted that other scales may be used, such as a number-based scale rating from 0 to 10 (where 0 is considered least productive and 10 is considered most productive). The overall productivity score is based, at least in part, on a culmination of data associated with the user's mental health status as well as other forms of data. For example, the productivity score may be based, at least in part, on user capacity, user energy, and user focus, each of which may be calculated and inferred based on passive data collected, for example.

The system is configured to further provide a suggestion or feedback based on the overall productivity score and the user-specific needs. For example, in the event that the overall productivity score is relatively high, the suggestion may simply include encouraging and positive feedback in which the user is commended on their efforts and encouraged to maintain their current working levels (i.e., maintain their current activities and mental health state). In the event that the overall productivity score is low or requires improvement, the suggestion may include one or more actions to be carried out by the user or a suggestion of one or more resources and/or intervening actions effective in addressing the any potential negative signs to subsequently have a positive impact on the user's mental health and productivity. Again, the suggestions or feedback may be provided via various forms of communication, including text messaging, email, push notification, and telecommunication means (i.e., phone call).

The interface further includes various features with which a user may interact to view certain metrics, such as an ability to view additional details concerning their productivity score (FIGS. 7C and 7D) as well as view communications and/or notifications (FIG. 7E). For example, upon selecting an input for checking their productivity score, the user may be presented with specific score metrics used in calculating their overall productivity score, which may include user capacity, user energy, and user focus, for example (see FIG. 7C), and the user may further view details for each metric, which may present a timeline (e.g., minutes, hours, days, weeks, months, years, etc.) of levels of each metric (see FIG. 7D), thereby allowing for a use to look back and see how their mental wellbeing has changed over time. It should be noted that the interface, specifically the timeline graph illustrated in FIG. 7D, may be interactive in that a user can pick a specific time to view the perceived level and observe how their scores have fluctuated over time. As illustrated in FIG. 7E, the user may further view all communications and/or notifications received as part of the health management services, which may help a user contextualize their wellbeing throughout the day, week, month, or year.

FIGS. 8 A, 8B, 8C, and 8D are screenshots of an interface of a mobile device 16a illustrating exemplary feedback provided to a user via a white-label software application provided on the mobile device (FIGS. 8A and 8C) and via direct API integration into an existing software applications on the mobile device (FIGS. 8B and 8D). As shown in FIG. 8A, the actionable feedback provided includes, for example, a text message, in which a suggested course of action is provided to the user with the intention to improve the user's mental health status, while FIG. 8C illustrates feedback (in the form of a text message) in which the user is encouraged to maintain their current actions (as their current mental health status is deemed healthy and positive). As shown in FIGS. 8B and 8D, the actionable feedback includes a text message (prompted via a push notification service, for example) from an existing software application (via direct API integration) in which a suggested course of action is provided to the user to improve the user's mental health status. It should be noted that the feedback may be provided via various forms of communication, including text messaging, email, push notification, and telecommunication means (i.e., phone call).

FIG. 9 is a flow diagram illustrating one embodiment of a method 200 for providing mental health management services. The method includes receiving, from a mobile device associated with a user, passive data captured by one or more sensors of the mobile device (operation 210). In contrast to active data, the passive data is data gathered without the direct and active involvement of the user and, unlike active data, the passive data is completely objective. For example, the passive data may include, but is not limited to, a user's mobility, geolocation, and/or social behavior within a given environment, for example, wherein such passive data may be captured via one or more sensors of the user's mobile device. Additionally, or alternatively, the passive data may be collected from sources beyond a user's mobile device and are not necessarily tied specifically to the user's mobile device. For example, the passive data may be collected from an entity's internal metrics (e.g., an employer's metrics concerning employee absences, work performance, meeting attendance, etc.). Additionally, or alternatively, the passive data may be collected from social media sources in which the user is a member, wherein such data may include postings or other forms of communication and/or participation from the user. Additionally, or alternatively, the passive data may include other forms of external data

The method 200 further includes assessing a mental health status of the user based entirely on analysis of the passive data relative to a set of baseline data to identify one or more changes in user behavior indicative of one or more changes in the mental health status of the user (operation 220). The set of baseline data, for example, may represent a baseline behavior pattern for a user in the given environment over a period of time. As such, assessing a mental health status of the user may be based entirely on a comparison of the passive data with a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the mental health status of the user.

The method 200 further includes generating and providing actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user (operation 230). The actionable feedback may be provided to at least one of the user and one or more secondary users associated with the user (i.e., the employer, insurer, healthcare network, researcher, family, and/or friends). In particular, the actionable feedback provided to the user may include a suggestion of one or more actions to be carried out by the user aimed at addressing the one or more identified changes and having a positive impact on the user's mental health. The actionable feedback provided to the one or more secondary users associated with the user may include a suggestion of one or more resources and/or intervening actions effective in addressing the one or more identified changes and having a positive impact on the user's mental health.

FIG. 10 is a flow diagram illustrating one embodiment of a method 300 for assessing wellness of a user. The method 300 includes receiving passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment (operation 310). The method 300 further includes providing a mental health assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior (operation 320). The mental health assessment may include a general wellness assessment concerning the user's mental health and/or a more detailed psychiatric assessment. For example, the psychiatric assessment may include a mental health diagnosis, including a specific determination of a mental health condition or mental illness, such as anxiety, depression, chronic stress, and post-traumatic stress disorder (PTSD).

Accordingly, the mental health management platform of the present disclosure addresses the drawbacks of current health service systems. In particular, the health management platform of the present invention leverages existing passive data available via mobile devices to track and identify early indicators of changes in mental health and further provides users and/or interested parties (i.e., employers, insurers, healthcare providers, researcher, family members or friends of the user, etc.) with such mental insights, including suggested minor interventions, before harmful symptoms manifest. Furthermore, by leveraging continuous streams of passive data, the platform is configured to provide early and accurate diagnosis, as well as constant monitoring, of users that may already be suffering from mental disorders. As such, the platform may help employers optimize productivity and reduce burnout through data-backed decisions, in that employers may better tailor resources to the needs of their teams and make smarter decisions on project management, to thereby maximize their team’s unique capabilities while ensuring employees remain healthy through data-driven preventative measures. Furthermore, because the platform runs on passive data, user participation improves as the platform does not require active user input and engage for the collection of data (i.e., the user is not burdened with daily, weekly, monthly questions). Additionally, the health management services provided by the platform are flexible and can be customized to fit any entity's culture and needs. For example, the health management services provided via the platform may be implemented via a cloud-based service, including, for example, a software as a service (SaaS) model, thereby providing an entity (i.e., an enterprise, insurer, healthcare provider, etc.) with on-demand mental-health insights using the existing passive data from the mobile device.

As used in any embodiment herein, the term “module” may refer to software, firmware and/or circuitry configured to perform any of the aforementioned operations. Software may be embodied as a software package, code, instructions, instruction sets and/or data recorded on non- transitory computer readable storage medium. Firmware may be embodied as code, instructions or instruction sets and/or data that are hard-coded (e.g., nonvolatile) in memory devices. “Circuitry”, as used in any embodiment herein, may comprise, for example, singly or in any combination, hardwired circuitry, programmable circuitry such as computer processors comprising one or more individual instruction processing cores, state machine circuitry, and/or firmware that stores instructions executed by programmable circuitry. The modules may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), system on-chip (SoC), desktop computers, laptop computers, tablet computers, servers, smart phones, etc.

Any of the operations described herein may be implemented in a system that includes one or more storage mediums having stored thereon, individually or in combination, instructions that when executed by one or more processors perform the methods. Here, the processor may include, for example, a server CPU, a mobile device CPU, and/or other programmable circuitry.

Also, it is intended that operations described herein may be distributed across a plurality of physical devices, such as processing structures at more than one different physical location. The storage medium may include any type of tangible medium, for example, any type of disk including hard disks, floppy disks, optical disks, compact disk read-only memories (CD-ROMs), compact disk rewritables (CD-RWs), and magneto-optical disks, semiconductor devices such as read-only memories (ROMs), random access memories (RAMs) such as dynamic and static RAMs, erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), flash memories, Solid State Disks (SSDs), magnetic or optical cards, or any type of media suitable for storing electronic instructions.

Other embodiments may be implemented as software modules executed by a programmable control device. The storage medium may be non-transitory.

As described herein, various embodiments may be implemented using hardware elements, software elements, or any combination thereof. Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth.

Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The term "non-transitory" is to be understood to remove only propagating transitory signals per se from the claim scope and does not relinquish rights to all standard computer- readable media that are not only propagating transitory signals per se. Stated another way, the meaning of the term "non-transitory computer-readable medium" and "non-transitory computer- readable storage medium" should be construed to exclude only those types of transitory computer-readable media which were found in In Re Nuijten to fall outside the scope of patentable subject matter under 35 U.S.C. § 101.

The terms and expressions which have been employed herein are used as terms of description and not of limitation, and there is no intention, in the use of such terms and expressions, of excluding any equivalents of the features shown and described (or portions thereof), and it is recognized that various modifications are possible within the scope of the claims. Accordingly, the claims are intended to cover all such equivalents. Incorporation by Reference

References and citations to other documents, such as patents, patent applications, patent publications, journals, books, papers, web contents, have been made throughout this disclosure. All such documents are hereby incorporated herein by reference in their entirety for all purposes.

Equivalents

Various modifications of the invention and many further embodiments thereof, in addition to those shown and described herein, will become apparent to those skilled in the art from the full contents of this document, including references to the scientific and patent literature cited herein. The subject matter herein contains important information, exemplification and guidance that can be adapted to the practice of this invention in its various embodiments and equivalents thereof.

Claims

1. A system for providing mental health management services, the system comprising a processor configured to: receive, from a mobile device associated with a user, passive data captured from one or more devices associated with the mobile device; assess a mental health status of the user based entirely on analysis of the passive data relative to a set of baseline data to identify one or more changes in user behavior indicative of one or more changes in the mental health status of the user; and generate and provide actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user.

2. The system of claim 1, wherein the processor is provided locally on the mobile device or provided on a server remote from the mobile device.

3. The system of claim 1, wherein the processor is configured to provide the actionable feedback to at least one of the user and one or more secondary users associated with the user.

4. The system of claim 3, wherein actionable feedback provided to the user comprises a suggestion of one or more actions to be carried out by the user aimed at addressing the one or more identified changes and having a positive impact on the user's mental health.

5. The system of claim 3, wherein actionable feedback provided to the one or more secondary users associated with the user comprises a suggestion of one or more resources and/or intervening actions effective in addressing the one or more identified changes and having a positive impact on the user's mental health.

6. The system of claim 5, wherein the one or more secondary users are associated with an employer of the user, a healthcare provider of the user, and/or a family member of the user.

7. The system of claim 1, wherein the processor is configured to generate and provide the actionable feedback via a cloud-based service, wherein the cloud-based service comprises a software as a service (SaaS) model.

8. The system of claim 1, wherein the set of baseline data represents a baseline behavior pattern for a user in a given environment.

9. The system of claim 8, wherein the mental health status of the user is assessed over a period of time to identify one or more trending changes in the user's behavior relative to the baseline behavior pattern in the given environment.

10. The system of claim 8, wherein the given environment comprises a place of work or employment.

11. A method for providing mental health management services, the method comprising: receiving, from a mobile device associated with a user, passive data captured by one or more sensors of the mobile device; assessing a mental health status of the user based entirely on analysis of the passive data relative to a set of baseline data to identify one or more changes in user behavior indicative of one or more changes in the mental health status of the user; and generating and providing actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user.

12. The method of claim 11, wherein the actionable feedback is provided to at least one of the user and one or more secondary users associated with the user.

13. The method of claim 12, wherein actionable feedback provided to the user comprises a suggestion of one or more actions to be carried out by the user aimed at addressing the one or more identified changes and having a positive impact on the user's mental health.

14. The method of claim 12, wherein actionable feedback provided to the one or more secondary users associated with the user comprises a suggestion of one or more resources and/or intervening actions effective in addressing the one or more identified changes and having a positive impact on the user's mental health.

15. The method of claim 14, wherein the one or more secondary users are associated with an employer of the user, a healthcare provider of the user, and/or a family member of the user.

16. The method of claim 11, wherein the actionable feedback is provided via a cloud-based service.

17. The method of claim 16, wherein the cloud-based service comprises a software as a service (SaaS) model.

18. The method of claim 11, wherein the set of baseline data represents a baseline behavior pattern for a user in a given environment.

19. The method of claim 18, wherein the mental health status of the user is assessed over a period of time to identify one or more trending changes in the user's behavior relative to the baseline behavior pattern in the given environment.

20. The method of claim 18, wherein the given environment comprises a place of work or employment.

21. A system for providing mental health management services, the system comprising a processor configured to: receive, from a mobile device associated with a user, passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; assess a mental health status of the user based entirely on a comparison of the passive data with a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the mental health status of the user; and in the event that one or more changes to the user's behavior are identified, generate and provide actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user.

22. The system of claim 21, wherein the processor is configured to provide the actionable feedback to at least one of the user and one or more secondary users associated with the user.

23. The system of claim 22, wherein actionable feedback provided to the user comprises a suggestion of one or more actions to be carried out by the user aimed at addressing the one or more identified changes and having a positive impact on the user's mental health and wherein actionable feedback provided to the one or more secondary users associated with the user comprises a suggestion of one or more resources and/or intervening actions effective in addressing the one or more identified changes and having a positive impact on the user's mental health.

24. The system of claim 22, wherein the one or more secondary users are associated with an employer of the user, a healthcare provider of the user, and/or a family member of the user.

25. The system of claim 21, wherein the passive data comprises data captured by at least one of a motion sensor and a global positioning system (GPS) sensor.

26. The system of claim 25, wherein the motion sensor comprises at least one of an accelerometer, one or more gyroscopes, and a magnetometer for capturing motion of the mobile device and/or a direction of travel or orientation of the mobile device to thereby provide corresponding motion of the user.

27. The system of claim 21, wherein the passive data associated with social behavior comprises data associated with a log of computing devices connected to, or otherwise associated with, a wireless network associated with the user's mobile device to thereby identify computing devices associated with individuals having a personal relationship with the user.

28. The system of claim 21, wherein the set of baseline data represents a baseline behavior pattern for a user in the given environment, the baseline data associated with at least a user's mobility behavior, geolocation behavior, and social behavior in the given environment over a period of time.

29. The system of claim 28, wherein the mental health status of the user is assessed over a period of time to identify one or more trending changes in the user's behavior relative to the baseline behavior pattern in the given environment.

30. The system of claim 28, wherein the given environment comprises a place of work or employment.

31. A method for providing mental health management services, the method comprising: receiving, from a mobile device associated with a user, passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; assessing a mental health status of the user based entirely on a comparison of the passive data with a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the mental health status of the user; and in the event that one or more changes to the user's behavior are identified, generating and providing actionable feedback to address one or more identified changes in user behavior and to improve the mental health status of the user.

32. The method of claim 31, wherein the actionable feedback is provided to at least one of the user and one or more secondary users associated with the user.

33. The method of claim 32, wherein actionable feedback provided to the user comprises a suggestion of one or more actions to be carried out by the user aimed at addressing the one or more identified changes and having a positive impact on the user's mental health and wherein actionable feedback provided to the one or more secondary users associated with the user comprises a suggestion of one or more resources and/or intervening actions effective in addressing the one or more identified changes and having a positive impact on the user's mental health.

34. The method of claim 32, wherein the one or more secondary users are associated with an employer of the user, a healthcare provider of the user, and/or a family member of the user.

35. The method of claim 31, wherein the passive data comprises data captured by at least one of a motion sensor and a global positioning system (GPS) sensor.

36. The method of claim 35, wherein the motion sensor comprises at least one of an accelerometer, one or more gyroscopes, and a magnetometer for capturing motion of the mobile device and/or a direction of travel or orientation of the mobile device to thereby provide corresponding motion of the user.

37. The method of claim 31, wherein the passive data associated with social behavior comprises data associated with a log of computing devices connected to, or otherwise associated with, a wireless network associated with the user's mobile device to thereby identify computing devices associated with individuals having a personal relationship with the user.

38. The method of claim 31, wherein the set of baseline data represents a baseline behavior pattern for a user in the given environment, the baseline data associated with at least a user's mobility behavior, geolocation behavior, and social behavior in the given environment over a period of time.

39. The method of claim 38, wherein the mental health status of the user is assessed over a period of time to identify one or more trending changes in the user's behavior relative to the baseline behavior pattern in the given environment.

40. The method of claim 38, wherein the given environment comprises a place of work or employment.

41. A system for assessing wellness of a user, the system comprising: a processor; and memory coupled to the processor for storing instructions executable by the processor to cause the processor to: receive passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; and provide a wellness assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a wellness status is known with respect to associated user behavior.

42. The system of claim 41, wherein the wellness assessment comprises a mental health and/or physical health context.

43. The system of claim 42, wherein the wellness assessment comprises an assessment of a mental health status of the user.

44. The system of claim 42, wherein the wellness assessment is provided to at least one of the user and one or more secondary users associated with the user.

45. The system of claim 44, wherein the processor is configured to generate and provide actionable feedback to at least one of the user and the one or more secondary users associated with the user, wherein the actionable feedback comprises suggested actions and/or resources having a positive impact on at least one of the user's mental health or physical health to thereby improve the wellness status of the user.

46. The system of claim 44, wherein the one or more secondary users are associated with an employer of the user, a healthcare provider of the user, and/or a family member of the user.

47. The system of claim 41, wherein the algorithm is run on data resulting from a comparison between the passive data and a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the wellness status of the user.

48. The system of claim 47, wherein the set of baseline data represents a baseline behavior pattern for a user in the given environment, the baseline data associated with at least a user's mobility behavior, geolocation behavior, and social behavior in the given environment over a period of time.

49. The system of claim 41, wherein the processor is configured to provide the wellness assessment via a cloud-based service, wherein the cloud-based service comprises a software as a service (SaaS) model.

50. The system of claim 41, wherein the database of constructed profiles of plurality of users is obtained from academic research.

51. A method for assessing wellness of a user, the method comprising: receiving passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; and providing a wellness assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a wellness status is known with respect to associated user behavior.

52. The method of claim 51, wherein the wellness assessment comprises a mental health and/or physical health context.

53. The method of claim 52, wherein the wellness assessment comprises an assessment of mental health status of the user.

54. The method of claim 52, wherein the wellness assessment is provided to at least one of the user and one or more secondary users associated with the user.

55. The method of claim 54, wherein the method further comprises generating and providing actionable feedback to at least one of the user and the one or more secondary users associated with the user, wherein the actionable feedback comprises suggested actions and/or resources having a positive impact on at least one of the user's mental health or physical health to thereby improve the wellness status of the user.

56. The method of claim 54, wherein the one or more secondary users are associated with an employer of the user, a healthcare provider of the user, and/or a family member of the user.

57. The method of claim 51, wherein the algorithm is run on data resulting from a comparison between the passive data and a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the wellness status of the user.

58. The method of claim 57, wherein the set of baseline data represents a baseline behavior pattern for a user in the given environment, the baseline data associated with at least a user's mobility behavior, geolocation behavior, and social behavior in the given environment over a period of time.

59. The method of claim 51, wherein the processor is configured to provide the wellness assessment via a cloud-based service, wherein the cloud-based service comprises a software as a service (SaaS) model.

60. The method of claim 51, wherein the database of constructed profiles of plurality of users is obtained from academic research.

61. A system for providing a psychiatric assessment of a user, the system comprising: a processor; and memory coupled to the processor for storing instructions executable by the processor to cause the processor to: receive passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; and provide a psychiatric assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior.

62. The system of claim 61, wherein the psychiatric assessment comprises a mental health diagnosis.

63. The system of claim 62, wherein the mental health diagnosis comprises a determination of a mental health condition or mental illness associated with the user's mental health status.

64. The system of claim 63, wherein the mental health condition or mental illness includes at least one of anxiety, depression, chronic stress, and post-traumatic stress disorder (PTSD).

65. The system of claim 62, wherein the psychiatric assessment is provided to at least one of the user and one or more secondary users associated with the user.

66. The system of claim 65, wherein the processor is configured to generate and provide actionable feedback to at least one of the user and the one or more secondary users associated with the user, wherein the actionable feedback comprises suggested actions and/or resources having a positive impact on at least one of the user's mental health to thereby improve the mental health status of the user.

67. The system of claim 65, wherein the one or more secondary users are associated with an employer of the user, a healthcare provider of the user, and/or a family member of the user.

68. The system of claim 61, wherein the algorithm is run on data resulting from a comparison between the passive data and a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the mental health status of the user.

69. The system of claim 68, wherein the set of baseline data represents a baseline behavior pattern for a user in the given environment, the baseline data associated with at least a user's mobility behavior, geolocation behavior, and social behavior in the given environment over a period of time.

70. The system of claim 61, wherein the database of constructed profiles of plurality of users is obtained from academic research.

71. A method for providing a psychiatric assessment of a user, the method comprising: receiving passive data associated with at least one of user mobility, user geolocation, and user social behavior relative to a given environment; and providing a psychiatric assessment of the user as a result of running an algorithm on the passive data, the algorithm having been trained on a reference set of data from a database of constructed profiles of a plurality of users for whom a mental health status is known with respect to associated user behavior.

72. The method of claim 71, wherein the psychiatric assessment comprises a mental health diagnosis.

73. The method of claim 72, wherein the mental health diagnosis comprises a determination of a mental health condition or mental illness associated with the user's mental health status.

74. The method of claim 73, wherein the mental health condition or mental illness includes at least one of anxiety, depression, chronic stress, and/or post-traumatic stress disorder.

75. The method of claim 72, wherein the psychiatric assessment is provided to at least one of the user and one or more secondary users associated with the user.

76. The method of claim 75, wherein the method further comprises generating and providing actionable feedback to at least one of the user and the one or more secondary users associated with the user, wherein the actionable feedback comprises suggested actions and/or resources having a positive impact on at least one of the user's mental health to thereby improve the mental health status of the user.

77. The method of claim 75, wherein the one or more secondary users are associated with an employer of the user, a healthcare provider of the user, and/or a family member of the user.

78. The method of claim 71, wherein the algorithm is run on data resulting from a comparison between the passive data and a set of baseline data to thereby identify one or more changes in the user's behavior indicative of a negative change in the mental health status of the user.

79. The method of claim 78, wherein the set of baseline data represents a baseline behavior pattern for a user in the given environment, the baseline data associated with at least a user's mobility behavior, geolocation behavior, and social behavior in the given environment over a period of time.

80. The method of claim 71, wherein the database of constructed profiles of plurality of users is obtained from academic research.