WO2024249474A2 - System and method for integrated lifestyle medicine - Google Patents

Abstract

A system for integrated lifestyle medicine tracking and digital therapy comprises a central computing system comprises a device interface subsystem, a user data interface subsystem, a data visualization engine, and a set of instructions, which when executed by the processor, perform steps comprising collecting a first data stream from the at least one sensor via the device interface subsystem, collecting a first user-reported data stream from the at least one user via the user data interface subsystem, calculating a probability of at least one lifestyle risk factor from the first data stream and the first user-reported data stream, and presenting the user with at least one intervention to mitigate the at least one lifestyle risk factor. A method for integrated lifestyle medicine tracking and digital therapy is also disclosed.

Description

SYSTEM AND METHOD FOR INTEGRATED LIFESTYLE MEDICINE

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to US Provisional Patent Application No. 63/504,920, filed on May 30, 2023, incorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under DK129441, AA028886, DA009241, DK045735, AA026021, AA020000 awarded by the National Institutes of Health. The government has certain rights in the invention.

BACKGROUND OF THE INVENTION

[0003] Chronic physical disease and mental health concerns are common and costly. 6 in 10 U.S. adults have a chronic physical disease and 1 in 5 U.S. adults have a mental health concern, which together contribute to the greater than $4 trillion in annual healthcare costs. Examples of chronic physical diseases include, but are not limited to, alcohol use disorder, other substance use disorders, tobacco use and related conditions, heart disease, cognitive disease, chronic obstructive pulmonary disease (COPD), obesity, stroke, lung cancer, colorectal cancer, other cancers, depression, anxiety, type 2 diabetes, arthritis, osteoporosis, asthma, liver failure, or chronic kidney disease. Typical courses of treatment for chronic diseases involve pharmaceutical and/or surgical treatments, including but not limited to chemotherapy, immunotherapy, thrombectomy, partial or full surgical resection, dialysis, organ transplantation, acetylcysteine, antihypertensives, antidiabetics, antilipidemics, weight loss drugs, antidepressants, benzodiazepines, anti-inflammatories, steroids, erythropoietin, FDA-approved pharmacotherapies for alcohol, tobacco, and other substance use disorders. [0004] Many chronic physical diseases are largely preventable. An estimated 40% of chronic disease risk, outcome, and cost is attributable to key modifiable lifestyle factors which co-occur, cluster, and interact. Most adults have two or more lifestyle risk factors. Thus, integrated approaches are needed for health monitoring and management. Lifestyle Medicine applies evidence-based lifestyle therapeutic interventions for improving core lifestyle factors in order to prevent, manage, or even reverse chronic physical disease. Lifestyle Medicine recognizes mental health as a core pillar of care; improving lifestyle factors can improve mental health.

[0005] At present, there are no digital solutions that address all lifestyle factors of Lifestyle Medicine. Available solutions include a thicket of separate digital platforms for each factor, and/or biosensor monitoring tools that track some but not all factors with little integration and intervention support. The digital information is siloed from the biobehavioral monitoring and feedback of the resulting behaviors, missing an opportunity to close this loop. Current biosensors that address physical activity and sleep, for example, require separate mobile applications for each device, target a single audience that is largely advantaged and healthy, and emphasize data volume and organization - “all your all health data all in one place.” But this strategy places substantial burden on users to process large volumes of health data and make implicit connections about their lifestyle factors and biometric outcomes. Health feedback is largely just summaries of individual factors/behaviors daily and over time. Thus, there is a need in the art for a digital platform for Lifestyle Medicine monitoring, analysis, and intervention that is able to better leverage biosensor technology for chronic disease prevention and management.

SUMMARY OF THE INVENTION

[0006] In one aspect, a system for integrated lifestyle medicine tracking and digital therapy comprises a central computing system comprising a processor and a non-transitory computer- readable medium, a data storage communicatively connected to the central computing system and comprising at least one relational database, a device interface subsystem configured to collect data from at least one sensor, a user data interface subsystem configured to collect user- reported data from at least one user, a data visualization engine configured to display raw or processed data from the at least one sensor, a set of instructions stored on the non-transitory computer-readable medium, which when executed by the processor, perform steps comprising collecting a first data stream from the at least one sensor via the device interface subsystem, collecting a first user-reported data stream from the at least one user via the user data interface subsystem, calculating a probability of at least one lifestyle risk factor from the first data stream and the first user-reported data stream, and presenting the user with at least one intervention to mitigate the at least one lifestyle risk factor.

[0007] In one embodiment, the step of calculating the probability of the at least one lifestyle risk factor includes the steps of providing the first data stream and the first user-reported data stream to a machine learning model, and inferring the probability of the at least one lifestyle risk factor from the machine learning model. In one embodiment, the first data stream comprises biosensor data selected from cardiovascular data, respiration data, sleep data, activity data, diet data, nutrition data, temperature data, stress data, menstrual data, or sexual health data. In one embodiment, the first user-reported data stream is selected from alcohol consumption, tobacco consumption, substance use, diet, physical activity, sleep quality, social connection, mood, stress, or mental health data.

[0008] In one embodiment, the instructions further comprise prompting the user to provide the first user-reported data stream. In one embodiment, the device interface subsystem comprises at least one application programming interface (API). In one embodiment, the device interface subsystem comprises at least one function of a system development kit (SDK). In one embodiment, the at least one sensor is selected from a location sensor, an accelerometer, a gyroscope, an optical sensor, a temperature sensor, or a skin conductance sensor.

[0009] In one aspect, a method for integrated lifestyle medicine tracking and digital therapy comprises providing a mobile computing device and at least one sensor, transmitting a first data stream from the at least one sensor to a central computing system, reporting, via the mobile computing device, a first user-reported data stream to the central computing system, and receiving, from the central computing system, at least one intervention to mitigate at least one lifestyle risk factor having a probability computed from the first data stream and the first user- reported data stream.

[0010] In one embodiment, the first data stream comprises biosensor data selected from cardiovascular data, respiration data, sleep data, activity data, diet data, nutrition data, temperature data, stress data, menstrual data, or sexual health data. In one embodiment, the first user-reported data stream is selected from alcohol consumption, tobacco consumption, substance use, diet, physical activity, sleep quality, social connection, mood, stress, or mental health data. In one embodiment, the first user-reported data stream comprises a diary.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The foregoing purposes and features, as well as other purposes and features, will become apparent with reference to the description and accompanying figures below, which are included to provide an understanding of the invention and constitute a part of the specification, in which like numerals represent like elements, and in which:

Fig. 1 is an exemplary computing device.

Fig. 2 is an overall system diagram of a platform disclosed herein.

Fig. 3 is a diagram of various graphical user interface (GUI) and data visualization elements which may be used in connection with embodiments of the disclosed platform.

Fig. 4 is an exemplary GUI for user-reporting of alcohol consumption.

Fig. 5A - Fig. 5E, Fig. 6A - Fig. 6E, Fig. 7A - Fig. 7E, and Fig. 8A - Fig. 8E are exemplary feedback reports generated by the disclosed platform.

DETAILED DESCRIPTION

[0012] It is to be understood that the figures and descriptions of the present invention have been simplified to illustrate elements that are relevant for a clear understanding of the present invention, while eliminating, for the purpose of clarity, many other elements found in related systems and methods. Those of ordinary skill in the art may recognize that other elements and/or steps are desirable and/or required in implementing the present invention. However, because such elements and steps are well known in the art, and because they do not facilitate a better understanding of the present invention, a discussion of such elements and steps is not provided herein. The disclosure herein is directed to all such variations and modifications to such elements and methods known to those skilled in the art.

[0013] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, exemplary methods and materials are described.

[0014] As used herein, each of the following terms has the meaning associated with it in this section.

[0015] The articles “a” and “an” are used herein to refer to one or to more than one (z.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.

[0016] “About” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, and ±0.1% from the specified value, as such variations are appropriate.

[0017] Throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, 6 and any whole and partial increments therebetween. This applies regardless of the breadth of the range.

[0018] In some aspects of the present invention, software executing the instructions provided herein may be stored on a non-transitory computer-readable medium, wherein the software performs some or all of the steps of the present invention when executed on a processor. [0019] Aspects of the invention relate to algorithms executed in computer software. Though certain embodiments may be described as written in particular programming languages, or executed on particular operating systems or computing platforms, it is understood that the system and method of the present invention is not limited to any particular computing language, platform, or combination thereof. Software executing the algorithms described herein may be written in any programming language known in the art, compiled or interpreted, including but not limited to C, C++, C#, Objective-C, Java, JavaScript, MATLAB, Python, PHP, Perl, R, Ruby, or Visual Basic. It is further understood that elements of the present invention may be executed on any acceptable computing platform, including but not limited to a server, a cloud instance, a workstation, a thin client, a mobile device, an embedded microcontroller, a television, or any other suitable computing device known in the art.

[0020] Parts of this invention are described as software running on a computing device. Though software described herein may be disclosed as operating on one particular computing device (e.g. a dedicated server or a workstation), it is understood in the art that software is intrinsically portable and that most software running on a dedicated server may also be run, for the purposes of the present invention, on any of a wide range of devices including desktop or mobile devices, laptops, tablets, smartphones, watches, virtual reality headsets, augmented reality headsets, wearable electronics or other wireless digital/cellular phones, televisions, cloud instances, embedded microcontrollers, thin client devices, or any other suitable computing device known in the art.

[0021] Similarly, parts of this invention are described as communicating over a variety of wireless or wired computer networks. For the purposes of this invention, the words “network”, “networked”, and “networking” are understood to encompass wired Ethernet, fiber optic connections, wireless connections including any of the various 802.11 standards, cellular WAN infrastructures such as 3G, 4G/LTE, or 5G networks, Bluetooth®, Bluetooth® Low Energy (BLE) or Zigbee® communication links, or any other method by which one electronic device is capable of communicating with another. In some embodiments, elements of the networked portion of the invention may be implemented over a Virtual Private Network (VPN). [0022] Fig. 1 and the following discussion are intended to provide a brief, general description of a suitable computing environment in which the invention may be implemented. While the invention is described above in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a computer, those skilled in the art will recognize that the invention may also be implemented in combination with other program modules.

[0023] Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0024] Fig. 1 depicts an illustrative computer architecture for a computer 100 for practicing the various embodiments of the invention. The computer architecture shown in Fig. 1 illustrates a conventional personal computer, including a central processing unit 150 (“CPU”), a system memoryl05, including a random access memory 110 (“RAM”) and a read-only memory (“ROM”) 115, and a system bus 135 that couples the system memory 105 to the CPU 150. A basic input/output system containing the basic routines that help to transfer information between elements within the computer, such as during startup, is stored in the ROM 115. The computer 100 further includes a storage device 120 for storing an operating system 125, application/program 130, and data.

[0025] The storage device 120 is connected to the CPU 150 through a storage controller (not shown) connected to the bus 135. The storage device 120 and its associated computer-readable media provide non-volatile storage for the computer 100. Although the description of computer- readable media contained herein refers to a storage device, such as a hard disk or CD-ROM drive, it should be appreciated by those skilled in the art that computer-readable media can be any available media that can be accessed by the computer 100.

[0026] By way of example, and not to be limiting, computer-readable media may comprise computer storage media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data.

Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.

[0027] According to various embodiments of the invention, the computer 100 may operate in a networked environment using logical connections to remote computers through a network 140, such as TCP/IP network such as the Internet or an intranet. The computer 100 may connect to the network 140 through a network interface unit 145 connected to the bus 135. It should be appreciated that the network interface unit 145 may also be utilized to connect to other types of networks and remote computer systems.

[0028] The computer 100 may also include an input/output controller 155 for receiving and processing input from a number of input/output devices 160, including a keyboard, a mouse, a touchscreen, a camera, a microphone, a controller, a joystick, fitness videogaming device, other gaming device, or other type of input device. Similarly, the input/output controller 155 may provide output to a display screen, a printer, a speaker, or other type of output device. The computer 100 can connect to the input/output device 160 via a wired connection including, but not limited to, fiber optic, Ethernet, or copper wire or wireless means including, but not limited to, Wi-Fi, Bluetooth, Near-Field Communication (NFC), infrared, or other suitable wired or wireless connections.

[0029] As mentioned briefly above, a number of program modules and data files may be stored in the storage device 120 and/or RAM 110 of the computer 100, including an operating system 125 suitable for controlling the operation of a networked computer. The storage device 120 and RAM 1 10 may also store one or more applications/programs 130. In particular, the storage device 120 and RAM 110 may store an application/program 130 for providing a variety of functionalities to a user. For instance, the application/program 130 may comprise many types of programs such as a word processing application, a spreadsheet application, a desktop publishing application, a database application, a gaming application, internet browsing application, electronic mail application, messaging application, and the like. According to an embodiment of the present invention, the application/program 130 comprises a multiple functionality software application for providing word processing functionality, slide presentation functionality, spreadsheet functionality, database functionality and the like.

[0030] The computer 100 in some embodiments can include a variety of sensors 165 for monitoring the environment surrounding and the environment internal to the computer 100. These sensors 165 can include a Global Positioning System (GPS) sensor, a photosensitive sensor, a gyroscope, a magnetometer, thermometer, a proximity sensor, an accelerometer, a microphone, biometric sensor, barometer, humidity sensor, radiation sensor, or any other suitable sensor.

[0031] In various embodiments, disclosed herein is an integrated, holistic platform that tracks and provides evidence-based tools for all lifestyle medicine factors together in a single digital framework with biosensor integration, comprising for example one mobile application across devices. The disclosed platform provides integrated feedback about lifestyle factors & links together, which provides an advantage over existing implementations which silo data into separate servers and in differing formats.

[0032] Additionally, the disclosed platform provides focused health knowledge and insights based on the gathered data, which goes beyond simply presenting a large volume of health data. In doing so, the disclosed platform helps users better connect their lifestyle factors & biometrics, identify goals, and track progress.

Data Gathering

[0033] One aspect of the disclosed platform comprises one or more subsystems for gathering data from a user. The data may be gathered for example from a smartphone app, smartwatch app, virtual reality app, augmented reality app, or other software application, for example a web portal, a standalone computer software application, or any other suitable data gathering method. In various embodiments, the data gathering subsystems comprise at least one device interface subsystem configured to collect data from at least one sensor, and/or at least one user data interface subsystem configured to collect user-reported data from at least one user.

[0034] Gathered data may be transmitted in raw, processed, and/or encrypted form over a variety of computer networks to a remote server platform or cloud computing infrastructure for storage and further processing. In one example, data is gathered via a mobile application running on a smartphone or other portable computing device, for example a tablet or wearable computing device. The mobile application may be further configured to provide feedback to the user, for example health feedback and tailored health advice computationally determined via the collected data. The mobile application may in some embodiments further present the user with links to evidence-based therapeutic tips and/or tools for lifestyle factors.

[0035] Some examples of data visualizations and processed representations of the data are shown in Fig. 3. The depicted visualizations include for example summary tables, for example summary table 301 showing a count of heavy drinking episodes and a peak blood BAC level. Graph 302 shows a representation of BAC over time with two datasets overlaid (measurements from a transdermal BAC sensor in blue, and entries from a diary overlaid in purple). The user’s sleep period, which may for example have been gathered by any of the sleep sensors contemplated herein, is shown along the X axis. Exemplary raw data from a sleep biosensor is shown in graphs 303, which derives sleep data and sleep quality data from activity data gathered from a wearable biosensor. The sleep biosensor data is overlaid in the graphs 303 with BAC levels estimated from a user diary.

[0036] Depicted data may further include potential sleep and alcohol associations, for example as shown in summary table 305, or a summary table showing various correlated data as shown in summary table 306 - which shows that sleep quality score and sleep duration are improved on non-drinking nights versus drinking nights.

[0037] In some embodiments, the gathered data about a user comprises user-reported data, collected for example via daily diaries and/or context-based prompting. Examples of user- reported data include, but are not limited to, alcohol consumption, for example daily or weekly, which may include for example a number of standard drinks, guided tabulation of standard drinks based on examples, timing of those drinks, an estimated blood alcohol content (BAC), drinking context, and/or perceived subjective effects of drinking. One example of a GUI which may be used to gather user-reported alcohol consumption data is shown in Fig. 4. In some embodiments, alcohol consumption may be quantitatively measured, for example via a wirelessly connected breathalyzer or other device for calculating an estimated BAC based on one or more measured physiological or hand motion factors. In some embodiments, a user may be prompted to provide feedback on alcohol consumption based on a location sensor integrated into the mobile computing device, for example a GPS sensor, which may prompt the user to provide feedback about alcohol consumption when the location sensor in the mobile computing device indicates that the mobile computing device (and the user) are in a bar, restaurant, or other location where the user is known to consume alcohol. In some embodiments, a prompt may be presented to the user some fixed or variable time after the user has left the bar, restaurant, or location where the user is known to consume alcohol. In some embodiments, based on potential detection that a user may have consumed alcohol or be at risk of consuming alcohol, a prompt may be presented to the user asking for confirmation of drinking and/or offer proactive support in the moment via various options (e.g., coach support, evidence-based digital tools to manage/avoid drinking).

[0038] Another example of user-reported data may comprise data related to tobacco use. Such data may include, but is not limited to, product type, flavor, nicotine strength, timing, estimated occasions or number of tobacco products consumed, use context, and/or perceived subjective effects. In some embodiments, data may be collected via a diary or via prompting in the mobile application. In some embodiments, user-reported data may be supplemented by gathered data, for example where users allow the mobile application to monitor location and/or financial activity of the user, a user may be prompted to comment on tobacco use when the user is detected to be at a location where the user is known to purchase tobacco products, for example a supermarket, convenience store, or gas station. In some embodiments, where a credit card charge for an amount known to be the price of a certain tobacco product, for example a pack of cigarettes or a quantity of chewing tobacco, is detected, such a purchase may be used to present the user with a prompt asking the user about their past or planned tobacco use. In some embodiments, data may comprise data collected from a third-party biosensor, for example accelerometer data from a wearable (e.g. a smart watch or smart ring) able to detect a user’s hand motion from smoking. Third party biosensor data may further comprise biochemical verification systems that measure, for example, breath carbon monoxide level or saliva cotinine. In some embodiments, user-reported smoking data may comprise barcode scans of tobacco products or photography of tobacco products with image/logo recognition.

[0039] Similarly, the system disclosed herein may comprise user-reported data about other substance use, for example illicit drugs. Users may be prompted to provide data such as product type, timing of use, estimated occasions, quantity consumed, use context, and/or perceived subjective effects. In some embodiments, a software platform as disclosed herein may be anonymized and the relevant data stored and transmitted in an encrypted and anonymized way, for example to assure users that they can report potentially illegal activity to the mobile application without fear of the information being used to alert local authorities. In some embodiments, a platform as disclosed herein is compliant with the Health Insurance Portability and Accountability Act (HIPAA) in its handling of confidential patient data.

[0040] In some embodiments, the user reported data contemplated herein may comprise data about diet, including meal timing, consumed foods, types of foods consumed, amount of water or other beverages consumed, etc. In some embodiments, a user may report dietary intake via photography with automated image recognition, scanning bar codes, selection from drop-down menus from nutrition databases, free text, survey instruments, or the like. Such user-reported data may be supplemented for example by data collected from third party data sources, for example meal delivery service receipts, grocery recei pts/ custom er loyalty programs, etc. In some embodiments, a mobile application as disclosed herein may be configured to collect data from a third-party calorie or diet tracking application, for example to prevent a user from having to input data multiple times into multiple diaries.

[0041] In some embodiments, user reported data contemplated herein may comprise data about physical activity, for example the timing, duration, and type of activity. User-reported physical activity data may be supplemented for example by data measured from one or more sensors, for example fitness tracking devices, Bluetooth heart rate monitors, fitness watches, or other suitable devices which may be communicatively connected to the mobile computing device. The mobile application may be configured in some embodiments to provide the user with an estimated physical activity data structure calculated after a particular workout based on one or more communicatively connected sensors, and then prompt the user to verify or correct the physical activity data structure. In one example, a mobile application may calculate, based on location data, accelerometer data, heart rate data, and gyroscope data of a mobile computing device, that a user ran a particular distance, for example 0.8 km and burned a particular number of calories based on the distance. The user may in some embodiments be presented with that estimated distance via a graphical user interface, and may then correct the distance if needed. Such user- reported data may be used in such embodiments to fine-tune computationally gathered data. Data from multiple devices gathering data related to the same activity may be integrated to improve accuracy, for example where multiple biosensors have divergent validity standards from benchtop testing. In such situations, data gathered from different sensors may be assigned different mathematical weights according to the degree of validity. Certain biosensor data may further be supplemented by correction factors released via software updates, or made available to researchers, in order to improve the accuracy of the measured data post collection.

[0042] In some embodiments, a platform as disclosed herein may comprise triangulation of parameters related to different behaviors. For example, sleep and/or alcohol consumption data from one day may be used to supplement, validate, or improve the accuracy of activity data from the following day.

[0043] In some embodiments, the user-reported data may comprise data relating to sleep quality. Such data may comprise, for example, self-ratings of how well the user slept the night before, self-reporting of naps, self-reporting of perceived sleepiness at different parts of the day, selfreporting of energy at waking, etc. In some embodiments, sleep quality data may comprise administration of one or more simple cognitive tests presented via a mobile application in order to gauge alertness. In some embodiments, user-reported sleep data may be supplemented with data gathered from one or more sensors, for example motion sensors on a smart watch worn during sleep, microphones to monitor breathing and/or snoring, etc. [0044] In some embodiments, user-reported data may comprise data relating to social interactions. Such data may include a periodic log of the number of people the user interacted with, types of interactions (e.g. in-person, telephonic, virtual, social media) and/or participation in social events. In some embodiments, some social interaction data may be gathered from third party data sources, for example logs of video chat conversations, mobile phone call logs, text message logs, location data, or by collecting data from one or more social media platforms about the user’s engagement with social media and/or users of social media. Particular data related to mobile phone calls may comprise number of outgoing calls, total outgoing call duration, total number of unique calls initiated by the user, total number of calls received by the user, total amount of time spent on calls received by the user, total number of unique calls received by the user, total number of times a call is received or sent to a unique person on a day without response, mean time before initiating a call after receiving a call, or any other parameters. Particular data related to text messages includes, but is not limited to, number of outgoing texts, total outgoing text length, total number of unique destinations to which the user sent texts, number or length of incoming texts, total unique individuals who sent text messages to the user, total number of times a text message is received or sent to a unique person on a day without response, and mean time before sending a text message after a text message is received.

[0045] In some embodiments, user-collected data may further comprise mental and/or emotional well-being data, for example self-ratings of mood, stress level, and/or PHQ-2 self-ratings for depression and/or anxiety.

[0046] In some embodiments, the disclosed platform may comprise connectivity to an outside database, for example one or more electronic medical record (EMR) databases or database entries containing clinician-provided data about a user.

[0047] As discussed above, in some embodiments, a mobile application will have access, which in some situations may need to be granted by the user, to data from one or more third-party biosensors (e.g., Fitbit, Garmin, Apple Watch) and/or third-party health data sources connected to their mobile computing device (e.g., Apple Health, Google Health). The availability of data for a given user will vary by whether they wear biosensors and/or connect their phone to other health data sources, how often they sync their device in the device app or connect to the Internet, and whether a device requires user input to track an outcome (e.g., recording a workout). Likewise, the amount of data may also vary by device type/data source with some permitting collection of intraday data (i.e., multiple data recorded throughout the day), some permitting collection of daily data (i.e., single value recorded for the day), and others only permitting collection if user recorded. In some embodiments, third party biosensors may be sensors integrated into the mobile computing device which is executing the mobile application of the system of the disclosed invention.

[0048] In some embodiments, third party biosensor data may comprise cardiovascular data, for example heart rate, resting heart rate, sleeping heart rate, walking heart rate, active heart rate, blood pressure, heart rate variability (HRV), oxygen saturation (SpO2), maximal oxygen consumption (V02max), heart rate zones and duration, atrial fibrillation detection, arrythmia detection, time elapsed between successive R waves (RR interval), etc. Cardiovascular data may be collected via any suitable biosensor, for example a pulse oximeter, chest strap, smart watch, smart ring, or adhesive electrocardiographic patch. Cardiovascular data may in some embodiments be collected via a sensor integrated into the mobile computing device on which the mobile application is executing, and/or may be collected via one or more remote sensors communicatively connected to the mobile computing device via a wired or wireless connection.

[0049] In some embodiments, third party biosensor data may comprise respiration data, for example average respiration rate, resting respiration rate, sleep respiration rate, active respiration rate, etc. Respiration data may be collected via any suitable biosensor, for example a chest strap, smart watch, smart phone with microphone, smart ring, adhesive electroencephalographic patch, adhesive electrocardiographic patch. Respiration data may in some embodiments be collected via a sensor integrated into the mobile computing device on which the mobile application is executing, and/or may be collected via one or more remote sensors communicatively connected to the mobile computing device via a wired or wireless connection.

[0050] In some embodiments, third party biosensor data may comprise sleep data, for example sleep start time, sleep end time, overall duration, rapid-eye-movement (REM) sleep duration, different stage sleep duration, sleep efficiency, wake after sleep onset, number of waking events, duration of waking events, latency, and/or sleep consistency. Sleep data may be collected via any suitable biosensor, for example a smart watch, a smart phone, an accelerometer, a gyroscope, a microphone, smart ring, adhesive electroencephalographic patch(es). Sleep data may in some embodiments be collected via a sensor integrated into the mobile computing device on which the mobile application is executing, and/or may be collected via one or more remote sensors communicatively connected to the mobile computing device via a wired or wireless connection.

[0051] In some embodiments, third party biosensor data may comprise activity data, for example steps, calories burned, activity type, activity duration, duration of inactivity/sitting, distance moved, elevation & floors climbed, duration & distance covered by specific activities such as running, walking, biking, speed/pace. Activity data may be collected via any suitable biosensor, for example a smart watch, a smart phone, an accelerometer, a gyroscope, a global positioning system (GPS) sensor, a wireless signal strength sensor, a pedometer, or the like. Activity data may in some embodiments be collected via a sensor integrated into the mobile computing device on which the mobile application is executing, and/or may be collected via one or more remote sensors communicatively connected to the mobile computing device via a wired or wireless connection.

[0052] In some embodiments, third party biosensor data may comprise diet and/or nutrition data, for example blood glucose, blood glucose postprandial change from baseline, duration in blood glucose target range vs. outside range, sweat electrolytes, or hydration. Diet and/or nutrition data may be collected via any suitable biosensor, for example a skin conductance sensor, a blood glucose monitor, or electromyographic recording of swallowing action. Diet and/or nutrition data may in some embodiments be collected via a sensor integrated into the mobile computing device on which the mobile application is executing, and/or may be collected via one or more remote sensors communicatively connected to the mobile computing device via a wired or wireless connection.

[0053] In some embodiments, third party biosensor data may comprise temperature data, for example body temperature and/or skin temperature. Temperature data may be collected via any suitable biosensor, for example a thermistor, thermal couple, infrared camera, or the like. Temperature data may in some embodiments be collected via a sensor integrated into the mobile computing device on which the mobile application is executing, and/or may be collected via one or more remote sensors communicatively connected to the mobile computing device via a wired or wireless connection.

[0054] In some embodiments, third party biosensor data may comprise stress data, for example as measured via skin conductance. Stress data may be collected via any suitable biosensor, for example a skin conductance sensor, skin temperature sensor, heart rate photoplethysmography sensor with beat-to-beat precision to measure heart rate variability, or the like. Stress data may in some embodiments be collected via a sensor integrated into the mobile computing device on which the mobile application is executing, and/or may be collected via one or more remote sensors communicatively connected to the mobile computing device via a wired or wireless connection.

[0055] In some embodiments, third party biosensor data may comprise menstrual cycle data, for example cycle length in days, cycle start and stop, cycle phase, predicted fertility, menstrual symptoms and intensity. Menstrual cycle data may be collected via any suitable biosensor, for example a heart rate photoplethysmography sensor with or without beat-to-beat precision to measure heart rate variability, a respiratory sensor, a skin perfusion sensor, or the like. In some embodiments, menstrual cycle data provided to a mobile application may comprise data gathered from any other mobile application the user uses for gathering or tracking menstrual cycle data, for example if the menstrual cycle tracking mobile application allows other mobile applications to access said data, and if the user gives permission on their mobile computing device for the mobile application disclosed herein to retrieve the menstrual cycle tracking data from the menstrual cycle tracking mobile application. Menstrual cycle data may in some embodiments be collected via a sensor integrated into the mobile computing device on which the mobile application is executing, and/or may be collected via one or more remote sensors communicatively connected to the mobile computing device via a wired or wireless connection.

[0056] In one aspect, the disclosed system comprises one or more graphical user interfaces (GUIs) configured to provide users with integrated health feedback and advice based on the collected biosensor and/or user-supplied data. Examples of GUIs suitable for use with the disclosed system include GUIs presented by mobile applications, web portals for display in a browser window, one or more indicators or screens on a connected device, for example a smart 1 watch or activity tracker, or any other suitable GUT. Users in some embodiments receive or are presented with integrated summaries of their daily diary and third-party biosensor/other health data periodically, for example daily or approximately daily, and/or over longer time intervals (e.g., weekly, monthly, etc.) using some or all available data from the user. Data may in some embodiments be presented in overlay illustrations to help users make more explicit connections among their lifestyle factors and related biometric outcomes. Displayed data may comprise corresponding text which explains any trends and/or associations among the displayed factors and biometrics. In some embodiments, a GUI of the disclosure further comprises tailored health advice for making improvements and/or maintaining progress. A GUI may further display realtime or averaged data from one or more sensors, and/or the user’s progress relative to any goals they elect to track. In providing such health feedback to the user, the disclosed system advantageously increases users’ motivation to improve lifestyle factors and overall health.

[0057] Examples of integrated health reports are shown in Fig. 5A - Fig. 5E, Fig. 6A - Fig. 6E, Fig. 7A - Fig. 7E, and Fig. 8A - Fig. 8E. As shown in the depicted examples, integrated reports as contemplated herein may comprise tables or graphs comparing a user’s statistics with health guidelines or ideal values (see Fig. 5A, Fig. 6A, Fig. 7A, Fig. 8A) with for example color coding to distinguish between poor or good values. Reports may further include text explaining the parameters being displayed, or explanations for why certain values are associated with good health and/or fitness. Reports may further include annotations on graphs, for example as shown in Fig. 5B, to denote where one measured parameter (e.g. sleeping heart rate variability) attained a good or poor value, and how that good or poor value was correlated with a good or poor value of another measured parameter (e.g. sleep duration or blood alcohol level). A report may include assessments of data over time, including noting when certain parameters improved over time, where certain parameters regressed over time, or providing the user with parameters they should seek to improve in the coming week (see e.g. Fig. 5D). Finally, a report may include brief descriptions of certain topics, or tips to the user based on the data recorded, for example how the user might improve certain parameters for a future report. Such descriptions may provide hyperlinks to literature or outside sources of information for the user to consult (see e.g. Fig 5E).

[0058] Software disclosed herein may in some embodiments comprise a data visualization engine, executing on either or both of the central computing system 201 or the mobile computing device 206. The data visualization engine may comprise a variety of functions and steps for composing graphical displays of quantitative data, for example line graphs, bar charts, scatter plots, waterfall plots, distribution curves, time series tracings, dimensionality reductions, or the like. The data visualization engine may interface with the GUI in order to provide data visualizations to the user and/or to a third party, for example a clinician.

[0059] In some embodiments, system disclosed herein may provide a user with evidence-based tips and or tools tailored to their health data, for example stored and presented from a library of tips or tools. Examples of such tips or tools include, but are not limited to, short lessons, videos, audio descriptions, text content, and exercises. These tips and tools may include psychoeducation, consensus national guideline recommendations and brief advice, evidencebased self-management interventions and techniques, and goal-setting for lifestyle factors. In addition to these tips and tools, in some embodiments, a system disclosed herein may allow users to connect to peer support groups and or communities, or may provide virtual and/or live coaching. Coaching may be provided for example via a GUI of a mobile application of the disclosed system, a web-based GUI of the disclosed system, or via an outside communication channel.

[0060] With reference to Fig. 2, an overall architecture diagram of one embodiment of the disclosed system is shown. The depicted system comprises a central computing system 201 , which may be for example a cloud computing system, one or more dedicated servers, or a process running on one or more processors. The central computing system 201 may comprise a device interface subsystem, which may receive or be configured to receive data from one or more devices 204, 205 via an application programming interface (API) 203. The API 203 may comprise one or more third party APIs, for example an API created by a manufacturer or service provider related to one or more of the devices 204, 205. In one embodiment the API 203 standardizes data received from multiple devices 204, 205 into a standard format. The API 203 may interface with any number of devices regardless of manufacturer or software platform by way of a data standardization engine embedded in the API 203 and/or the central computing system 201. The central computing system 201 may further collect user-provided or user- provided data directly via a communication interface with a mobile computing device 206, which may for example comprise a smartphone, or via a portable or stationary computing device 207, for example a laptop or desktop computer, via for example a web interface.

[0061] The architecture of Fig. 2 may further comprise storage 202, which may comprise a transitory or non-transitory computer-readable memory. The central computing system 201 may store raw, standardized, and/or processed data collected from devices 204, 205, or from users via computing devices 206, 207, in the storage 202. The stored data may comprise raw or summarized raw data, processed data, or may comprise health feedback data that was provided to the user or which may be provided to the user at a future time. The storage 202 may in some embodiments comprise at least one relational database.

[0062] In some embodiments, the central computing system 201 and/or the API 203 may be executed in a cloud computing environment, for example an Amazon Web Services (AWS) cloud instance. The cloud computing environment may protect and support overall data collection, storage, processing, and access. The cloud computing environment may provide encryption of communication or data. The cloud computing environment may perform computing steps to harmonize data into one or more standardized formats, for example converting different formats of heart rate data into a single heart rate data format, or converting different formats of respiration data into a single respiration data format. In some embodiments, some or all components of API 203 may execute on mobile computing device 206, for example where devices 204, 205 are communicatively connected to central computing system 201 via mobile computing device 206, e.g. via a wired or wireless connection as contemplated herein.

[0063] In some embodiments, software executing on the central computing system 201 may comprise a machine learning or other statistical algorithm configured to query data from data storage 202 and process and/or analyze different data streams from one or more users. Data streams suitable for use with a machine learning algorithm as disclosed herein include, but are not limited to, mobile app daily diaries, users’ third-party biosensor data, mobile application usage data, smartphone sensor data, or the like. The machine learning or other statistical algorithm may be configured to summarize some or all of these data.

[0064] In some embodiments, software executing on the central computing system 201 and/or the mobile computing device 206 may comprise a visual presentation engine configured to compile queried raw, summarized, or processed data for one or more users and display the raw, summarized, or processed data in a graphical or textual format. Such visual presentation of data may be used for example in an integrated feedback profile for the user. Examples of integrated feedback profiles are included in Fig. 5 A - Fig. 5E, Fig. 6A - Fig. 6E, Fig. 7A - Fig. 7E, and Fig. 8A - Fig. 8E.

[0065] Some embodiments of the disclosed system may comprise a software development kit (SDK) comprising a set of pre-programmed functions and instructions. Such an SDK may provide functions to communicate with a central computing system via the pre-programmed functions in order to allow third parties to develop software easily to interface with the central computing system. Such an SDK may provide easy connectivity for other mobile sensors and devices, and/or may provide functions to query data from one or more databases or data sources in storage 202, for example in order to display data in a graphical or textual format. In some embodiments, the central computing system may comprise one or more SDKs provided by a third party, for example to communicate with third-party biosensors and other health data sources, for example Apple Health, Fitbit, or the like.

[0066] The disclosed system is the only digital program that derives simultaneous and integrated health feedback to highlight the relationship across lifestyle factors and health biometrics. The system solves an important problem for customers by providing a single mobile application configured to interface with a wide variety of health wearables. The system provides smarter, more focused health data feedback for explicit rather than implicit insights, and provides digital therapeutics across Lifestyle Medicine factors in one place.

[0067] The disclosed system also solves an important problem for healthcare providers. The disclosed single mobile application generates valid, reliable integrated patient health profiles, reduces resources & time needed to process and score data, make recommendations, and connect patients to care.

[0068] The disclosures of each and every patent, patent application, and publication cited herein are hereby incorporated herein by reference in their entirety. While this invention has been disclosed with reference to specific embodiments, it is apparent that other embodiments and variations of this invention may be devised by others skilled in the art without departing from the true spirit and scope of the invention. The appended claims are intended to be construed to include all such embodiments and equivalent variations.

References

[0069] The following publications are incorporated herein by reference in their entirety.

[0070] DeMartini, K. S., Fucito, L. M. (2014). Variations in sleep characteristics and sleep- related impairment in at-risk college drinkers: a latent profde analysis. Health Psychology, 33, 1164-1173.

[0071] Fucito, L. M., DeMartini, K. S., Hanrahan, T. H., Yaggi, H. K., Heffern, C., Redeker, N. S. (2017). Using sleep interventions to engage and treat heavy-drinking college students: a randomized pilot study. Alcoholism: Clinical & Experimental Research, 41, 798-809.

[0072] Fucito, L. M., Bold, K. W ., Van Reen, E., Redeker, N. S., O’Malley, S. S., Hanrahan, T. H., DeMartini, K. S. (2018). Reciprocal variations in sleep and drinking over time among heavydrinking young adults. Journal of Abnormal Psychology, 127, 92-103.

[0073] Fucito, L. M., Ash, G. I., DeMartini K. S., Pittman, B , Barnett, N. P., Li, C. R., Redeker, N. S. OMalley S. S. (2021) A multimodal mobile sleep intervention for young adults engaged in risky drinking: protocol for a randomized controlled trial. JMIR Research Protocols, 10, e26557.

[0074] Ash GI, Jeon S, Conley S, Knies AK, Yaggi HK, Jacoby D, Hollenbeak CS, Linsky S, O'Connell M, Redeker NS. Day-to-day Relationships between Physical Activity and Sleep Characteristics among People with Heart Failure and Insomnia (2021). Behavioral Sleep Medicine, 19: 602-614.

[0075] Ash, G. I., Nally, L., Stults-Kolehmainen, M., De-Los-Santos, M., Jeon, S., Brandt, C., Gulanski, B. I., Spanakis, E. K., Baker, J. S., Whittemore, R., Weinzimer, S. A., Fucito, L. M. Personalized big data for type 1 diabetes exercise support. SportRxiv, (https:// osf. io/preprints/sportrxiv/34vdc/) (January 2023)

[0076] Fucito, L. M. (2022, September). Applying sleep health/wellness technology for alcohol prevention in young adults. Invited oral presentation. Institute for Collaboration on Health, Intervention, & Policy (InCHIP) Lecture Series. University of Connecticut, Storrs, Connecticut. Public YouTube talk link: https:// www.youtube.com/watch?v=tQxgVIbNhgw

[0077] Fucito, L. M., Ash, G. I., DeMartini, K. S., Pittman, B., Barnett, N. P., Li, C. R., Redeker, N. S. & O’Malley, S. S. (2022, June). A multimodal mobile sleep intervention to reduce drinking and alcohol-related harm among heavy-drinking young adults. Symposium: Sleep and alcohol use associations among children, youth, and young adults: Insights from multi-method research.

[0078] Augustine, M., Ash, G. I., DeMartini, K. S., Haughton, A., O’Malley, S. S., Redeker, N. S., Fucito, L. M. (2022, April). Personalized feedback about sleep and alcohol use to promote behavior change in heavy-drinking young adults: Acceptability and preliminary efficacy. Poster presented at the 43^rd Annual Meeting of Society of Behavioral Medicine, Baltimore, MD.

[0079] Ash, G. I., Nally, L., Stults-Kolehmainen, M., et al., Spanakis, E., Baker, J. S., Weinzimer, S. A., Fucito, L. M. (2021, September). Personalized digital health information to substantiate human-delivered exercise support for adults with type 1 diabetes. Oral presentation at the Annual IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Special Session: Al-driven Informatics and Technologies for Cardiovascular Care using multi-modal data from EMRs and/or Wearables, Athens, Greece.

[0080] Ash, G. I., Nally, L., Stults-Kolehmainen M., et al., Spanakis, E., Baker, J. S., Weinzimer, S. A., Fucito, L. M. (2020, October). Personalized dig data for type 1 diabetes exercise support: feasibility and acceptability. Oral presentation at the 15^th Annual Meeting of the International Conference on Genomics, Wuhan, China.

[0081] Fucito, L. M. (2019, October). Novel approaches to individual alcohol interventions for young adults: leveraging other health concerns and technology. Invited presentation at the National Institute on Alcohol Abuse and Alcoholism’s (NIAAA) Division of Epidemiology and Prevention Research (DEPR) Workshop entitled, “Brief Alcohol Interventions for College and Young Adults: Strengthening Effects and Disentangling Mechanisms of Action to Build Personalized Interventions for Widespread Uptake.”

[0082] Ash, G. I., DeMartini, K. S., OMalley, S. S., Redeker, N. S., Fucito, L. M., Presenting young adults with the intersection of alcohol consumption behavior and sleep outcomes as measured by wearable technology: A pilot study. Poster presented at the 3rd Annual Northeast Computational Health Summit (NECHS). Brown University, Providence, RI. (2019, April).

[0083] Fucito, L. M., Bold, K. W., Van Reen, E., Redeker, N. S., O’Malley, S. S., Hanrahan, T. H., DeMartini, K. S. (2017, June). Using wearable sleep/wake activity monitors to assess poor sleep as a mechanism of alcohol use in young adults. Oral presentation at the Annual Meeting of the Research Society on Alcoholism, Denver, CO.

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[0085] Fucito, L. M. (2016, March). Young adults’ sleep: a risk factor for heavy drinking and a novel intervention target? Invited presentation at the Sleep Research Interest Group Seminar at the E.P. Bradley Sleep Laboratory, Department of Psychiatry and Human Behavior, Brown Medical School, Providence. RI.

Claims

CLAIMS What is claimed is:

1. A system for integrated lifestyle medicine tracking and digital therapy, comprising: a central computing system comprising a processor and a non-transitory computer- readable medium; a data storage communicatively connected to the central computing system and comprising at least one relational database; a device interface subsystem configured to collect data from at least one sensor; a user data interface subsystem configured to collect user-reported data from at least one user; a data visualization engine configured to display raw or processed data from the at least one sensor or the user-reported data; a set of instructions stored on the non-transitory computer-readable medium, which when executed by the processor, perform steps comprising: collecting a first data stream from the at least one sensor via the device interface subsystem; collecting a first user-reported data stream from the at least one user via the user data interface subsystem; calculating a probability of at least one lifestyle risk factor from the first data stream and the first user-reported data stream; and presenting the user with at least one intervention to mitigate the at least one lifestyle risk factor.

2. The system of claim 1, wherein the step of calculating the probability of the at least one lifestyle risk factor includes the steps of providing the first data stream and the first user-reported data stream to a machine learning model; and inferring the probability of the at least one lifestyle risk factor from the machine learning model.

3. The system of claim 1, wherein the first data stream comprises biosensor data selected from cardiovascular data, respiration data, sleep data, activity data, diet data, nutrition data, temperature data, stress data, menstrual data, or sexual health data.

4. The system of claim 1, wherein the first user-reported data stream is selected from alcohol consumption, tobacco consumption, substance use, diet, physical activity, sleep quality, social connection, mood, stress, or mental health data.

5. The system of claim 1, wherein the instructions further comprise prompting the user to provide the first user-reported data stream.

6. The system of claim 1, wherein the device interface subsystem comprises at least one application programming interface (API).

7. The system of claim 1, wherein the device interface subsystem comprises at least one function of a system development kit (SDK).

8. The system of claim 1, wherein the at least one sensor is selected from a location sensor, an accelerometer, a gyroscope, an optical sensor, a temperature sensor, or a skin conductance sensor.

9. A method for integrated lifestyle medicine tracking and digital therapy, comprising: providing a mobile computing device and at least one sensor; transmitting a first data stream from the at least one sensor to a central computing system; reporting, via the mobile computing device, a first user-reported data stream to the central computing system; and receiving, from the central computing system, at least one intervention to mitigate at least one lifestyle risk factor having a probability computed from the first data stream and the first user-reported data stream.

10. The method of claim 10, wherein the first data stream comprises biosensor data selected from cardiovascular data, respiration data, sleep data, activity data, diet data, nutrition data, temperature data, stress data, menstrual data, or sexual health data.

11. The method of claim 10, wherein the first user-reported data stream is selected from alcohol consumption, tobacco consumption, substance use, diet, physical activity, sleep quality, social connection, mood, stress, or mental health data.

12. The method of claim 10, wherein the first user-reported data stream comprises a diary. 1