System and method for assisting an individual in a behavior change program

Abstract

A method and system for enhancing electronic interaction between a behavior modification program and a user of the program by providing customized content specific to the user. The system and method enable coach-counselor assistance or automated content delivery to individual users.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application is a non - provisional application of U.S. Provisional Patent Application No. 62 / 955,214 (filed on December 30, 2019) and No. 62 / 955,219 (filed on December 30, 2019), the entire contents of which are incorporated herein by reference.

[0002] (Field of the Invention) This disclosure relates to methods and systems for enhancing the electronic interaction between a behavior modification program and a user in the program by providing customized content specific to the user. The systems and methods enable coach - counselor assistance or automated content delivery for individual users.

Background Art

[0003] Behavior modification programs include programs that attempt to assist registered individuals in attempts to improve their physical and / or mental health, or programs that attempt to reduce or discontinue undesirable behaviors following such programs (e.g., individual users). Many behavior modification programs attempt to change behavior or reduce undesirable behaviors by techniques including negative and positive reinforcement, imposition of restrictions, goal - setting, and training of individual users.

[0004] The success rate of behavior modification programs ultimately depends on the actions of individual users within the program, and it is very important to provide support to those individual users when they participate in the program. Thus, effective coaching can often improve the success rate of such behavior modification programs.

[0005] Often, unhealthy behaviors are learned over a significant period of time. Thus, individuals who are trying to discard such behaviors or change future behaviors may face various levels of difficulty that vary according to many factors specific to that individual user. Therefore, any coach attempting to assist that individual user will be more effective if the coaching techniques used by the coach incorporate information specific to that individual user, whether that information is the individual's background or the individual user's activities / actions within the program. For example, an individual user in the initial stage 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 for the continued compliance of the modified behavior. Further, the coaching support for an individual user who strictly adheres to the program will likely be different from the coaching support required by another user who cannot remain compliant with the program.

Summary of the Invention

Problems 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, this disclosure can benefit any number of behavior modification programs, including, but not limited to, programs that assist individuals with e-cigarette cessation, nicotine addition, weight loss, drug treatment compliance, addiction, depression management, and improvement of physical and / or mental activities.

Means for Solving the Problems

[0007] The systems and methods described herein enable the support of individuals in behavior modification programs through individualized coaching and individualized program feedback, where both the individualized coaching and the individualized program feedback are specific to either an individual user or the activities of an individual user within a 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. Still further variations of the methods and systems disclosed herein include behavior modification programs that use biological feedback / measurements from an individual user.

[0008] The present disclosure includes methods for enhancing electronic interaction between coaches - counselors who support individual users participating in behavior modification programs. For example, such methods include providing electronic access to an information database during an electronic interaction between a coach - counselor and an individual user, where the information database includes a plurality of user - specific input data specific to the individual user, with at least some 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, where the background data includes historical information regarding the activities of the individual user in the behavior modification program, enabling the coach - counselor to review historical information about the individual user during the electronic interaction; electronically supplying to the coach - counselor at least one prompt for a communication topic from a general information database applicable to the behavior modification program, where the at least one prompt provides coaching topics to the coach - counselor to improve the efficiency and accuracy of the interaction between the coach - counselor and the individual user for supporting 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 can 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 can include a subset of individual user biological input data accompanied by at least one of a first subset of individual user psychological information and a second subset of individual user personal information, any additional information, and / or combinations thereof.

[0010] Variations of the methods and systems described herein can include ways in which at least one prompt is automatically sent electronically without input from a coach-counselor. Alternatively, or in combination, sending at least one prompt electronically requires input from a coach-counselor.

[0011] Variations of the methods and systems can further require establishing an electronic report interface for a coach-counselor, the electronic report interface enabling the coach-counselor to electronically access a database of batch data including information from a plurality of users who have participated in the behavior modification program.

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

[0013] The information database can be automatically updated by monitoring the activities of the user, and / or the coach-counselor can update the information database regarding an individual user.

[0014] In an additional variant, the second subset of the individual user personal information in the information database includes information from the group consisting of the background, constitution, subject attributes, and prior notes regarding the individual user. A variant of the method can include a first subset of the individual user psychological information including the user's milestones and targets.

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

[0016] The prompt can be a reusable prompt applicable to a plurality of different users. Additionally or alternatively, at least one prompt can include a partially written description, and the coach-counselor needs to complete the partially written description before sending the description to the individual user.

[0017] A variant of the method includes a coach-counselor selecting at least one prompt, and at least one prompt includes an encoded variable that is pre-filled when at least one prompt is selected by the coach-counselor.

[0018] The methods and systems described herein can include checks such as when the prompt includes a placeholder, and this method further includes preventing the electronic transmission of at least one prompt until the coach-counselor replaces the placeholder with text.

[0019] Another variant of the method includes tagging coach messages to assign related categories. The related categories can include triggers or actions.

[0020] In some variants, the coach messages are added to the information database regarding the user and can be made either private or public.

[0021] Additional variations of the method include enabling the coach-counselor to select data from the information database and enabling the coach-counselor to search with the content of at least one prompt.

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

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

[0024] The behavior modification program disclosed herein may include preventing behaviors selected from the group consisting of cigarette smoking, e-cigarette vaping, alcohol consumption, tobacco use, and drug use.

[0025] The present disclosure also includes a method of providing customized content to individual users participating in a behavior modification program. Examples of such a method include providing an information database composed of a plurality of user-specific data unique to an individual user, at least a portion of the plurality of user-specific input data having been previously collected, electronically monitoring the activities of the individual user, 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 user as an electronic message, and monitoring the electronic interaction of the individual user with the program-related content.

[0026] Although it is repetitive, a variant of the method can 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 an additional variant, the user-specific data can include a subset of individual user biological input data accompanied by at least one of a first subset of individual user psychological information and a second subset of individual user personal information, any additional information, and / or combinations thereof.

[0027] A variant of the method further includes an electronic message including at least one data item from one 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.

[0028] Electronically transmitting program-related content to an individual user can be done automatically or can require input from the individual user. In an additional variant, the information database further includes a summary of an individual's actions, the summary of actions including an association of biological input data from the individual with at least one of a plurality of action data items supplied by the individual, the action data being non-biological data.

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

[0030] The method can further include adding program-related content to an information database regarding the user. An additional variant includes enabling a coach-counselor to select data from the information database and enabling the coach-counselor to search with the content of at least one prompt.

[0031] This application is related to patents and applications by the same applicant as follows. Such patents include U.S. Patent No. 10,306,922 issued on June 4, 201, U.S. Patent No. 9,861,126 issued on January 9, 2018, U.S. Patent No. 10,674,761 issued on June 9, 2020, U.S. Patent No. 10,206,572 issued on February 19, 2019, U.S. Patent No. 10,335,032 issued on July 2, 2019, U.S. Patent No. 10,674,913 issued on June 9, 2020, and U.S. Patent No. 10,306,922 issued on June 4, 2019. Such applications include U.S. Patent Application No. 16 / 889,617 published as U.S. Patent Application Publication No. 20200288785 on September 17, 2020, U.S. Patent Application No. 15 / 782,718 published as U.S. Patent Application Publication No. 20190113501 on April 18, 2019, and U.S. Patent Application No. 16 / 890,253 published as U.S. Patent Application Publication No. 20200288979 on September 17, 2020. The content of each of the above patents and applications is incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0032]

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

[0034] In a first variation, the method and system for enhanced coaching of an individual are discussed in the context of a smoking cessation program. However, the method and system can be applied to any behavior modification program intended to increase an individual's health and / or the user's well-being.

[0035] The behavior modification programs described herein rely on electronic communication 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 represents a diagram of an individual user 10 using an electronic personal device configured to provide biological data / measurements from an individual. The methods and systems include any personal electronic device capable of providing biological data / measurements, but for illustrative purposes, FIG. 1 shows a smartwatch 52 or an exhalation sensor 54 that can be used to communicate biological data to a cloud server 60 either directly or via any intermediate device such as a smartphone 56 or other personal computing device. In a variation discussed below where a smoking cessation program is provided as the behavior modification program, the user 10 uses a portable device 56 that acquires multiple samples of exhalation from an individual using a sensor that measures the amount of carbon monoxide in the exhalation sample (also called exhaled carbon monoxide or ECO). The biological input data can include data measured by the device (e.g., exhalation via device 54). Alternatively, or in combination, the biological data can include data manually input by the user 10 as discussed below.

[0036] In a first variation, as conceptually shown in FIG. 2A, the method and system for enhanced coaching of an individual requires building and / or compiling one or more databases 70 that contain information specific to the user 10. This data can include, but is not limited to, data subsets (e.g., 72, 74, 76) that build one or more databases 70. In some variations, the submission of biological data 76 does not occur until the user engages with the program.

[0037] The transmission 62 or input of data 72, 74, 76 to one or more databases 70 can occur via any number of ways. For example, data 72, 74, 76 can be compiled by one or more individuals associated with the program before or during the initial stages of the behavior modification program. Alternatively, or in combination, user 10 can use an electronic interface to perform for some or all of data 72, 74, 76. A user-specific database 70 (which may include one or more databases) can be compiled and / or updated within any time frame. However, the behavior modification program can set the minimum level of information necessary to initiate or register an individual with the program. FIG. 2 shows the data transmission 62 to the cloud or cloud server 60, but variations of the methods and systems within the present disclosure can include local storage of databases.

[0038] Figures 2B and 2C show a non-exhaustive list of inputs 73, 75 that send data subsets 72, 74 to build one or more databases of information specific to an individual user. As shown, in FIG. 2B, user personal information 72 can include information, in this example subject attribute information. Typically, such user personal information 72 includes information unique to the history or identification of an individual user. Such information inputs include, but are not limited to, gender, age, tobacco products used and usage amounts, previous attempts to quit smoking, geographic regions, languages spoken, cultures 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, smoking family members, presence or absence of a spouse, children, living situation at home, community factors (e.g., poverty, crime, quality of education), access to health care, and health insurance.

[0039] FIG. 2C shows some examples of inputs 75 that can be used to collect a specific subset of data containing individual user psychological information. The psychological inputs 75 enable the construction of a database of psychological information 74 that allows for the study and classification of individual users according to the individual user's way of thinking, desires, and other psychological criteria with respect to behavior modification programs. Again, in a smoking cessation program, the psychological inputs vary according to the specific behavior modification program, and such psychological inputs 75 can include, but are not limited to, goals for changing tobacco habits, motivation to quit smoking, well-being, confidence in quitting smoking, preference for nicotine replacement therapy, personal sense of crisis, situational factors (e.g., business trips on holidays, periods of stressful work), and co-existing mental or physical health disorders (e.g., major depression, obesity).

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

[0041] Figures 3B and 3C represent the various inputs 77, 79 used to generate biological 76 and app / sensor 78 data subsets. Biological input 77 can be any input relevant to the user. Typically, biological data is input 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 way required by the specific data useful for 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 rate, oxygen levels, blood pressure, and hemoglobin A1c measurements. It should be noted 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 while participating in the program, etc.).

[0042] Figure 4 shows the conceptual electronic interaction 30 while counselor - coach 20 is corresponding to individual user 10 participating in the behavior modification program. As shown, the electronic interaction 30 can occur via one or more electronic devices 56. The present method and system also contemplate person - to - person or real - time voice or messaging communications, but the electronic interaction 30 enables an on - demand coaching system. The coach - counselor 20 accesses, via an electronic device 94, a server / system 60 that improves and enhances the interaction with user 10. The server / system 60 can draw from one or more databases 70 containing user - specific data as described above. This configuration enables the coach - counselor 20 to access a wide variety of data useful to the coach - counselor 20 to provide meaningful support to individual user 10. For example, the server / system 60 can display a plurality of user - specific data specific to the individual user received from the database 70, and the user - specific data can include 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 also provides the coach - counselor 20 with any background data including historical information regarding the activities of the individual user in the behavior modification program, enabling the coach - counselor to review historical information about the individual user during the electronic interaction. Typically, such background data includes app / sensor usage input 79 (shown in FIG. 3C). However, the background data can also include information from previous sessions between the coach - counselor 20 and the user 10. The ability to provide a wide variety of data specific to the user enables any number of coach - counselors 20 to get to know user 10 well.

[0043] Another function of system 60 is the ability to extract information from one or more databases 90 that contain information specific to the behavior modification program. For example, system 60 can electronically supply at least one prompt of a communication topic from database 90 to coach-counselor 20, and the prompt or communication topic is general information applicable to the behavior modification program, and at least one prompt improves the efficiency and accuracy of the interaction between the coach-counselor and the individual user. The coach-counselor 20 will have the ability to electronically send (30) a message containing one or more prompts to the individual user 10. In an additional variant, the coach-counselor 20 will have the option to customize the prompt prior to the discussion or before sending it to the user 10.

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

[0045] FIG. 5A typically shows a display 94 that provides a plurality of biological data in relation to an individual's behavioral input, including, as described above, an application, an app, and / or sensor data 78. The system submits one or more prompts 40 based on a comparison of the biological data and the behavioral data 78. FIG. 5 shows biological data 76 including Bio_Data.sub.n(1 to y), meaning that the displayed biological data 76 can include any information in the above database. 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 an individual's unique background or other data, such as historical information regarding the activities of an individual user in a behavior modification program. The prompt 40 can be derived 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 the behavior modification program. The goal of the prompt is to improve the efficiency and accuracy of the interaction between the coach-counselor and the individual user, and to provide coaching topics to the coach-counselor to assist the individual user in the behavior modification program.

[0046] FIG. 5A also shows a display that provides additional information that is general about an individual but information 90 about the program, as well as personal information 72 related to the user. Additional variations of the systems and methods described herein can include the display of any relevant information to the coach-counselor and / or the user. Such information includes, 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] Figures 5B and 5C show one variation of display 94 according to the display described in Figure 5A, where the coach-counselor accesses information intended to improve interaction with the user. As described herein, display 94 can include information specific to an individual user. In the illustrated example, the display includes a combination 82 of biological data having an application, app, and / or sensor data, along with a prompt 40 associated with a particular combination of information 82. In the first example on the left, the combination 82 information notifies the coach that the user already aims to reduce smoking but still needs to pair with a CO breath sensor for information submission, and 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 combined data includes background data of historical information regarding the activities of an individual user in a behavior modification program. As shown, the system also provides various prompts 40 to the coach-counselor related to an associated subset of the combined data 82. In this example, the prompts to the coach-counselor include "Strongly recommend [to the user] pairing the CO sensor to the mobile app to better track the reduction", "[Propose to the user] a lesson - 'Use of the breath sensor'" [media content provided in the smoking cessation program], and "[Propose to the user] a conversation with the coach about the benefits and reduction of the sensor". As described above, prompt 40 is a communication topic from a general information database applicable to the smoking cessation program. In some cases, the prompt is a combination of general information and specific patient information. In any case, the prompt is a discussion topic customized based on the activities of a particular user. Such customized discussion prompts improve the efficiency and accuracy of the interaction between the coach-counselor and the individual user, providing coaching topics to the coach-counselor to assist the individual user in a behavior modification program.

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

[0049] Program-related content can include URL links, information cards (e.g., electronic "cards" discussed below, lessons, videos, assignments, activities, media content, any variation of information content including but not limited to tasks). Program-related content can 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 include graphical elements that prompt the user to perform some targeted action such as engaging in an assignment related to behavior modification, performing an activity related to the program (e.g., contacting a family member), or making a request to obtain biological data (e.g., using an exhalation sensor), which may require behavior elicitation.

[0050] In another variation of the system and method, customizing / personalizing the card with user-specific data is included. 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 can generally include the user of "the top 3 reasons for cigarette packs logged in the past 48 hours" or "the highest / lowest CO readings in the most recent 48 hours". Each user then receives a customized card that includes general information with the user-specific information incorporated into the general message on the card.

[0051] As described above, and as conveyed in FIG. 6, system 60 can monitor the activities of user 10 via electronic interactions 30. The system can then use algorithms to derive from information within one or more databases 90 that are common to the program. The system then selects an information card having content applicable to user 10. Thus, the information card can be communicated directly to the user's electronic device 56, or the information card can be compiled / added to one or more of the user's personal databases 70 (66). In some variations, the information cards remain on the general database 90, but the system uses user-specific information stored in those personal databases 70 to pull relevant general information from database 90.

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

[0053] Figures 7A and 7B also show an information data card 44 selected to provide individualized information unique to the user. In the illustrated example, Figure 7A shows an information data card 44 unique to a person who has just entered a smoking cessation behavior modification program (referred to as "PIVOT"). As shown in Figure 7B, when the user engages an information card 40 that may simply provide information or may require interaction from the user, the system marks the information card 44 as complete. However, in a variation of the system, an individual may be able to revisit any information card 44 at a later time. As described herein, the information data card is an example of program-related content (as described above) that is electronically transmitted to the user 10.

[0054] Figure 8 shows an electronic interface 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 8 also shows that the information card 44 can include content 48 related to a behavior modification program. As noted above, the content may be general to the program but can be selected based on specific criteria regarding the user. Alternatively, or in combination, the content 48 can be customized for the user. In this example, the data card 48 shows providing media content to explain the advantages of ordering a nicotine replacement medication along with facts about the medication. The content 48 further enables the user to interact with one or more buttons 49, which in the illustrated example enables the user to order the medication.

[0055] The systems and methods described herein also enable supporting users in a very user - centric way. For example, the systems and methods can enable maintaining user autonomy by monitoring and identifying cards that a user ignores or does not follow. To maintain user autonomy, the systems and methods can delay for a specified period the prompting of the user using the same or similar cards. Such a feature enables an “auto - snooze” of information that the user saw but did not follow. The period of the time delay can be selected by the user and / or by the system configuration.

[0056] In another variation, the systems and methods can include a card “disable” function that stops the system from prompting the user with a card (and / or a similar card) if the user has seen the card (and / or a similar card) but has not followed it multiple times. In one example, the system stops prompting the user with the card after three impressions (when the system determines that the user has received the card but has not followed it three times).

[0057] The systems and methods can further include a card chaining function where the system prompts card “A” a specified number of times and then selects card “B” to disable card “A”, and cards “A” and “B” can have related or mutually exclusive information. The system can prompt card “B” a specified number of times and then select card “A” or another card “C” to disable card “B”. This sequence can be repeated so that the card chaining can be made longer or shorter 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 wish to quit smoking on a specific day, while other users may desire a regular smoking cessation plan (i.e., quit smoking over a longer period). In such cases, the system / method can assign the user either a "quick quit" plan or a "regular quit" plan. As an example, a user identified as a "quick quit" user will receive highly prioritized lists of coach-counseled prompts and / or cards for steps that are extremely important in the behavior modification program. In contrast, a user identified as a "regular quit" user will receive customized content that allows more time in the program sequence.

[0059] Additional examples of customized feedback include repeatedly prompting the user. For example, information prompted this week is of high priority if the user completes their interaction with the card / counseling for that week, and then the information from the previous week that was not completed can be prompted by the coach / system. The card / counseling can also prompt the user to maintain goals and current usage. For example, the system can prompt the user (via coaching and / or card) for a weekly update of the number of cigarettes smoked per day to ensure that the user's goals are up-to-date at least every two weeks. When the user is sufficiently engaged / successful in the program, the system can provide customized program-related content (e.g., coaching and / or other information) that recommends setting aggressive behavioral goals. For example, if the user has reduced their smoking by 50%, the system can prompt and instruct the user to move forward and quit smoking. In another variation, a user who has completed all lessons and is still active can be prompted by the system if they wish to change, such as reducing their smoking amount or quitting completely.

[0060] In some embodiments, the systems and methods described herein provide a system that includes one or more mobile devices and a server that communicates with the mobile devices. FIG. 9 shows an exemplary embodiment 100 of such a system that includes device 102, device 104, and server 106 that communicates with devices 102 and 104. Device 102 assists in detecting a patient's smoking behavior. Device 102 includes a processor, a memory, and a communication link for transmitting and receiving data from device 104 and / or server 106. Device 102 includes one or more sensors for measuring a patient's smoking behavior based on one or more measurements of the patient's CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, skin electrical response, pupil diameter, geographical location, environment, ambient temperature, stress factors, life events, and other suitable parameters. For example, device 102 may include a PPG-based sensor for measuring CO, eCO, SpCO, and SpO2, an electrocardiogram recording-based sensor for measuring heart rate and blood pressure, an acoustic signal processing-based sensor for measuring respiratory rate, a wearable temperature sensor for measuring body temperature, a skin conductance, a skin potential-based sensor for measuring brain waves, an implantable sensor disposed in the skin, fat, or muscle for measuring CO and other variables, an intraoral CO sensor, a surrounding CO sensor, and other suitable sensors. These sensors may have various positions on or in the body for optimal monitoring.

[0061] Device 102 may be portable or wearable. For example, Device 102 may be wearable in a manner similar to a wristwatch. In another example, Device 102 is portable or wearable and may be attached to a fingertip, earlobe, auricle, toe, chest, ankle, wrist, skin wrinkle, or another suitable body part. 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, Device 102 may be attached to a fingertip via a finger clip. In another example, Device 102 may be attached to an earlobe or auricle via an ear clip. In yet another example, Device 102 may be attached to a toe via a toe clip. In yet another example, Device 102 may be attached to the chest via a chest strap. In yet another example, Device 102 may be attached to the ankle via an ankle band. In yet another example, Device 102 may be attached to the wrist via a biceps or triceps strap. In yet another example, Device 102 may be attached to a skin wrinkle via a sensor pad.

[0062] Device 102 can prompt a patient to provide a sample, or the device can take a sample without requiring the patient's will when worn. Sampling may be sporadic, continuous, nearly continuous, periodic, or based on any other suitable interval. In some embodiments, sampling is performed continuously at a frequency at which the sensor can make measurements. In some embodiments, sampling is performed continuously after a set time interval such as 5 minutes or 15 minutes, or another suitable time interval.

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

[0064] Patient input data received by device 104 can include stress factors, life events, geographical location, daily events, administration of nicotine patches or other prescriptions, administration of other drugs for smoking cessation, and other suitable patient input data. For example, some of the patient input data can include information regarding phone calls, sports competitions, work, exercise, stress, gender, alcohol consumption, smoking, and other suitable patient input data. The use of a smartphone for text messaging, making calls, surfing, playing games, and other suitable uses can also be correlated with smoking behavior, and these correlations are utilized to predict behavior and change behavior. Device 104 or server 106 can (after receiving the data) compile the data, analyze the data for trends, and correlate the data in real time or after a specified period has elapsed. Server 106 includes a processor, a memory, and a communication link for transmitting and receiving data from device 102 and / or device 104. Server 106 can be located remotely from devices 102 and 104, for example, at a healthcare provider site or another suitable location.

[0065] FIG. 10 shows an exemplary embodiment 200 of a system including a device 202 and a server 204 that communicates with the device 202. The device 202 assists in detecting a patient's smoking behavior. The device 202 includes a processor, a memory, and a communication link for transmitting and receiving data from the server 204. The device 202 can be portable or wearable. For example, the device 202 can be wearable in a manner similar to a wristwatch. The device 202 includes one or more sensors 206 for measuring a patient's smoking behavior based on one or more measurements of the patient's CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, skin electrical response, pupil diameter, geographical location, environment, ambient temperature, stress factors, life events, and other suitable parameters.

[0066] The device 202 can include one or more sensors 208 for measuring specific parameters including, but not limited to, movement, position, date and time, patient input data, and other suitable parameters. Patient input data can include stress factors, life events, location, daily events, administration of nicotine patches or other prescriptions, administration of other drugs for smoking cessation, and other suitable patient input data. Patient input data can be received, for example, in response to a prompt to the patient on a mobile device such as device 104, or can be input at the patient's discretion without a prompt instruction. For example, a portion of the patient input data can include information regarding phone calls, sports competitions, work, sports, stress, gender, alcohol consumption, smoking, and other suitable patient input data. The device 202 or the server 204 can (after data reception) compile the data, analyze the data for trends, and correlate the data in real time or after a specified period has elapsed. The server 204 includes a processor, a memory, and a communication link for transmitting and receiving data from the device 202. The server 204 can be located remotely from the device 202, for example, at a healthcare provider site or another suitable location.

[0067] In some embodiments, device 102 or 202 includes a detector unit and a communication unit. Device 102 or 202 may include a user interface as required for its particular function. The user interface can receive input via a touch screen, keyboard, or another suitable input mechanism. The detector unit includes at least one test element capable of detecting a substance using input of biological parameters from a patient indicating smoking behavior. The detector unit analyzes biological input from the patient, such as the wavelength of light directed through exhaled breath, saliva, or tissue from the lungs, or the wavelength of light reflected by the tissue. In some embodiments, the detector unit monitors the patient's SpCO using PPG. The detector unit can optionally measure a number of other variables including, but not limited to, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, skin electrical response, pupil diameter, geographical 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 body fluid-based sensor, patient input may include placing a fluid sample in a test chamber provided within the detector unit.

[0068] In the case of a light-based sensor such as PPG, patient input may include placing 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 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 suitable circuitry for establishing a communication link with another device, such as device 104, via a wired or wireless connection. The wireless connection can be established using WI-FI, BLUETOOTH, radio frequency, or another suitable protocol.

[0069] Figure 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 exhalation 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 an earlobe, auricle, fingertip, toe tip, skin wrinkle, or another suitable body part, and analyzing the attenuation at 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 range of 500-1000 nm. Such PPG sensors can be implemented via a finger clip, band, adhesively attached sensor pad, or another suitable medium. The PPG sensor can be transmissive, as is used in many pulse oximeters. In a transmissive PPG sensor, 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 waveform to determine SpCO. Alternatively, the PPG sensor can be reflective. In a reflective PPG sensor, light is shone onto a target, such as a finger, and the receiver / sensor captures the reflected light and determines a measurement of SpCO. Further details are provided below.

[0070] Transcutaneous or transmucosal sensors can non-invasively determine blood CO levels and other parameters based on an analysis of the attenuation of an optical signal passing through tissue. Transmissive sensors are typically placed in contact with a thin body part, such as an earlobe, auricle, fingertip, toe tip, skin wrinkle, or another suitable body part. Light is shone from one side of the tissue and detected on the other side. The light diode on one side is tuned to a specific set of wavelengths. The receiver or detector on the other side detects which waveforms are transmitted and how much the waveforms are attenuated. This information is used to determine the binding rate of O2 and / or CO to hemoglobin molecules, i.e., SpO2 and / or SpCO.

[0071] Reflective sensors can be used on thicker body parts such as the wrist. The light illuminated on the 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 patient wrist movement 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. Patent No. 8,224,411 entitled "Noninvasive Multi-Parameter Patient Monitor". Another example of a suitable sensor is disclosed in U.S. Patent No. 8,311,601 entitled "Reflectance and / or Transmissive Pulse Oximeter". These two U.S. patents include all the content incorporated by reference in their specifications, and the entire content is incorporated herein by reference.

[0072] In some embodiments, device 102 or 202 is configured to recognize unique patient characteristics such as fingerprints, retinal scans, voice labels, or other biometric identifiers to prevent an agent from responding to signaling and test prompts to scratch the back of the system. For this purpose, a patient identification subunit may be included in device 102 or 202. One skilled in the art can configure the identification subunit as needed to include one or more of a fingerprint scanner, retinal scanner, voice analyzer, or face recognition, as is known in the art. Examples of suitable identification subunits are disclosed, for example, in U.S. Patent No. 7,716,383 entitled "Flash-interfaced Fingerprint Sensor" and U.S. Patent Application Publication No. 2007 / 0005988 entitled "Multimodal Authentication", and the entire content is incorporated herein by reference.

[0073] The identification subunit may include a built-in still camera or video camera for automatically recording a patient's photo or video when a biological input is provided to the test element. Regardless of the type of identification protocol used, device 102 or 202 can associate the identification with a specific biological input, for example by time reference, and store it along with other information regarding the specific biological input for later analysis.

[0074] The patient may also attempt to scratch the back of the detector by blowing into the detector using a pump, an air bag, bellows, or other device, for example when testing exhalation. In embodiments of a saliva test, the patient may attempt to use a clean liquid such as water instead. In the case of a light-based sensor, the patient may ask a friend to act on their behalf. Means to overcome these attempts can be incorporated into the system. For example, device 102 or 202 can incorporate the ability to distinguish between actual and simulated exhalation delivery. This functionality can be incorporated by configuring the detector unit to sense oxygen and carbon dioxide, as well as a target substance (e.g., carbon monoxide). In this way, the detector unit can confirm that the gas being analyzed is derived from exhalation having lower oxygen and higher carbon dioxide than ambient air. In another example, the detector unit can be configured to detect enzymes that naturally occur in saliva to distinguish saliva from other liquids. In yet another example, a light-based sensor can be used to measure blood chemical composition parameters other than CO levels, and thus the results can be compared to a known sample representing the patient's blood chemical composition.

[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 at appropriate intervals. Device 104 can provide a user interface for prompting a patient for specific inputs. Device 104 can provide a user interface for displaying a specific output of the collected data. Device 104 can enable a patient to input information that the patient believes is relevant to their condition, either without prompting or in response to a prompt. Such information can include information regarding the patient's psychology, such as feeling stress or anxiety. Such spontaneous information can be correlated to biological inputs based on a predetermined algorithm, such as being associated with the biological input that is closest in time to the spontaneous input, or being associated with the first biological input that occurs after the spontaneous input. Server 106 (e.g., a healthcare database server) can receive such data from one or both of devices 102 and 104. In some embodiments, the data can be stored in one or more combinations of devices 102, 104, and 106. The data can 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, such as device 102 or 202, is applied to a patient during their normal annual visit, for example, to detect smoking behavior, and can then direct smokers to a smoking cessation program. The patient is provided with a wearable device to wear as an outpatient for a period of time, such as one day, one week, or another suitable period. Longer wear times can provide higher sensitivity in detecting smoking behavior and higher accuracy in quantifying variables related to smoking behavior. Figure 15 below provides an exemplary flowchart for detecting smoking behavior and is described in more detail below.

[0077] In some embodiments, the employer requests that employees voluntarily wear a wearable device for a certain period, such as one day, one week, or another suitable period. The incentive program can be similar to programs for biometric screening for obesity, dyslipidemia, diabetes, hypertension, and other suitable health conditions. In some embodiments, the medical insurance company requests that subscribers wear a wearable device for a suitable period to detect smoking behavior. Based on the quantified smoking behavior, these patients can be directed to a smoking cessation program as described in the present disclosure.

[0078] When a wearable device is worn for a suitable period, such as five days, several parameters can be measured in real-time or near real-time. These parameters can 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, skin electrical response, pupil diameter, geographical location, environment, ambient temperature, stress factors, life events, and other suitable parameters. FIG. 12 shows an exemplary chart 400 of various levels of SpCO for a patient over a typical five-day monitoring period. Data points 402 and 404 indicate high levels of CO, which are likely to indicate high smoking events. Data points 406 and 408 indicate low levels of CO, perhaps because the patient was sleeping or otherwise busy. One or more algorithms can be applied to the fine data points on the curve to detect smoking events with sufficient sensitivity and specificity. For example, the algorithm can analyze one or more of the shape, start point, upstroke, slope, peak, delta, downslope, upstroke, change time, area under the curve, and other suitable factors of the SpCO curve to detect smoking events.

[0079] Data from the wearable device can be sent in real time, at the end of each day, or according to another suitable time interval, to a smartphone, such as device 104, or a cloud server, such as server 106 or 204. The smartphone can measure parameters including, but not limited to, movement, location, date and time, patient input data, and other suitable parameters. Patient input data can include stress factors, life events, location, daily events, administration of nicotine patches or other prescriptions, administration of other drugs for smoking cessation, and other suitable patient input data. For example, a portion of the patient input data can include information regarding phone calls, athletic competitions, work, sports, stress, gender, alcohol consumption, smoking, and other suitable patient input data. The received data can be compiled, analyzed for trends, and correlated either in real time or after a period has been completed.

[0080] From the measured parameters described above, information regarding smoking can be derived, for example, via a processor located in devices 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 during the day, and how the vitals change before, during, and after smoking. FIG. 13 shows an exemplary diagram 500 of the analyzed information. A patient can reach FIG. 13 by zooming in on a given day in FIG. 12. Data point 502 indicates the SpCO level when the patient is asleep. Data point 504 indicates that the SpCO level is lowest when the patient wakes up. Data points 506, 508, and 510 indicate that high SpCO levels are associated with triggers such as break times, lunch, and commuting. The processor can analyze the SpCO trend in FIG. 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 amount of each cigarette smoked, how the patient's smoking events appear on the curve for later use in a smoking cessation program, time, day of the week, related stress factors, geography, location, and exercise. For example, the total number of peaks on a given day can indicate the number of cigarettes smoked, while the slope of each peak can 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 the data points of FIG. 13, e.g., data point 508. Patient data 602 includes identifying information about the patient such as patient name 604 and patient age 606. Patient data 602 includes curve data 608 corresponding to the curve of FIG. 13. For example, curve data 608 includes a curve identifier 610 corresponding to data point 508. The data corresponding to data point 508 can be collected by devices 102, 104, or 202, and / or server 106 or 204 or combinations thereof. The data associated with curve identifier 610 includes date, time, and location information 612. The data includes patient vital signs such as CO level and O2 level 614. The data includes patient input data such as trigger 616. Patient input data can be input, for example, in response to a prompt to the patient on device 104 or at the patient's discretion without a prompt instruction. Curve data 608 includes curve identifiers 618 for additional data points of FIG. 13. Data structure 600 can be adapted as needed to store patient data.

[0082] FIG. 15 shows an exemplary flowchart 700 for detecting a patient's smoking behavior over a suitable evaluation period. When a patient wears a wearable device for an appropriate 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 can 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, skin electrical response, pupil diameter, geographical location, environment, ambient temperature, stress factors, life events, and other suitable parameters. A wearable device or another suitable device can measure parameters including, but not limited to, movement, location, date and time, and other suitable parameters.

[0083] In step 702, a processor of a smartphone, such as device 104, or a cloud server, such as server 106 or 204, receives the described patient data. In step 704, the processor receives patient input data in response to a prompt displayed to the patient on, for example, the smartphone and / or receives patient data entered at the patient's discretion without a prompt. The patient input data can include stress factors, life events, location, daily events, administration of a nicotine patch or other prescription, administration of other drugs for smoking cessation, and other suitable patient input data. In step 706, the processor sends an instruction to update the patient database with the received data. For example, the processor can send the patient data to a healthcare provider server or a cloud server hosting the patient database.

[0084] In step 708, the processor analyzes the patient data to determine a smoking event. The processor can 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 can analyze the information to determine CO trends, averages, peaks, curve shapes, and correlations, other vital sign trends during the day, and how the vitals change before, during, and after smoking. The processor can analyze the 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, the time, day of the week, related stress factors, geography, location, and exercise. For example, the total number of peaks on a given day can indicate the number of cigarettes smoked, while the slope of each peak can indicate the strength of each cigarette smoked.

[0085] In step 710, the processor transmits the determined smoking event and related analysis to the patient database for storage. In step 712, the processor determines whether the evaluation period has ended. For example, the evaluation period can be 5 days or another suitable period. If the evaluation period has not ended, 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, in step 714, the processor ends the data collection and analysis. For example, the processor can evaluate all the collected data at the end of the evaluation period and create a report, as described with respect to FIG. 16 below.

[0087] It is contemplated that the steps or descriptions of FIG. 15 can be used in conjunction with any other embodiment of the present disclosure. Additionally, the steps and descriptions associated with FIG. 15 can be performed in an alternative order or in parallel for further purposes of the present disclosure. For example, each of these steps can be performed in any order, appropriately or in parallel or substantially simultaneously, to reduce delays or improve the speed of the system or method. Further, note that any of the devices or apparatuses discussed in relation to FIG. 9 (e.g., devices 102, 104, or 106) or FIG. 10 (e.g., devices 202 or 204) can be used to perform one or more of the steps of FIG. 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 has completed a five-day assessment while wearing a wearable device, such as device 102 or 202, a complete data set is compiled and analyzed by the system and delivered to the patient or physician for the smoking cessation program. FIG. 16 shows an exemplary embodiment 800 of a sample report from this analysis. For example, the report indicates that from October 1 to October 6, Mr. Jones smoked a total of 175 cigarettes, with an average of 35 cigarettes smoked per day and a maximum of 45 cigarettes smoked in a day. Mr. Jones' CO levels ranged from a maximum of 20.7% to an average of 5.5%, remaining above 4% for 60% of the duration of the five-day assessment period. Mr. Jones' triggers included work, home stress factors, and commuting. This report recommends high-dose and high-frequency nicotine level predictions for initiating nicotine replacement therapy, from the perspective of Mr. Jones' smoking habits.

[0089] In some embodiments, the patient works with a physician or counselor to initiate the process of entering a smoking cessation program. In some embodiments, the system automatically sets a smoking cessation program based on data from the evaluation period. The sample report of FIG. 16 is an example of generating a report on measuring SpCO and predicting CO exposure, related stress factors, and starting nicotine dosage requirements. For example, heavy and high-intensity smokers may be more nicotine-dependent at the time of entering a smoking cessation program, which can be estimated by the processor based on five days of behavior, and the smoking cessation program starts this patient at a higher nicotine replacement therapy dosage. This can avoid many patients failing the smoking cessation program early due to withdrawal symptoms. Based on the report data including the average and maximum number of cigarettes smoked, SpCO levels, and triggers, the processor can determine the dosage of nicotine for administration to the patient. For example, the processor can determine a high nicotine dosage for patients who smoke more than the threshold number of cigarettes per day on average. When the report data is updated, the processor can also update the dosage of nicotine.

[0090] The data collected may affect the initiation and patient setting of a smoking cessation program by assisting in drug selection and administration immediately before entering the smoking cessation program. For example, higher smoking metrics can prompt to start a higher nicotine replacement therapy dosage or multiple drugs (e.g., adding a drug used to treat nicotine addiction such as varenicline). The data collected may affect the initiation and setting of a smoking cessation program by determining the frequency, type, and duration of counseling needed for the patient. The data can lead to stratification of smokers' needs. For example, the highest-risk smokers with the highest usage may receive more interventions, while lower-risk smokers may receive fewer interventions. For example, an 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 has a tendency to smoke.

[0091] The collected data may affect the initiation and setting of a smoking cessation program by correlating the smoking behavior with all of the above variables, such as stress factors that promote smoking, date and time, and other suitable variables used in prior patient counseling to recognize these triggers. Counseling interventions can target these stress factors and there can be timely interventions for patients, such as text messages or timely calls. The collected data may affect the initiation and setting of a smoking cessation program by assigning peer groups based on smoking behavior. The collected data can be used to predict and / or avoid smoking events. For example, if tachycardia or heart rate variability or a set of appropriate variables precede most smoking events, an alarm will sound and the patient can administer a dose of the drug or receive a call from a peer group, physician, or counselor. FIG. 20 shows an exemplary embodiment of preventing a smoking event and will be discussed in more detail below. FIGS. 22 and 23 show exemplary flow diagrams for predicting and preventing an anticipated smoking event and 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. When entering a smoking cessation program, a patient may continue to wear a wearable device for monitoring, such as device 102 or 202. The system can use analysis tools, such as setting a SpCO baseline and tracking progress against this baseline. The trend may drop to zero and stay 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 upwards towards recurrence (indicating a relapse).

[0093] The system can engage patients using a patient engagement strategy by providing small, infrequent rewards for group or individual progress. The system can provide employer rewards, payers, spouses, or peer groups to engage patients. The system can present the process for the patient as a game and improve visibility of progress. FIG. 21 provides an exemplary embodiment of such a user interface and will be discussed in more detail below. In some embodiments, the system can send data in real time to a healthcare provider for remote monitoring, enabling the provider to efficiently monitor and adjust a patient's treatment without the need to have data in the office daily. For example, the provider can send commands to the system to adjust drug type and dosage, change the intensity of counseling, calls, and texts to actively promote progress, or initiate an intervention if the patient is unable to refrain from smoking. This can efficiently automate the process, replacing the high-cost quit phone line staffed by employees. The system can improve patient outcomes using increased intensity and frequency. The system can encourage patients through support from spouses, employers, healthcare providers, peers, friends, and other suitable relationships via scheduled phone calls, text messages, or other suitable communications.

[0094] FIG. 17 shows an exemplary graph 900 for tracking the average daily SpCO trend of patients who prepare for and then enter a smoking cessation program. The average trend is tracked daily as it improves. A physician or counselor can expand on a particular day (current or past) to view details and the relationship with other measured parameters of CO and related stress factors 910. Visibility of the trend of CO over time in the smoking cessation program can prevent patient dropout, prevent relapse of smoking, set drug dosages, and improve outcomes. For example, data point 902 indicates the CO level before the patient enters the smoking cessation program. Data points 904 and 906 indicate the CO levels when nicotine replacement therapy and varenicline therapy are administered during the smoking cessation program. Data point 908 indicates that the patient has successfully quit smoking. At this point, the system may recommend that the patient enter a maintenance prevention program to prevent relapse.

[0095] In some embodiments, the systems and methods described herein provide a follow-up observation program after a patient has successfully quit smoking. After verified smoking cessation success by the system, the patient wears a wearable device, such as device 102 or 202, as a relapse early detection system for a long period of time, e.g., several months to two years. The system can collect data and use counseling strategies as described above for the smoking cessation program.

[0096] In some embodiments, the patient receives an application for a wearable device, such as device 102 or 202, and their own smartphone, such as device 104, which enables them to remotely and privately evaluate their health by tracking several different parameters. The patient can submit an exhaled breath sample or place a finger inside or on top of the sensor of the wearable device several times a day as needed. The patient can wear the wearable device to obtain more frequent or more continuous measurements. At the end of a test period, such as 5 - 7 days or another suitable period, the processor of the smartphone can calculate the patient's CO exposure and related parameters. FIG. 18 shows an exemplary app screen 1000 showing measurements such as SpCO 1002, SpO2 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 warning indicator 1014 or 1016 is activated.

[0097] The system can recommend that the patient enter a smoking cessation program and can 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 can share this data with the patient's spouse, doctor, or another suitable stakeholder involved in the patient's smoking cessation program. For example, the system can share data with an application on a stakeholder's mobile device or send a message containing the data via email, phone, social network, or another suitable medium. Triggers for getting the patient to participate in a smoking cessation program can include a spouse's suggestion, an employer's incentive, peer pressure, personal choice, illness, or another suitable trigger. The patient can start a smoking cessation program on their own or can bring the data to a doctor and receive assistance in participating in a smoking cessation program.

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

[0099] FIG. 19 shows an exemplary embodiment of an app screen 1100 for receiving patient input data. The app screen 1100 can be displayed when a smartphone app receives an indication of a smoking event, for example, due to a sudden increase in the patient's CO level. The app screen 1100 prompts the user to enter a trigger for the smoking event. For example, the patient can select one of options 1102, 1104, 1106, and 1108 as causing the smoking event, or select option 1110 and provide further information about the trigger. Other triggers for smoking events can include phone calls, sports competitions, sports, stress, gender, and other suitable patient input data. The patient may voluntarily call up the app screen 1100 and enter trigger information for the smoking event. In some embodiments, the app screen 1100 for receiving patient data is displayed to the patient during a 5-day evaluation period to collect information about smoking behavior before the patient enters a smoking cessation program.

[0100] In some embodiments, the collected data is used by a smartphone app to avoid smoking events. A processor executing the app, or a processor within another device (e.g., device 102 or 202 or server 106 or 204, etc.), can analyze information regarding heart rate and other vital signs over a period leading up to a smoking event. The processor can correlate changes in heart rate, such as tachycardia, that can predict when a patient is about to smoke. This information can be used to initiate a prevention protocol to stop a smoking event. For example, the prevention protocol can include delivering a rapid dose of nicotine. The nicotine can be delivered via transdermal transfer from a container of nicotine stored in a transdermal patch or wearable device, e.g., 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. FIGS. 22 and 23 provide flow diagrams for predicting smoking events based on a patient's vital signs and are described in more detail below.

[0101] FIG. 20 shows an exemplary embodiment of an app screen 1200 implementing such a prevention protocol. For example, if a patient tends to have 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 dosage via option 1204. In some embodiments, the nicotine is administered automatically. The amount can be determined based on the patient's current SpCO 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 refrain from smoking and suggest looking for other activities to distract the patient.

[0102] In some embodiments, the smartphone app presents the patient's process as a game to improve progress visibility. The app can use the patient engagement strategy to engage the patient by providing small, frequent, or rare rewards for group or individual progress. The app can provide an employer reward, payer, spouse, or peer group to engage the patient. FIG. 21 shows an exemplary app screen 1300 of such an embodiment. The app screen 1300 offers a reward for the patient to refrain from smoking for 15 days. The prompt 1302 proposes that the patient refrain for another 15 days. The patient can select option 1304 to accept the reward and continue to monitor progress while remaining smoke-free. However, the patient may have difficulty refraining 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 the patient's group. The group can track each other's progress and provide support. For example, group members can be part of a social network that allows them to view each other's statistics and encourage refraining from smoking. In another example, a message, such as a tweet, can be sent to group members of the patient's social network, such as followers, when it is detected that the patient is smoking. The message can notify group members that the patient may need assistance. The group can contact the patient in various ways to provide assistance. This interaction may enable the patient to further refrain from smoking that day.

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

[0105] Wearable devices, such as device 102 or 202, and a smartphone app, such as device 104, can continuously monitor a patient's health parameters, such as SpCO levels, in real-time or near real-time and process the data for observation by the patient, physician, or any other suitable party. The smartphone app can also provide data in a form that is easy to summarize for daily or weekly disclosure 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 expand for a specific day to observe further details. The physician can record the patient in a medical database stored on server 106 or 204 that communicates with a mobile device running the smartphone app and continue to receive data from the smartphone app via the Internet or another suitable communication 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 communication link.

[0106] The patient and the physician can set a future quit date and send the patient home without using any drugs or with drugs to assist the patient in quitting smoking. The patient can start working towards the agreed-upon quit date. Feedback from the wearable device and / or smartphone app can assist the patient in smoking less by the quit date than at the start when starting, and can also assist in being better prepared on the quit date when actually quitting smoking. Once the patient starts a smoking cessation program, the patient can receive daily or weekly feedback from the patient's spouse, physician, nurse, counselor, peer, friend, or any other suitable person.

[0107] Drug therapy, if prescribed, can be obtained based on the physician or can be automatically adjusted based on the patient's performance. For example, the physician can remotely increase or decrease the administration of nicotine dosage based on the patient's CO, eCO, SpCO levels. In another example, a processor of a wearable device, such as device 102 or 202, a smartphone, such as device 104, or a remote server, such as server 106 or 204, can increase or decrease the nicotine dosage administration based on the CO trend from the patient's past measurements. Similarly, drug therapy can be made shorter or longer according to the data collected.

[0108] FIG. 22 shows an exemplary flowchart 1400 for predicting smoking events based on the patient's CO, eCO, SpCO measurements, and other suitable factors. The patient can be provided with a smartphone app for a wearable device, such as device 102 or 202, and the patient's own mobile phone, such as device 104. The wearable device can include a PPG sensor for measuring the patient's SpCO level. In step 1402, a processor in the wearable device or the patient's mobile phone receives the patient's SpCO level and the PPG measurements of the associated time and location. The processor can also receive other information such as heart rate, respiratory rate, and other suitable factors in predicting smoking events.

[0109] In step 1404, the processor updates a patient database stored locally or at a remote location, such as the medical database in server 106, using the received patient data. In step 1406, the processor analyzes the current and previous measurements of the patient parameters to determine whether a smoking event is expected. For example, the SpCO trend can reach a minimum value indicating that the user can reach for a cigarette and increase the SpCO level. The processor can apply a gradient descent algorithm to determine the minimum value. In step 1408, the processor determines whether the SpCO trend indicates an expected smoking event. If the processor determines that no smoking event is expected, in step 1410, the processor determines whether the time and / or location indicates an expected smoking event. For example, the processor can determine that the patient usually smokes when waking up around 7:00 am. In another example, the processor can determine that the patient usually smokes immediately after arriving at the company. In yet another example, the processor can determine that the patient always smokes when visiting a particular restaurant or bar in the evening.

[0110] If the processor determines that a smoking event is expected from either step 1408 or 1410, at 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 the memory of device 102, 104, or 202, or server 106 or 204, or a combination thereof. The information of the prevention protocol may include instructions for one or more intervention options to be initiated when the patient attempts to smoke. For example, the processor can initiate an alarm on the patient's mobile phone and display an app screen similar to FIG. 20. The app screen may provide patient options to administer nicotine or receive a call from a peer group, a physician, or another suitable person. Alternatively, the prevention protocol may include automatically administering a rapid dose of nicotine to the patient from a container of nicotine stored in the patient's wearable device. In another example, the app screen may indicate that a message, such as a tweet, is sent to the group members of the patient's social network, such as followers, when it is detected that the patient has failed to refrain from smoking. The patient can refrain from smoking to prevent the message indicating their failure from 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 expected. For example, the processor may determine that a smoking event is expected 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 expected based on a series of steps for analyzing one or more of the patient's SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, skin electrical response, pupil diameter, geographical location, environment, ambient temperature, stress factors, life events, and other suitable parameters.

[0112] In step 1414, the processor determines whether the prevention protocol was successful. If a smoking event has occurred, in step 1418, the processor updates the patient database to indicate that the prevention protocol was not successful. If a smoking event has not occurred, in step 1416, the processor updates the patient database to indicate that the prevention protocol was successful. The processor returns to step 1402 and continues to receive 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 return to a smoking event again.

[0113] The steps or descriptions of FIG. 22 are intended to be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions discussed in connection with FIG. 22 can be performed in an alternative order or in parallel for further purposes of the present disclosure. For example, each of these steps can be performed in any order, appropriately or in parallel or substantially simultaneously, to reduce delays or improve the speed of the system or method. Further, note that any of the devices or apparatuses discussed in connection with FIG. 9 (e.g., devices 102, 104, or 106) or FIG. 10 (e.g., devices 202 or 204) can be used to perform one or more of the steps of FIG. 22.

[0114] FIG. 23 shows an exemplary flow diagram 1500 for determining whether a prevention protocol has been successful in relation to step 1414 of FIG. 22. At step 1502, the processor receives patient data for determining whether a smoking event has occurred. At step 1504, the processor analyzes the currently received patient data and the previously received patient data. At step 1506, the processor determines, based on the analysis, whether a smoking event has occurred. For example, if nicotine has not been 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, at step 1508, the processor returns a message indicating that the prevention protocol has not been successful. In another example, if the patient's vital signs do not indicate an increase or indicate a decrease in the SpCO level, the processor may determine that a smoking event has not occurred. In such a situation, at step 1510, the processor returns a message indicating that the prevention protocol has been successful.

[0115] The steps or descriptions of FIG. 23 are intended to be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions discussed in relation to FIG. 23 may be performed in an alternative order or in parallel for further purposes of the present disclosure. For example, each of these steps may be performed in any order, appropriately or in parallel or substantially simultaneously, to reduce latency or improve the speed of the system or method. Further, note that any of the devices or apparatuses discussed in relation to FIG. 9 (e.g., devices 102, 104, or 106) or FIG. 10 (e.g., devices 202 or 204) may be used to perform one or more of the steps of FIG. 23.

[0116] FIG. 24 shows an exemplary flow diagram 1600 for a 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 a one-time measurement of the patient's SpCO level. At step 1602, a processor within the wearable device receives PPG measurements for the patient's SpCO level, as well as any other suitable data such as, for example, time, location, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, skin electrical response, pupil diameter, geographical 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 that exceeds a particular 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 exceeding a specified threshold may indicate a smoking event. In another example, one or more of the shape of the SpCO curve, start point, upstroke, slope, peak, delta, downslope, upslope, change time, area under the curve, and other suitable factors may indicate a smoking event. One or more of these factors may assist in quantifying the smoking event. For example, the total number of peaks on a given day may indicate the number of cigarettes smoked, while the gradient shape and size of each peak and other characteristics may indicate the strength and amount of each cigarette smoked. If the processor determines that the SpCO level indicates that no smoking event has occurred, in step 1608, the processor returns a negative message indicating that the patient has had no recent smoking events. The patient's physician can find this information useful for evaluating the patient's smoking behavior. If the processor determines that the SpCO level indicates that a smoking event has occurred, 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 as described above.

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

[0119] The steps or descriptions of FIG. 24 are intended to be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions discussed in relation to FIG. 24 can be performed in an alternative order or in parallel for further purposes of the present disclosure. For example, each of these steps can be performed in any order, appropriately or in parallel or substantially simultaneously, to reduce latency or improve the speed of the system or method. Further, note that any of the devices or apparatuses discussed in relation to FIG. 9 (e.g., devices 102, 104, or 106) or FIG. 10 (e.g., devices 202 or 204) can be used to perform one or more of the steps of FIG. 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 device records multiple biometric variables and context variables in real time or near real time. For example, biometric variables can include CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, skin electrical response, pupil diameter, and other suitable biometric variables. For example, context variables can include GPS location, patient activity (e.g., sports, gym, shopping, or another suitable patient activity), patient environment (e.g., at work, at home, in a vehicle, in a bar, or another suitable patient environment), stress factors, life events, and other suitable context variables. The collected data can also include in-person observations of the patient's smoking behavior. A spouse or friend or companion can input data that the patient smoked and correlate that data with the SpCO reading to determine accuracy.

[0121] Server 106 includes a processor that receives data of a plurality of patients over a certain period of time and analyzes data on the tendency to occur at the time of an actual smoking event. Based on the tendency, the processor determines a diagnosis and / or detection test for the smoking event. The test may include one or more algorithms applied to the data determined by the processor. For example, the processor may analyze a rapid increase in the patient's CO level. Detecting a rapid increase may include determining that the CO level exceeds a specific designated level. Detecting a rapid increase may include detecting a relative increase in the patient's CO level from a previously measured baseline. The processor can detect a rapid increase as a change in the slope of the patient's CO tendency over a certain period of time. For example, a CO tendency that changes from a negative slope to a positive slope may indicate a rapid increase in the CO level. In another example, the processor may apply one or more algorithms to changes in heart rate, an increase in heart rate variability, changes in blood pressure, or other suitable data fluctuations to detect a smoking event.

[0122] FIG. 25 shows an exemplary flow diagram 1700 for detecting the above-described smoking event. A processor (e.g., within server 106 or 204) may determine a smoking event diagnosis and / or detection test according to flow diagram 1700. At step 1702, the processor receives current patient data. At step 1704, the processor retrieves previously stored data of the patient from a database, e.g., the patient database stored in server 106 or 204. At step 1706, the processor compares the current and previous patient data to detect a smoking event. For example, the processor may analyze a sudden increase in the patient's CO level. Detecting a sudden increase may include detecting a relative increase in the patient's CO level from a previously measured baseline. The processor can 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 that changes 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, an increase in heart rate variability, changes in blood pressure, or other suitable data fluctuations to detect a smoking event. At step 1708, the processor determines whether a smoking event has occurred based on, for example, a sudden increase in the patient's CO level as described. If no smoking event is detected, at step 1710, the processor returns a message indicating that no smoking event has occurred. If a smoking event is detected, at step 1712, the processor returns a message indicating that a smoking event has occurred. At step 1714, the processor updates the patient database using the result from either step 1710 or 1712.

[0123] The steps or descriptions of FIG. 25 are intended to be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions described in relation to FIG. 25 may be performed in an alternative order or in parallel for further purposes of the present disclosure. For example, each of these steps may be performed in any order, appropriately or in parallel or substantially simultaneously, to reduce the latency or improve the speed of the system or method. Further, note that any of the devices or apparatuses discussed in relation to FIG. 9 (e.g., devices 102, 104, or 106) or FIG. 10 (e.g., devices 202 or 204) may be used to perform one or more of the steps of FIG. 25.

[0124] In some embodiments, the processor analyzes the initially received data to measure when a person smokes and associates variables with an algorithm for diagnosing and / or detecting smoking events. The processor continues to analyze other variables while receiving additional patient data. The processor may determine another variable that changes when the patient smokes and use that variable instead to operate the algorithm. For example, the processor can choose to use that 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 a percentage of the number of actual smoking events detected by the sensor and the algorithm. For example, if a patient smokes 20 times a day and the algorithm identifies all smoking events, it has 100% high sensitivity.

[0126] In some embodiments, the algorithm for detecting a smoking event has a high specificity. Specificity is defined as the ability of a test (i.e., a positive test without a smoking event) not to make a false positive call for a smoking event. If the sensor and algorithm do not make a false positive call in a day, it has 100% specificity.

[0127] In another example, if a patient smokes 20 times and the algorithm identifies 18 out of the 20 actual smoking events and indicates 20 other false smoking events, the algorithm has 90% sensitivity (i.e., detected 90% of the smoking events) and 50% specificity (i.e., over-called the number of smoking events by a factor of two).

[0128] In some embodiments, after the processor determines one or more algorithms and applies them to the SpCO measurement to detect a smoking event with sufficient sensitivity and specificity, the processor determines whether there is an association between other biometric variables or context variables that can be used alone (without SpCO) to detect a smoking event and the SpCO result. The processor may determine another variable that changes when the patient smokes and operate the algorithm using that variable instead. For example, the processor can choose to use that 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 patient data received over a period of time, such as 5 minutes, 10 minutes, 15 minutes, 20 minutes, or another suitable time interval, prior to the smoking event to determine one or more triggers. For example, some smoking events may be preceded by a context trigger (e.g., at a bar, before, during, or after a meal, before, during, or after sexual activity, or another suitable context trigger). In another example, some smoking events may be preceded by a change in a biometric variable, such as heart rate or another suitable biometric variable. The determined variables may overlap with those selected for diagnosis and detection and can thus 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 operate 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, for example, a patient's smoking event entered in a smoking cessation program, track the received patient data, and analyze the trends. The processor can determine the patient's goals and provide a reward when the 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 rapid dose of nicotine (e.g., as discussed with respect to FIG. 20).

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

[0132] In some embodiments, the processor detects smoking in a binary-like format with a positive or negative sign indication. The processor first uses observational studies and SpCO measurements from the patient to detect smoking behavior. For example, the processor receives data regarding true positives of 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 of a smoking event. If there is a match, the processor applies an algorithm to other received patient data including the patient's SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, skin electrical response, pupil diameter, geographical 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 can be used in algorithms of non-SpCO devices such as wearable smartwatches or heart rate monitor straps or other devices to detect smoking events.

[0133] When a smoking event is detected based on the received patient data, the processor can quantify the smoking behavior. For example, the processor analyzes the SpCO data trend to indicate the intensity with which the patient smoked each cigarette, the number of cigarettes the patient smoked in a day, the amount of each cigarette smoked, and / or the time taken to smoke each cigarette. The processor can similarly use other biometric or context variables for the metrics. The processor uses the received patient data to predict the likelihood of a smoking event occurring in the near future, e.g., within the next 10 minutes. The processor can analyze the patient data received over a lead period, e.g., 5 minutes, 10 minutes, 15 minutes, 20 minutes, or another suitable time interval, prior to the smoking event to determine one or more triggers.

[0134] In some embodiments, the systems and methods described herein provide for evaluating a patient's smoking behavior. During a 5-day test period, the patient behaves as normal. Devices 102, 104, and / or 106 or devices 202 and / or server 204 receive patient data related to the patient's smoking behavior. Since the purpose of the test period is to observe the patient's smoking pattern, the patient engagement is very little to none. The test period can be extended to a second 5-day period if necessary. Alternatively, the first and second periods can be shorter, e.g., 2 or 3 days, or longer, e.g., more than 1 week. Prior to the second test period, the processor determines a model of the patient's smoking method.

[0135] In the second test phase, the processor applies a series of causes of fluctuations to the model to see if the smoking behavior changes. There can be several types of causes of fluctuations, each having several dimensions. For example, the cause of the fluctuation can be whether sending a text message before or during a smoking event avoids or shortens the smoking event. The dimensions within the cause of the fluctuation can be different senders, different timings, and / or different contents of the text message. In another example, the cause of the fluctuation can be whether a phone call at a particular date and time, or before or during a smoking event, avoids or shortens the smoking event. The dimensions within the cause of the fluctuation can be different callers, different timings, and / or different contents of the phone call. In yet another example, the cause of the fluctuation can be whether alerting the patient to review the patient's smoking behavior at several points during the day avoids smoking in a subsequent period. The dimensions can include determining whether and when that avoidance dissipates. In other examples, the cause of the fluctuation can be a reward, a team play, or other suitable triggers that avoid or shorten the patient's smoking event.

[0136] In some embodiments, the processor uses a machine learning process to deliver the cause of the fluctuation to the patient's smoking model. The machine learning process delivers the cause of the fluctuation, tests the results, and adjusts the cause of the fluctuation accordingly. The processor determines what works best to achieve the identified behavior change by attempting options via the machine learning process. The machine learning process can be applied during the second test phase as a cause of minor fluctuations. The machine learning process can also be applied during the patient's smoking cessation phase using a cause of significant fluctuations to enhance the effort to attempt to get the patient to quit smoking or continue refraining from smoking.

[0137] FIG. 26 shows an exemplary flowchart 1800 for applying one or more causes of perturbation to a patient's smoking model in a second test phase. At 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 a first test phase. At step 1804, the processor analyzes the received patient data to determine a model of the patient's smoking behavior. At step 1806, the processor applies one or more causes of perturbation to the model to check whether the smoking behavior changes. The cause of perturbation can be applied to the model using a machine learning process. There can be several types of causes of perturbation, each having several dimensions. For example, the cause of perturbation can be whether sending a text message before or during a smoking event avoids or shortens the smoking event. The dimensions within the cause of perturbation can be different senders, different timings, and / or different contents of the text message.

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

[0139] If it is necessary to apply another cause of agitation, at step 1816, the processor determines another cause of agitation to apply to the model. For example, the processor can adjust the current cause of agitation to send a text message to the patient at a different time or with different content. In another example, the processor can apply a different cause of agitation by initiating a phone call to the patient before or during a smoking event. The processor returns to step 1806 to apply the cause of agitation to the model. The processor can use a machine learning process to deliver the cause of agitation, test the results, and accordingly, adjust the cause of agitation or select another cause of agitation. In this way, the processor determines what works best to achieve the identified behavior change in the patient by trying different options through the machine learning process.

[0140] The steps or descriptions of FIG. 26 are intended to be used with any other embodiment of the present disclosure. Additionally, the steps and descriptions described in connection with FIG. 26 can be performed in an alternative order or in parallel for further purposes of the present disclosure. For example, each of these steps can be performed in any order, appropriately or in parallel or substantially simultaneously, to reduce delays or improve the speed of the system or method. Further, note that any of the devices or apparatuses discussed in connection with FIG. 9 (e.g., devices 102, 104, or 106) or FIG. 10 (e.g., devices 202 or 204) can be used to perform one or more of the steps of FIG. 26.

[0141] In an example, a 52-year-old male patient is incentivized by his employer to be screened for smoking behavior. The patient enters the evaluation 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 a sensor connected to the patient, e.g., wearable device 102 or 202. The coordinator notifies the patient to smoke and act normally over a 5-day test period and respond to prompts from the app when they occur. After the 5-day period has elapsed, the coordinator places the patient in an additional test period where the app prompts more frequently (e.g., to apply the cause of the fluctuations). The coordinator notifies the patient that it is up to the patient to respond as desired at that point. The coordinator establishes a target date of June 10, 2015 to include a 10-day test.

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

[0143] During the additional 5-day test period, a processor of 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 factors 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 intensity of smoking, 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 am every day with a display including the number of cigarettes smoked on the previous day. The net effect is studied on how the prompts affect the patient's smoking behavior for the rest of the day. The machine learning process can adjust the time and content of the display as needed to change the agitation factor dimensions.

[0144] In another example, the processor applies the perturbation cause 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 perturbation cause by having a different sender, different timing, sending before or during smoking, different content of the message, different images in the message, and / or different rewards for refraining. In another example, the processor applies the perturbation cause 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 perturbation cause by having a different caller, different timing, calling before or during smoking, different content of the call, different tones in the call, and / or different rewards for refraining.

[0145] In another example, the processor applies the cause of the perturbation via a machine learning process in the form of a prompt regarding a particular activity on the patient's mobile device. The prompt indicates that the patient is smoking but should consider cutting the cigarette smoking in half and then going outside. During the long time between cigarette events, or when an event is predicted, the machine learning process applies the cause of the perturbation to attempt to completely avoid the smoking event. For example, the mobile device displays a prompt notifying 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 test period, the coordinator enrolls the patient in a smoking cessation program. During the smoking cessation period, the processor receives patient data and applies the algorithm to the data as described. The processor uses all the data from the first and second test periods to customize the algorithm and start regimen and smoking cessation program intervention for a particular patient. The diagnostic and detection algorithm may use one or more biometric variables of the patient, such as SpCO, to detect smoking behavior. The smoking cessation program includes a nicotine regimen that starts on the first day as part of nicotine replacement therapy. The nicotine can be delivered via a transdermal patch or via transdermal transfer from a container of nicotine stored in a wearable device given 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 works best to change the patient's smoking behavior. The processor can evoke a plurality of individualized interventions from the stakeholders as a cause of perturbation via a machine learning process and test which one works best to change the patient's smoking behavior. It is possible to retain the cause of the perturbation that most affects the patient's smoking model, but on the other hand, it is not possible to further use those that have little or no effect.

[0147] Exemplary embodiments of the systems and methods described above have focused on smoking behavior, examples of which include smoking tobacco via cigarettes, pipes, cigars, and water pipes, as well as smoking illegal products such as marijuana, cocaine, heroin, etc., and alcohol-related behaviors, but are not limited thereto, and 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. Such other examples include the oral placement of certain substances (specific examples include, but are not limited to, placing chewing tobacco and sniffing tobacco in the mouth), the transdermal absorption of certain substances (specific examples include, but are not limited to, the application of certain creams, ointments, gels, patches, or other products containing dependence drugs (e.g., narcotics and LSD) to the skin), and inhaling dependence drugs or substances through the nose (including, but not limited to, inhaling cocaine).

[0148] Generally, the basic configurations of devices 102 and 104 or device 202, as well as the related steps and methods disclosed herein, are similar among the different behaviors being addressed. The devices may vary somewhat in design, taking into account the different markers required by different tests or the different target substances required by different test methodologies associated with a particular undesirable behavior.

[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 aid in the cessation program. The patient may be independent and voluntary, and thus it is equally understood that the systems and methods may be beneficially utilized to unilaterally quit an undesirable behavior outside of a formal cessation program.

[0150] In a further exemplary embodiment, the systems and methods disclosed herein may be readily adapted for the collection of reliable, verifiable data for data collection and, in particular, research related to undesirable behaviors for which the present invention is well suited for testing. Such research can be accomplished with substantially no modification to the underlying device or method, except that it may not have been necessary to update the test protocol or treatment protocol based on user input when no treatment was included.

[0151] FIG. 27 shows another variation of a system and / or method for influencing an individual's smoking behavior and further for quantifying an individual's exposure to cigarette smoke using some of the aspects 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 cigarette smoke, such that the quantified information can be relayed to the individual, a healthcare provider, and / or other parties having an interest in the individual's health. The example discussed below uses a portable device 1900 that obtains multiple samples of breath from an individual using a commonly available sensor that measures the amount of carbon monoxide in the breath sample (also referred to as exhaled carbon monoxide or ECO). However, the quantification and information transfer are not limited to 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 combine or supplement such sampling means, if possible, while still remaining within the scope of the present invention. In addition, 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] The measurement of exhaled CO levels is known to function as an immediate non-invasive 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, V77, No. 11. These articles consider that the exhaled CO ("eCO") levels of non-smokers can range from 3.61 ppm to 5.6 ppm. In one example, the cut-off level for eCO exceeded 8 - 10 ppm to identify smokers.

[0153] Returning to FIG. 27, as illustrated, portable or personal sampling unit 1900 communicates with either personal electronic device 110 or computer 112. Here, personal electronic device 110 includes, but is not limited to, smartphones, regular phones, mobile phones, or other personal transmission devices exclusively designed to receive data from portable sampling unit 1900. Similarly, computer 112 is intended to include personal computers, local servers, or remote servers. Data transmission 114 from portable sampling unit 1900 can occur to either or both of personal electronic device 110 and / or computer 112. Further, synchronization 116 between personal electronic device 110 and computer 112 is optional. Any of personal electronic device 110, computer 112, and / or portable sampling unit 1900 can send data to a remote server for the data analysis described herein. Alternatively, the data analysis can occur in whole or in part on a local device (such as a computer or personal electronic device). In any case, personal electronic device 110 and / or computer 112 can provide information to an individual, caregiver, or other person, as shown in FIG. 27.

[0154] In the illustrated example of FIG. 27, portable sampling unit 1900 receives a sample of exhaled breath 108 from an individual via collection tube 1902. The hardware within portable 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, mobile phone, or other radio waves that provide for data transmission). The transmitted data and associated measurements and quantifications are then displayed on either or both of computer display 112 or personal electronic device 110. Alternatively, or in combination, any of the information can be selectively displayed on portable sampling unit 1900.

[0155] The personal sampling unit (or personal breathing unit) can also use a standard port to enable direct wired communication with each of the devices 110 and 112. In certain variations, the personal sampling unit 1900 can also include either a removable or an embedded memory storage device, and such memory enables the recording of data and the separate transmission of data. Alternatively, the personal sampling unit can enable the simultaneous storage and transmission of data. Additional variations of the device 1900 do not require memory storage. Further, the unit 1900 can use any number of GPS components, inertial sensors (for tracking movement), and / or other sensors that provide additional information regarding the patient's behavior.

[0156] The personal sampling unit 1900 can also include any number of input triggers (such as switches or sensors) 1904, 1906. As described below, the input triggers 1904, 1906 enable an individual to prepare the device 1900 for the delivery of an exhaled sample 108 or to record other information regarding a smoked cigarette, such as the quantity and strength of the smoked cigarette. Additionally, variations of the personal sampling unit 1900 can also associate a time stamp of the input with the device 1900. For example, the personal sampling unit 1900 can associate the time at which a sample is provided and provide the measured or input data along with the time of measurement when transmitting the data 114. Alternatively, the personal sampling device 1900 can use alternative means to identify the time at which a sample is acquired. For example, instead of recording the time stamp of each sample, when a series of samples is given, the period between each sample within the series can be recorded. Thus, the identification of the time stamp of any one sample enables the determination of the time stamp for each of the samples within the series.

[0157] In certain variations, the personal sampling unit 1900 has a minimal form factor and is designed to be easily carried by an individual with minimal effort. Thus, the input trigger 1904 can include a thin tactile switch, an optical switch, a capacitive touch switch, or any commonly used switch or sensor. The portable sampling unit 1900 can also use any number of commonly known techniques to provide feedback or information to the user. For example, as shown, the portable sampling unit 1900 can include a screen 1908 that displays selected information as discussed below. Alternatively, or in addition, the feedback can be in the form of a vibration element, an audible element, and a visual element (e.g., an illumination source of one or more colors). Any of the feedback components can be configured to provide an alert to the individual that can function as a reminder to provide a sample and / or to provide feedback related to the measurement of smoking behavior. Further, the feedback components can provide alerts to the individual on a repeating basis in an attempt to remind the individual to provide periodic samples of exhaled breath to extend the period during which the system captures biometric (e.g., eCO, CO levels) and other behavioral data (position entered manually or via a GPS component coupled to the unit, number of cigarettes, or other triggers). In certain cases, the reminder can be triggered more frequently during an initial program or data capture. Once sufficient data has been acquired, the reminder frequency can be reduced.

[0158] Figure 28A shows a visual representation of data that can be collected in a variation of the system shown in Figure 27. As described above, an individual provides an exhaled 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 within the portable sampling unit to measure the amount of CO. The CO measurement typically corresponds to an inflection point 410 on the graph of Figure 28A. Each CO measurement 410 corresponds to a time stamp as shown on the horizontal axis. The data accumulated via the portable sampling unit enables the collection of a data set that includes at least the CO measurement of the sample and the time, and this data set can be graphed to obtain an eCO curve showing the amount of CO due to an individual's smoking behavior over time.

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

[0160] Figure 28B shows a partial graphical representation of the data collected as described above. However, in this variant, the quantification of an individual's smoking behavior can use the behavioral data to better approximate the CO values between eCO readings. For example, in some variants, the eCO measurements between any two points 410 can be approximated using a linear approximation between the two points. However, it is known that the CO level in the bloodstream decays when not exposed to new CO. This decay can be approximated using a reference ratio, a ratio based on the patient's biometric information (weight, heart rate, activity, etc.). As shown in Figure 28B, when the patient is between cigarettes 414, the calculated CO level can follow a decay rate 440. When an individual records a cigarette 414, the CO increase 442 can be approximated again by using the reference ratio, or a ratio calculated using biometric data as described above, or based on the strength, duration, and amount 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. Using such an improved eCO rate, an improved eCO curve 438 can also be determined while the individual is sleeping. 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 that communicates with the system.

[0161] This approximated or improved eCO curve 438 can be displayed to the individual (or third party) as a means of assisting in behavior modification since the individual can view 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 amount of CO increase by each cigarette based on the starting CO value.

[0162] FIG. 29 shows an example of a data set used to determine the eCO curve 412 over a period of time, and the eCO resulting from an individual's smoking behavior can be quantified to determine the per-interval eCO burden or eCO load for various time intervals. As shown, the period extends along the horizontal axis and includes the history and ongoing data captured / transmitted by the portable sampling unit. With respect to an individual's smoking behavior, the eCO curve 412 during a particular time interval can be quantified to provide effective feedback to the individual. In the illustrated example, the time interval between times 416 and 418 includes a 24-hour time interval. The subsequent 24-hour interval is defined between times 418 and 420. A time interval or time intervals can include any time between two points within the period 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 the eCO burden / load over a time interval is to use a dataset as shown in the graph of FIG. 29 to obtain the area defined by or under the eCO curve 412 for a given time interval (e.g., 416 - 418, 418 - 420, etc.). In the illustrated example, the eCO burden / load 422 for the first interval (416 - 418) includes 41 (measured in COppm*t), while the eCO burden 422 for the second interval (418 - 420) includes 37. As described above, along with the eCO burden / load 422, the dataset can include the number of cigarettes smoked 414 along with the time stamp of each cigarette. This cigarette data can also be summarized at 426 for any given time interval, along with the eCO burden / load 422. In the illustrated example, the eCO burden / load is a daily load, allowing an individual to track their CO exposure. Since smokers smoke differently, determining the CO 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 may smoke less deeply and less strongly. Both individuals may smoke one pack per day, but the former will have a much higher daily CO load due to the intensity with which the smoke is inhaled. The CO load is also important when an individual becomes a patient in a smoking cessation program. In such cases, quantification allows a caregiver or counselor to understand the patient while they are reducing 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 unchanged Daily CO Load as they compensate when smoking the reduced number of cigarettes (i.e., the patient smokes more vigorously, deeply, and strongly). The reduction of a patient's smoking exposure only occurs when the patient's CO load decreases.

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

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

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

[0167] Figure 30 also shows a second graphic output display 122 of the eCO curve 412 over an alternative period. In this example, the first graphic 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 the latest eCO reading from the latest sample), the number of cigarettes smoked 126 over a defined period such as the current date, and the amount of nicotine 128. Further, the dashboard 118 can also include a count of the number of samples 130 provided by an individual over a defined period (such as a daily to monthly count).

[0168] The dashboard 118 can also display information that can assist an individual in reducing or quitting smoking. For example, Figure 30 also shows the cost 132 of cigarettes using the count 126 or 426 of the portions of cigarettes smoked by an individual. The dashboard can also display social connections 146, 142, 140 to assist in quitting smoking. For example, the dashboard can display a physician or counselor 140 to whom a direct message can be sent. Further, the information can be displayed to social acquaintances 142 who also seek to reduce their smoking behavior.

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

[0170] Dashboard 118 can also selectively display any of the information discussed herein based on an individual's analysis. For example, it may be possible to associate specific means effective in characterizing an individual's smoking behavior and assisting the individual in reducing or quitting smoking with such behavior. In these cases, an individual's behavior can classify one or more phenotypes (the observable constitution of the individual enables classification within one or more groups). The dashboard can display information found to be effective for that phenotype. Further, the information on the dashboard can be selectively adjusted by the user to enable customization for the individual to recognize what is effective as a non-smoking motivation.

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

[0172] Figures 32A-32C show another variation of a data set that is quantified and presented to aid an individual in understanding their smoking behavior, including data on exhaled carbon monoxide, collection time, and cigarettes. Figure 32A shows an example where a patient collected exhaled samples over a number of days. The exemplary data shown in Figures 32A-32C shows data 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, period 432 is shown along the horizontal axis, with the time intervals being daily within the period. Although not shown, during the initial stages of sample collection, the time intervals themselves may include more than one day with time intervals that are 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] Figure 32A shows a variation of dashboard 118 where 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, the individual provides exhaled samples either regularly or randomly. In certain variations, a portable sampling unit (not shown) prompts the individual to provide a sample for CO measurement. The portable sampling unit associates the sample with a timestamp and is able to transmit 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 a period of time (e.g., per day as shown in Figure 32A).

[0175] Figure 32A also shows the ability to show historical data contemporaneously with current data. For example, CO load data 140 shows the CO load from the previous day, as well as the highest CO reading, lowest CO reading, and average CO reading. Similar histories are shown for cigarette data, as well as the results of the smoking questionnaire 136.

[0176] Figures 32B and 32C show a dataset in graphical form when an individual reduces their smoking behavior. As shown in Figure 32C, as the individual continues to provide samples for the measurement of CO, the graphical representation of the dataset shows the individual's self-report of smoking fewer cigarettes, which is verified by the reduced value of the CO load of 124.

[0177] The systems and methods described herein, namely the quantification and display of smoking behavior and other behavioral data, provide a basis that can be utilized by medical professionals in an effective program designed to reduce the effects of cigarette smoke. For example, using the systems and methods described herein, a group of smokers can be simply identified from within a general population. Once this group is identified, a dataset of an individual's specific smoking behavior can be constructed before attempting to enroll the individual in a smoking cessation program. As described above, the quantification of the smoking burden (or CO burden) along with the time data of the smoking activity can, in combination with other behavioral data, identify the 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. Further, the systems and methods described herein can be easily adapted to monitor an individual's behavior once the individual enters a smoking cessation program, to ensure that the smoking cessation program remains effective when the individual stops smoking, and to monitor the individual to ensure that the individual refrains from smoking.

[0178] Further, using the above-described systems and methods for the quantification of smoking behavior, the model of the above-described smoking behavior can be constructed, updated, and improved, and ultimately a cause of the fluctuations can be provided to assist in reducing an individual's smoking behavior.

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

[0180] For example, FIG. 33A shows an exemplary overview of a multi - stage regimen / program 440 intended to ultimately assist an individual in incorporating the teachings found herein to reduce and / or cease smoking behavior. As shown, each stage 442, 444, 446, 448, 450, 452 of the program can be associated with a display 438. The illustrated display 438 represents a portable device (e.g., smartphone, tablet, computer), but the display can include any display or dedicated electronic device with which a user can receive and / or interact with a user interface and the 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 content based on smoking behavior. Further, the content can change based on the individual's tracked behavior or the content can change based on other factors independent of the individual's tracked behavior.

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

[0182] Furthermore, the content relayed to the individual may include a general reminder that the amount of smoking behavior is not safe. Such a warning is intended to prevent the individual from attempting to use the system to reduce or maintain their smoking behavior at a relative level that the individual may incorrectly perceive as a safe smoking level. For example, such a warning may be triggered at a specific level of exhaled CO, such as 0-6 ppm. The warning states that a low level of CO in the breath does 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, the stages 442, 444, 446, 448, 450, 452 of program 440 can be divided into individual time spans or periods, and each stage provides different goals that enable the individual to construct and proceed when attempting to suppress smoking behavior. For example, in the method / system example, the initial stage 442 enables the user to explore their smoking behavior with little or no attempt to implement an immediate change in smoking behavior. Such an exploration stage 442 provides the individual with information that enables the individual to identify their smoking behavior. In the illustrated example shown in FIG. 33A, the program stages are separated as follows in an exemplary period: exploration 442 (9 days), construction 444 (1 day to 4 weeks), consolidation 446 (1 week), abstinence 448 (1 week), secure 450 (11 weeks), and maintenance 552 (40 weeks). Clearly, any variation of the periods can be associated with each stage.

[0184] Using the exploration 442, actions related to smoking behavior can be taken up to stimulate an individual's interest in quitting smoking. Using the construction 444, skills can be built to encourage an individual to decide to quit smoking. Using the aggregation 446, an individual can be made ready to quit smoking. Using the quitting 448, an individual can be supported when quitting smoking. For example, this stage can be used to provide support to an individual during the quitting process, and the support can include facilitating two-way communication with a counselor, facilitating two-way communication of peer support, displaying information content to support quitting smoking, or a combination thereof. Using the secure 450, skills can be provided to an individual to be able to continue quitting smoking. Using the maintenance 452, support can be provided to an individual to prevent relapse and solidify non-smoking behavior.

[0185] For example, as described above, such a first stage 442 can include recording a plurality of behavioral data from an individual, and such behavioral data can include the number of cigarettes smoked, the number of times (plural possible) a cigarette was smoked, the individual's location, the location of the individual while smoking, the mental state, and any other data indicating the individual's behavior. In relation to the behavioral data, the method can enable an individual to submit a plurality of biological data from the individual. For example, the biological data can include an exhaled breath sample submitted to the above-described electronic device. Alternatively, or in combination, the submission of the biological data can occur passively through any number of sensors that actively measure the individual's biological information (e.g., via blood, breath, temperature, etc.).

[0186] Next, the biological information is quantified, thereby enabling the individual to understand the impact of smoking exposure. As described above, when the biological data includes exhaled carbon monoxide, the quantification of smoking exposure can include the exhaled carbon monoxide burden.

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

[0188] Note that the system can also evaluate the 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 the samples are submitted at undesirable intervals. For example, the system can provide a warning to an individual if multiple biological data are obtained from the individual within a predetermined time of a previous submission of biological data. In some cases, especially for biological data, submission of samples without allowing sufficient time between samples can reduce the validity of the measurements. In an additional variation, the warning to the individual can further include rejecting at least one of the multiple biological data from the individual in addition to providing the warning. Such warnings can be provided visually, auditorily, sensorily, and / or through a visual display of the content discussed herein.

[0189] FIG. 33C shows an additional example of an action summary including action data 460 in combination with biological data 124. In this example, the user can select between the display of biological data (e.g., exhaled carbon monoxide load) and the number of cigarettes smoked. Further, the display 438 enables the user to interact with the data by selecting specific information such as a smoking map showing action data in the form of a smoking location 464. The present disclosure includes any number of variations of displaying all or at least a portion of the action summary to an individual to notify the individual about their smoking behavior.

[0190] FIG. 33D shows another example of the method disclosed herein using one or more two-way activities to engage an individual in a program. In this variation, the two-way activity can span through the first stage of the program. For example, the first stage of the program can include any time span, but in the illustrated variation, the first stage is separated into nine days with markers 468, 470, 474 where each day represents an activity for that date. The markers can be two-way such that they enable the individual to access the activity or the markers can be on-directional in providing information to the user. Optionally, this activity can provide content to the individual regarding their smoking behavior. For example, as shown in FIG. 33D, the initial activity 468 can function as a reminder or, as described above, can initiate the submission of biological and / or action data by the individual, and the method can generate content incorporating any of the data to provide feedback to the user. In FIG. 33D, the first activity 468 provides content feedback to the user regarding the need for a biological sample (e.g., an exhaled sample) and can provide any information related to a biological or action sample such as the current count, the minimum number of required samples, or a countdown until the minimum number of samples is met.

[0191] Figure 33D also shows additional markers 468, 474 that represent additional activities and / or days in the first stage of the program. As shown in Figure 33D, the content 472 can be pure information such as providing information on how useful measured CO is as an indicator of an individual's exposure to the toxins of cigarette smoking. In other variations, as shown in Figure 33E, the content can include two-way activities. For example, as shown in Figure 25E, the central screen image can prompt an individual about the costs associated with cigarette smoking or smoking and can calculate information as shown. The activity can then, as shown on the right screen, combine the prompted information to provide additional information indicating the individual's smoking behavior 478. For example, in this example, the information can include the estimated cost of smoking, the reasons for smoking, and the estimated extrapolated savings when quitting smoking. The two-way activity can also provide a reward 480 to the individual to provide biological and / or behavioral data.

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

[0193] Figure 33G, in this example, shows another activity (associated with activity 6 488 in Figure 33F) as shown on the central screen, and the two-way data prompted by the program is related to environmental factors associated with the individual (e.g., home information). When the individual enters environmental information, the program can, as shown on the right screen, combine the prompted environmental information and use the environmental data to provide additional information indicating the individual's smoking behavior 478.

[0194] Figure 33H represents activity or days 7 to 9, 490, 496, 498. As the first program stage approaches completion, the individual can increase their confidence 494 in their ability to quit smoking, which can be affected, if they think the program will continue to provide the individual with the above metrics regarding their smoking behavior. When the first program stage is completed, the individual will have an individualized smoking behavior profile compiled using metrics specific to that individual, as indicated by the completion marker 498. The two-way activity can then prompt the individual to enter the next stage of the program (as outlined in Figure 33A).

[0195] Numerous embodiments of the present invention have been described. However, it will be understood that various modifications can be made without departing from the spirit and scope of the present invention. Combinations of aspects of the above-described variations are intended to be within the scope of the present disclosure, just as combinations of the variations themselves are.

[0196] Without departing from the true spirit and scope of the present invention, various changes may be made to the described invention, and equivalents (enumerated herein or not included for the sake of brevity) may be substituted. Also, any feature of a variation of the present invention may be discussed and claimed independently or in combination with any one or more of the features described herein. Accordingly, the present invention contemplates, where possible, combinations of various aspects of the embodiments or combinations of the embodiments themselves. References to singular items include the possibility that multiple identical items exist. More specifically, as used in this specification and the appended claims, unless otherwise specified, the singular forms "a", "an", "said", and "the" include plural referents.

[0197] 〔Embodiments〕 (1) A method for enhancing electronic interaction between a coach and a counselor to assist individual users participating in a behavior modification program, the method comprising Providing electronic access to an information database during the electronic interaction between the coach-counselor and the individual user, wherein the information database includes a plurality of user-specific input data unique to the individual user, the plurality of user-specific input data includes 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, and at least a part of the plurality of user-specific input data has been collected in advance, and Electronically displaying background data to the coach-counselor during the electronic interaction, wherein the background data includes historical information regarding the activities of the individual user in the behavior modification program, enabling the coach-counselor to review the historical information regarding the individual user during the electronic interaction, and Electronically supplying at least one prompt of communication topics to the coach-counselor from a general information database applicable to the behavior modification program, wherein the at least one prompt improves the efficiency and accuracy of the interaction between the coach-counselor and the individual user and provides coaching topics to the coach-counselor to assist the individual user in the behavior modification program, and Electronically transmitting the at least one prompt to the individual user as a coaching message. A method comprising: (2) The method according to embodiment 1, wherein electronically transmitting the at least one prompt is automatically performed without input from the coach-counselor. (3) The method according to embodiment 1, wherein electronically transmitting the at least one prompt is performed and requires input from the coach-counselor. (4) further comprising establishing an electronic report interface for the coach-counselor, the electronic report interface enabling the coach-counselor to electronically access a database of batch data including information from a plurality of users who participated in the behavior modification program, the method according to Embodiment 1. (5) the information database further including a behavior summary of the individual user, the behavior summary including an association between the individual user biological input data from the individual user and at least one of a plurality of behavior data supplied by the individual user, the plurality of behavior data being non-biological, the method according to Embodiment 1.

[0198] (6) further comprising enabling the coach-counselor to update the information database regarding the individual user, the method according to Embodiment 1. (7) a subset of the individual user personal information in the information database includes information from the group consisting of the background, constitution, subject attributes, and prior notes regarding the individual user, the method according to Embodiment 1. (8) a subset of the individual user psychological information in the information database includes milestones and targets, the method according to Embodiment 1. (9) displaying the background data includes displaying a conversation history between the individual user and the coach-counselor, the method according to Embodiment 1. (10) the at least one prompt includes a reusable prompt applicable to a plurality of other users, the method according to Embodiment 1.

[0199] (11) the at least one prompt includes a partially written description, and the coach-counselor needs to complete the partially written description into the completed description before sending the completed description to the individual user, the method according to Embodiment 1. (12) The method according to embodiment 1, wherein the at least one prompt requires selection by the coach-counselor, and the at least one prompt includes an encoded variable that is pre-filled when the at least one prompt is selected by the coach-counselor. (13) The method according to embodiment 1, further comprising preventing the at least one prompt from being electronically transmitted until the coach-counselor replaces the placeholder with text, wherein the at least one prompt includes a placeholder. (14) The method according to embodiment 1, further comprising tagging the coach message to assign a related category. (15) The method according to embodiment 14, wherein the related category includes a trigger or an action.

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

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

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

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

Claims

Claim 1 A method of operating a system including a server, a coach-counselor device, and a personal device for enhancing the electronic interaction between coaches and counselors who assist individual users participating in a behavior modification program, the operating method comprising: the server 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 unique to the individual user, the plurality of user-specific input data including a subset of individual user biological input data and at least one of a subset of individual user psychological information and a subset of individual user personal information, at least a part of the plurality of user-specific input data having been collected in advance; the server sending background data to the coach-counselor device during the electronic interaction, and the coach-counselor device electronically displaying the background data to the coach-counselor, the background data including historical information regarding the activities of the individual user in the behavior modification program, enabling the coach-counselor to review the historical information regarding the individual user during the electronic interaction; the server electronically supplying at least one prompt of a communication topic as a coaching topic to the coach-counselor device from a general information database applicable to the behavior modification program, the at least one prompt improving the efficiency and accuracy of the interaction between the coach-counselor and the individual user and assisting the individual user in the behavior modification program; the coach-counselor device electronically transmitting the at least one prompt to the personal device as a coaching message to the individual user; comprising The at least one prompt is generated by the server comparing the individual user's biological input data with the individual user's behavioral data, and the server generates at least one coaching topic for use by the coach-counselor to assist the individual user based on the comparison between the individual user's biological input data and the individual user's behavioral data, The at least one coaching topic is a communication topic from a database of general information applicable to the behavior modification program, and the server supplies the at least one coaching topic to the coach-counselor device, and the coach-counselor device electronically displays the at least one coaching topic to the coach-counselor, including an operating method.

2. The method of operation according to claim 1, wherein electronically transmitting the at least one prompt is either automatically performed without input from the coach-counselor or requires input from the coach-counselor.

3. The method of operation according to claim 1, further comprising the server establishing an electronic report interface for the coach-counselor by transmitting an email to the coach-counselor device, the electronic report interface enabling the coach-counselor to electronically access a database of batch data including information from a plurality of users who participated in the behavior modification program.

4. The method of operation according to claim 1, further comprising adding the coach message to the information database regarding the individual user and assigning the coach message as either private or public.

5. The information database further includes a behavioral summary of the individual user, the behavioral summary including an association between the individual user's biological input data and at least one of a plurality of behavioral data supplied by the individual user, the plurality of behavioral data being non-biological, the method of operation according to claim 1.

6. A subset of the individual user's personal information in the information database includes information from the group consisting of background, constitution, subject attributes, and prior notes regarding the individual user, and / or The method of operation according to claim 1, wherein the subset of the individual user psychological information in the information database includes milestones and targets.

7. The method of operation according to claim 1, wherein the behavior modification program includes preventing a behavior selected from the group consisting of cigarette smoking, e-cigarette vaping, and tobacco use.

8. The method of operation according to any one of claims 1 to 7, wherein the at least one coaching topic includes suggesting the use of the CO breath sensor when the individual user does not have a corresponding CO breath sensor.

9. The method of operation according to any one of claims 1 to 7, wherein the server generates the at least one prompt by comparing the historical information of the individual user with the individual user biological input data and the behavior data.

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