Systems and methods for assisting individual users in behavioral-change program

Abstract

To provide systems and methods allowing for coach-counselor assistance to the individual-user or for automated content delivery.SOLUTION: An interface of an electronic device 56 to demonstrate an example of a user's direct interaction with the smoking cessation behavior modification program system / method includes any number of data subsets previously including, but not limited to, subsets of data, and shows submitted biological data 76, applications 78, and prompts 40. Biological data 76 shows a measured exhaled CO reading value (shown as "3") submitted by the user by using an exhaled breath sensor. Application data shows an application entry of the number of cigarettes smoked over a period of time (shown as "8"). A display includes any associated text as needed, as well as a control panel 46 for interacting with the system and / or coaches.SELECTED DRAWING: Figure 7A

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application is a non-provisional adaptation of U.S. Provisional Patent Application Nos. 62 / 955,214 (filed December 30, 2019) and 62 / 955,219 (filed December 30, 2019), which are incorporated herein by reference in their entireties.

[0002] FIELD OF THE INVENTION The present disclosure relates to methods and systems for enhancing electronic interactions between behavior modification programs and users of the programs by providing user-specific, customized content. The systems and methods enable coach-counselor assisted or automated content delivery to individual users. [Background technology]

[0003] Behavior modification programs include programs that attempt to assist enrolled individuals in an attempt to improve their physical and / or mental health, or to reduce or discontinue undesirable behaviors following the program (e.g., individual users). Many behavior modification programs attempt to change behavior or reduce undesirable behaviors through techniques including negative and positive reinforcement, limit setting, goal setting, and individual user training.

[0004] The success rate of a behavior modification program ultimately depends on the actions of the individual users within the program, and providing support to those individuals as they participate in the program is crucial. Effective coaching can therefore often improve the success rate of such behavior modification programs.

[0005] Unhealthy behaviors are often learned over a significant period of time. Therefore, individuals attempting to unlearn such behaviors or change future behaviors may face varying levels of difficulty depending on many factors unique to the individual user. Therefore, any coach attempting to assist the individual user will be more effective if the coach can use coaching techniques that incorporate information specific to the individual user, whether that information is the individual's background or the individual user's activities / behaviors within the program. For example, an individual user in the early stages of a behavior modification program may require a different type of coaching support than an individual user who has completed the behavior modification program but continues to engage in ongoing adherence to the modified behaviors. Furthermore, the coaching support of an individual user who is strictly adhering to the program will be different from the coaching support needed by another user who is unable to remain adherent to the program. Summary of the Invention [Problem to be solved by the invention]

[0006] There remains a need to provide improved and effective coaching support for individual users of any behavior modification program. While this disclosure discusses smoking cessation programs, the disclosure may benefit any number of behavior modification programs, including, but not limited to, programs assisting individuals with e-cigarette smoking cessation, nicotine addition, weight loss, medication compliance, addiction, managing depression, increasing physical and / or mental activity, etc. [Means for solving the problem]

[0007] The systems and methods described herein enable assisting individuals in behavior modification programs through personalized coaching and personalized program feedback, both specific to either the individual user or the individual user's activities in the program. In one example, the behavior modification program is a smoking cessation program. However, additional variations of the methods and systems described herein may be applied to any number of behavior modification programs. Yet additional variations of the methods and systems disclosed herein include behavior modification programs that use biological feedback / measurements from individual users.

[0008] The present disclosure includes a method for enhancing electronic interactions between a coach-counselor supporting an individual user participating in a behavior modification program. For example, such a method may include: providing electronic access to an information database during electronic interaction between the coach-counselor and the individual user, the information database including a plurality of user-specific input data specific to the individual user, at least a portion of the plurality of user-specific input data having been collected in advance; electronically displaying background data to the coach-counselor during the electronic interaction, the background data including historical information regarding the individual user's activities in the behavior modification program to enable the coach-counselor to review the historical information regarding the individual user during the electronic interaction; electronically supplying the coach-counselor with at least one prompt of a communication topic from a general information database applicable to the behavior modification program, the at least one prompt providing the coach-counselor with a coaching topic to improve the efficiency and accuracy of interactions between the coach-counselor and the individual user to support the individual user in the behavior modification program; and electronically transmitting the at least one prompt to the individual user as a coaching message.

[0009] Variations of the method may include user-specific data including at least one of a first subset of individual user psychological information, a second subset of individual user personal information, a third subset of individual user biological input data, and / or combinations thereof. In additional variations, the user-specific data includes the subset of individual user biological input data with at least one of the first subset of individual user psychological information and the second subset of individual user personal information, any additional information, and / or combinations thereof.

[0010] Variations of the methods and systems described herein may include methods in which electronically sending at least one prompt occurs automatically without input from the coach-counselor, or alternatively, or in combination, electronically sending at least one prompt requires input from the coach-counselor.

[0011] Variations of the methods and systems may further involve establishing an electronic reporting interface for the coach-counselor, which allows the coach-counselor to electronically access a database of batch data containing information from multiple users who participated in the behavior modification program.

[0012] The information database may further include a behavioral summary of the individual user, the behavioral summary including an association between biological input data from the individual user and at least one of a plurality of behavioral data provided by the individual user, the behavioral data being non-biological data.

[0013] The information database can be updated automatically by monitoring user activity and / or a coach-counselor can update the information database regarding individual users.

[0014] In an additional variation, the second subset of the individual user personal information in the information database includes information from the group consisting of background, disposition, subject attributes, and prior notes about the individual user. A variation of the method can include the first subset of the individual user psychographic information including milestones and targets for the user.

[0015] In a variation of the method and system, displaying the data includes displaying a conversation history between an individual user and a coach-counselor.

[0016] The prompts may be reusable prompts applicable to multiple different users. Additionally or alternatively, at least one prompt may include a partially written statement that the coach-counselor must complete before sending the statement to the individual user.

[0017] A variation of the method includes the coach-counselor selecting at least one prompt, the at least one prompt including coded variables that are pre-filled when the at least one prompt is selected by the coach-counselor.

[0018] The methods and systems described herein can include checks such as if a prompt includes a placeholder, and the methods further include preventing the at least one prompt from being sent electronically until the coach-counselor replaces the placeholder with text.

[0019] Another variation of this method involves tagging the coaching message and assigning it an associated category, which may include a trigger or an action.

[0020] In some variations, the coaching message is added to a database of information about the user and can be either private or public.

[0021] An additional variation of the method includes allowing the coach-counselor to select data from the information database to allow the coach-counselor to search by content of at least one prompt.

[0022] The prompts discussed herein may be modified to maintain a stylistic similarity to the coach-counselor.

[0023] In an additional variation, the method may further include selecting an automated message based on an inquiry from the individual user and automatically sending the automated message to the individual user.

[0024] The behavior modification programs disclosed herein can include preventing a behavior selected from the group consisting of cigarette smoking, vaping, alcohol consumption, tobacco use, and drug use.

[0025] The present disclosure also includes a method for providing customized content to individual users participating in a behavior modification program. An example of such a method includes providing an information database comprised of a plurality of user-specific data specific to the individual users, at least a portion of the plurality of user-specific input data having been previously collected, electronically monitoring the activities of the individual users, using the activities to customize program-related content including electronic media content from a general information database applicable to the behavior modification program, electronically transmitting the program-related content to the individual users as electronic messages, and monitoring the individual users' electronic interactions with the program-related content.

[0026] Again, variations of the method may include user-specific data including at least one of a first subset of individual user psychological information, a second subset of individual user personal information, a third subset of individual user biological input data, and / or combinations thereof. In additional variations, the user-specific data includes the subset of individual user biological input data together with at least one of the first subset of individual user psychological information and the second subset of individual user personal information, any additional information, and / or combinations thereof.

[0027] Variations of this method include the electronic message further including at least one data item from one of the first subset of individual user psychological information, the second subset of individual user personal information, or the third subset of individual user biological input data.

[0028] The electronic transmission of program-related content to the individual user may be automatic or may require input from the individual user. In an additional variation, the information database further includes a behavioral summary of the individual, the behavioral summary including an association of biological input data from the individual with at least one of a plurality of behavioral data provided by the individual, the behavioral data being non-biological data.

[0029] The second subset of the individual user personal information in the information database may include information from the group consisting of background, disposition, subject attributes, and prior notes about the individual user. The first subset of the individual user psychological information in the information database may include milestones and targets.

[0030] The method may further include adding program-related content to an information database about the user. An additional variation includes enabling the coach-counselor to select data from the information database including enabling the coach-counselor to search by content of at least one prompt.

[0031] This application is related to the following commonly assigned patents and applications, including U.S. Patent No. 10,306,922 issued June 4, 2019, U.S. Patent No. 9,861,126 issued January 9, 2018, U.S. Patent No. 10,674,761 issued June 9, 2020, U.S. Patent No. 10,206,572 issued February 19, 2019, U.S. Patent No. 10,335,032 issued July 2, 2019, U.S. Patent No. 10,674,913 issued June 9, 2020, and U.S. Patent No. 10,306,922 issued June 4, 2019. Such applications include U.S. Patent Application No. 16 / 889,617, published on September 17, 2020 as U.S. Patent Application Publication No. 20200288785, U.S. Patent Application No. 15 / 782,718, published on April 18, 2019 as U.S. Patent Application Publication No. 20190113501, and U.S. Patent Application No. 16 / 890,253, published on September 17, 2020 as U.S. Patent Application Publication No. 20200288979. The contents of each of the above patents and applications are incorporated by reference. [Brief explanation of the drawings]

[0032] [Figure 1] 1 illustrates an individual user using an electronic personal device configured to provide biological data / measurements from the individual. [Figure 2A] A method and system for enhanced personalized coaching is presented that requires building and / or compiling one or more databases containing user-specific information. [Figure 2B] A non-exhaustive list of inputs that contribute data subsets 72, 74 that build one or more databases of information specific to individual users is shown. [Figure 2C] A non-exhaustive list of inputs that contribute data subsets 72, 74 that build one or more databases of information specific to individual users is shown. [Figure 3A] It represents compiling one or more databases using additional subsets of biological data and application, app, and / or sensor data. [Figure 3B] Represents the various inputs used to generate the biological and app / sensor data subsets. [Figure 3C] Represents the various inputs used to generate the biological and app / sensor data subsets. [Figure 4] 1 illustrates a conceptual electronic interaction between a counselor-coach working with an individual user participating in a behavior modification program. [Figure 5A] 1 illustrates an example display, e.g., via an electronic display, of information provided to a coach-counselor to enhance interaction when assisting an individual user during a behavior modification program. [Figure 5B] 5B shows a variation of a display according to the display of FIG. 5A, in which the coach-counselor has access to information intended to improve interaction with the user. [Figure 5C] 5B shows a variation of a display according to the display of FIG. 5A, in which the coach-counselor has access to information intended to improve interaction with the user. [Figure 6] FIG. 1 is a conceptual diagram of one embodiment of a system / method for enhancing direct electronic interaction between one or more systems / servers of a behavior modification program and individual users. [Figure 7A] Also shown is an information data card selected to provide personalized information specific to the user. [Figure 7B] Also shown is an information data card selected to provide personalized information specific to the user. [Figure 8] An electronic interface is shown having different information cards 44 that display selected and customized information to the user based on any subset of the data as described above. [Figure 9] 1 illustrates an exemplary system including a wearable device, a mobile device, and a remote server in communication with the wearable device and the mobile device, according to some embodiments of the present disclosure. [Figure 10]1 illustrates another exemplary system including a wearable device and a remote server in communication with the wearable device, according to some embodiments of the present disclosure. [Figure 11] 1 illustrates exemplary light absorption curves for various types of hemoglobin, enabling measurement of carboxyhemoglobin (SpCO) and oxyhemoglobin (SpO2) levels using a photoplethysmography (PPG) sensor according to some embodiments of the present disclosure. [Figure 12] 1 shows a chart of a patient's changing SpCO levels over a typical five-day monitoring period prior to starting a smoking cessation program, according to some embodiments of the present disclosure. [Figure 13] 1 shows trends in a patient's SpCO levels and smoking triggers throughout a typical day before starting a smoking cessation program, according to some embodiments of the present disclosure. [Figure 14] 1 illustrates a data structure for storing a patient's SpCO levels and smoking triggers throughout a typical day, according to some embodiments of the present disclosure. [Figure 15] 1 illustrates an exemplary flow diagram for detecting a patient's smoking behavior, according to some embodiments of the present disclosure. [Figure 16] 10 shows a sample report after a 5-day evaluation of a patient, according to some embodiments of the present disclosure. [Figure 17] 1 shows an exemplary chart of patient SpCO levels during run-in and smoking cessation programs, according to some embodiments of the present disclosure. [Figure 18] 10A-10C illustrate exemplary smartphone app screens showing measurements such as SpCO, SpO2, heart rate, respiratory rate, blood pressure, and temperature, according to some embodiments of the present disclosure. [Figure 19] 10A-10C illustrate exemplary smartphone app screens for receiving patient input data, according to some embodiments of the present disclosure. [Figure 20] 10A-10C illustrate exemplary smartphone app screens implementing a smoking prevention protocol, according to some embodiments of the present disclosure. [Figure 21]10A-10C illustrate exemplary smartphone app screens for presenting the smoking cessation process as a game for patients, according to some embodiments of the present disclosure. [Figure 22] FIG. 1 shows an exemplary flow diagram for predicting and preventing anticipated smoking events, according to some embodiments of the present disclosure. [Figure 23] 23 shows an example flow diagram of step 1414 of FIG. 22 for determining whether a prevention protocol was successful, according to some embodiments of the present disclosure. [Figure 24] FIG. 1 shows an exemplary flow diagram for one-time measurement of a patient's SpCO level using a PPG sensor, according to some embodiments of the present disclosure. [Figure 25] 1 shows an exemplary flow diagram for detecting a smoking event according to some embodiments of the present disclosure. [Figure 26] 1 shows an exemplary flow diagram for applying one or more perturbations to a model of a patient's smoking behavior, according to some embodiments of the present disclosure. [Figure 27] Another variation of a system and / or method for influencing an individual's smoking behavior and further quantifying an individual's exposure to cigarette smoke using some embodiments described herein is shown. [Figure 28A] 28 shows a visual representation of data that can be collected with a variation of the system shown in FIG. 27. [Figure 28B] 28 shows a visual representation of data that can be collected with a variation of the system shown in FIG. 27. [Figure 29] 1 shows an example data set used to determine an eCO curve over a period of time, where the eCO due to an individual's smoking behavior can be quantified to determine interval-by-interval eCO Burden or eCO Load for various time intervals. [Figure 30] 10 illustrates an example of displaying biometric data as well as various other information for assessing an individual's smoking behavior. [Figure 31]31 shows another variation of a dashboard displaying information similar to that shown in FIG. 30. [Figure 32A] 10 shows another variation of a dataset including exhaled carbon monoxide, collection time, and cigarette data quantified and displayed to benefit individuals seeking to understand their smoking behavior. [Figure 32B] 10 shows another variation of a dataset including exhaled carbon monoxide, collection time, and cigarette data quantified and displayed to benefit individuals seeking to understand their smoking behavior. [Figure 32C] 10 shows another variation of a dataset including exhaled carbon monoxide, collection time, and cigarette data quantified and displayed to benefit individuals seeking to understand their smoking behavior. [Figure 33A] Another variation of the above system and method is shown, which is used to implement a treatment plan for identifying an individual's smoking behavior to ultimately assist the individual in quitting smoking and maintaining the individual's status as a non-smoker. [Figure 33B] Another variation of the above system and method is shown, which is used to implement a treatment plan for identifying an individual's smoking behavior to ultimately assist the individual in quitting smoking and maintaining the individual's status as a non-smoker. [Figure 33C] Another variation of the above system and method is shown, which is used to implement a treatment plan for identifying an individual's smoking behavior to ultimately assist the individual in quitting smoking and maintaining the individual's status as a non-smoker. [Figure 33D] Another variation of the above system and method is shown, which is used to implement a treatment plan for identifying an individual's smoking behavior to ultimately assist the individual in quitting smoking and maintaining the individual's status as a non-smoker. [Figure 33E] Another variation of the above system and method is shown, which is used to implement a treatment plan for identifying an individual's smoking behavior to ultimately assist the individual in quitting smoking and maintaining the individual's status as a non-smoker. [Figure 33F]Another variation of the above system and method is shown, which is used to implement a treatment plan for identifying an individual's smoking behavior to ultimately assist the individual in quitting smoking and maintaining the individual's status as a non-smoker. [Figure 33G] Another variation of the above system and method is shown, which is used to implement a treatment plan for identifying an individual's smoking behavior to ultimately assist the individual in quitting smoking and maintaining the individual's status as a non-smoker. [Figure 33H] Another variation of the above system and method is shown, which is used to implement a treatment plan for identifying an individual's smoking behavior to ultimately assist the individual in quitting smoking and maintaining the individual's status as a non-smoker. DETAILED DESCRIPTION OF THE INVENTION

[0033] The present disclosure includes methods for enhanced coaching of individual users participating in a behavior modification program. Coaching can be performed using a live coach, which is an individual trained to assist the user during the program. Alternatively, or in combination, coaching can include automated electronic communications pushed or requested by the individual, which can provide repeated interactions with the individual user to maintain engagement with the program. As described herein, coaching can include customized information as well as general information. For example, customized information is information intended to be specifically applied to an individual user based on any number of criteria specific to that user. General information can include information that applies to one or more users regardless of the user's specific circumstances.

[0034] In a first variant, the methods and systems for enhanced coaching of individuals are discussed specifically for smoking cessation programs, however, the methods and systems may be applied to any behavior modification program intended to increase an individual's health and / or user's sense of well-being.

[0035] The behavior modification programs described herein rely on electronic communications to facilitate the exchange of information between an individual user 10 and the behavior modification program (e.g., the program's computer / database system and / or live coach). For example, FIG. 1 depicts a diagram of an individual user 10 using an electronic personal device configured to provide biological data / measurements from the individual. While the present methods and systems include any personal electronic device capable of providing biological data / measurements, for illustrative purposes, FIG. 1 depicts a smartwatch 52 or breath sensor 54 that may be used to communicate biological data to a cloud server 60, either directly or through any intermediate device, such as a smartphone 56 or other personal computing device. In a variation discussed below that provides a smoking cessation program as a behavior modification program, the user 10 uses a portable device 56 that obtains multiple samples of breath from the individual using a sensor that measures the amount of carbon monoxide in the breath samples (also referred to as exhaled carbon monoxide, or ECO). Biological input data may include data measured by the device (e.g., breath via device 54). Alternatively, or in combination, the biological data may include data manually entered by the user 10, as discussed below.

[0036] In a first variation, as conceptually illustrated in Figure 2A, methods and systems for enhanced personalized coaching require building and / or compiling one or more databases 70 containing information specific to user 10. This data may include, but is not limited to, data subsets (e.g., 72, 74, 76) that make up one or more databases 70. In some variations, submission of biological data 76 does not occur until the user engages with the program.

[0037] The transmission 62 or entry of data 72, 74, 76 into one or more databases 70 can occur via any number of methods. For example, data 72, 74, 76 can be compiled before or during the initial phase of the behavior modification program by one or more individuals associated with the program. Alternatively, or in combination, user 10 can use an electronic interface to compile some or all of the data 72, 74, 76. User-specific databases 70 (which may include one or more databases) can be compiled and / or updated at any time frame. However, the behavior modification program can set a minimum level of information required to initiate or enroll an individual in the program. While FIG. 2 illustrates data transmission 62 to a cloud or cloud server 60, variations of the methods and systems within this disclosure can include local storage of databases.

[0038] 2B and 2C show non-exhaustive lists of inputs 73, 75 that feed data subsets 72, 74 to build one or more databases of information specific to individual users. As shown, in FIG. 2B, user personal information 72 can include information, in this example, demographic information. Typically, such user personal information 72 includes information specific to the history or identity of an individual user. Such information inputs include, but are not limited to, gender, age, tobacco products used and amount of use, previous quit attempts, geographic region, languages ​​spoken, culture or cultural aspects, race, country, nicotine replacement therapy experience and medical history, income, education, socioeconomic status, family history of tobacco use, weight or body mass index, family members who smoke, marital status, children, home living situation, community factors (e.g., poverty, crime, quality of education), access to health care, and health insurance.

[0039] 2C shows examples of some inputs 75 that can be used to collect specific subsets of data, including individual user psychological information. The psychological inputs 75 allow for the construction of a database of psychological information 74 that allows for the study and classification of individual users according to their mindsets, aspirations, and other psychological criteria with respect to behavior modification programs. Again, in a smoking cessation program, the psychological inputs will vary depending on the particular behavior modification program, and such psychological inputs 75 may include, but are not limited to, goals for changing tobacco habits, motivation to quit smoking, feelings of well-being, confidence in quitting smoking, preference for nicotine replacement therapy, personal crisis, situational factors (e.g., holiday business trips, stressful work periods), and comorbid mental or physical health disorders (e.g., major depression, obesity).

[0040] 3A depicts compiling one or more databases using additional subsets of biological data 76 and application, app, and / or sensor data 78. As noted above, the use of additional biological data 76 and app / sensor data 78 often occurs during user 10's participation in a behavior modification program and after the databases have been compiled as shown in FIG. 2A. However, the methods and systems described herein may include any sequence for compiling a database(s).

[0041] 3B and 3C depict various inputs 77, 79 used to generate the biological 76 and app / sensor 78 data subsets. Biological input 77 can be any input related to the user. Typically, biological data is entered using a personal device (e.g., 52 and / or 54 as shown in FIG. 1). However, biological data 76 can be generated or measured in any manner as required by the particular data useful to the behavior modification program. Examples of biological inputs 77 for use in a smoking cessation behavior modification program include, but are not limited to, carbon monoxide (CO) levels, respiration rates, oxygen levels, blood pressure, and hemoglobin A1c measurements. Note that application data 78 can include data actively submitted by the user or data passively recorded by the system (e.g., duration in the program, time between program participation, etc.).

[0042] FIG. 4 illustrates a conceptual electronic interaction 30 during which a counselor-coach 20 works with an individual user 10 participating in a behavior modification program. As illustrated, the electronic interaction 30 can occur via one or more electronic devices 56. While the present method and system also contemplates in-person or real-time voice or messaging communication, the electronic interaction 30 enables an on-demand system of coaching. The coach-counselor 20 accesses a server / system 60 via an electronic device 94 to refine and enhance interactions with the user 10. The server / system 60 can draw from one or more databases 70 containing user-specific data, as described above. This configuration allows the coach-counselor 20 to access a wide variety of data useful to the coach-counselor 20 to provide meaningful support to the individual user 10. For example, the server / system 60 may display a plurality of user-specific data specific to an individual user received from the database 70, the user-specific data including any of a first subset of individual user psychological information, a second subset of individual user personal information, or a third subset of individual user biological input data. The system 60 may also provide the coach-counselor 20 with optional background data, including historical information regarding the individual user's activity in a behavior modification program, to allow the coach-counselor to review historical information about the individual user during an electronic interaction. Typically, such background data includes app / sensor usage input 79 (shown in FIG. 3C ). However, the background data may also include information from previous sessions between the coach-counselor 20 and the user 10. The ability to provide a wide variety of user-specific data allows any number of coach-counselor 20 to become familiar with the user 10.

[0043] Another feature of system 60 is the ability to pull information from one or more databases 90 containing information specific to behavior modification programs. For example, system 60 can electronically provide coach-counselor 20 with at least one prompt for a communication topic from database 90, where the prompt or communication topic is general information applicable to behavior modification programs, and the at least one prompt improves the efficiency and accuracy of interactions between the coach-counselor and the individual user. Coach-counselor 20 would have the ability to electronically send (30) a message to individual user 10 that includes one or more prompts. In an additional variation, coach-counselor 20 would have the option to customize the prompt prior to the discussion or before sending to user 10.

[0044] 5A shows an example display, such as via electronic display 94, of information provided to a coach-counselor to enhance interaction when assisting an individual user during a behavior modification program. FIG. 5 is intended to illustrate variations in the information that may be relayed to the coach-counselor. However, any variations in the data subsets may be provided at any time depending on the behavior modification program.

[0045] FIG. 5A illustrates a display 94 that typically provides multiple biological data in association with an individual's behavioral input, including application, app, and / or sensor data 78, as described above. The system submits one or more prompts 40 based on a comparison of the biological data with the behavioral data 78. FIG. 5 illustrates biological data 76, including Bio_Data.sub.n(1 to y), meaning that the displayed biological data 76 can include any information found in the databases described above. Similarly, the display can show the same or various different biological data 76. The methods and systems described herein compare various biological data 76 with behavioral data 78 to generate prompts 40 for use by a coach-counselor in assisting a particular user. As described above, the behavioral data 78 can include background or other data specific to the individual, such as historical information about the individual user's activity in a behavior modification program. The prompts 40 can be drawn from information in one or more databases (90 in FIG. 4) specific to the behavior modification program. Such information can be general information applicable to behavior modification programs. The goal of the prompts is to improve the efficiency and accuracy of the interaction between the coach-counselor and the individual user, and to provide the coach-counselor with coaching topics to assist the individual user in a behavior modification program.

[0046] 5A also shows a display that provides additional information about the individual generally, such as information about the program 90, as well as personal information 72 related to the user. Additional variations of the systems and methods described herein may include the display of any relevant information to the coach-counselor and / or user. Such information may include, but is not limited to, information related to the user, information related to the program, or information unrelated to either the user and / or the program.

[0047] 5B and 5C show a variation of a display 94 according to the display of FIG. 5A, in which the coach-counselor accesses information intended to improve interaction with the user. As described herein, the display 94 can include information specific to an individual user. In the illustrated example, the display includes a combination 82 of biological data with application, app, and / or sensor data, along with a prompt 40 associated with the particular combination of information 82. In the first example on the left, the combination 82 information informs the coach that the user is already aiming to reduce smoking but still needs to pair with a CO breath sensor for submission of the information; the information also indicates that the user has received the breath sensor. Again, such information can be a combination of previously submitted data from a user-specific database (e.g., database 70, described above). This combination data includes background data of historical information regarding the individual user's activity in the behavior modification program. As shown, the system also provides the coach-counselor with various prompts 40 related to associated subsets of the combination data 82. In this example, the prompts to the coach-counselor include "We strongly recommend that [the user] pair a CO sensor to the mobile app to better track reduction," "We suggest a lesson [for the user]—'Using the breath sensor'" [media content provided in the smoking cessation program], and "We suggest that [the user] dialogue with the coach about the benefits of the sensor and reduction." As noted above, the prompts 40 are communication topics from a general information database applicable to smoking cessation programs. In some cases, the prompts are a combination of general information and specific patient information. In either case, the prompts are custom-created discussion topics based on the activity of a particular user. Such custom-created discussion prompts improve the efficiency and accuracy of interactions between the coach-counselor and the individual user and provide the coach-counselor with coaching topics to assist the individual user in the behavior modification program.

[0048] FIG. 6 is another conceptual diagram of one embodiment of a system / method for enhancing direct electronic interaction 30 between one or more systems / servers 60 of a behavior modification program and an individual user 10. In this variation, the system / server 60 can provide feedback or personalized recommendations to the user 10 with or without a coach-counselor. Direct interaction between the user 10 and the behavior modification program can provide real-time, personalized advice to the user 10 while providing the user with a sense of understanding progress, completion, and encouragement. The direct interaction system described in FIG. 6 can incorporate additional information instead of or in addition to coaching from the coach-counselor. This additional information can be customized to the individual based on the individual's interaction with the program (e.g., see app data subset 78 described above in FIGS. 2A-3C). Additionally, the information can include general information about the behavior modification program. For clarity, information provided to an individual via direct electronic interaction 30 can be referred to as program-related content. As discussed herein, program-related content can be personalized to an individual user's goals, the effectiveness of the program's features, an individual's medical history during and / or prior to participating in the program, and can be customized to include the user's personal information (e.g., username, coach name, selected goals, etc.). Because the program-related content is provided via electronic communication, the system can use the program-related content to track the user's activities to determine whether the user's activities have started, are in progress, and the degree of progress or have been completed without requiring the user to affirmatively report their progress.

[0049] Program-related content may include any variation of informational content, including, but not limited to, URL links, information cards (e.g., electronic "cards" discussed below), lessons, videos, assignments, activities, media content, and tasks. Program-related content may include content intended to be reviewed (passive content) or information that requires activity or action by the user (action). For example, some program-related content may require a call to action, including graphical elements that prompt the user to perform some targeted action, such as engaging in a task related to behavior modification, an activity related to the program (e.g., contacting a family member), or a request to obtain biological data (e.g., using a breath sensor).

[0050] Another variation of the system and method involves customizing / personalizing the card with user-specific data. In such cases, the card is generic but has electronic placeholders that are filled with user-specific data when sent to the user. For example, the card could generically include the user's "top three reasons for cigarettes logged in the last 48 hours" or "highest / lowest CO readings in the last 48 hours." Each user then receives a customized card containing generic information, with the user-specific information incorporated into the generic message on the card.

[0051] As described above and conveyed in FIG. 6 , the system 60 can monitor the activities of the user 10 via electronic interactions 30. The system can then use an algorithm to draw from information in one or more databases 90 that are general to the program. The system then selects an information card with content that applies to the user 10. Thus, the information card can be conveyed directly to the user's electronic device 56, or the information card can be compiled / added 66 to one or more of the user's personal databases 70. In some variations, the information card remains on the general database 90, but the system uses user-specific information stored in those personal databases 70 to draw relevant general information from the databases 90.

[0052] 7A and 7B show an interface of an electronic device 56 to demonstrate an example of a user's direct interaction with a smoking cessation behavior modification program system / method. As shown, the interface 56 can include any number of data subsets described above, including but not limited to the subsets of data previously disclosed herein. For example, FIGS. 7A and 7B show the submitted biological data 76, application 78, and prompt 40 described above. In the illustrated variation, the biological data 76 indicates a measured exhaled CO reading (shown as "3") submitted by the user using a breath sensor. The application data indicates an application entry of the number of cigarettes smoked over a period of time (shown as "8"). The display can include any associated text as needed, as well as a control panel 46 for interacting with the system and / or coach.

[0053] 7A and 7B also show information data cards 44 selected to provide personalized information specific to the user. In the illustrated example, FIG. 7A shows an information data card 44 specific to a person who is just entering a smoking cessation behavior modification program (called "PIVOT"). As shown in FIG. 7B, when a user engages with an information card 40, which may simply provide information or may require interaction from the user, the system marks the information card 44 as complete. However, variations of the system allow an individual to revisit any information card 44 at a later time. As described herein, an information data card is an example of program-related content (as described above) that is electronically transmitted to user 10.

[0054] FIG. 8 illustrates an electronic interface with different information cards 44 that display selected and customized information to the user based on any subset of data, as described above. FIG. 8 also illustrates that the information cards 44 can include content 48 related to a behavior modification program. As described above, the content can be general to the program, but can also be selected based on specific criteria related to the user. Alternatively, or in combination, the content 48 can be customized for the user. In this example, the data card 48 is shown to provide media content to explain the benefits of ordering a nicotine replacement medication along with facts about the medication. The content 48 further allows the user to interact with one or more buttons 49, which, in the illustrated example, allow the user to order the medication.

[0055] The systems and methods described herein also enable supporting users in a highly user-centric manner. For example, the systems and methods can enable preserving user autonomy by monitoring and identifying cards that the user ignores or disobeys. To preserve user autonomy, the systems and methods can delay prompting the user with the same or similar cards for a specified period of time. Such a feature allows for "auto-snoozing" of information that the user has viewed but not acted upon. The period of the time delay can be selected by the user and / or can be selected by system configuration.

[0056] In another variation, the system and method may include a card "expiry" feature that causes the system to stop prompting the user with the card if the user views the card (and / or similar cards) but fails to comply multiple times. In one example, the system stops prompting the user with the card after three impressions (when the system determines that the user received the card but failed to comply three times).

[0057] The system and method may further include a card chaining feature in which the system prompts for card "A" any specified number of times and then selects card "B" to revoke card "A," where cards "A" and "B" may have related or mutually exclusive information. The system may prompt for card "B" any specified number of times and then selects card "A" or another card "C" to revoke card "B." This sequence may be repeated so that the card chain may be as long or short as desired. Such an approach maintains the novelty of coaching / counseling, as opposed to simply repeating the same information.

[0058] Variations of the systems and methods described herein can select content based on user-specific information. In such cases, counselor-coaching prompts and / or cards can be tailored to that information. For example, some users may want to quit smoking on a specific date, while others desire a regular quitting plan (i.e., a longer period of time). In such cases, the system / method can assign the user either a "rapid quit" plan or a "regular quit" plan. As an example, a user identified as a "rapid quit" user would receive coach-counseled prompts and / or cards with a highly prioritized list of critical steps in a behavior modification program. In contrast, a user identified as a "regular quit" user would get customized content that allows for a longer time in the planning sequence.

[0059] Additional examples of customized feedback include repeated user prompts. For example, information prompted this week is given high priority if the user completes their interaction with that week's card / coaching, after which the incomplete information from the previous week can be prompted by the coach / system. The card / coaching can also prompt the user to maintain their goals and current usage. For example, the system may prompt the user (via coaching and / or cards) for a weekly update of cigarettes per day to ensure the user's goals are up to date at least every two weeks. If the user is sufficiently engaged / successful with the program, the system may provide customized program-related content (e.g., coaching and / or other information) encouraging them to set positive behavioral goals. For example, if the user reduces their smoking by 50%, the system may prompt the user to go ahead and quit smoking. In another variation, a user who has completed all lessons and is still active may be prompted by the system if they wish to make changes, such as reducing how much they smoke or quitting completely.

[0060] In some embodiments, the systems and methods described herein provide a system including one or more mobile devices and a server in communication with the mobile devices. FIG. 9 shows an exemplary embodiment 100 of such a system including a device 102, a device 104, and a server 106 in communication with the devices 102 and 104. The device 102 assists in detecting a patient's smoking behavior. The device 102 includes a processor, memory, and a communication link for sending and receiving data from the device 104 and / or the server 106. The device 102 includes one or more sensors for measuring the patient's smoking behavior based on measurements of one or more of the patient's CO, eCO, SpCO, SpO, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil diameter, geographic location, environment, ambient temperature, stress factors, life events, and other suitable parameters. For example, device 102 may include PPG-based sensors for measuring CO, eCO, SpCO, and SpO2, electrocardiography-based sensors for measuring heart rate and blood pressure, acoustic signal processing-based sensors for measuring respiratory rate, wearable temperature sensors for measuring body temperature, electrodermal activity-based sensors for measuring skin conductance, electroencephalography-based sensors for measuring brain waves, implantable sensors placed in skin, fat, or muscle to measure CO and other variables, oral CO sensors, ambient CO sensors, and other suitable sensors. These sensors may have various locations on or within the body for optimal monitoring.

[0061] The device 102 may be portable or wearable. For example, the device 102 may be wearable in a manner similar to a wristwatch. In another example, the device 102 may be portable or wearable and attached to a fingertip, earlobe, pinna, toe, chest, ankle, arm, skin crease, or another suitable body part. The device 102 may be attached to a suitable body part via a clip, band, strap, adhesively attached sensor pad, or another suitable medium. For example, the device 102 may be attached to a fingertip via a finger clip. In another example, the device 102 may be attached to an earlobe or pinna via an ear clip. In yet another example, the device 102 may be attached to a toe via a toe clip. In yet another example, the device 102 may be attached to a chest via a chest strap. In yet another example, the device 102 may be attached to an ankle via an ankle band. In yet another example, the device 102 may be attached to an arm via a biceps or triceps strap. In yet another example, the device 102 may be attached to a skin crease via a sensor pad.

[0062] The device 102 can prompt the patient for a sample, or the device can take a sample without the patient's consent when worn. Sampling can be sporadic, continuous, near-continuous, periodic, or based on any other suitable interval. In some embodiments, sampling is performed continuously as often as the sensors can take measurements. In some embodiments, sampling is performed continuously after a set time interval, such as 5 or 15 minutes, or another suitable time interval.

[0063] In some embodiments, device 102 includes one or more sensors for monitoring SpCO using a transcutaneous method, such as PPG. The transcutaneous monitoring may use a transmission or reflection method. Device 104 may be a smartphone or another suitable mobile device. Device 104 includes a processor, memory, and a communication link for sending and receiving data from device 102 and / or server 106. Device 104 may receive data from device 102. Device 104 may include an accelerometer, a global positioning system-based sensor, a gyroscope sensor, and other suitable sensors for tracking the described parameters. Device 104 may measure certain parameters, including, but not limited to, movement, location, date and time, patient-entered data, and other suitable parameters.

[0064] The patient-input data received by device 104 may include stressors, life events, geographic location, daily events, administration of nicotine patches or other prescriptions, administration of other medications for smoking cessation, and other suitable patient-input data. For example, some of the patient-input data may include information about phone calls, athletics, work, sports, stress, gender, alcohol consumption, smoking, and other suitable patient-input data. A patient's use of their smartphone for text messaging, calling, surfing, playing games, and other suitable uses may also be correlated with smoking behavior, and these correlations may be utilized to predict behavior and change behavior. Device 104 or server 106 (after receiving the data) may compile the data, analyze the data for trends, and correlate the data in real time or after a specified period of time has completed. Server 106 includes a processor, memory, and communications links for transmitting and receiving data from device 102 and / or device 104. Server 106 may be located remotely from devices 102 and 104, for example, at a healthcare provider site or another suitable location.

[0065] 10 illustrates an exemplary embodiment 200 of a system including a device 202 and a server 204 in communication with the device 202. The device 202 assists in detecting a patient's smoking behavior. The device 202 includes a processor, memory, and a communication link for transmitting and receiving data from the server 204. The device 202 may be portable or wearable. For example, the device 202 may be wearable in a manner similar to a wristwatch. The device 202 includes one or more sensors 206 for measuring the patient's smoking behavior based on measurements of one or more of the patient's CO, eCO, SpCO, SpO, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil diameter, geographic location, environment, ambient temperature, stress factors, life events, and other suitable parameters.

[0066] The device 202 may include one or more sensors 208 for measuring certain parameters, including, but not limited to, movement, location, date and time, patient-input data, and other suitable parameters. The patient-input data may include stressors, life events, location, daily events, administration of a nicotine patch or other prescription, administration of other medications for smoking cessation, and other suitable patient-input data. The patient-input data may be received in response to a prompt for the patient on a mobile device, such as the device 104, or may be entered voluntarily by the patient without a prompt. For example, some of the patient-input data may include information about phone calls, athletic events, work, sports, stress, gender, alcohol consumption, smoking, and other suitable patient-input data. The device 202 or the server 204 (after receiving the data) may compile the data, analyze the data for trends, and correlate the data in real time or after a specified period of time has completed. The server 204 includes a processor, memory, and communications links for receiving and transmitting data from the device 202. The server 204 may be located remotely relative to the device 202, for example, at a healthcare provider site or another suitable location.

[0067] In some embodiments, the device 102 or 202 includes a detector unit and a communications unit. The device 102 or 202 may include a user interface as required for its particular function. The user interface may receive input via a touchscreen, keyboard, or another suitable input mechanism. The detector unit includes at least one test element capable of detecting a substance using biological parameter input from the patient indicative of smoking behavior. The detector unit analyzes biological input from the patient, such as exhaled breath from the lungs, saliva, or wavelengths of light directed through or reflected by tissue. In some embodiments, the detector unit monitors the patient's SpCO2 using PPG. The detector unit can optionally measure numerous other variables, including, but not limited to, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil diameter, geographic location, environment, ambient temperature, stress factors, life events, and other suitable parameters. In the case of a breath-based sensor, patient input may include blowing into a tube that is part of the detector unit. In the case of a saliva or other bodily fluid based sensor, the patient input may include placement of a fluid sample within a test chamber provided within the detector unit.

[0068] In the case of a light-based sensor such as PPG, patient input may include placement of an emitter-detector on a finger or other area of ​​exposed skin. The detector unit records the date and time, quantifies the presence of the target substance, stores the data for future analysis, and / or transmits the data to another location, such as device 104 or server 106, for analysis. The communication unit includes appropriate circuitry for establishing a communication link with another device, such as device 104, via a wired or wireless connection. A wireless connection may be established using Wi-Fi, Bluetooth, radio frequency, or another suitable protocol.

[0069] FIG. 11 shows an exemplary embodiment 300 of suitable wavelengths for analyzing SpO2 and SpCO using an optical-based sensor. A patient's SpCO can be measured by intermittently testing the patient's exhaled breath with a suitable sensor. In another example, a patient's SpCO can be measured using a transcutaneous method, such as photoplethysmography (PPG). SpCO is detected by passing light through patient tissue, such as the earlobe, pinna, fingertip, toe, skin crease, or another suitable body part, and analyzing the attenuation of various wavelengths. SpO2 is typically measured using two wavelengths, such as 302 (660 nm) and 306 (940 nm). SpCO can be measured using three wavelengths, such as 302 (660 nm), 304 (810 nm), and 306 (940 nm), or up to seven or more wavelengths, for example, in the 500-1000 nm range. Such PPG sensors may be implemented via a finger clip, band, adhesively attached sensor pad, or another suitable medium. PPG sensors may be transmissive, as used in many pulse oximeters. In transmissive PPG sensors, two or more optical waveforms are transmitted through patient tissue, such as a finger, and a sensor / receiver on the other side of the target analyzes the received waveforms to determine SpCO. Alternatively, PPG sensors may be reflective. In reflective PPG sensors, light is shone onto a target, such as a finger, and a receiver / sensor captures the reflected light to determine a measurement of SpCO. Further details are provided below.

[0070] Transcutaneous or transmucosal sensors can noninvasively determine blood CO2 levels and other parameters based on analysis of the attenuation of an optical signal passing through tissue. Transmission sensors are typically placed in contact with a thin body part, such as the earlobe, pinna, fingertip, toe, skin crease, or another suitable body part. Light is shone from one side of the tissue and detected on the other side. A photodiode on one side is tuned to a specific set of wavelengths. A receiver or detector on the other side detects which waveform is transmitted and how much it is attenuated. This information is used to determine the rate at which O2 and / or CO bind to hemoglobin molecules, i.e., SpO2 and / or SpCO.

[0071] Reflectance sensors can be used on thicker body parts, such as the wrist. Light shone on a surface is not measured on the other side, but instead is measured in the form of light reflected from the surface on the same side. The wavelength and attenuation of the reflected light are used to determine SpO2 and / or SpCO. In some embodiments, problems due to movement of the patient's wrist are corrected using an accelerometer. For example, information from the accelerometer is used to correct errors in SpO2 and SpCO values ​​due to movement. An example of such a sensor is disclosed in U.S. Patient Monitor No. 8,224,411, entitled "Noninvasive Multi-Parameter Patient Monitor." Another example of a suitable sensor is disclosed in U.S. Pat. No. 8,311,601, entitled "Reflectance and / or Transmissive Pulse Oximeter." These two U.S. patents are incorporated herein by reference in their entireties, including the entire contents thereof.

[0072] In some embodiments, device 102 or 202 is configured to recognize a unique characteristic of the patient, such as a fingerprint, retinal scan, voice label, or other biometric identifier, to prevent a representative from responding to signaling and test prompts in an attempt to circumvent the system. To this end, a patient identification subunit may be included in device 102 or 202. One skilled in the art may configure the identification subunit as needed to include one or more of a fingerprint scanner, a retinal scanner, a voice analyzer, or facial recognition, as known in the art. Examples of suitable identification subunits are disclosed, for example, in U.S. Pat. No. 7,716,383, entitled "Flash-interfaced Fingerprint Sensor," and U.S. Patent Application Publication No. 2007 / 0005988, entitled "Multimodal Authentication," both of which are incorporated herein by reference in their entireties.

[0073] The identification subunit may include a built-in still or video camera for automatically recording photographs or videos of the patient as the biological input is provided to the test element. Regardless of the type of identification protocol used, the device 102 or 202 may associate the identification with a particular biological input, for example, by a time reference, and may store that information along with other information about the particular biological input for later analysis.

[0074] Patients may also attempt to circumvent the detector, for example, when testing breath, by blowing into the detector using a pump, air bag, billows, or other device. In saliva-testing embodiments, patients may attempt to substitute a clean liquid, such as water. In the case of light-based sensors, patients may ask a friend to act on their behalf. Means for overcoming these attempts may be incorporated into the system. For example, device 102 or 202 may incorporate the ability to distinguish between actual and simulated breath delivery. This functionality may be incorporated by configuring the detector unit to sense oxygen and carbon dioxide, as well as target substances (e.g., carbon monoxide). In this way, the detector unit can confirm that the analyzed gas originates from breath with lower oxygen and higher carbon dioxide than ambient air. In another example, the detector unit may be configured to detect enzymes naturally occurring in saliva to distinguish saliva from other liquids. In yet another example, light-based sensors may be used to measure blood chemistry parameters other than CO levels, so that the results can be compared to known samples representative of the patient's blood chemistry.

[0075] In some embodiments, device 104 (e.g., a smartphone) receives measurements from device 102 (e.g., a wearable device) in real time, near real time, or periodically according to appropriate intervals. Device 104 may provide a user interface for prompting the patient for specific inputs. Device 104 may provide a user interface for displaying specific outputs of collected data. Device 104 may allow the patient to input information they believe is relevant to their condition, either without prompting or in response to a prompt. Such information may include information about the patient's state of mind, such as feeling stressed or anxious. Such spontaneous information may be correlated to biological inputs based on a predetermined algorithm, such as being associated with the biological input closest in time to the spontaneous input or the first biological input occurring after the spontaneous input. Server 106 (e.g., a healthcare database server) may receive such data from one or both of devices 102 and 104. In some embodiments, data may be stored in one or more combinations of devices 102, 104, and 106. The data may be reported to various stakeholders, such as the patient, the patient's physician, peer groups, family, counselors, employers, and other suitable stakeholders.

[0076] In some embodiments, a wearable device, e.g., device 102 or 202, is applied to a patient, e.g., during their regular annual visit, to detect smoking behavior, after which the smoker can be referred to a smoking cessation program. The patient is provided with a wearable device to wear as an outpatient for a period of time, e.g., one day, one week, or another suitable period. A longer wearing time can provide greater sensitivity in detecting smoking behavior and greater accuracy in quantifying variables related to smoking behavior. Figure 15 below provides an exemplary flow diagram for detecting smoking behavior and is described in more detail below.

[0077] In some embodiments, employers ask employees to voluntarily wear a wearable device for a set period of time, such as a day, a week, or another suitable period. Incentive programs can be similar to programs for biometric screening for obesity, dyslipidemia, diabetes, hypertension, and other suitable health conditions. In some embodiments, health insurance companies ask subscribers to wear a wearable device for a suitable period of time to detect smoking behavior. Based on the quantified smoking behavior, these patients can be referred to smoking cessation programs, as described in this disclosure.

[0078] When the wearable device is worn for a suitable period of time, e.g., five days, several parameters can be measured in real time or near real time. These parameters may include, but are not limited to, CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil diameter, geographic location, environment, ambient temperature, stress factors, life events, and other suitable parameters. Figure 12 shows an exemplary chart 400 of a patient's varying levels of SpCO over a typical five-day monitoring period. Data points 402 and 404 indicate high levels of CO, which likely indicate a high smoking event. Data points 406 and 408 indicate low levels of CO, perhaps because the patient was asleep or otherwise busy. One or more algorithms can be applied to the precise data points on the curve to detect smoking events with sufficient sensitivity and specificity. For example, the algorithm may analyze one or more of the shape, onset point, upstroke, slope, peak, delta, downslope, upslope, time of change, area under the curve, and other suitable factors of the SpCO curve to detect smoking events.

[0079] Data from the wearable device may be transmitted to a smartphone, e.g., device 104, or a cloud server, e.g., server 106 or 204, in real time, at the end of each day, or according to another suitable time interval. The smartphone may measure parameters including, but not limited to, movement, location, date and time, patient-input data, and other suitable parameters. Patient-input data may include stressors, life events, location, daily events, administration of nicotine patches or other prescriptions, administration of other medications for smoking cessation, and other suitable patient-input data. For example, some of the patient-input data may include information about phone calls, athletic events, work, sports, stress, gender, alcohol consumption, smoking, and other suitable patient-input data. Received data may be compiled, analyzed for trends, and correlated either in real time or after a period is completed.

[0080] From the above measured parameters, information regarding smoking can be derived, for example, via a processor located in device 102, 104, or 202 or server 106 or 204. For example, the processor can analyze the information to determine CO trends, averages, peaks, and correlations; other vital sign trends throughout the day; and how vitals change before, during, and after smoking. FIG. 13 shows an exemplary diagram 500 of the analyzed information. A patient can arrive at FIG. 13 by zooming in on a given day in FIG. 12. Data point 502 shows the SpCO level when the patient is asleep. Data point 504 shows the lowest SpCO level when the patient wakes up. Data points 506, 508, and 510 show that high SpCO levels are associated with triggers such as breaks, lunch, and commuting. 13 to determine parameters such as the total number of cigarettes smoked, the average number of cigarettes smoked per day, the maximum number of cigarettes smoked per day, the strength of each cigarette smoked, the quantity of each cigarette smoked, what the patient's smoking events look like on the curve, time of day, day of the week, associated stressors, geography, location, and exercise that may later be used for smoking cessation programming. For example, the total number of peaks on a given day may indicate the number of cigarettes smoked, while the slope of each peak may indicate the strength of each cigarette smoked.

[0081] FIG. 14 shows an exemplary data structure for storing patient data. In this embodiment, data structure 600 shows patient data 602 associated with a data point, e.g., data point 508, from FIG. 13 . Patient data 602 includes identifying patient information such as patient name 604 and patient age 606. Patient data 602 includes curve data 608 corresponding to the curve in FIG. 13 . For example, curve data 608 includes a curve identifier 610 corresponding to data point 508. Data corresponding to data point 508 may be collected by device 102, 104, or 202 and / or server 106 or 204, or a combination thereof. Data associated with curve identifier 610 includes date, time, and location information 612. Data includes patient vital signs, such as CO2 level and O2 level 614. Data includes patient-input data, such as trigger 616. Patient-input data may be entered, for example, in response to a prompt for the patient on device 104 or at the patient's discretion without a prompt. The curve data 608 includes the curve identifier 618 of the additional data points of Figure 13. The data structure 600 can be adapted as needed to store patient data.

[0082] 15 shows an exemplary flow diagram 700 for detecting a patient's smoking behavior over a suitable evaluation period. Once the patient wears the wearable device for a suitable period, e.g., five days, several parameters can be measured in real time, near real time, at the end of each day, or according to another suitable time interval. These parameters may include, but are not limited to, CO, eCO, SpCO, SpO, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil diameter, geographic location, environment, ambient temperature, stress factors, life events, and other suitable parameters. The wearable device or another suitable device can measure parameters including, but not limited to, movement, location, time and date, and other suitable parameters.

[0083] At step 702, a processor of a smartphone, e.g., device 104, or a cloud server, e.g., server 106 or 204, receives the described patient data. At step 704, the processor receives patient-entered data, e.g., in response to prompts displayed to the patient on the smartphone, and / or patient data voluntarily entered by the patient without prompting. The patient-entered data may include stressors, life events, location, daily events, administration of nicotine patches or other prescriptions, administration of other medications for smoking cessation, and other suitable patient-entered data. At step 706, the processor transmits instructions to update a patient database with the received data. For example, the processor can transmit the patient data to a healthcare provider server or a cloud server hosting the patient database.

[0084] At step 708, the processor analyzes the patient data to determine smoking events. The processor may compile the data, analyze the data for trends, and correlate the data in real time or after the evaluation period is complete. For example, the processor may analyze the information to determine CO trends, averages, peaks, curve shapes, and relationships, other vital sign trends throughout the day, and how vitals change before, during, and after smoking. The processor may analyze SpCO trends to determine parameters such as the total number of cigarettes smoked, the average number of cigarettes smoked per day, the maximum number of cigarettes smoked per day, the strength of each cigarette smoked, time of day, day of week, associated stress factors, geography, location, and exercise. For example, the total number of peaks for a given day may indicate the number of cigarettes smoked, while the slope of each peak may indicate the strength of each cigarette smoked.

[0085] At step 710, the processor transmits the determined smoking events and associated analyses to a patient database for storage. At step 712, the processor determines whether the evaluation period has expired. For example, the evaluation period may be five days or another suitable period. If the evaluation period has not expired, the processor returns to step 702 to receive additional patient data, analyze the data, and update the patient database accordingly.

[0086] If the evaluation period has ended, the processor terminates data collection and analysis in step 714. For example, the processor may evaluate all collected data and generate a report at the end of the evaluation period, as described below with respect to FIG.

[0087] It is contemplated that the steps or descriptions of Figure 15 may be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions described in connection with Figure 15 may be performed in alternate orders or in parallel for further purposes of the present disclosure. For example, each of these steps may be performed in any order, as appropriate, in parallel, or substantially simultaneously, to reduce delay or improve the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed in connection with Figure 9 (e.g., devices 102, 104, or 106) or Figure 10 (e.g., devices 202 or 204) may be used to perform one or more of the steps of Figure 15.

[0088] In some embodiments, the systems and methods described herein provide for initiating and setting up a smoking cessation program for a patient. After a patient completes a five-day assessment while wearing a wearable device, e.g., device 102 or 202, the complete data set is compiled and analyzed by the system and delivered to the patient or physician for smoking cessation programming. FIG. 16 shows an exemplary embodiment 800 of a sample report from this analysis. For example, the report shows that from October 1 to October 6, Mr. Jones smoked a total of 175 cigarettes, with an average number of cigarettes smoked per day of 35 and a maximum number of cigarettes smoked in a single day of 45. Mr. Jones's CO2 levels averaged 5.5%, with a maximum of 20.7%, and remained above 4% for 60% of the duration of the five-day assessment period. Mr. Jones's triggers included work, home stressors, and commuting. The report recommends high doses and frequent predicted nicotine levels for initiation of nicotine replacement therapy in light of Mr. Jones's smoking habits.

[0089] In some embodiments, the patient works with a doctor or counselor to begin the process of entering a smoking cessation program. In some embodiments, the system automatically configures the smoking cessation program based on data from the evaluation period. The sample report in FIG. 16 is an example of measuring SpCO and generating a report on CO exposure, associated stress factors, and a prediction of starting nicotine dose requirements. For example, heavy and intense smokers may be more nicotine dependent at the time of smoking cessation program entry, which the processor can estimate based on five days of behavior, and the smoking cessation program would start the patient on a higher nicotine replacement therapy dose. This may prevent many patients from prematurely failing the smoking cessation program due to withdrawal symptoms. Based on report data including the average and maximum number of cigarettes smoked, SpCO levels, and triggers, the processor may determine a nicotine dosage for administration to the patient. For example, the processor may determine a higher nicotine dosage for patients who, on average, smoke more than a threshold number of cigarettes per day. As report data is updated, the processor may also update the nicotine dosage.

[0090] The collected data may influence the initiation and placement of a smoking cessation program by assisting in medication selection and administration immediately prior to entry. For example, higher smoking indicators may prompt the initiation of a higher nicotine replacement therapy dose or multiple medications (e.g., adding a medication used to treat nicotine addiction, such as varenicline). The collected data may influence the initiation and placement of a smoking cessation program by determining the frequency, type, and duration of counseling required for the patient. The data may lead to stratification of smokers' needs. For example, the highest-risk smokers with the highest use may receive more intervention, while lower-risk smokers may receive less intervention. For example, intervention may include a text message, phone call, social networking message, or another suitable event from the patient's spouse, friend, physician, or another suitable stakeholder when the patient is prone to smoking.

[0091] The collected data may influence the initiation and setup of smoking cessation programs by correlating smoking behavior with all of the variables described above, such as stressors that prompt smoking, time of day, and other suitable variables used in advance patient counseling to recognize these triggers. Counseling interventions can target these stressors, and there may be time-sensitive interventions for patients, such as text messages or ad hoc calls. The collected data may influence the initiation and setup of smoking cessation programs by assigning peer groups based on smoking behavior. The collected data may be used to predict and / or prevent smoking events. For example, if tachycardia or heart rate variability or a suitable set of variables precedes most smoking events, an alarm will sound and the patient can administer a dose of medication or receive a call from a peer group, doctor, or counselor. FIG. 20 illustrates an exemplary embodiment for preventing smoking events, which will be discussed in more detail below. FIGS. 22 and 23 illustrate exemplary flow diagrams for predicting and preventing anticipated smoking events, which will be discussed in more detail below.

[0092] In some embodiments, the systems and methods described herein provide for maintaining a patient's participation in a smoking cessation program. Once in the smoking cessation program, the patient may continue to wear a wearable device for monitoring, e.g., device 102 or 202. The system may use analytical tools, such as establishing an SpCO baseline and tracking progress against this baseline. The trend may drop to zero and remain there (indicating no more smoking). The trend may drop slowly with peaks and dips (indicating a reduction in smoking). The trend may drop to zero and then spike back up again (indicating relapse).

[0093] The system can engage patients using patient engagement strategies by offering small, infrequent rewards for group or individual progress. The system can provide employer compensation, payer, spouse, or peer group engagement. The system can gamify the process for patients and improve visibility of progress. FIG. 21 provides an exemplary embodiment of such a user interface, which is discussed in more detail below. In some embodiments, the system can transmit data to healthcare providers in real time for remote monitoring, allowing providers to efficiently monitor and adjust patient treatment without having to have data in the office every day. For example, providers can adjust medication types and doses, change the intensity of counseling, calls, and texts to proactively promote progress, or send commands to the system to trigger intervention if the patient is unable to abstain from smoking. This can replace expensive staffed quit phone lines and efficiently automate the process. The system can use increased intensity and frequency to improve patient outcomes. The system can encourage patients through support from spouses, employers, healthcare providers, peers, friends, and other suitable parties via scheduled phone calls, text messages, or other suitable communications.

[0094] FIG. 17 shows an exemplary graph 900 for tracking average daily SpCO trends for a patient preparing for and then entering a smoking cessation program. The average trend is tracked daily as the patient improves. A physician or counselor can zoom in on a specific day (current or past) to view details and the relationship of CO to other measured parameters and associated stressors 910. Visibility into CO trends over time in a smoking cessation program can prevent patient dropout, prevent relapse, titrate medications, and improve outcomes. For example, data point 902 shows CO levels before the patient entered the smoking cessation program. Data points 904 and 906 show CO levels when nicotine replacement therapy and varenicline therapy are administered during the smoking cessation program. Data point 908 indicates the patient has successfully quit smoking. At this point, the system may recommend that the patient enter an addiction prevention program to prevent relapse.

[0095] In some embodiments, the systems and methods described herein provide a follow-up program after a patient successfully quits smoking. After successful quitting as verified by the system, the patient wears a wearable device, such as device 102 or 202, for an extended period of time, such as several months to two years, as an early detection system for relapse. The system collects data and can use counseling strategies as described above for smoking cessation programs.

[0096] In some embodiments, patients receive a wearable device, e.g., device 102 or 202, and an app on their smartphone, e.g., device 104, that allows them to remotely and privately assess their health by tracking several different parameters. Patients can submit breath samples or place their finger in or on the sensor of the wearable device several times per day as needed. Patients can wear the wearable device to obtain more frequent or even continuous measurements. At the end of a test period, e.g., 5-7 days or another suitable period, the smartphone processor can calculate the patient's CO exposure and related parameters. FIG. 18 shows an example app screen 1000 showing measurements such as SpCO 1002, SpO 1004, heart rate 1006, respiratory rate 1008, blood pressure 1010, and body temperature 1012. Warning indicators 1014 and 1016 can be provided for abnormal measurements that may indicate the effects of smoking on the body. The system can prompt the patient with an alert when the warning indicator 1014 or 1016 is activated.

[0097] The system may recommend that the patient enter a smoking cessation program and provide options for such a program. The patient may agree to enter a smoking cessation program upon seeing such objective evidence of smoking. The system may share this data with the patient's spouse, physician, or another suitable stakeholder involved in the patient's smoking cessation program. For example, the system may share the data with an application on the stakeholder's mobile device or send a message containing the data via email, telephone, social network, or another suitable medium. Triggers for enrolling the patient in a smoking cessation program may include a spouse's suggestion, an employer's encouragement, peer pressure, personal choice, illness, or another suitable trigger. The patient can start the smoking cessation program on their own or bring the data to their physician for assistance in enrolling in a smoking cessation program.

[0098] While the patient is initiated in a smoking cessation program, a wearable device, such as device 102 or 202, can continue to monitor the patient's health parameters, such as heart rate, movement, location, etc., that precede smoking behavior and transmit the data to the patient and / or physician to improve treatment. For example, a smartphone app on device 104 may receive patient-input data, including, but not limited to, stressors, life events, location, daily events, administration of nicotine patches or other prescriptions, administration of other medications for smoking cessation, and other suitable patient-input data.

[0099] FIG. 19 shows an example embodiment of an app screen 1100 for receiving patient input data. The app screen 1100 may be displayed when the smartphone app receives an indication of a smoking event, for example, due to a sudden increase in the patient's CO levels. The app screen 1100 prompts the user to enter a smoking event trigger. For example, the patient can select one of options 1102, 1104, 1106, and 1108 as triggering a smoking event, or select option 1110 and provide further information about the trigger. Other triggers for a smoking event may include a phone call, an athletic event, a sport, stress, gender, and other suitable patient input data. The patient may also independently invoke the app screen 1100 and enter the smoking event trigger information. In some embodiments, the app screen 1100 for receiving patient data is displayed to the patient during a five-day assessment period to collect information about their smoking behavior before they enter a smoking cessation program.

[0100] In some embodiments, the collected data is used by a smartphone app to prevent smoking events. A processor running the app, or a processor in another device (e.g., device 102 or 202 or server 106 or 204), can analyze information relating to heart rate and other vital signs in the period leading up to a smoking event. The processor can correlate changes in heart rate, such as tachycardia, that can predict when a patient will smoke. This information can be used to initiate a prevention protocol to halt a smoking event. For example, the prevention protocol can include delivering a rapid dose of nicotine. The nicotine can be delivered via transdermal delivery from a transdermal patch or a wearable device, e.g., a reservoir of nicotine stored in device 102 or 202. In another example, the prevention protocol can include calling the patient's physician, peer group, or another suitable stakeholder. The processor can send instructions to an automated call system, e.g., resident on server 106 or 204, to initiate the call. 22 and 23 provide a flow diagram for predicting smoking events based on a patient's vital signs and are described in more detail below.

[0101] 20 shows an example embodiment of an app screen 1200 implementing such a prevention protocol. For example, if a patient is prone to tachycardia 20 minutes before each cigarette, the processor can detect the tachycardia and prompt the patient to administer nicotine via option 1202. The patient can vary the nicotine dose via option 1204. In some embodiments, the nicotine is administered automatically. The amount can be determined based on the patient's current SpCO2 level or another suitable parameter. The patient can receive calls from a peer group via option 1206, a physician via 1208, or another suitable stakeholder. The caller can encourage the patient to abstain from smoking and suggest finding other activities to distract the patient.

[0102] In some embodiments, the smartphone app presents the process as a game for the patient to improve visibility of progress. The app can engage the patient using patient engagement strategies by offering small, frequent, or infrequent rewards for group or individual progress. The app can provide employer rewards, payer, spouse, or peer group to engage the patient. FIG. 21 shows an example app screen 1300 of such an embodiment. The app screen 1300 offers the patient a reward for abstaining from smoking for 15 days. Prompt 1302 suggests the patient abstain for another 15 days. The patient can select option 1304 to accept the reward and continue monitoring their progress while abstaining. However, the patient may have difficulty abstaining and can select option 1306 to contact a peer group, counselor, family member, physician, or another suitable party.

[0103] In some embodiments, the patient is a peer and supporter of others in a patient group. The group can track each other's progress and offer support. For example, group members can be part of a social network that allows them to see each other's statistics and encourage each other to abstain from smoking. In another example, a message, e.g., a tweet, can be sent to group members, e.g., followers, in the patient's social network when the patient is detected smoking. The message can notify the group member that the patient needs assistance. The group can contact the patient in various ways to offer assistance. This interaction can enable the patient to further abstain from smoking that day.

[0104] In some embodiments, during a primary care visit, the patient provides a sample and is asked whether they smoke. For example, a wearable device, e.g., device 102 or 202, is applied to the patient and receives a sample for a one-time, on-site measurement of the patient's SpCO level. The SpCO level may exceed a certain threshold, indicating the patient is smoking. FIG. 24 provides a flow diagram for a one-time measurement of a patient's SpCO level. The patient may be provided with a wearable device to wear as an outpatient for a period of time, e.g., one day, one week, or another suitable period. A longer wearing time can provide more sensitivity in detecting smoking behavior and greater accuracy in quantifying variables related to smoking behavior.

[0105] A wearable device, e.g., device 102 or 202, and a smartphone app, e.g., device 104, can continue to monitor a patient's health parameters, such as SpCO levels, in real time or near real time and process the data for review by the patient, a physician, or any other suitable party. The smartphone app can also provide the data in a form that is easy to summarize for daily or weekly review by the patient and / or physician. For example, the smartphone app can generate a display similar to FIG. 17 showing daily progress with the option to zoom in on a particular day to view further details. The physician can log the patient in a medical database stored, for example, on server 106 or 204, which communicates with a mobile device running the smartphone app, and continue to receive data from the smartphone app via the internet or another suitable communications link. The smartphone app can receive data from the sensor via a wired connection to the mobile device running the app or via a wireless connection, such as Wi-Fi, Bluetooth, radio frequency, or another suitable communications link.

[0106] The patient and doctor can set a future quit date and have it sent to the patient's home without any medication or with medication to help the patient quit. The patient can begin working toward the agreed-upon quit date. Feedback from the wearable device and / or smartphone app can help the patient smoke less on the quit date than when they first started, and be more prepared on the quit date when they actually quit. Once the patient begins the smoking cessation program, they can receive daily or weekly feedback from their spouse, doctor, nurse, counselor, peer, friend, or any other suitable party.

[0107] Medication, if prescribed, can be physician-based or automatically adjusted based on patient performance. For example, a physician can remotely increase or decrease nicotine dosage based on the patient's CO, eCO, and SpCO levels. In another example, a processor in a wearable device, e.g., device 102 or 202, a smartphone, e.g., device 104, or a remote server, e.g., server 106 or 204, can increase or decrease nicotine dosage based on CO trends from the patient's past measurements. Similarly, medication can be shortened or lengthened in duration according to collected data.

[0108] 22 shows an example flow diagram 1400 for predicting a smoking event based on a patient's CO, eCO, SpCO measurements, and other suitable factors. The patient may be provided with a wearable device, e.g., device 102 or 202, and a smartphone app for their mobile phone, e.g., device 104. The wearable device may include a PPG sensor for measuring the patient's SpCO level. At step 1402, a processor within the wearable device or the patient's mobile phone receives the patient's SpCO level and associated time and location PPG measurements. The processor may also receive other information, such as heart rate, respiratory rate, and other suitable factors in predicting a smoking event.

[0109] At step 1404, the processor uses the received patient data to update a patient database stored locally or remotely, such as a medical database in server 106. At step 1406, the processor analyzes current and previous measurements of patient parameters to determine whether a smoking event is expected. For example, the SpCO trend may result in a local minimum indicating that the user may reach for a cigarette, increasing the SpCO level. The processor may apply a gradient descent algorithm to determine the local minimum. At step 1408, the processor determines whether the SpCO trend indicates an expected smoking event. If the processor determines that a smoking event is not expected, at step 1410, the processor determines whether the time and / or location indicate an expected smoking event. For example, the processor may determine that the patient typically smokes when they wake up around 7:00 a.m. In another example, the processor may determine that the patient typically smokes immediately after arriving at work. In yet another example, the processor may determine that the patient typically smokes whenever they visit a particular restaurant or bar in the evening.

[0110] If the processor determines from either step 1408 or 1410 that a smoking event is expected, then in step 1412, the processor initiates a prevention protocol for the patient to prevent the smoking event. Information regarding the prevention protocol may be stored in memory of device 102, 104, or 202, or server 106 or 204, or a combination thereof. The prevention protocol information may include instructions for one or more intervention options to initiate when the patient attempts to smoke. For example, the processor may initiate an alert on the patient's mobile phone and display an app screen similar to FIG. 20. The app screen may provide the patient with options to administer nicotine or receive a call from a peer group, a physician, or another suitable party. Alternatively, the prevention protocol may include automatically administering a nicotine bolus to the patient from a nicotine container stored on the patient's wearable device. In another example, the app screen may indicate that if it is detected that the patient has failed to abstain from smoking, a message, e.g., a tweet, is sent to group members, e.g., followers, of the patient's social network. Patients can abstain from smoking to prevent messages of personal failure being sent.

[0111] In some embodiments, steps 1408 and 1410 are combined into one step or include two or more steps for the processor to determine that a smoking event is predicted. For example, the processor may determine that a smoking event is predicted based on a combination of SpCO trend, the patient's location, and / or the current time. In another example, the processor may determine that a smoking event is predicted based on a series of steps for analyzing one or more of the patient's SpCO, SpO, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil diameter, geographic location, environment, ambient temperature, stress factors, life events, and other suitable parameters.

[0112] At step 1414, the processor determines whether the prevention protocol was successful. If a smoking event occurred, at step 1418, the processor updates the patient database to indicate that the prevention protocol was not successful. If a smoking event did not occur, at step 1416, the processor updates the patient database to indicate that the prevention protocol was successful. The processor returns to step 1402 to continue receiving PPG measurements for the patient's SpCO level and associated data. The processor can continuously monitor the patient's vital signs to ensure that the patient does not relapse into a smoking event.

[0113] It is contemplated that the steps or descriptions of Figure 22 may be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions described in connection with Figure 22 may be performed in alternate orders or in parallel for further purposes of the present disclosure. For example, each of these steps may be performed in any order, as appropriate, in parallel, or substantially simultaneously, to reduce delay or improve the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed in connection with Figure 9 (e.g., devices 102, 104, or 106) or Figure 10 (e.g., devices 202 or 204) may be used to perform one or more of the steps of Figure 22.

[0114] FIG. 23 shows an example flow diagram 1500 for determining whether a prevention protocol was successful in connection with step 1414 of FIG. 22 . In step 1502, the processor receives patient data for determining whether a smoking event occurred. In step 1504, the processor analyzes the currently received patient data and the previously received patient data. In step 1506, the processor determines whether a smoking event occurred based on the analysis. For example, if nicotine was not administered but the patient's current SpCO level is higher than the previous SpCO level, the processor may determine that the patient has relapsed and smoked a cigarette. In such a situation, in step 1508, the processor returns a message indicating that the prevention protocol was not successful. In another example, if the patient's vital signs do not indicate an increase or a decrease in SpCO level, the processor may determine that a smoking event did not occur. In such a situation, in step 1510, the processor returns a message indicating that the prevention protocol was successful.

[0115] It is contemplated that the steps or descriptions of Figure 23 may be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions described in connection with Figure 23 may be performed in alternate orders or in parallel for further purposes of the present disclosure. For example, each of these steps may be performed in any order, as appropriate, in parallel, or substantially simultaneously, to reduce delay or improve the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed in connection with Figure 9 (e.g., devices 102, 104, or 106) or Figure 10 (e.g., devices 202 or 204) may be used to perform one or more of the steps of Figure 23.

[0116] FIG. 24 shows an example flow diagram 1600 for one-time measurement of a patient's SpCO level using a PPG sensor. For example, a wearable device, such as device 102 or 202, is applied to a patient and receives a sample of the one-time measurement of the patient's SpCO level. At step 1602, a processor within the wearable device receives PPG measurements of the patient's SpCO level and any other suitable data, such as time, location, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil diameter, geographic location, environment, ambient temperature, stress factors, life events, and other suitable parameters. At step 1604, the processor analyzes the received data to determine a recent smoking event. For example, an elevated SpCO level above a certain threshold may suggest that the patient has recently smoked a cigarette.

[0117] In step 1606, the processor determines whether the patient's SpCO level indicates that a smoking event has occurred. For example, an SpCO level above a specified threshold may indicate a smoking event. In another example, one or more of the shape, onset, upstroke, slope, peak, delta, downslope, upslope, time of change, area under the curve, and other suitable factors of the SpCO curve may indicate a smoking event. One or more of these factors may assist in quantifying a smoking event. For example, the total number of peaks on a given day may indicate the number of cigarettes smoked, while the slope shape and size of each peak and other characteristics may indicate the strength and quantity of each cigarette smoked. If the processor determines that the SpCO level indicates that a smoking event has not occurred, in step 1608, the processor returns a negative message indicating that the patient has not had a recent smoking event. The patient's physician may find this information useful in evaluating the patient's smoking behavior. If the processor determines that the SpCO level indicates that a smoking event has occurred, then in step 1610 the processor returns a confirmation message indicating that the patient has had a recent smoking event. In this case, the collected data can be used to set up a smoking cessation program for the patient.

[0118] After steps 1608 or 1610, in step 1612, the processor updates the patient database to record this information. In step 1614, the processor completes the evaluation of the patient's SpCO level. The patient may be provided with a wearable device to wear as an outpatient for a period of time, e.g., one day, one week, or another suitable period. Longer wear times can provide more sensitivity in detecting smoking behavior and greater accuracy in quantifying variables related to smoking behavior.

[0119] It is contemplated that the steps or descriptions of Figure 24 may be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions described in connection with Figure 24 may be performed in alternate orders or in parallel for further purposes of the present disclosure. For example, each of these steps may be performed in any order, as appropriate, in parallel, or substantially simultaneously, to reduce delay or improve the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed in connection with Figure 9 (e.g., devices 102, 104, or 106) or Figure 10 (e.g., devices 202 or 204) may be used to perform one or more of the steps of Figure 24.

[0120] In some embodiments, data from one or more devices associated with a patient, such as devices 102 and 104 or device 202, is received at a central location, such as server 106 or 204. The patient devices record multiple biometric and contextual variables in real time or near real time. For example, biometric variables may include CO, eCO, SpCO, SpO, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil diameter, and other suitable biometric variables. For example, contextual variables may include GPS location, patient activity (e.g., sports, gym, shopping, or another suitable patient activity), patient environment (e.g., work, home, vehicle, bar, or another suitable patient environment), stressors, life events, and other suitable contextual variables. Collected data may also include in-person observations of the patient's smoking behavior. A spouse, friend, or companion may enter data that the patient has smoked and correlate that data with the SpCO reading to determine accuracy.

[0121] The server 106 includes a processor for receiving data for multiple patients over a period of time and analyzing the data for trends occurring around actual smoking events. Based on the trends, the processor determines diagnostic and / or detection tests for smoking events. The tests may include one or more algorithms determined by the processor and applied to the data. For example, the processor may analyze a sudden increase in the patient's CO level. Detecting a sudden increase may include determining that the CO level exceeds a certain specified level. Detecting a sudden increase may include detecting a relative increase in the patient's CO level from a previously measured baseline. The processor may detect a sudden increase as a change in the slope of the patient's CO trend over a period of time. For example, a CO trend changing from a negative slope to a positive slope may indicate a sudden increase in CO levels. In another example, the processor may apply one or more algorithms to changes in heart rate, increased heart rate variability, changes in blood pressure, or other suitable data fluctuations to detect smoking events.

[0122] FIG. 25 shows an example flow diagram 1700 for detecting a smoking event, as described above. A processor (e.g., within the server 106 or 204) can determine diagnostic and / or detection tests for a smoking event according to the flow diagram 1700. In step 1702, the processor receives current patient data. In step 1704, the processor retrieves previously stored data for the patient from a database, such as a patient database stored on the server 106 or 204. In step 1706, the processor compares the current and previous patient data to detect a smoking event. For example, the processor may analyze a sudden rise in the patient's CO level. Detecting a sudden rise may include detecting a relative increase in the patient's CO level from a previously measured baseline. The processor can detect a sudden rise as a change in the slope of the patient's CO trend over a period of time. For example, a CO trend changing from a negative slope to a positive slope may indicate smoking behavior. In another example, the processor may apply one or more algorithms to changes in heart rate, increases in heart rate variability, changes in blood pressure, or other suitable data fluctuations to detect a smoking event. In step 1708, the processor determines whether a smoking event has occurred based on, for example, a sudden increase in the patient's CO levels as described. If a smoking event is not detected, in step 1710, the processor returns a message indicating that a smoking event has not occurred. If a smoking event is detected, in step 1712, the processor returns a message indicating that a smoking event has occurred. In step 1714, the processor updates the patient database with the results from either step 1710 or 1712.

[0123] It is contemplated that the steps or descriptions of Figure 25 may be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions described in connection with Figure 25 may be performed in alternate orders or in parallel for further purposes of the present disclosure. For example, each of these steps may be performed in any order, as appropriate, in parallel, or substantially simultaneously, to reduce delay or improve the speed of the system or method. Furthermore, it should be noted that any of the devices or apparatuses discussed in connection with Figure 9 (e.g., devices 102, 104, or 106) or Figure 10 (e.g., devices 202 or 204) may be used to perform one or more of the steps of Figure 25.

[0124] In some embodiments, the processor initially analyzes the received data to measure when a person smokes and associates the algorithm with a variable that operates the algorithm to diagnose and / or detect a smoking event. As the processor receives additional patient data, it continues to analyze other variables. The processor may determine another variable that changes when the patient smokes and operate the algorithm using that variable instead. For example, the processor may select to use another variable because it is less invasive or easier to measure than the initially selected variable.

[0125] In some embodiments, the algorithm for detecting smoking events has high sensitivity. Sensitivity is defined as the percentage of the number actual smoking events detected by the sensor and algorithm. For example, if a patient smokes 20 times a day and the algorithm identifies all smoking events, it has a high sensitivity of 100%.

[0126] In some embodiments, the algorithm for detecting smoking events has high specificity. Specificity is defined as the ability of a test (i.e., a positive test without a smoking event) to not make a false positive call of a smoking event. If the sensor and algorithm make no false positive calls in a day, it has 100% specificity.

[0127] In another example, if a patient smokes 20 cigarettes and the algorithm identifies 18 of the 20 real smoking events and 20 other false smoking events, the algorithm has a sensitivity of 90% (i.e., it detected 90% of the smoking events) and a specificity of 50% (i.e., it overcalled the number of smoking events by 2-fold).

[0128] In some embodiments, after the processor determines and applies one or more algorithms to SpCO measurements to detect smoking events with sufficient sensitivity and specificity, the processor determines whether there is an association between the SpCO result and other biometric or contextual variables that can be used alone (without SpCO) to detect smoking events. The processor may determine another variable that changes when the patient smokes and operate the algorithm using that variable instead. For example, the processor may choose to use another variable because it is less invasive, easier to measure, or more reliable than the initially selected variable.

[0129] In some embodiments, the processor analyzes the received patient data to predict the likelihood of a smoking event before it occurs. The processor can analyze the received patient data over a period of time, e.g., 5 minutes, 10 minutes, 15 minutes, 20 minutes, or another suitable time interval, to determine one or more triggers prior to the smoking event. For example, some smoking events may be preceded by a contextual trigger (e.g., at a bar, before, during, or after a meal, before, during, or after sexual activity, or another suitable contextual trigger). In another example, some smoking events may be preceded by a change in a biometric variable, e.g., heart rate or another suitable biometric variable. The determined variables may overlap with those selected for diagnosis and detection and therefore may also be used for prediction. Alternatively, the determined variables may not overlap with those selected for diagnosis.

[0130] The processor can notify the patient of the likelihood of a smoking event and activate a prevention protocol (e.g., as discussed with respect to FIGS. 22 and 23) to prevent a change in smoking behavior. The processor can detect the patient's smoking events entered in, for example, a smoking cessation program, track received patient data, and analyze trends. The processor can determine patient goals and provide rewards when set goals are achieved (e.g., as discussed with respect to FIG. 21). The processor can predict when the patient is about to smoke and intervene just in time by suggesting a call to a peer group or a physician, or by administering a nicotine bolus (e.g., as discussed with respect to FIG. 20).

[0131] In one example, the processor predicts a patient's smoking event during the diagnosis based on 75% of the patient's smoking events being preceded by an increased heart rate (or a suitable change in another variable). During a smoking cessation program, the processor can apply one or more algorithms to the received patient data to predict a smoking event and initiate a prevention protocol. For example, the prevention protocol may engage the patient in time by having the patient contact a supporter, such as a doctor, counselor, peer, team member, nurse, spouse, friend, robot, or another suitable supporter. In some embodiments, the processor applies algorithms to adjust settings, such as baseline, threshold, sensitivity, and other suitable settings, for each patient based on their five-day run-in diagnosis period. The processor can then use these customized algorithms for the particular patient's smoking cessation program. The described combination of technologies for modifying smoking behavior in patients may be referred to as digital drugs.

[0132] In some embodiments, the processor detects smoking in a binary manner with a positive or negative sign indication. The processor first detects smoking behavior using observational studies and SpCO measurements from the patient. For example, the processor receives data regarding true positive smoking events from observational data of the patient's smoking behavior. The processor determines whether the detection based on the SpCO measurement matches a true positive smoking event. If there is a match, the processor applies an algorithm to other received patient data, including the patient's SpCO, SpO, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil diameter, geographic location, environment, ambient temperature, stress factors, life events, and other suitable parameters. The processor determines whether any patterns in the non-SpCO variable data also indicate a smoking event. Such variables may be used in algorithms in non-SpCO devices, such as wearable smartwatches or heart rate monitor straps or other devices, to detect smoking events.

[0133] The processor can quantify smoking behavior when a smoking event is detected based on the received patient data. For example, the processor can analyze SpCO data trends to indicate the strength at which the patient smoked each cigarette, the number of cigarettes the patient smoked per day, the amount of each cigarette smoked, and / or the time it took to smoke each cigarette. The processor may similarly use other biometric or contextual variables for indicators. The processor can use the received patient data to predict the likelihood of a smoking event occurring in the near future, for example, within the next 10 minutes. The processor can analyze the received patient data over a preceding period, for example, 5 minutes, 10 minutes, 15 minutes, 20 minutes, or another suitable time interval, to determine one or more triggers prior to the smoking event.

[0134] In some embodiments, the systems and methods described herein provide for evaluating a patient's smoking behavior. During a five-day test period, the patient behaves as they normally would. Devices 102, 104, and / or 106 or device 202 and / or server 204 receive patient data related to the patient's smoking behavior. Because the purpose of the test period is to observe the patient's smoking patterns, patient engagement ranges from very little to none. The test period can be extended to a second five-day period, if necessary. Alternatively, the first and second periods can be shorter, such as two or three days, or longer, such as one week or more. Prior to the second test period, a processor determines a model of the patient's smoking habits.

[0135] In a second testing phase, the processor applies a series of perturbation causes to the model to determine whether smoking behavior changes. There may be several types of perturbation causes, each with several dimensions. For example, a perturbation cause may be whether sending a text message before or during a smoking event averts or shortens the smoking event. Dimensions within a perturbation cause may be different senders, different timing, and / or different content of the text message. In another example, a perturbation cause may be whether a phone call at a specific date and time, or before or during a smoking event averts or shortens the smoking event. Dimensions within a perturbation cause may be different callers, different timing, and / or different content of the phone call. In yet another example, a perturbation cause may be whether alerting a patient to review their smoking behavior at several points in the day averts smoking for a subsequent period of time. Dimensions may include determining whether and when the avertance disappears. In another example, a perturbation cause may be a reward, team play, or other suitable trigger that averts or shortens a smoking event for the patient.

[0136] In some embodiments, the processor uses a machine learning process to deliver perturbations to the patient's smoking model. The machine learning process delivers the perturbations, tests the results, and adjusts the perturbations accordingly. The processor tries options through the machine learning process to determine which one works best to achieve the identified behavioral change. The machine learning process can be applied during a second testing phase as minor perturbations. The machine learning process can also be applied with major perturbations during the patient's smoking cessation phase to increase efforts to try to get the patient to quit or remain abstain from smoking.

[0137] FIG. 26 shows an example flow diagram 1800 for applying one or more perturbation causes to a patient's smoking model in a second testing phase. In step 1802, a processor in the wearable device 102 or 202, the mobile device 104, or the server 106 or 204 receives patient data related to the patient's smoking behavior in the first testing phase. In step 1804, the processor analyzes the received patient data to determine a model of the patient's smoking behavior. In step 1806, the processor applies one or more perturbation causes to the model to see if the smoking behavior changes. The perturbation causes may be applied to the model using a machine learning process. There may be several types of perturbation causes, each with several dimensions. For example, a perturbation cause may be whether sending a text message before or during a smoking event prevents or shortens the smoking event. Dimensions within a perturbation cause may be different senders, different timing, and / or different content of the text message.

[0138] In step 1808, the processor determines whether the perturbation cause changed the patient's smoking behavior. For example, the processor determines whether receiving a text message before or during a smoking event caused the patient to abstain or shorten their smoking. If the perturbation cause caused a change in the patient's smoking behavior, in step 1810, the processor updates the model of the patient's smoking behavior to reflect the positive outcome of the applied perturbation cause. The processor then proceeds to step 1812. Otherwise, the processor proceeds directly from step 1808 to step 1812 to determine whether to apply another perturbation cause or a variation of the dimension of this perturbation cause. The processor can use a machine learning process to determine whether to apply additional perturbation causes to the model. If no more perturbation causes need to be applied, in step 1814, the processor ends the process of applying perturbation causes.

[0139] If more perturbation causes need to be applied, then in step 1816, the processor determines another perturbation cause to apply to the model. For example, the processor may adjust the current perturbation cause to send a text message to the patient at a different time or with different content. In another example, the processor may apply a different perturbation cause by initiating a phone call to the patient before or during a smoking event. The processor returns to step 1806 to apply the perturbation cause to the model. The processor may use a machine learning process to deliver the perturbation cause, test the results, and adjust the perturbation cause or select another perturbation cause accordingly. In this manner, the processor tries different options via the machine learning process to determine which one works best to achieve the patient's identified behavior change.

[0140] It is contemplated that the steps or descriptions of Figure 26 may be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions described in connection with Figure 26 may be performed in alternate orders or in parallel for further purposes of the present disclosure. For example, each of these steps may be performed in any order, as appropriate, in parallel, or substantially simultaneously, to reduce delay or improve the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed in connection with Figure 9 (e.g., devices 102, 104, or 106) or Figure 10 (e.g., devices 202 or 204) may be used to perform one or more of the steps of Figure 26.

[0141] In an illustrative example, a 52-year-old male patient is encouraged by his employer to be screened for smoking behavior. The patient enters the assessment program on June 1, 2015. The patient reports smoking 20 cigarettes per day. A program coordinator, such as a physician or counselor, loads an app onto the patient's smartphone, e.g., mobile device 104, and provides the patient with a connected sensor, e.g., wearable device 102 or 202. The coordinator instructs the patient to smoke and behave normally over a five-day test period and respond to prompts from the app as they occur. After the five-day period has elapsed, the coordinator places the patient in an additional test period in which the app prompts more frequently (e.g., to apply agitation factors). The coordinator informs the patient that it is up to the patient to respond as they wish at that point. The coordinator establishes a target date of June 10, 2015 to encompass the 10-day test.

[0142] After the 5-day testing period, the coordinator receives a report (e.g., a 5-day report card as discussed with respect to FIG. 16). The report shows 150 cigarette smoking events detected using CO compared to 100 cigarette smoking events based on the patient's estimate. The report shows associated contextual variables include alcohol, location, stress, and other suitable data. The report shows associated biometric variables include increased heart rate without exercise as preceding 50% of the smoking events. The report shows that stress level prompts indicated increased stress in 20% of the smoking events.

[0143] During an additional five-day test period, a processor in the mobile device, e.g., device 104, the wearable device, e.g., device 102 or 202, or a remote server, e.g., server 106 or 204, applies the agitation triggers via a machine learning process. For example, the mobile device prompts the patient four times a day with a display including the number of cigarettes smoked, the smoking intensity, and the date and time. As the day progresses, the prompts cause the patient to reduce smoking for longer periods of time. The net effect is that the patient smokes fewer cigarettes in the second half of the day compared to the first half. In another example, the mobile device prompts the patient at 10:00 AM each day with a display including the number of cigarettes smoked on the previous day. The net effect is studied for how the prompts affect the patient's smoking behavior for the remainder of the day. The machine learning process can adjust the time and content of the display as needed to change the agitation trigger dimensions.

[0144] In another example, the processor applies the trigger via a machine learning process in the form of a text message sent to the patient during a smoking event. The machine learning process varies the dimension of the trigger by having a different sender, different timing, sending before or during smoking, different content of the message, different images in the message, and / or a different reward for abstaining. In another example, the processor applies the trigger via a machine learning process in the form of a phone call to the patient during a smoking event. The machine learning process varies the dimension of the trigger by having a different caller, different timing, calling before or during smoking, different content of the call, different tone in the call, and / or a different reward for abstaining.

[0145] In another example, the processor applies a trigger for agitation via a machine learning process in the form of a prompt for a particular activity on the patient's mobile device. The prompt indicates that the patient is smoking but should consider smoking only half a cigarette and then going outside. During long periods between cigarette events or when an event is predicted, the machine learning process applies a trigger for agitation to attempt to avoid the smoking event altogether. For example, the mobile device displays a prompt informing the patient that the patient is in a high-risk zone and should consider an alternative activity or location or a call to a friend.

[0146] After the trial period, the coordinator places the patient in a smoking cessation program. During the smoking cessation period, the processor receives the patient data and applies the algorithm to the data, as described above. The processor uses all data from the first and second trial periods to customize the algorithm and initiation regimen and smoking cessation program interventions for the specific patient. The diagnostic and detection algorithm may use one or more patient biometric variables, such as SpCO, to detect smoking behavior. The smoking cessation program includes a nicotine regimen beginning on day 1 as part of nicotine replacement therapy. Nicotine may be delivered via a transdermal patch or transdermal delivery from a reservoir of nicotine stored in a wearable device provided to the patient. The processor applies the algorithm to the received patient data to determine the most effective intervention. The processor applies the intervention and further adjusts the intervention as needed. The processor can set a target event count and determine which method best works to change the patient's smoking behavior. The processor can generate multiple personalized interventions from stakeholders as a trigger via a machine learning process and test which one best works to change the patient's smoking behavior. The sources of perturbation that have the most impact on the patient's smoking model can be retained, while those that have less or no impact cannot be used further.

[0147] While the exemplary embodiments of the systems and methods described above have focused on smoking behavior, it will be readily apparent to those skilled in the art that the teachings of the present invention are equally applicable to any number of other undesirable behaviors, including, but not limited to, tobacco smoking via cigarettes, pipes, cigars, and water pipes, as well as smoking illicit products such as marijuana, cocaine, heroin, and alcohol-related behaviors. Other such examples include oral placement of certain substances (specific examples include, but are not limited to, placing chewing tobacco and snuff in the oral cavity), transdermal absorption of certain substances (specific examples include, but are not limited to, applying to the skin certain creams, ointments, gels, patches, or other products containing addictive drugs (e.g., narcotics and LSD)), and snorting addictive drugs or substances (including, but not limited to, snorting cocaine).

[0148] Generally, the basic configuration of devices 102 and 104 or device 202, and the associated steps and methods disclosed herein, are similar between the different behaviors being addressed. Devices may vary somewhat in design to account for different target substances required for testing or different test methodologies required by different markers associated with particular undesirable behaviors.

[0149] It will also be understood by those skilled in the art that patients participating in a formal cessation program may utilize the systems and methods disclosed herein as an adjunct to the cessation program. It will equally be understood that patients may be independent and self-motivated and therefore may beneficially utilize the systems and methods to unilaterally quit undesirable behaviors outside of a formal cessation program.

[0150] In further exemplary embodiments, the systems and methods disclosed herein may be readily adapted for data collection and the collection of reliable and verifiable data for studies, particularly those related to undesirable behaviors that the present invention is well suited to testing. Such studies may be accomplished substantially without modification to the underlying devices or methods, except that updating test or treatment protocols based on user input would not necessarily be necessary if treatment was not included.

[0151] FIG. 27 illustrates another variation of a system and / or method for influencing an individual's smoking behavior and further quantifying the individual's exposure to cigarette smoke using several embodiments described herein. In the illustrated example, multiple samples of biometric data are obtained from an individual and analyzed to quantify the individual's exposure to tobacco smoke, so that quantified information can be relayed to the individual, a medical caregiver, and / or other parties with a stake in the individual's health. The example discussed below uses a portable device 1900 that obtains multiple samples of exhaled breath from an individual using a commonly available sensor that measures the amount of carbon monoxide in the exhaled breath samples (also referred to as exhaled carbon monoxide, or ECO). However, quantification and communication is not limited to exhaled breath-based smoking exposure. As noted above, there are many sampling means for obtaining an individual's smoking exposure. The methods and devices described in this example can be combined with or supplemented with such sampling means, where possible, while still remaining within the scope of the present invention. Additionally, while this example discusses the use of a portable sampling unit, the methods and procedures described herein can be used with dedicated or non-portable sampling units.

[0152] Measurement of exhaled CO levels is known to serve as an immediate, noninvasive method for assessing an individual's smoking status. See, for example, "The Measurement of Exhaled Carbon Monoxide in Healthy Smokers and Non-smokers," S. Erhan Devecia, et al., Department of Public Health, Medical Faculty of Firat University, Elazig, Turkey 2003, and "Comparison of Tests Used to Distinguish Smokers from Nonsmokers," M.J. Jarvis et al., American Journal of Public Health, November 1987, Vol. V77, No. 11. These articles discuss that exhaled CO ("eCO") levels in non-smokers can range from 3.61 ppm to 5.6 ppm. In one example, the cutoff level for eCO to identify smokers is greater than 8-10 ppm.

[0153] Returning to FIG. 27 , as shown, the portable or personal sampling unit 1900 communicates with either a personal electronic device 110 or a computer 112. Here, the personal electronic device 110 includes, but is not limited to, a smartphone, regular phone, cellular phone, or other personal transmitting device designed exclusively for receiving data from the personal sampling unit 1900. Similarly, the computer 112 is intended to include a personal computer, a local server, or a remote server. Data transmission 114 from the personal sampling unit 1900 can occur to both or either the personal electronic device 110 and / or the computer 112. Furthermore, synchronization 116 between the personal electronic device 110 and the computer 112 is optional. Any of the personal electronic device 110, the computer 112, and / or the personal sampling unit 1900 can transmit data to a remote server for data analysis as described herein. Alternatively, data analysis can occur in whole or in part on a local device (such as a computer or personal electronic device). In either case, the personal electronic device 110 and / or computer 112 can provide information to the individual, a caregiver, or other individuals, as shown in FIG.

[0154] 27 , a personal sampling unit 1900 receives a sample of exhaled breath 108 from an individual via a collection tube 1902. The hardware within the personal sampling unit 1900 includes any commercially available electrochemical gas sensor that detects carbon monoxide (CO) gas in the exhaled breath sample, and commercially available transmission hardware that transmits data 114 (e.g., via Bluetooth, cellular, or other radio waves that provide for transmission of data). The transmitted data and associated measurements and quantifications are then displayed on either (or both) the computer display 112 or the personal electronic device 110. Alternatively, or in combination, any of the information can be selectively displayed on the portable sampling unit 1900.

[0155] The personal sampling unit (or personal respiratory unit) may also allow for direct wired communication with the respective devices 110 and 112 using standard ports. In certain variations, the personal sampling unit 1900 may also include memory storage, either removable or built-in, where such memory allows for data recording and separate transmission of data. Alternatively, the personal sampling unit may allow for simultaneous storage and transmission of data. Additional variations of the device 1900 do not require memory storage. Furthermore, the unit 1900 may use any number of GPS components, inertial sensors (to track movement), and / or other sensors that provide additional information about the patient's behavior.

[0156] The personal sampling unit 1900 may also include any number of input triggers (such as switches or sensors) 1904, 1906. As described below, the input triggers 1904, 1906 allow an individual to prepare the device 1900 for delivery of a breath sample 108 or to record other information about the cigarettes, such as the quantity, strength, etc. of cigarettes smoked. In addition, variations of the personal sampling unit 1900 also associate timestamps of inputs to the device 1900. For example, the personal sampling unit 1900 may associate the time the sample is provided and provide the measured or input data with the time of measurement when transmitting data 114. Alternatively, the personal sampling device 1900 may use alternative means to identify the time at which the sample is taken. For example, given a series of samples, rather than recording the timestamp of each sample, the time period between each sample in the series may be recorded. Thus, identifying the timestamp of any one sample allows the determination of the timestamp for each of the samples in the series.

[0157] In certain variations, the personal sampling unit 1900 is designed to have a minimal profile and be easily carried by an individual with minimal effort. Accordingly, the input trigger 1904 can include a low-profile tactile switch, an optical switch, a capacitive touch switch, or any commonly used switch or sensor. The portable sampling unit 1900 can also provide feedback or information to the user using any number of commonly known techniques. For example, as shown, the portable sampling unit 1900 can include a screen 1908 that displays selection information, as discussed below. Alternatively, or in addition, feedback can be in the form of a vibrating element, an audible element, and a visual element (e.g., one or more colored illumination sources). Any of the feedback components can be configured to provide an alert to the individual that can serve as a reminder to provide a sample and / or to provide feedback related to the measurement of smoking behavior. Additionally, the feedback component can provide alerts to the individual on a recurring basis in an attempt to remind the individual to provide periodic samples of exhaled breath to extend the period during which the system captures biometrics (eCO, CO levels, etc.) and other behavioral data (location entered manually or via a GPS component coupled to the unit, number of cigarettes smoked, or other triggers). In certain cases, reminders may be triggered more frequently during initial programming or data capture. Once sufficient data has been acquired, the reminder frequency can be reduced.

[0158] FIG. 28A shows a visual representation of data that may be collected with the variation of the system shown in FIG. 27. As described above, an individual provides a breath sample using a portable sampling unit. The individual may be reminded at regular or random intervals, depending on the nature of the treatment or intervention program. Each sample is evaluated by one or more sensors in the portable sampling unit to measure the amount of CO. The CO measurements typically correspond to inflection points 410 on the graph in FIG. 28A. Each CO measurement 410 corresponds to a timestamp as shown on the horizontal axis. The data accumulated via the portable sampling unit allows for the collection of a data set including at least the CO measurements and time of the sample, which can be graphed to obtain an eCO curve that shows the amount of CO attributable to an individual's smoking behavior over time.

[0159] As described herein, an individual may further track additional information, such as cigarettes smoked. As indicated by bar 414, a cigarette smoked may be associated with its own timestamp. In one variation of the methods and systems under this disclosure, an individual may use an input trigger on the portable sampling unit to input the number of cigarettes smoked or fractions thereof. For example, each actuation of the input trigger may be associated with a fraction of a cigarette (e.g., 1 / 2, 1 / 3, 1 / 4, etc.).

[0160] FIG. 28B shows a portion of a graphical representation of the data collected as described above. However, in this variation, quantification of an individual's smoking behavior can use behavioral data to better approximate CO values ​​between eCO readings. For example, in some variations, eCO measurements between any two points 410 can be approximated using a linear approximation between the two points. However, it is known that CO levels in the bloodstream decay when not exposed to new CO. This decay can be approximated using a baseline ratio, a ratio based on the patient's biometric information (weight, heart rate, activity, etc.). As shown in FIG. 28B, when the patient is between cigarettes 414, the calculated CO level can follow a decay rate 440. Once the individual logs a cigarette 414, the CO increase 442 can be re-approximated by using the baseline ratio, or a ratio calculated using biometric data as described above, or based on the strength, duration, and quantity of the cigarette smoked. Thus, the methods and systems described herein can optionally use an improved (or approximated) eCO curve 438 using the behavioral data described above. Such improved eCO rate can also be used to determine an improved eCO curve 438 while the individual is asleep. This improved eCO curve can then provide the improved eCO load described herein. The biometric information used to determine the decay rate can be measured by a portable sampling device or by an external biometric measurement device in communication with the system.

[0161] This approximated or improved eCO curve 438 can be displayed to the individual (or a third party) as a means to aid in behavioral change, as the individual can see their real-time approximated CO levels (i.e., the rate of decrease when not smoking and the rate of increase when smoking). Additional information can also be displayed; for example, the system can also calculate the CO increase from each cigarette based on the starting CO values.

[0162] FIG. 29 illustrates an example data set used to determine an eCO curve 412 over a period of time, where the eCO attributable to an individual's smoking behavior can be quantified to determine interval-by-interval eCO burden or eCO load for various time intervals. As shown, the time period extends along the horizontal axis and includes historical and ongoing data captured / transmitted by the portable sampling unit. The eCO curve 412 during specific time intervals can be quantified to provide more effective feedback to the individual regarding their smoking behavior. In the illustrated example, the time interval between times 416 and 418 comprises a 24-hour time interval. A subsequent 24-hour interval is defined between times 418 and 420. A time interval or time interval can include any time between two points within the time span spanned by the data set. In most cases, a time interval is compared to other time intervals having the same time duration (i.e., each interval can include M minutes, H hours, D days, etc.).

[0163] One way to quantify eCO burden / burden over an interval of time is to obtain the area defined by or under the eCO curve 412 during a given time interval (e.g., 416-418, 418-420, etc.) using a dataset such as that shown in the graph of FIG. 29. In the illustrated example, the eCO burden / burden 422 for the first interval (416-418) comprises 41 (measured in CO ppm*t), while the eCO burden 422 for the second interval (418-420) comprises 37. As noted above, along with the eCO burden / burden 422, the dataset can include the number of cigarettes smoked 414 along with a timestamp for each cigarette. This cigarette data can also be summarized in 426 along with the eCO burden / burden 422 for any given time interval. In the illustrated example, the eCO burden / burden is a daily burden, allowing an individual to track their CO exposure. Because smokers smoke differently, determining CO2 load more accurately reflects total smoking exposure compared to simply counting cigarettes. One smoker may smoke an entire cigarette completely, deeply, and strongly, while another smokes less deeply and strongly. While both individuals may smoke a pack per day, the former will have a much higher daily CO2 load due to the intensity with which the smoke is inhaled. CO2 load is also important, such as when an individual becomes a patient in a smoking cessation program. In such cases, quantification can help caregivers or counselors understand the patient as they reduce their smoking activity. For example, a patient may reduce from 20 cigarettes per day to 18-16 cigarettes per day. However, at 10 cigarettes per day, the patient may still have an undecreased Daily CO2 Load because they compensate when smoking the reduced number of cigarettes (i.e., they smoke more intensely, deeply, and strongly). A reduction in a patient's smoking exposure only occurs when the patient's CO2 load decreases.

[0164] 29 is intended for illustrative purposes only, and the duration of a given data set period will depend on the amount of time an individual spends using a portable sampling unit to capture biometric and behavioral data. Quantifying exhaled carbon monoxide exposure involves using the data set to correlate a function of exhaled carbon monoxide versus time over that period to obtain the area under the eCO curve 412. In variations of the method and system, the eCO curve can be calculated or approximated.

[0165] Figure 30 shows an example of displaying biometric data as well as various other information for the benefit of a user, caregiver, or other party interested in assessing an individual's smoking behavior. The data shown in Figure 30 is for illustrative purposes and may be displayed on a portable electronic device (e.g., see 110 in Figure 27) or one or more computers. Additionally, any of the biometric or other data may be displayed on the portable sampling unit 1900.

[0166] 30 shows a "dashboard" view 118 of an individual's smoking behavior data, including a graphical output 120 of the eCO curve 412 over a period of time, as well as cigarette counts for any given time interval within the period. The graphical output 120 may also provide a measured or calculated nicotine trend 424. This nicotine trend 424 may be determined from the number of cigarettes smoked 426 rather than a direct measurement of nicotine.

[0167] 30 also shows a second graphical output display 122 of an eCO curve 412 over an alternative time period. In this example, the first graphical display 120 shows the eCO curve 412 over a seven-day period, while the second display 122 shows data over a three-day period. The dashboard view 118 can also include additional information, including the latest eCO burden / load 124 (or latest eCO reading from the most recent sample), the number of cigarettes 126 over a defined period, such as the current date, and the amount of nicotine 128. Furthermore, the dashboard 118 can also include a count of the number of samples 130 provided by the individual over a defined period, such as daily to monthly counts.

[0168] The dashboard 118 can also display information that can assist an individual in reducing or quitting smoking. For example, FIG. 30 also shows the cost of cigarettes 132 using a count 126 or 426 of the portions of cigarettes smoked by the individual. The dashboard can also display social connections 146, 142, 140 to assist in quitting smoking. For example, the dashboard can display a doctor or counselor 140 that can be messaged directly. Additionally, information can be displayed to social contacts 142 who are also seeking to reduce their smoking behavior.

[0169] The dashboard 118 may also display information about smoking triggers 134, as described above, for the individual as a reminder to avoid the triggers. The dashboard may also provide the user with additional behavioral information, including, but not limited to, the results of a behavioral questionnaire 136 that the individual previously completed with their physician or counselor.

[0170] The dashboard 118 can also selectively display any of the information discussed herein based on an analysis of the individual. For example, it may be possible to characterize an individual's smoking behavior and associate such behavior with specific measures effective in assisting the individual in reducing or quitting smoking. In these cases, the individual's behavior can classify one or more phenotypes (the individual's observable dispositions allow for classification within one or more groups). The dashboard can display information found to be effective for that phenotype. Furthermore, the information on the dashboard can be selectively adjusted by the user to allow customization that an individual recognizes as effective non-smoking motivators.

[0171] FIG. 31 shows another variation of dashboard 118 displaying information similar to that shown in FIG. 30. As noted above, the displayed information is customizable. For example, this variation shows eCO load 140 in a graphic display showing historical data (yesterday's load), current eCO burden or load, and a target level for a non-smoker's eCO load. As shown in FIGS. 30 and 31, an individual's previous quit attempts 138 can be displayed. Additionally, a graphic representation 120 of eCO trend 412 can be shown with individual eCO readings (for each sample), along with information regarding smoking time 426 and a graphic showing the time or duration of smoking (as indicated by circles of various diameters). As noted above, such information can be entered by a portable sampling unit and displayed in additional forms, as shown at 126 and 127, which show historical and current data regarding the number of times smoked and the total number of cigarettes smoked, respectively.

[0172] 32A-32C show another variation of a dataset including exhaled carbon monoxide, collection time, and cigarette data quantified and displayed to aid individuals seeking to understand their smoking behavior. FIG. 32A shows an example where a patient collected breath samples over a period of days. The exemplary data shown in FIGS. 32A-32C shows data presented over a 21-day period, although any time range is within the scope of the systems and methods described herein.

[0173] As shown in Figure 32A, the time period 432 is shown along the horizontal axis, with the time intervals being each day within the period. Although not shown, during the initial stages of sample collection, the time interval itself may include one or more days with time intervals being multiples of hours or minutes. Clearly, the longer the period, the greater the ability of the program to select meaningful time intervals within the period.

[0174] FIG. 32A shows a variation of the dashboard 118 in which smoking data (including total number of cigarettes 428 and associated curve 430) is overlaid on a graph showing the eCO curve 412. As described above, an individual provides breath samples on a regular or random basis. In certain variations, a portable sampling unit (not shown) prompts the individual to provide a sample for measurement of CO. The portable sampling unit associates the sample with a timestamp, allowing for transmission of other user-generated data as described above. The CO data is then quantified to provide a value for CO exposure (eCO of exhaled CO) over an interval of time (e.g., per day as shown in FIG. 32A).

[0175] 32A also illustrates the ability to show historical data simultaneously with current data. For example, CO load data 140 shows the CO load from the previous day, as well as the highest, lowest, and average CO readings. A similar history is shown for cigarette data and smoking cessation questionnaire results 136.

[0176] Figures 32B and 32C show the dataset in graphical form as the individual reduces their smoking behavior. As shown in Figure 32C, as the individual continues to provide samples for measurement of CO, the graphical representation of the dataset shows the individual self-reporting that they are smoking fewer cigarettes, which is verified by the reduced value of CO burden 124.

[0177] The systems and methods described herein, i.e., the quantification and display of smoking behavior and other behavioral data, provide a basis that medical professionals can leverage for effective programs designed to reduce the effects of cigarette smoke. For example, the systems and methods described herein can be used to simply identify a group of smokers within the general population. Once this group is identified, a data set on an individual's specific smoking behavior can be constructed before attempting to enroll the individual in a smoking cessation program. As described above, quantification of smoking burden (or CO burden) along with temporal data of smoking activity can be combined with other behavioral data to identify smoking triggers specific to that individual. Thus, an individual's smoking behavior can be fully understood by a medical professional before selecting a smoking cessation program. Furthermore, the systems and methods described herein can be easily adapted to monitor an individual's behavior once they enter a smoking cessation program, and once they have stopped smoking, the individual can be monitored to ensure that the smoking cessation program remains effective and that the individual abstains from smoking.

[0178] Furthermore, the above-described systems and methods for quantifying smoking behavior can be used to build, update, and improve the above-described models of smoking behavior, and ultimately provide perturbations to help individuals reduce their smoking behavior.

[0179] 33A-33H illustrate another variation of the above-described systems and methods used to implement a treatment plan for identifying an individual's smoking behavior to ultimately assist the individual in quitting smoking and maintaining the individual's status as a non-smoker.

[0180] For example, FIG. 33A illustrates an exemplary overview of a multi-phase regimen / program 440 incorporating the teachings found herein to ultimately assist an individual in reducing and / or ceasing smoking behavior. As shown, each phase 442, 444, 446, 448, 450, 452 of the program can be associated with a display 438. While the illustrated display 438 represents a handheld device (e.g., a smartphone, tablet, computer), the display can include any display or dedicated electronic device that allows a user to receive and / or interact with a user interface and content provided by the program. The content provided by the program can be smoking-related content intended to inform the individual about the effects of smoking and / or can be content based on smoking behavior. Additionally, the content can change based on the individual's tracked behavior, or the content can change based on other factors unrelated to the individual's tracked behavior.

[0181] Smoking-related content may also include information and / or warnings regarding the appropriate use of devices and systems used to compile smoking behavior. For example, warnings may include warnings against using the device / system, including but not limited to, as a measure of potential carbon monoxide poisoning, a measure of non-tobacco smoke inhalation (e.g., from a fire or chemical release). In some cases, the system may instruct the individual to call emergency medical services (e.g., 911) if non-tobacco CO exposure occurs. The system may also provide system-specific warnings, such as warnings against sharing breath sensors between different individuals.

[0182] Additionally, the content relayed to the individual may include a general reminder that the amount of smoking is unsafe. Such a warning is intended to prevent the individual from attempting to use the system to reduce or maintain their smoking at a relative level that the individual may mistakenly perceive as a safe smoking level. For example, such a warning may be triggered at a specific level of exhaled CO, e.g., 0-6 ppm. The warning states that low levels of CO in exhaled breath do not indicate that it is safe to start or increase smoking, or that the current level is safe. The warning further states that smoking is harmful to a person's health and that no amount of smoking is safe.

[0183] In the illustrated example, stages 442, 444, 446, 448, 450, and 452 of program 440 can be divided into distinct time spans or periods, with each stage providing a different goal that allows an individual to build and progress as they attempt to curb their smoking behavior. For example, in the example method / system, initial stage 442 allows a user to explore their smoking behavior with little or no attempt to implement immediate changes in their smoking behavior. Such exploration stage 442 provides the individual with information that allows them to identify their smoking behavior. In the illustrated example shown in FIG. 33A, the program stages are separated by exemplary time periods as follows: Explore 442 (9 days), Build 444 (1 day to 4 weeks), Rally 446 (1 week), Quit 448 (1 week), Secure 450 (11 weeks), and Maintain 552 (40 weeks). Clearly, any variation in time periods can be associated with each stage.

[0184] Explore 442 can be used to address behaviors related to smoking behavior and stimulate an individual's interest in quitting smoking. Build 444 can be used to build skills to encourage an individual to decide to quit smoking. Rally 446 can be used to prepare an individual to quit smoking. Quit 448 can be used to support an individual in quitting smoking. For example, this stage can be used to provide support to an individual during smoking cessation, where support can include facilitating two-way communication with a counselor, facilitating two-way communication of peer support, displaying informational content to support smoking cessation, or a combination thereof. Secure 450 can be used to provide an individual with skills to continue quitting smoking. Maintain 452 can be used to provide an individual with support to prevent relapse and solidify non-smoking behaviors.

[0185] For example, as described above, such a first stage 442 may include recording a plurality of behavioral data from the individual, where such behavioral data may include the number of cigarettes smoked, the number(s) of times the cigarettes were smoked, the individual's location, the individual's location while smoking, their mood, as well as any other data indicative of the individual's behavior. In conjunction with the behavioral data, the method may enable the individual to submit a plurality of biological data from the individual. For example, the biological data may include a breath sample submitted to the electronic device described above. Alternatively, or in combination, the submission of biological data may occur passively through any number of sensors that actively measure the individual's biological information (e.g., via blood, breath, temperature, etc.).

[0186] The biological information is then quantified, thereby allowing an individual to understand the effects of smoking exposure. As noted above, if the biological data includes exhaled carbon monoxide, quantification of smoking exposure can include exhaled carbon monoxide load.

[0187] The method then includes compiling a behavioral summary that combines at least some of the behavioral data with the smoking exposure. FIG. 33B shows an example display of a behavioral summary showing several behavioral data, including, but not limited to, smoking exposure / exhaled CO2 load 124, as well as the number of cigarettes smoked, estimated smoking cost, and time since the last cigarette was smoked. The visual display may also provide various menu options 462 to allow the individual to interact with various content items related to smoking behavior, as well as counselors. The display 438 may also allow the individual to view the behavioral summary based on daily values ​​or for a set period of time (e.g., 7 days, 30 days, entire history, etc.).

[0188] It should be noted that the system can also evaluate submitted data (either biological and / or behavioral) to ensure data accuracy. For example, the system can evaluate the time span between submitted samples and provide a warning if samples are submitted at undesirable intervals. For example, the system can provide a warning to an individual if obtaining multiple biological data from an individual occurs within a predetermined time of the previous submission of biological data. In some cases, particularly for biological data, submitting samples without allowing sufficient time between samples may reduce the validity of the measurements. In additional variations, warning the individual may further include rejecting at least one of the multiple biological data from the individual in addition to providing a warning. Such warnings may be provided visually, audibly, sensorily, and / or through a visual display of the subject matter discussed herein.

[0189] 33C shows an additional example of an example behavioral summary that includes behavioral data 460 in conjunction with biological data 124. In this example, the user can select between displaying biological data (e.g., breath carbon monoxide load) and the number of cigarettes smoked. Additionally, display 438 allows the user to interact with the data by selecting specific information, such as a smoking map, showing behavioral data in the form of smoking locations 464. The present disclosure encompasses any number of variations of displaying either all or at least a portion of the behavioral summary to an individual to inform the individual about their smoking behavior.

[0190] FIG. 33D illustrates another example of a method disclosed herein that uses one or more interactive activities to engage an individual during a program. In this variation, the interactive activities may span the first phase of the program. For example, while the first phase of the program can encompass any time span, in the illustrated variation, the first phase is separated into nine days with markers 468, 470, 474, each day representing that day's activity. The markers may be interactive, meaning they allow the individual to access the activity, or they may be on-directional in providing information to the user. Alternatively, the activity may provide content to the individual regarding smoking behavior. For example, as shown in FIG. 33D, the initial activity 468 may serve as a reminder or, as described above, initiate the individual's submission of biological and / or behavioral data, and the method may generate content incorporating any of the data to provide feedback to the user. In FIG. 33D, a first activity 468 provides content feedback to the user regarding the need for a biological sample (e.g., a breath sample) and may provide any information related to the biological or behavioral sample, such as a current count, a minimum number of samples required, or a countdown until the minimum number of samples is met.

[0191] FIG. 33D also shows additional markers 468, 474 representing additional activities and / or days in the first phase of the program. As shown in FIG. 33D, content 472 can be purely informational, such as providing information about how measured CO is a useful indicator of an individual's exposure to cigarette toxins. In other variations, as shown in FIG. 33E, the content can include interactive activities. For example, as shown in FIG. 25E, a center screen image can prompt an individual about costs associated with cigarettes or smoking and calculate the information as shown. An activity can then combine the prompted information to provide additional information indicative of the individual's smoking behavior 478, as shown in the right screen. For example, in this example, the information includes the estimated cost of smoking, reasons for smoking, and estimated extrapolated savings upon quitting. The interactive activity can also provide rewards 480 to individuals for providing biological and / or behavioral data.

[0192] 33F shows additional markers 482, 486, 488, 490, and 496 representing themes such as reasons for smoking 482, additions 486, home 488, time (spent smoking) 490, and confidence 496. As shown, the content displayed can be purely informational (e.g., displaying reasons for smoking 484) or can be combined with data entered into the program (e.g., displaying amount of time spent smoking per week 492).

[0193] Figure 33G shows another activity (associated with Activity 6 488 in Figure 33F), in this example, as shown in the center screen, where the interactive data prompted by the program relates to environmental factors associated with the individual (e.g., household information). Once the individual enters the environmental information, the program can combine the prompted environmental information and use the environmental data to provide additional information indicative of the individual's smoking behavior 478, as shown in the right screen.

[0194] Figure 33H depicts activities or days 7-9 490, 496, 498. As the first program phase nears completion, individuals may gain increased confidence 494 in their ability to affect smoking cessation, knowing that the program will continue to provide them with the above-described metrics regarding their smoking behavior. Upon completion of the first program phase, as indicated by a completion marker 498, the individual will have a personalized smoking behavior profile compiled using metrics specific to that individual. An interactive activity may then prompt the individual to enter the next phase of the program (as outlined in Figure 33A).

[0195] A number of embodiments of the invention have been described. It will be understood, however, that various modifications can be made without departing from the spirit and scope of the invention. Combinations of aspects of the above-described variations, as well as combinations of the variations themselves, are intended to be within the scope of the disclosure.

[0196] Various modifications may be made to the invention as described, and equivalents (whether listed herein or not included for some brevity) may be substituted, without departing from the true spirit and scope of the invention. Also, any feature of the variations of the invention may be discussed and claimed independently or in combination with any one or more of the features described herein. Accordingly, the invention contemplates combinations of various aspects of the embodiments or combinations of the embodiments themselves, where possible. Reference to a singular item includes the possibility that multiple of the same items are present. More specifically, as used in this specification and the appended claims, the singular forms "a," "an," "said," and "the" include plural referents unless expressly stated otherwise.

[0197] [Embodiment] (1) A method for enhancing electronic coach-counselor interactions supporting individual users participating in a behavior modification program, said method comprising: providing electronic access to an information database during the electronic interaction between the coach-counselor and the individual user, the information database including a plurality of user-specific input data specific to the individual user, the plurality of user-specific input data including at least one of a subset of individual user psychological information, a subset of individual user personal information, and a subset of individual user biological input data, at least a portion of the plurality of user-specific input data having been collected in advance; electronically displaying background data to the coach-counselor during the electronic interaction, the background data including historical information regarding the individual user's activity in the behavior modification program, allowing the coach-counselor to review the historical information regarding the individual user during the electronic interaction; electronically providing said coach-counselor with at least one prompt of a communication topic from a general information database applicable to said behavior modification program, said at least one prompt providing said coaching topic to said coach-counselor for improving the efficiency and accuracy of interactions between said coach-counselor and said individual user to assist said individual user in said behavior modification program; and electronically transmitting the at least one prompt to the individual user as a coaching message. (2) The method of embodiment 1, wherein electronically sending the at least one prompt occurs automatically without input from the coach-counselor. (3) The method of embodiment 1, wherein transmitting the at least one prompt electronically requires input from the coach-counselor. (4) The method of claim 1, further comprising establishing an electronic reporting interface for the coach-counselor, the electronic reporting interface enabling the coach-counselor to electronically access a database of batch data containing information from multiple users who participated in the behavior modification program. (5) The method of embodiment 1, wherein the information database further includes a behavioral summary of the individual user, the behavioral summary including an association between the individual user biological input data from the individual user and at least one of a plurality of behavioral data provided by the individual user, and the plurality of behavioral data is non-biological.

[0198] (6) The method of claim 1, further comprising enabling the coach-counselor to update the information database regarding the individual user. (7) The method of claim 1, wherein the subset of the individual user personal information in the information database includes information from the group consisting of background, physical characteristics, subject attributes, and prior notes about the individual user. (8) The method of claim 1, wherein the subset of individual user psychological information in the information database includes milestones and targets. (9) The method of embodiment 1, wherein displaying the background data includes displaying a conversation history between the individual user and the coach-counselor. (10) The method of embodiment 1, wherein the at least one prompt comprises a reusable prompt applicable to multiple different users.

[0199] (11) The method of embodiment 1, wherein the at least one prompt includes a partially written statement, and the coach-counselor must complete the partially written statement into the completed statement before sending the completed statement to the individual user. (12) The method of claim 1, wherein the at least one prompt requires selection by the coach-counselor, and the at least one prompt includes coded variables that are pre-filled when the at least one prompt is selected by the coach-counselor. (13) The method of embodiment 1, wherein the at least one prompt includes a placeholder, the method further comprising preventing electronic transmission of the at least one prompt until the coach-counselor replaces the placeholder with text. (14) The method of embodiment 1, further comprising tagging the coaching message and assigning an associated category. (15) The method of embodiment 14, wherein the related category includes a trigger or an action.

[0200] (16) The method of embodiment 1, wherein the coaching message is added to the information database about the individual user. (17) The method of embodiment 16, further comprising assigning the coaching message as either private or public. (18) The method of embodiment 1, wherein enabling the coach-counselor to select data from the information database includes enabling the coach-counselor to search by content of the at least one prompt. (19) The method of claim 1, further comprising modifying the at least one prompt to maintain stylistic similarity with the coach-counselor. (20) The method of embodiment 1, further comprising selecting an automated message based on an inquiry from the individual user and automatically sending the automated message to the individual user.

[0201] (21) The method of embodiment 1, wherein the behavior modification program comprises preventing a behavior selected from the group consisting of cigarette smoking, vaping, alcohol consumption, tobacco use, and drug use. (22) A method for providing customized content to individual users participating in a behavior modification program, said method comprising: providing an information database consisting of a plurality of user-specific data specific to the individual user, the plurality of user-specific data including at least one of a subset of individual user psychological information, a subset of individual user personal information, and a subset of individual user biological input data, at least a portion of the plurality of user-specific data having been collected in advance; electronically monitoring the activities of said individual users; using the activity to customize program-related content, including electronic media content from a general information database applicable to the behavior modification program; electronically transmitting the program-related content to the individual user as an electronic message; and monitoring electronic interactions between said individual users and program-related content. (23) The method of embodiment 22, wherein the electronic message further includes at least one data item from one of the subset of the individual user psychological information, the subset of the individual user personal information, or the subset of the individual user biological input data. (24) The method of embodiment 22, wherein electronically transmitting the program-related content to the individual user is performed automatically. (25) The method of embodiment 22, wherein electronically transmitting program-related content requires input from the individual user.

[0202] (26) The method of embodiment 22, wherein the information database further includes a behavioral summary of the individual user, the behavioral summary including an association between the individual user biological input data and at least one of a plurality of behavioral data provided by the individual user, the plurality of behavioral data being non-biological. (27) The method of embodiment 22, wherein the subset of the individual user personal information in the information database includes information from the group consisting of background, physical characteristics, subject attributes, and prior notes about the individual user. (28) The method of claim 22, wherein the subset of individual user psychological information in the information database includes milestones and targets. (29) The method of embodiment 22, further comprising adding the program-related content to the database of information about the individual user. (30) The method of claim 22, further comprising enabling a coach-counselor to select data from the information database for electronic communication with the individual user.

[0203] (31) The method of embodiment 30, further comprising electronically providing at least one prompt to the coach-counselor. (32) The method of embodiment 22, further comprising selecting an automated message based on an inquiry from the individual user and automatically sending the automated message to the individual user. (33) The method of embodiment 22, wherein the behavior modification program comprises preventing a behavior selected from the group consisting of cigarette smoking, vaping, alcohol consumption, tobacco use, and drug use.

Claims

1. 1. A method of operation of a system including a server and a personal device for providing customized content to individual users participating in a behavior modification program via the personal device, the method comprising: the server providing an information database consisting of a plurality of user-specific data specific to the individual user, the plurality of user-specific data including at least one of a subset of individual user psychological information, a subset of individual user personal information, and a subset of individual user biological input data, at least a portion of the plurality of user-specific data having been collected in advance; said server electronically monitoring the activities of said individual users; the server using the activity to customize program-related content, including electronic media content from a general information database applicable to the behavior modification program; the server electronically transmitting the program-related content to the personal device as an electronic message; said server monitoring electronic interactions between said individual users and said program-related content.

2. 2. The method of claim 1, wherein the electronic message further includes at least one data item from one of the subset of individual user psychological information, the subset of individual user personal information, or the subset of individual user biological input data.

3. 10. The method of claim 1, wherein electronically transmitting the program-related content to the personal device is either automatic or requires input from the individual user.

4. 2. The method of claim 1, wherein the information database further includes a behavioral summary of the individual user, the behavioral summary including an association between the individual user biological input data and at least one of a plurality of behavioral data provided by the individual user, the plurality of behavioral data being non-biological.

5. The subset of individual user personal information in the information database includes information from the group consisting of background, physical characteristics, subject attributes, and prior notes about the individual user; and / or The method of claim 1 , wherein the subset of individual user psychological information in the information database includes milestones and targets.

6. 2. The method of claim 1, further comprising adding said program-related content to said database of information about said individual user.

7. 2. The method of claim 1, wherein the system includes a coach-counselor device, the method further comprising the server enabling the coach-counselor to select data from the information database for electronic communication with the individual user using the coach-counselor device.

8. The method of claim 7 further comprising the server electronically providing at least one prompt to the coach-counselor device.

9. The method of claim 1 further comprising the server selecting an automated message based on an inquiry from the individual user and automatically transmitting the automated message to the personal device.

10. 10. The method of claim 1, wherein the behavior modification program comprises preventing a behavior selected from the group consisting of cigarette smoking, vaping, alcohol consumption, tobacco use, and drug use.

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