HK1247542B - Systems and methods for quantification of, and prediction of smoking behavior - Google Patents

Description

Systems and methods for quantification and prediction of smoking behavior

Related applications

This application is a non-provisional application of U.S. Provisional Application No. 62/143,924, filed on April 7, 2015, the entire contents of which are incorporated by reference.

Field of the Invention

The present disclosure relates to systems and methods for monitoring biometrics and contextual variables to aid in screening for smoking cessation. The systems and methods can non-invasively detect a patient's smoking behavior. The systems and methods can quantify and/or predict a patient's smoking behavior. The systems and methods may aid in smoking cessation. In some embodiments, the systems and methods provide for screening the general population during medical and dental visits and other appropriate health-related appointments. In some embodiments, the systems and methods provide for initiating and setting up smoking cessation programs for smoking patients. In some embodiments, the systems and methods provide follow-up programs after a patient successfully quits smoking.

background

The health problems associated with smoking are well known. Cigarette smoke contains nicotine as well as numerous other compounds and additives. Tobacco smoke exposes individuals to carbon monoxide and these other compounds, many of which are carcinogenic and toxic to both the smoker and those around them. The presence and level of carbon monoxide in a smoker's exhaled breath can provide a marker for identifying that individual's overall smoking behavior and their overall exposure to other toxic compounds.

Because of the health risks and problems associated with smoking, in addition to its effects on exposed non-smokers, many programs exist to help individuals stop smoking or at least reduce the amount they smoke per day.

Smoking cessation programs and products often attempt to reduce smoking among patients without fully understanding smoking behaviors, which can vary among patients. Furthermore, understanding a patient's smoking behavior can be difficult given that self-reporting of smoking behavior relies on strict adherence to reporting smoking activities. Furthermore, in many cases, individuals may not strictly adhere to reporting such activities due to shame, carelessness, and/or human error associated with tracking and assessing smoking.

There remains a need for addressing an individual's smoking by first understanding the individual's smoking behavior and then engaging the individual in effective means for reducing and terminating smoking based on this understanding.

SUMMARY OF THE INVENTION

The system and method described herein allow the individual of smoking to participate and quantify its smoking behavior to better help the individual to ultimately achieve the multi-stage method of stopping smoking.The method and system described herein allow even before the individual is given the difficult task of trying to quit smoking, the measurement and quantification of the improvement of smoker's behavior.For example, the system and method described herein can be used for using objective criteria to identify a smoker group from a larger group.Once an individual smoker is identified, the same method and system just allow the learning and exploration stage in which the specific smoking behavior of the individual can be tracked and quantified.The method and system also allow tracking individual's behavioral data, to identify the potential triggering factors of smoking, or simply educate the individual for the degree of its smoking.The method and system also allow the individual who has decided to participate in the "quit smoking" project to be more actively monitored, wherein this monitoring allows the individual to carry out self-monitoring and monitor by companion, coach or counselor.Finally, the method and system disclosed herein can be used for monitoring the individual who has successfully quit smoking, to ensure that smoking behavior no longer occurs.

This paper describes a system and method for evaluating an individual's smoking behavior. The system can allow an individual's smoking behavior to be quantified by measuring biometric data and evaluating factors attributable to cigarette smoke and evaluating behavioral data associated with smoking or the individual's general activities.

The system and method can detect and quantify the patient's smoking behavior non-invasively based on measuring one or more of the patient's biometric data (such as CO level or exhaled CO level). However, other biometric data can also be used. These data include carboxyhemoglobin (SpCO), oxyhemoglobin (SpO2), heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse speed, skin galvanic response, pupil size, geographic location, environment, ambient temperature, stressors, life events and other suitable parameters. Such measurement or data collection can use a portable measurement unit or a fixed measurement unit, either of which communicates with one or more electronic devices for performing quantitative analysis. Alternatively, analysis can be performed in the portable/fixed unit. For example, the portable unit can be coupled to a keychain, an individual's cigarette lighter, a mobile phone or other items that will often be with the individual. Alternatively, the portable unit can be a stand-alone unit or can be worn by the individual.

In one variation, the methods described herein allow for quantifying an individual's smoking behavior by: obtaining multiple samples of exhaled breath from the individual over a period of time and recording the collection time associated with each sample of exhaled breath; measuring the amount of exhaled carbon monoxide for each of the samples of exhaled breath; compiling a dataset comprising the amount of exhaled carbon monoxide and the collection time for each sample of exhaled breath; using the dataset to quantify exhaled carbon monoxide exposure over time intervals within the period of time and assigning an exhaled carbon monoxide burden to the time intervals; and displaying the exhaled carbon monoxide burden. Displaying the quantified results can occur at one or more locations to provide feedback to the individual, a caregiver, or any other individual relevant to understanding and/or reducing the individual's smoking behavior.

In further variations, obtaining the plurality of samples of exhaled breath from the individual over a period of time and recording the time of collection of each sample of exhaled breath includes obtaining the plurality of samples of exhaled breath sequentially.

Quantifying the exposure to exhaled carbon monoxide can include using the data set to correlate exhaled carbon monoxide with respect to a function of time over the time period. Alternatively, the quantification can include a mathematical product of CO levels and time over the time interval. Such quantification allows for improved observation of smoking behavior because it allows observation of the body's overall exposure to CO over a given time interval.

In another variation, the method further includes obtaining an area of exhaled carbon monoxide and time over the time interval according to a curve defined by the function.

The method and system may also include generating a signal, for example using a portable device that utilizes the individual's location, to remind the individual to provide at least one sample of exhaled breath. In further variations, the method may include reminding the individual on a recurring basis to provide a sample of exhaled breath within a time period.

The above method may also include receiving input data from the individual and recording the time of the input. Such data may include behavioral data such as a count of a portion of cigarettes smoked by the individual. Alternatively, the data may include information about location (via a GPS unit), diet, activity (e.g., driving, watching TV, dining, working, socializing, etc.).

The method may include visually displaying any of the input data, including a count of the number of partial cigarettes smoked by an individual. Such data may also allow for the display of calculated information. For example, cigarette counts may be used to determine an individual's nicotine exposure from cigarettes, when direct biometric measurements may also incorrectly measure nicotine from nicotine patches or nicotine gum. Additionally, cigarette data may be used to estimate the cost associated with the number of cigarettes smoked by an individual and display such cigarette cost.

The method may also include determining a nicotine estimate using the count of portions of the cigarette smoked by the individual and displaying the nicotine estimate.

The method may further include providing a visual display of the exhaled carbon monoxide load over a period of time associated with the visual display time interval. Additionally, the method may include providing a visual display of a count of the number of the plurality of exhaled breath samples.

The method may also include determining a series of exhaled carbon monoxide loads for a series of time intervals within a time period.In addition to the information discussed herein, these values may also be displayed.

In the method, measuring the amount of exhaled carbon monoxide may include using a portable sensor.

The method may also include transmitting the collection time and the amount of exhaled carbon monoxide associated with each sample of exhaled breath from the portable sensor to the electronic device.

The method may further include obtaining a location of the individual when obtaining at least one of the plurality of exhaled breath samples.

The method may also include obtaining results of a behavioral questionnaire from the individual and displaying the results of the behavioral questionnaire with the exhaled carbon monoxide burden.

In the method, quantifying the exposure to exhaled carbon monoxide over time intervals within a time period may include estimating a decay rate of carbon monoxide.

In the method, quantifying the exposure to exhaled carbon monoxide over time intervals within the time period may include estimating the rate at which carbon monoxide increases while the individual smokes a cigarette.

Another variation of quantifying an individual's smoking behavior may include obtaining multiple carbon monoxide samples from the individual over a period of time and recording a collection time associated with each sample of carbon monoxide; compiling a data set including the collection time and the amount of carbon monoxide for each sample of carbon monoxide; quantifying carbon monoxide exposure over time intervals over the period of time and assigning a carbon monoxide burden to the time intervals using the data set; and displaying the carbon monoxide burden. In this case, the amount of carbon monoxide is determined based on any type of measurement that identifies the level of carbon monoxide in the body.

The method may also include obtaining multiple samples of carbon monoxide, which includes obtaining multiple samples of exhaled breath from the individual over time and measuring the amount of exhaled carbon monoxide for each of the samples of exhaled breath, and wherein recording the collection time associated with each sample of carbon monoxide includes recording the collection time associated with each sample of exhaled breath.

The present disclosure also includes a device for obtaining data to quantify an individual's smoking behavior. Such a device can assist or perform the functions described herein.

In one example, the device includes a portable breathing unit configured to receive multiple samples of exhaled breath from an individual over a time period and configured to record a collection time associated with each sample of exhaled breath; a sensor located within the unit and configured to measure the amount of exhaled carbon monoxide for each of the samples of exhaled breath; at least one input switch configured to record input data from the individual; a storage unit configured to store at least the amount of exhaled carbon monoxide and the collection time; a transmitter configured to transmit the amount of exhaled carbon monoxide, the collection time, and the input data to an external electronic device; and an alarm unit configured to provide an alarm to a user to submit multiple samples of exhaled breath.

The methods described herein may also include methods for developing a program to help a patient who smokes stop smoking. For example, the method may include measuring at least one biological indicator that determines whether the patient smokes; capturing a plurality of patient data, wherein the patient data includes information temporally related to when the patient smokes; combining at least one of the plurality of patient data and at least one biological indicator to determine a smoking behavior model of the patient during a first test period; evaluating the smoking behavior model to assess the level of intervention required for the smoking cessation program; and providing a summary report of the smoking behavior model and the level of intervention.

In another embodiment, the present disclosure includes a system for preventing a patient from smoking. For example, the system may include a sensor for measuring at least one biological indicator that determines whether the patient is smoking; a data collection device configured to capture a plurality of patient data, wherein the patient data includes information temporally associated with when the patient smokes; a processor in communication with the sensor and the data collection device, the processor configured to compile the plurality of patient data and the at least one biological indicator to determine a smoking behavior model of the patient during a first test period; the processor configured to generate at least one perturbation signal before a second test period, wherein the processor analyzes at least one of the patient data captured during the second test period to determine a test smoking behavior; and wherein the processor compares the test smoking behavior to the model smoking behavior to determine whether the patient is prevented from smoking.

The present disclosure also includes a method for deterring a patient from smoking. For example, such a method may include measuring at least one biological indicator that determines whether the patient smokes; collecting a plurality of patient data, wherein the patient data includes information temporally associated with when the patient smokes; compiling the plurality of patient data with the at least one biological indicator to determine a smoking behavior model for the patient; generating at least one perturbation signal prior to a second test period, wherein the at least one perturbation signal affects the patient; analyzing at least one of the patient data captured during the second test period to determine a test smoking behavior; and comparing the test smoking behavior to a model smoking behavior to determine a change from the smoking behavior model, thereby determining whether the patient is deterred from smoking.

In another embodiment, a system for preventing a patient from smoking may include: a database containing a smoking behavior model of the patient, wherein the smoking behavior model includes a plurality of historical patient data temporally correlated with when the patient smoked; a sensor for measuring at least one biological indicator for determining whether the patient smokes; a processor configured to determine an expected smoking event when analyzing the smoking behavior model, and after determining the expected smoking event, the processor generates at least one disturbance signal before a test period; the processor is configured to check the at least one biological indicator during the test period to determine whether the patient is prevented from smoking during the test period; and wherein after determining whether the patient is prevented from smoking during the test period, the processor updates the database containing the smoking behavior model.

Another variation of a method for deterring smoking may include: accessing a database containing a smoking behavior model of a patient, wherein the smoking behavior model includes a plurality of historical patient data temporally correlated with when the patient smoked; estimating an expected smoking event when analyzing the smoking behavior model; generating at least one disturbance signal before a test period after a processor determines the expected smoking event; measuring at least one biological indicator that determines whether the patient smokes during the test period; examining the at least one biological indicator during the test period to determine whether the patient is deterred from smoking during the test period; and updating the database containing the smoking behavior model after determining whether the patient is deterred from smoking during the test period.

The above is a brief description of some methods and systems for quantifying smoking behavior and programs for effectively stopping smoking. Other features, advantages, and embodiments of the present invention will become apparent to those skilled in the art from the following description and accompanying drawings, in which specific forms of the invention are described in detail for illustrative purposes only. Variations of the access devices and processes described herein include combinations of features of the various embodiments or combinations of the embodiments themselves (where possible).

Although the present disclosure discusses cigarettes in various examples, the methods, systems, and improvements disclosed herein can be applied to any type of tobacco smoke or other inhaled smoke. In this case, the present disclosure contemplates replacing "cigarette" with an appropriate type of tobacco or smoke-generating product (including but not limited to cigars, pipes, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

1 depicts an illustrative system including a wearable device, a mobile device, and a remote server in communication with the wearable device and the mobile device, according to some embodiments of the present disclosure;

2 depicts another illustrative system including a wearable device and a remote server in communication with the wearable device, according to some embodiments of the present disclosure;

FIG3 depicts illustrative light absorption curves for various types of hemoglobin that allow for measurement of carboxyhemoglobin (SpCO) and oxyhemoglobin (SpO2) levels using a photoplethysmography (PPG) sensor, according to some embodiments of the present disclosure;

4 depicts a graph of different SpCO levels for a patient over a typical five-day monitoring period prior to starting a smoking cessation program, according to some embodiments of the present disclosure;

5 depicts a trend graph of a patient's SpCO levels and smoking triggers over a typical day prior to starting a smoking cessation program, according to some embodiments of the present disclosure;

FIG6 depicts a data structure for storing SpCO levels and smoking triggers for a patient during a typical day, according to some embodiments of the present disclosure;

FIG7 depicts an illustrative flow chart for detecting a patient's smoking behavior, according to some embodiments of the present disclosure;

FIG8 depicts a sample report after five days of evaluation of a patient, according to some embodiments of the present disclosure;

FIG9 depicts an illustrative graph of patient SpCO levels during a run-in and smoking cessation program, according to some embodiments of the present disclosure;

FIG10 depicts an illustrative smartphone application screen displaying measurements such as SpCO, SpO2, heart rate, respiratory rate, blood pressure, and temperature, in accordance with some embodiments of the present disclosure;

FIG11 depicts an illustrative smartphone application screen for receiving patient-entered data, according to some embodiments of the present disclosure;

FIG12 depicts an illustrative smartphone application screen that implements a smoking prevention program, according to some embodiments of the present disclosure;

FIG13 depicts an illustrative smartphone application screen for presenting the smoking cessation process as a game for a patient, according to some embodiments of the present disclosure;

FIG14 depicts an illustrative flow chart for predicting and preventing anticipated smoking events, according to some embodiments of the present disclosure;

FIG15 depicts an illustrative flow chart of step 1414 of FIG14 for determining whether the prevention regimen was successful, according to some embodiments of the present disclosure;

FIG16 depicts an illustrative flow chart for taking a measurement of a patient's SpCO level using a PPG sensor, according to some embodiments of the present disclosure;

FIG17 depicts an illustrative flow chart for detecting a puffing event, according to some embodiments of the present disclosure; and

18 depicts an illustrative flow chart for applying one or more perturbations to a model of a patient's smoking behavior, according to some embodiments of the present disclosure.

19 illustrates another variation of a system and/or method for influencing an individual's smoking behavior and further quantifying an individual's exposure to cigarette smoke using aspects described herein.

FIG20A shows a visual representation of data that may be collected using a variation of the system shown in FIG19.

FIG20B shows a visual representation of data that may be collected using a variation of the system shown in FIG19.

21 shows an example of a data set for determining an eCO profile over a period of time, where the eCO attributable to an individual's smoking behavior may be quantified over various time intervals to determine an eCO burden or eCO load for each interval.

FIG. 22 shows an example of displaying biometric data and various other information used to assess an individual's smoking behavior.

FIG. 23 shows another variation of a dashboard displaying information similar to that shown in FIG. 22 .

24A-24C illustrate another variation of a data set that includes exhaled carbon monoxide, collection time, and cigarette data that are quantified and displayed to benefit individuals seeking to understand their smoking behavior.

Detailed Description of the Invention

Systems and methods for smoking cessation using interactive screening are described. The systems and methods non-invasively detect and quantify a patient's smoking behavior based on measuring one or more of the patient's carbon monoxide level, exhaled carbon monoxide level (eCO), carboxyhemoglobin (SpCO), oxyhemoglobin (SpO2), heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil size, geographic location, environment, ambient temperature, stressors, life events, and other suitable parameters. Notably, SpCO and eCO are two ways to measure CO levels in a patient's blood.

In some embodiments, the systems and methods described herein provide for screening the general population during medical and dental visits and other appropriate health-related appointments. The wearable device can be applied to patients during, for example, their annual doctor's visits to detect recent smoking behavior and, if positive, recommend the smoker for further testing and ultimately a smoking cessation program. In some embodiments, the wearable device is applied as a one-time, on-site measurement. In some embodiments, the patient is provided with the wearable device to wear as an outpatient for a period of time, such as a day, a week, or another appropriate period of time. Longer wearing times may provide greater sensitivity in detecting smoking behavior and greater accuracy in quantifying variables related to smoking behavior.

When the wearable device is worn in an appropriate time period (e.g., five days), multiple 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 velocity, skin galvanic response, pupil size, geographical location, environment, ambient temperature, stressor, life events, and other suitable parameters. The data from the wearable device can be sent to a smart phone or cloud server or another suitable device in real time, near real time, at the end of each day, or according to another suitable time interval. Wearable device or smart phone can measure parameters, including but not limited to movement, position, time of day, data entered by the patient, and other suitable parameters. The data entered by the patient may include the use of stressors, life events, position, daily events, nicotine patches or other nicotine formulations, the use of other smoking cessation drugs, and other suitable patient input data. For example, some of the data entered by the patient can include information about the data of phone calls, exercise, work, sports, stress, sex, drinking, smoking, and other suitable patient inputs. The received data may be compiled, trended, and correlated in real time or after a time period has been completed.

According to the parameters measured above, information about smoking can be obtained via a processor in a wearable device, a smart phone, a cloud server or another suitable device. For example, the processor can analyze information to determine CO, eCO, SpCO trends, mean values, peak values, variations, specific curve features, slopes of variations and other types of variations, and determine the association with other biometric and situational variable trends during a day, and how these variables change before, during and after smoking. The processor can analyze CO, eCO, 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 intensity of each cigarette smoked, the amount (quantity) of each cigarette smoked, the time of smoking each cigarette, the time of the day, the day of the week, the associated stressors, geography, location and motion. For example, the total number of peak values in a given day can indicate the number of cigarettes smoked, and the shape and size of each peak value and other characteristics can indicate the intensity and amount of each cigarette smoked. The processor can analyze heart rate and/or pulse rate data to determine the correlation between trend, mean value and peak value and the patient's smoking behavior. For example, the processor can correlate heart rate changes (such as tachycardia or heart rate variability) that occur before or during a smoking event that can predict when the patient will smoke. This information can be used to preempt smoking events during a smoking cessation program. For example, if a smoking event is predicted to occur within the next 10 minutes, the patient can be notified to deliver a dose of nicotine through any of a number of mechanisms, such as delivery via a transdermal patch or transdermal transfer from a reservoir of nicotine stored in a wearable device.

In some embodiments, the systems and methods described herein provide for assessing a patient's smoking behavior over two test periods. During the first test period, the patient behaves as usual. A processor located in a wearable device, smartphone, cloud server, or other suitable device receives patient data related to the patient's smoking behavior. Because the purpose of the test period is to observe the patient's smoking patterns, the patient rarely participates. Prior to the second test period, the processor determines a model of how the patient smokes.

During the second test period, the processor applies a series of perturbations to the model to see if smoking behavior changes. Perturbations can be applied to the model using a machine learning process. The machine learning process delivers the perturbations, tests the results, and adjusts the perturbations accordingly. The processor determines the most effective way to achieve the identified behavioral changes by trying options through the machine learning process.

There may be several types of disturbances, each with several dimensions. For example, a disturbance may be whether sending a text message before or during a smoking event causes the smoking event to be avoided or shortened. The dimensions within the disturbance may be different senders, different timings, and/or different contents for the text message. In another example, a disturbance may be whether a phone call at a certain time of day or before or during a smoking event causes the smoking event to be avoided or shortened. The dimensions within the disturbance may be different callers, different timings, and/or different contents for the phone call. In yet another example, a disturbance may be to warn the patient to check their smoking behavior at several points in the day to avoid smoking within a period of time thereafter. The dimensions may include determining whether and when to eliminate the avoidance. In other examples, the disturbance may be a reward, team action, or other suitable trigger to avoid or shorten the patient's smoking event.

In some embodiments, the systems and methods described herein provide patients with tools for initiating and setting up a smoking cessation program. After the patient completes a five-day assessment while wearing the wearable device, the complete dataset is compiled and analyzed by the system and delivered to the patient or physician for use in the smoking cessation program. For example, a sample report may indicate that Mr. Jones smoked a total of 175 cigarettes from October 1st to October 6th, averaging 35 cigarettes per day and a maximum of 45 cigarettes smoked in a single day. Mr. Jones's CO2 level averaged 5.5%, peaked at 20.7%, and remained above 4% for 60% of the five-day assessment period. Mr. Jones's triggers included work, family stressors, and commuting. Given Mr. Jones's smoking habits, the report recommends high-dose and frequent nicotine level predictions for initiating nicotine replacement therapy. If the treatment plan is tailored to the patient's needs from the outset, this may result in a higher likelihood of patient compliance in the smoking cessation program.

In some embodiments, data collected during a five-day assessment period while the patient was a regular smoker and prior to a smoking cessation program is used to establish a baseline for the patient's vital signs, such as CO, eCO, and SpCO levels. The system can generate a baseline curve for the patient's vital signs based on the differences in CO, eCO, and SpCO levels in the collected data. The baseline curve can serve as a reference for comparison with future measurements of the patient's CO, eCO, and SpCO levels.

In some embodiments, patients work with their doctors or counselors to begin the process of entering a smoking cessation program. Having objective data in front of doctors and patients can help set up smoking cessation programs and develop medication and counseling plans. In some embodiments, the system automatically sets up a smoking cessation program based on data from the evaluation period. The collected data may affect the initiation and setup of a smoking cessation program for patients before they enter the program by assisting with medication selection and dispensing. For example, higher and more frequent smoking indications may prompt the start of a higher nicotine replacement therapy dose or multiple medications (e.g., adding a medication for treating nicotine addiction, such as varenicline).

The data collected may influence the initiation and setting of smoking cessation programs by determining the frequency, type, and duration of counseling required for patients. The data may lead to a stratification of smokers' needs. For example, smokers with the highest risk of the highest use may receive more intervention, while smokers with lower risk may receive less intervention. The intervention may include a text message, phone call, social network message, or another suitable event from the patient's spouse, friend, doctor, or another suitable stakeholder at a specific time of day when the patient is likely to smoke.

The data collected may be used to influence the start-up and setting of a smoking cessation program by comparing smoking behavior with all the above variables (such as, the stressors that prompt smoking, the time of day, and other appropriate variables for consulting the patient in advance to be aware of these triggers). Counseling intervention may be directed to these stressors. Intervention can be directed to the patient at those times of the day, such as text messages or phone calls at those times of the day. The data collected may be used to influence the start-up and setting of a smoking cessation program by allocating a peer group based on smoking behavior. The data collected can be used to predict and/or avoid smoking events. For example, if tachycardia or heart rate variability occur before most smoking events, this can sound an alarm, and the patient can take a certain dose of medication, or can receive a phone call from a peer group, doctor, or counselor to avoid smoking events.

In some embodiments, the systems and methods described herein provide for maintaining patient participation in a smoking cessation program. Once in a smoking cessation program, the patient may continue to wear the wearable device for monitoring. The system can use analytical tools, such as setting a CO, eCO, SpCO baseline and tracking progress against the baseline. For example, a trend may drop to zero and stay there (indicating no more smoking). A trend may slowly drop with peaks and valleys (indicating a reduction in smoking). A trend may drop to zero and then spike again (indicating a relapse).

The system can present the patient's progress as a game and improve the visibility of progress. For example, the system can provide a small reward to the patient in exchange for quitting smoking within a certain period of time. In some embodiments, the system can transmit data to healthcare providers in real time for remote monitoring and allow providers to effectively monitor and adjust patient care without having to have the patient appear in the office every day.

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

In some embodiments, the systems and methods described herein provide a system comprising one or more mobile devices and a server in communication with the mobile devices. FIG1 shows an exemplary embodiment 100 for such a system comprising a device 102, a device 104, and a server 106 in communication with the devices 102 and 104. The device 102 assists in detecting a patient's smoking behavior. The device 102 includes a processor, a memory, and a communication link for sending and receiving data from the device 104 and/or the server 106. The device 102 includes one or more sensors to measure a patient's smoking behavior based on measuring one or more of the patient's CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil size, geographic location, environment, ambient temperature, stressors, life events, and other suitable parameters. For example, the device 102 may include PPG-based sensors for measuring CO, eCO, SpCO, and SpO2, electrocardiogram-based sensors for measuring heart rate and blood pressure, acoustic signal processing-based sensors for measuring respiratory rate, wearable temperature sensors for measuring body temperature, skin conductance, electroencephalogram-based sensors for measuring skin electrode activity, implantable sensors placed in the skin, fat, or muscle to measure CO and other variables, intraoral CO sensors, ambient CO sensors, and other suitable sensors. These sensors may have various locations on or within the body for optimal monitoring.

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 may be portable or wearable and attached to a fingertip, earlobe, auricle, toe, chest, ankle, arm, skin fold, or another suitable body part. Device 102 may be attached to a suitable body part via a clip, a strap, a band, an adhesively applied sensor pad, or other 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 a chest strap. In yet another example, device 102 may be attached to an ankle via an ankle strap. In yet another example, device 102 may be attached to an arm via a bicep or tricep strap. In yet another example, device 102 may be attached to a skin fold via a sensor pad.

The device 102 can prompt the patient for a sample or the device (if worn) can perform sampling without the patient's consent. Sampling can be sporadic, continuous, nearly continuous, periodic, or based on any other suitable interval. In some embodiments, sampling is performed continuously whenever the sensor is able to make a measurement. In some embodiments, sampling is performed continuously after a set time interval (such as five minutes or fifteen minutes) or another suitable time interval.

In some embodiments, device 102 includes one or more sensors that monitor SpCO using a transcutaneous method such as PPG. Transcutaneous monitoring can use either a transmissive or reflective method. Device 104 can be a smartphone or another suitable mobile device. Device 104 includes a processor, memory, and a communication link for sending and receiving data from device 102 and/or server 106. Device 104 can receive data from device 102. Device 104 can include an accelerometer, a GPS-based sensor, a gyroscopic sensor, and other suitable sensors for tracking the described parameters. Device 104 can measure specific parameters, including but not limited to movement, location, time of day, patient-entered data, and other suitable parameters.

The data of patient input received by device 104 can include stressors, life events, geographic location, daily events, the application of nicotine patches or other formulations, the application of other drugs for smoking cessation, and other suitable patient input data. For example, some of the data entered by the patient can include information about phone calls, exercise, work, sports, stress, gender, drinking, smoking, and other suitable patient input data. The patient's smartphone is used for texting, calling, surfing the Internet, playing games, and other suitable uses and may also be associated with smoking behavior, and these associations are used to predict behavior and change behavior. Device 104 or server 106 (after receiving the data) can compile data in real time or after a specified time period is completed, perform trend analysis on the data, and associate the data. Server 106 includes a processor, a memory, and a communication link for sending and receiving data from device 102 and/or device 104. Server 106 can be located at a location away from devices 102 and 104, such as a healthcare provider site or another suitable location.

FIG2 shows an exemplary embodiment 200 of a system including a device 202 and a server 204 in communication with the device 202. The device 202 assists in detecting a patient's smoking behavior. The device 202 includes a processor, a memory, and a communication link for sending 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 to measure the patient's smoking behavior based on measuring one or more of the patient's CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil size, geographic location, environment, ambient temperature, stressors, life events, and other suitable parameters.

Device 202 may include one or more sensors 208 to measure specific parameters, including but not limited to movement, location, time of day, patient-entered data and other suitable parameters. The data entered by the patient may include stressors, life events, location, daily events, the use of nicotine patches or other formulations, the use of other smoking cessation medications and other suitable patient-entered data. The data entered by the patient may be received on, for example, a mobile device (such as device 104) in response to a prompt to the patient, or may be entered without prompting according to the patient's wishes. For example, some of the data entered by the patient may include information about phone calls, exercise, work, sports, stress, gender, drinking, smoking and other suitable patient-entered data. Device 202 or server 204 (after receiving the data) may compile the data, analyze the data to obtain trends and correlate the data in real time or after a specified time period is completed. Server 204 includes a processor, a memory and a communication link for sending and receiving data from device 202. Server 204 may be located at a location away from device 202, for example, a healthcare provider site or another suitable location.

In some embodiments, device 102 or 202 includes a detector unit and a communication unit. Device 102 or 202 may include a user interface suitable for its specific function. The user interface may receive input via a touch screen, a keyboard, or another suitable input mechanism. The detector unit includes at least one test element capable of detecting substances indicative of smoking behavior using input from a patient's biological parameters. The detector unit analyzes biological input from the patient, such as exhaled air from the lungs, saliva, or the wavelength of light directed through or reflected by tissue. In some embodiments, the detector unit monitors the patient's SpCO using PPG. The detector unit may optionally measure a number of other variables, including but not limited to SpO2, heart rate, respiratory rate, blood pressure, body temperature, perspiration, heart rate variability, electrical rhythm, pulse rate, galvanic skin response, pupil size, geographic location, environment, ambient temperature, stressors, life events, and other suitable parameters. For breath-based sensors, patient input may include blowing into a tube that is part of the detector unit. For saliva or other body fluid-based sensors, patient input may include placing a fluid sample into a test chamber provided in the detector unit.

For light-based sensors (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 of day, quantifies the presence of the target substance, and stores the data for future analysis and/or sends the data to another location for analysis, such as device 104 or server 106. The communication unit includes appropriate circuitry for establishing a communication link with another device (e.g., device 104) via a wired or wireless connection. The wireless connection may be established using WI-FI, Bluetooth, radio frequency, or other suitable schemes.

FIG3 depicts an illustrative embodiment 300 of suitable wavelengths for analyzing SpO2 and SpCO using a light-based sensor. A patient's SpCO can be measured by intermittently testing the patient's exhaled breath using 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 the patient's tissue (e.g., earlobe, pinna, fingertip, toe, skin folds, or another suitable body part) and analyzing the attenuation of various wavelengths. SpO2 is typically measured using two wavelengths (e.g., 302 nm (660 nm) and 306 nm (940 nm). SpCO can be measured using three wavelengths (e.g., 302 nm (660 nm), 304 nm (810 nm), and 306 nm (940 nm)) or up to seven or more wavelengths (e.g., in the range of 500-1000 nm). Such a PPG sensor can be implemented via a finger clip, a strip, an adhesively applied sensor pad, or another suitable medium. The PPG sensor can be transmissive, such as used in many pulse oximeters. In a transmissive PPG sensor, two or more waveforms of light are transmitted through patient tissue (e.g., a finger), and a sensor/receiver on the other side of the target analyzes the received waveforms to determine SpCO. Alternatively, the PPG sensor can be reflective. In a reflective PPG sensor, light is impinged on a target (e.g., a finger), and a receiver/sensor picks up the reflected light to determine a measurement of SpCO. More details are provided below.

Transcutaneous or transmucosal sensors can noninvasively determine blood CO levels and other parameters based on analysis of the attenuation of light signals passing through tissue. Transmissive sensors are typically placed against a thin body part, such as the earlobe, pinna, fingertip, toe, skin fold, or another suitable body part. Light is shone from one side of the tissue and detected on the other. A light-emitting diode on one side is tuned to a specific set of wavelengths. A receiver or detector on the other side detects which waveforms were transmitted and how much they were attenuated. This information is used to determine the percentage of O2 and/or CO bound to hemoglobin molecules, i.e., SpO2 and/or SpCO.

Reflectance sensors can be used on thicker body parts, such as the wrist. Light irradiated at the surface is not measured on the other side, but rather is measured on the same side as light reflected from the surface. The wavelength and attenuation of the reflected light are used to determine SpO2 and/or SpCO. In some embodiments, an accelerometer is used to correct for problems due to movement of the patient's wrist. For example, information from the accelerometer is used to correct for 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 PulseOximeter." Both of these U.S. patents are incorporated herein by reference in their entirety, including all material incorporated therein by reference.

In some embodiments, device 102 or 202 is configured to recognize a patient's unique features, such as a fingerprint, retinal scan, voice tag, or other biometric identifier, to prevent having an agent respond to signaling and test prompts and thereby disable the system. To this end, a patient identification subunit can be included in device 102 or 202. One of ordinary skill in the art can configure the identification subunit as needed to include one or more of a fingerprint scanner, retinal scanner, voice analyzer, or facial recognition known in the art. Examples of suitable identification subunits are disclosed in, for example, U.S. Patent No. 7,716,383, entitled "Flash-interfaced Fingerprint Sensor," and U.S. Patent Application Publication No. 2007/0005988, entitled "Multimodal Authentication," each of which is incorporated herein by reference in its entirety.

The identification subunit may include a built-in still or video camera for automatically recording pictures or videos of the patient while providing a biological input to the test element. Regardless of the type of identification scheme used, the device 102 or 202 may associate an identification with a specific biological input, for example, by a time reference, and may store this information along with other information about the specific biological input for later analysis.

The patient may also attempt to disable the detector by blowing air into the detector with a pump, bladder, billow or other device, for example, when testing exhaled breath. In an embodiment of the saliva test, the patient may try to replace a clean liquid such as water. For a light-based sensor, the patient may ask a friend to replace him or her. Means for disabling these attempts can be incorporated into the system. For example, the device 102 or 202 may include the ability to distinguish between real and simulated exhaled breath delivery. This function can be incorporated by configuring the detector unit to sense oxygen and carbon dioxide and a target substance (e.g., carbon monoxide). In this way, the detector unit can confirm that the gas being analyzed comes from exhaled breath with lower oxygen and higher carbon dioxide than ambient air. In another example, the detector unit can be configured to detect enzymes naturally present in saliva to distinguish saliva from other liquids. In yet another example, a light-based sensor can be used to measure blood chemistry parameters other than CO levels, and thus the results can be compared with known samples representing the patient's blood chemistry.

In some embodiments, device 104 (e.g., a smartphone) receives measurements from device 102 (e.g., a wearable device) periodically, in real time, or near real time, at appropriate intervals. Device 104 may provide a user interface for prompting the patient for certain inputs. Device 104 may also provide a user interface for displaying certain outputs of the collected data. Device 104 may allow the patient to enter information they deem relevant to their condition without being prompted or responding to prompts. Such information may include information regarding the patient's psychological state, such as feelings of stress or anxiety. Such unprompted information may be associated with the biometric input based on a predetermined algorithm, such as with the biometric input closest in time to the unprompted input, or with the first biometric input that occurred after the unprompted input. Server 106 (e.g., a healthcare database server) may receive such data from one or both of devices 102 and 104. In some embodiments, the data may be stored on a combination of one or more of devices 102, 104, and 106. The data may be reported to various stakeholders, such as the patient, their physician, peer group, family, counselors, employer, and other appropriate stakeholders.

In some embodiments, a wearable device, such as device 102 or 202, can be applied to a patient during, for example, their typical annual doctor visit to detect smoking behavior and subsequently refer the smoker to a smoking cessation program. The patient is provided with the wearable device to wear as an outpatient for a period of time, such as a day, a week, or another suitable period of time. Longer wear times may provide greater sensitivity in detecting smoking behavior and greater accuracy in quantifying variables related to smoking behavior. FIG7 provides an illustrative flow chart for detecting smoking behavior, and is described in more detail below.

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

When the wearable device is worn for an appropriate period of time (e.g., five days), multiple parameters can be measured in real time or near real time. These parameters may include, but are not limited to, CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse velocity, skin galvanic response, pupil size, geographic location, environment, ambient temperature, stressors, life events, and other suitable parameters. FIG4 shows an illustrative chart 400 of SpCO levels for changes in a patient over a typical five-day monitoring period. Data points 402 and 404 indicate high CO levels, which are likely to indicate high smoking events. Data points 406 and 408 indicate low CO levels, likely because the patient is asleep or busy with other things. One or more algorithms can be applied to granular 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, starting point, ascending, slope, peak, increment, downslope, upslope, time of change, area under the curve, and other appropriate factors of the SpCO curve to detect smoking events.

The data from the wearable device can be sent to a smart phone (e.g., device 104) or a cloud server (e.g., server 106 or 204) in real time, at the end of each day, or according to another suitable time interval. The smart phone can measure parameters including, but not limited to, movement, location, time of day, patient-entered data, and other suitable parameters. The patient-entered data may include stressors, life events, location, daily events, administration of nicotine patches or other formulations, administration of other smoking cessation medications, and other suitable patient-entered data. For example, some of the patient-entered data may include information about phone calls, exercise, work, sports, stress, gender, drinking, smoking, and other suitable patient-entered data. The received data may be compiled, trended, and associated in real time or after a time period is complete.

Based on the parameters measured above, information about smoking can be obtained via a processor located in, for example, device 102, 104, or 202 or server 106 or 204. For example, the processor can analyze the information to determine CO trends, averages, peaks, and correlations throughout the day, other vital sign trends, and how these vital organs change before, during, and after smoking. FIG5 shows an illustrative diagram 500 of the analyzed information. The patient can obtain FIG5 by zooming in on a given day in FIG4. Data point 502 indicates the SpCO level when the patient is asleep. Data point 504 shows that the SpCO level is lowest when the patient is awake. Data points 506, 508, and 510 indicate that high SpCO levels are associated with triggers such as work breaks, lunch, and commuting. The processor can analyze the SpCO trend in FIG5 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 intensity of each cigarette smoked, the amount of each cigarette smoked, what the patient's smoking events look like on a curve for subsequent use in a smoking cessation program, time of day, day of the week, associated stressors, geography, location, and exercise. For example, the total number of peaks on a given day can indicate the number of cigarettes smoked, and the gradient of each peak can indicate the intensity of each cigarette smoked.

FIG6 depicts an illustrative data structure for storing patient data. In this embodiment, data structure 600 shows patient data 602 associated with a data point (e.g., data point 508) in FIG5 . Patient data 602 includes identifying information for the patient, such as patient name 604 and patient age 606. Patient data 602 includes curve data 608 corresponding to the curve in FIG5 . 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 device 102, 104, or 202, and/or server 106 or 204, or a combination thereof. Data associated with curve identifier 610 includes date, time, and location information 612. Data includes patient vital signs, such as CO and O2 levels 614. Data includes patient-entered data, such as triggers 616. Patient-entered data can be entered in response to a prompt to the patient, such as on device 104, or can be entered without a prompt, based on the patient's wishes. Curve data 608 includes curve identifiers 618 for additional data points in Figure 5. Data structure 600 may be adapted as appropriate for storing patient data.

Fig. 7 depicts an illustrative flow chart 700 for detecting a patient's smoking behavior in a suitable assessment period. When the patient wears the wearable device in a suitable time period (e.g., five days), multiple 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 speed, skin galvanic response, pupil size, geographical location, environment, ambient temperature, stressor, life events, and other suitable parameters. Wearable device or another suitable device can measure parameters, including but not limited to movement, position, time of day, and other suitable parameters.

At step 702, a processor in a smartphone (e.g., device 104) or a cloud server (e.g., server 106 or 204) receives the described patient data. At step 704, the processor receives patient-entered data in response to a prompt displayed to the patient on, for example, a smartphone and/or patient data entered without prompting according to the patient's wishes. The patient-entered data may include stressors, life events, location, daily events, the use of nicotine patches or other formulations, the use of other smoking cessation medications, and other suitable patient-entered data. At step 706, the processor sends an instruction to update a patient database with the received data. For example, the processor can transmit the patient data to a healthcare provider server or cloud server that hosts the patient database.

At step 708, the processor analyzes the patient data to determine the smoking event. The processor can compile data, perform trend analysis on the data and correlate the data in real time or after the evaluation period is complete. For example, the processor can analyze information to determine CO trends, average values, peak values, curve shapes and associations, other vital signs trends, and how these vital organs change before, during, and after smoking during the day. The processor can analyze SpCO trends to determine parameters such as the total number of cigarettes smoked, the average number of cigarettes smoked per day, the maximum number of cigarettes smoked per day, the intensity of each cigarette smoked, the time of day, the day of the week, the associated stressors, geography, location, and motion. For example, the total number of peak values on a given day can indicate the number of cigarettes smoked, and the gradient of each peak value can indicate the intensity of each cigarette smoked.

At step 710, the processor transmits the determined smoking events and associated analysis to the patient database for storage. At step 712, the processor determines whether the evaluation period has ended. For example, the evaluation period can be five days or another suitable time period. If the evaluation period has not yet ended, the processor returns to step 702 to receive additional patient data, analyze the data, and update the patient database accordingly. If the evaluation period has ended, at step 714, the processor ends data collection and analysis. For example, the processor can evaluate all collected data at the end of the evaluation period to prepare a report as described below with respect to FIG. 8 .

It is contemplated that the steps or descriptions of FIG. 7 may be used in conjunction with any other embodiment of the present disclosure. Additionally, the steps and descriptions described with respect to FIG. 7 may be performed in an alternate order or in parallel to facilitate the purposes of the present disclosure. For example, each of these steps may be performed in any order, in parallel, or substantially simultaneously, as appropriate, to reduce latency or increase the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed with respect to FIG. 1 (e.g., device 102, 104, or 106) or FIG. 2 (e.g., device 202 or 204) may be used to perform one or more of the steps in FIG. 7.

In some embodiments, the systems and methods described herein provide patients with a method for starting and setting up a smoking cessation program. After the patient completes a five-day assessment while wearing a wearable device (e.g., device 102 or 202), the complete data set is compiled and analyzed by the system and delivered to the patient or doctor for use in a smoking cessation program. FIG8 shows an illustrative embodiment 800 of a sample report from analysis. For example, the report indicates that from October 1 to October 6, Mr. Jones smoked a total of 175 cigarettes, an average of 35 cigarettes per day, and the maximum number of cigarettes smoked in a day was 45. Mr. Jones's CO level averaged 5.5%, with a maximum of 20.7%, and was consistently above 4% for 60% of the time during the five-day assessment period. Mr. Jones's triggers include work, family stressors, and commuting. In view of Mr. Jones's smoking habits, the report recommends high-dose and frequent nicotine level predictions for starting nicotine replacement therapy.

In some embodiments, patients work with their physician or counselor to begin the process of entering a smoking cessation program. In some embodiments, the system automatically sets up a smoking cessation program based on data from the assessment period. The sample report in Figure 8 is an example of measuring SpCO and generating a report on CO exposure, associated stressors, and predicted starting nicotine dosage requirements. For example, heavy and intensive smokers may be more dependent on nicotine upon entering a smoking cessation program. The processor can estimate this based on five-day behavior, and the smoking cessation program will start the patient with a higher dose of nicotine replacement therapy. This may prevent many patients from failing a smoking cessation program early due to withdrawal symptoms. Based on the reported data, including the average and maximum number of cigarettes smoked, SpCO levels, and triggers, the processor can determine the nicotine dosage to administer to the patient. For example, the processor can determine a high dose of nicotine for a patient who smokes more than a threshold number of cigarettes per day on average. The processor can also update the nicotine dosage when the reported data is updated.

The data collected can influence the initiation and setup of a smoking cessation program for patients before they enter the program by assisting with medication selection and dispensing. For example, higher indications of smoking may prompt the start of higher nicotine replacement therapy doses or multiple medications (e.g., adding medications for treating nicotine addiction, such as varenicline). The data collected can influence the initiation and setup of a smoking cessation program by determining the frequency, type, and duration of counseling required for the patient. The data may lead to a stratification of smoker needs. For example, smokers with the highest risk of the highest use may receive more intervention, while smokers with lower risk may receive less intervention. For example, an intervention may include a text message, phone call, social network message, or another suitable event from the patient's spouse, friend, doctor, or another suitable stakeholder at a specific moment when the patient is likely to smoke.

The data collected may be able to influence the start-up and setting of the smoking cessation program by associating smoking behavior with all above variables (such as, the stressor that prompts smoking, the time of day and other suitable variables for consulting the patient in advance to be aware of these triggers). Consultation intervention can be for these stressors, and there can be intervention for the patient at those times of the day, such as the text message or call at those times of the day. The data collected may be able to influence the start-up and setting of the smoking cessation program by distributing a peer group based on smoking behavior. The data collected can be used for predicting and/or avoiding smoking events. For example, if tachycardia or heart rate variability or a suitable set of variables occur before most smoking events, this will sound an alarm, and the patient can take a certain dose of medicine, or can receive a call from a peer group, doctor or counselor. Figure 12 shows an illustrative embodiment of stopping smoking events in advance, and will be discussed in more detail below. Figure 14 and Figure 15 show an illustrative flow chart for predicting and preventing expected smoking events, and will be discussed in more detail below.

In some embodiments, the systems and methods described herein provide for maintaining patient participation in a smoking cessation program. Once in a smoking cessation program, the patient may continue to wear a wearable device (e.g., device 102 or 202) for monitoring. The system can employ analytical tools such as setting an SpCO baseline and tracking progress against that baseline. The trend may drop to zero and stay there (indicating no more smoking). The trend may slowly drop with peaks and valleys (indicating a reduction in smoking). The trend may drop to zero and then spike again (indicating a relapse).

The system can employ a patient engagement strategy that encourages patient participation by offering small, infrequent rewards for group or individual progress. The system can offer incentives from employers, payers, spouses, or peer groups to engage patients. The system can present the patient's progress as a game and increase visibility of progress. FIG13 provides an illustrative example of such a user interface, which will be discussed in more detail below. In some embodiments, the system can transmit data to healthcare providers in real time for remote monitoring, allowing providers to effectively monitor and adjust patient care without having to be in the office every day. For example, providers can send instructions to the system to adjust medication type and dosage, change the intensity of counseling, calls, and text messages to actively encourage progress, or trigger intervention if a patient is unable to curb smoking. This could replace expensive, staffed smoking cessation phone lines and effectively automate the process. The system can employ increased intensity and frequency to improve patient outcomes. The system can encourage patients through support from spouses, employers, healthcare providers, peers, friends, and other appropriate parties via scheduled phone calls, text messages, or other suitable communications.

FIG9 shows an illustrative diagram 900 for tracking the average daily SpCO trend for patients who reach and then enter a smoking cessation program. The average trend is tracked daily as it improves. A doctor or counselor can zoom in on a specific day (present or past) to view granular details of CO and the association of CO with other measured parameters and associated stressors 910. Visibility of CO trends that change over time in a smoking cessation program may prevent patients from dropping out, prevent smoking relapse, titrate medications and counseling, and improve outcomes. For example, data point 902 indicates the CO level of a patient before entering a smoking cessation program. Data points 904 and 906 indicate CO levels when nicotine replacement therapy and varenicline therapy were administered during the smoking cessation program. Data point 908 indicates that the patient has successfully quit smoking. At this point, the system can recommend that the patient enter a recidivism prevention program to prevent relapse.

In some embodiments, the systems and methods described herein provide a follow-up program after a patient successfully quits smoking. After successful smoking cessation verified by the system, the patient will wear a wearable device (e.g., device 102 or 202) for an extended period of time, such as several months to two years, as an early detection system for relapse. The system can collect data and employ the counseling strategies described above for smoking cessation programs.

In some embodiments, patients receive a wearable device (e.g., device 102 or 202) and an application for their smartphone (e.g., device 104) that allows them to remotely and privately assess their health by tracking several different parameters. Patients can submit breath samples several times a day as needed or place their fingers in or on the sensors on the wearable device. They can wear the wearable device to obtain more frequent or even continuous measurements. At the end of a test period, such as five to seven days or another suitable time period, a processor in the smartphone can calculate their CO exposure and related parameters. FIG10 shows an exemplary application screen 1000, which shows 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 atypical measurements, which may indicate the effects of smoking on the body. When warning indicators 1014 or 1016 are activated, the system can prompt the patient with an alarm.

The system can recommend that patients enter smoking cessation programs and provide options for these programs. The patient may agree to enter a smoking cessation program after seeing this objective evidence of smoking. The system can share this data with the patient's spouse, their doctor, or another suitable stakeholder participating in the patient's smoking cessation program. For example, the system can share data with an application on the stakeholder's mobile device, or send a message including the data via email, phone, social network, or another suitable media. The trigger that enables the patient to join a smoking cessation program may include spouse advice, employer incentives, peer pressure, personal choice, disease, or another suitable trigger. The patient can voluntarily start a smoking cessation program or provide the data to a doctor to receive assistance for joining a smoking cessation program.

When a patient is active in a smoking cessation program, a wearable device (e.g., device 102 or 202) can continue to monitor the patient's health parameters (such as heart rate, movement, location prior to smoking behavior) and transmit the data to the patient and/or his physician to improve treatment. For example, a smartphone application on device 104 can receive patient-entered data, including but not limited to stressors, life events, location, daily events, application of nicotine patches or other formulations, application of other medications for smoking cessation, and other suitable patient-entered data.

Figure 11 shows an illustrative embodiment of an application screen 1100 for receiving the data input by the patient. When the smartphone application receives the indication of a smoking incident (for example, due to the spike in the patient's CO level), application screen 1100 can be displayed. Application screen 1100 prompts the user to input the trigger for the smoking incident. For example, the patient can select or select option 1110 and provide further information about the trigger from one of options 1102, 1104, 1106 and 1108 as triggering the smoking incident. Other triggers for the smoking incident can comprise the data input by phone call, competitive sports, athletics, pressure, sex and other suitable patients. The patient can also voluntarily call application screen 1100 to input the trigger information for the smoking incident. In certain embodiments, the application screen 1100 for receiving patient data is shown to the patient during the five-day assessment period, to collect information about smoking behavior before the patient enters the smoking cessation project.

In some embodiments, the data collected are used by smart phone applications to avoid smoking events. The processor running the application or the processor in another device (such as, device 102 or 202 or server 106 or 204) can analyze the information about what happened to heart rate and other vital signs in the period before the smoking event. The processor can associate the change (such as tachycardia) of heart rate, which can predict when the patient will smoke. This information can be used to start a preventive program for stopping smoking events. For example, a preventive program can include a pill for delivering nicotine. Nicotine can be delivered via a transdermal patch or from the transdermal transfer of the nicotine stored in a wearable device (such as device 102 or 202). In another example, a preventive program can include calling the patient's doctor, peer group or another suitable stakeholder. The processor can send an instruction to initiate a call to an automatic call system, such as one residing at server 106 or 204. Figures 14 and 15 provide a flow chart for predicting smoking events based on patient vital signs, and will be described in more detail below.

Figure 12 shows an illustrative embodiment of an application screen 1200 that implements such a preventive program. For example, if the patient tends to become tachycardic twenty minutes before each cigarette, the processor can detect the tachycardia and prompt the patient to administer nicotine via option 1202. The patient can change the nicotine dosage via option 1204. In certain embodiments, nicotine is automatically administered. This amount can be determined based on the patient's current SpCO level or another suitable parameter. The patient can receive a call from a peer group via option 1206, a call from a doctor or another suitable stakeholder via 1208. The caller can provide the patient with encouragement to quit smoking and suggest finding other activities to divert the patient's attention.

In some embodiments, the smartphone application presents the patient's process as a game to improve the visibility of progress. The application can adopt a patient engagement strategy by providing small, common or rare rewards for group or individual progress to get the patient involved. The application can provide rewards from employers, payers, spouses or peer groups to get the patient involved. Figure 13 shows an illustrative application screen 1300 for such an embodiment. Application screen 1300 provides the patient with a reward for quitting smoking for fifteen days. Prompt 1302 challenges the patient to further quit for another fifteen days. The patient can select option 1304 to accept the reward and continue to monitor progress while he remains non-smoking. However, the patient may find it difficult to quit and can select option 1306 to contact a peer group, counselor, family member, doctor or another appropriate party.

In some embodiments, the patient is a companion and supporter of other people in their group. The group can track each other's progress and provide support. For example, group members may be part of a social network that allows them to view each other's statistics and provide encouragement to quit smoking. In another example, when it is detected that the patient is smoking, a message (for example, a tweet) can be sent to the group members (for example, followers) of the patient's social network. The message can notify group members that the patient needs help. The group can be connected to the patient to provide help in various ways. This interaction can enable the patient to further quit smoking that day.

In some embodiments, during a primary care visit, the patient provides a sample and is asked if they smoke. For example, a wearable device (e.g., device 102 or 202) is applied to the patient and receives a sample for a single on-site measurement of the patient's SpCO level. The SpCO level may exceed a certain threshold, which suggests that the patient is smoking. FIG16 provides a flow chart for taking a single measurement of a patient's SpCO level. The patient may be provided with a wearable device to wear as an outpatient for a period of time, such as a day, a week, or another suitable period of time. Longer wearing times may provide greater sensitivity in detecting smoking behavior and greater accuracy in quantifying variables related to smoking behavior.

Wearable devices, such as devices 102 or 202, and smartphone applications, such as on device 104, can continue to monitor the patient's health parameters (such as SpCO levels) in real time or near real time and process the data for viewing by the patient, doctor, or any other suitable party. The smartphone application can also provide data in an easily understandable form for daily or weekly use by the patient and/or doctor. For example, the smartphone application can generate a display similar to that of FIG9 , which shows a daily progress with the option to zoom in on a specific day for viewing further details. The doctor can store the patient's record in a healthcare database at, for example, a server 106 or 204 that communicates with the mobile device running the smartphone application, and continue to receive data from the smartphone application via the Internet or another appropriate communication link. The smartphone application can receive data from the sensor via a wired connection to the mobile device running the application or via a wireless connection (such as WI-FI, Bluetooth, radio frequency) or another appropriate communication link.

The patient and the doctor can set a future quit date and send the patient home with or without medication to help the patient quit. The patient can start working towards the agreed quit date. Feedback from the wearable device and/or smartphone app can help the patient be better prepared to actually quit smoking on the quit date, and smoke less on the quit date than when they started. Once the patient begins the quit program, they may get daily or weekly feedback from their spouse, doctor, nurse, counselor, companion, friend, or any other appropriate party.

Medication (if prescribed) can be adjusted based on the doctor's instructions or automatically based on the patient's performance. For example, a doctor can remotely increase or decrease nicotine dosage based on the patient's CO, eCO, SpCO levels. In another example, a processor in a wearable device (e.g., device 102 or 202), a smartphone (e.g., device 104), or a remote server (e.g., server 106 or 204) can increase or decrease nicotine dosage based on CO trends from past measurements of the patient. Similarly, medication can be shortened or extended in duration based on the collected data.

FIG14 depicts an illustrative flow chart 1400 for predicting a smoking event based on a patient's CO, eCO, SpCO measurements, and other suitable factors. A patient may be given a wearable device (e.g., device 102 or 202) and a smartphone application for their mobile phone (e.g., device 104). The wearable device may include a PPG sensor for measuring the patient's SpCO level. At step 1402, a processor in the wearable device or the patient's mobile phone receives a PPG measurement of the patient's SpCO level and the associated time and location. The processor may also receive other information such as heart rate, respiratory rate, and other suitable factors when predicting a smoking event.

At step 1404, the processor updates a patient database stored locally or at a remote location (such as a healthcare database in server 106) using the received patient data. At step 1406, the processor analyzes the current and previous measurements of the patient parameters and determines whether a smoking event is expected. For example, the SpCO trend may be at a local minimum, indicating that the user may smoke to increase their SpCO level. The processor may apply a gradient descent algorithm to determine the local minimum. At step 1408, the processor determines whether the SpCO trend indicates an expected smoking event. If the processor determines that a smoking event is not expected, then at step 1410, the processor determines whether the time and/or location indicate an expected smoking event. For example, the processor may determine that the patient typically smokes when they wake up around 7 a.m. In another example, the processor may determine that the patient typically smokes soon after they arrive at work. In yet another example, the processor may determine that the patient typically smokes whenever they visit a particular restaurant or bar in the evening.

If the processor determines the expected smoking event from step 1408 or 1410, then at step 1412, the processor starts a prevention program for the patient to prevent the smoking event. Information about the prevention program can be stored in the memory of device 102, 104 or 202 or server 106 or 204 or its combination. Information for the prevention program can include instructions about one or more intervention options initiated when the patient is about to smoke. For example, the processor can start an alarm in the patient's mobile phone and display an application screen similar to Figure 12. The application screen can provide the patient with the option of administering nicotine or receiving a call from a peer group, a doctor or another suitable party. Alternatively, the prevention program can include automatically administering a nicotine pill to the patient from a nicotine reservoir stored in the patient's wearable device. In another example, the application screen can indicate that when detecting that the patient has failed to quit smoking, a message (for example, a tweet) will be sent to a group member (for example, a follower) of the patient's social network. The patient can suppress smoking to prevent the message indicating its failure from being sent out.

In some embodiments, steps 1408 and 1410 are combined into one step or include two or more steps for the processor to determine whether a smoking event is expected. For example, the processor can determine whether a smoking event is expected based on a combination of SpCO trends, the patient's location, and/or the current time. In another example, the processor can determine whether a smoking event is expected based on a series of steps that analyze 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, galvanic skin response, pupil size, geographic location, environment, ambient temperature, stressors, life events, and other suitable parameters.

At step 1414, the processor determines whether the prevention regimen was successful. If a smoking incident occurred, then at step 1418, the processor updates the patient database to indicate that the prevention regimen was unsuccessful. If no smoking incident occurred, then at step 1416, the processor updates the patient database to indicate that the prevention regimen was successful. The processor returns to step 1402 to continue receiving PPG measurements of the patient's SpCO level and associated data. The processor can continuously monitor the patient's vital signs to ensure that the patient does not relapse into a smoking incident.

It is contemplated that the steps or descriptions of Figure 14 may be used in conjunction with any other embodiment of the present disclosure. Additionally, the steps and descriptions described with respect to Figure 14 may be performed in an alternate order or in parallel to facilitate the purposes of the present disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously, as appropriate, to reduce delay or improve the speed of the system or method. Additionally, it should be noted that any one of the devices or equipment discussed with respect to Figure 1 (e.g., device 102, 104, or 106) or Figure 2 (e.g., device 202 or 204) may be used to perform one or more of the steps in Figure 14.

FIG15 depicts an illustrative flowchart 1500 for determining whether a prevention regimen was successful, with respect to step 1414 in FIG14 . At step 1502, a processor receives patient data for determining whether a smoking event occurred. At step 1504, the processor analyzes the currently received patient data and previously received patient data. At step 1506, the processor determines whether a smoking event occurred based on the analysis. For example, if nicotine was not administered, but the patient's SpCO level is currently higher than a previous SpCO level, the processor may determine that the patient has relapsed and is smoking. In this case, at step 1508, the processor returns a message indicating that the prevention regimen was unsuccessful. In another example, if the patient's vital signs indicate that the SpCO level has not increased or decreased, the processor may determine that a smoking event did not occur. In this case, at step 1510, the processor returns a message indicating that the prevention regimen was successful.

It is contemplated that the steps or descriptions of Figure 15 may be used in conjunction with any other embodiment of the present disclosure. Additionally, the steps and descriptions described with respect to Figure 15 may be performed in an alternate order or in parallel to facilitate the purposes of the present disclosure. For example, each of these steps may be performed in any order, in parallel, or substantially simultaneously, as appropriate, to reduce delay or improve the speed of the system or method. Additionally, it should be noted that any one of the devices or equipment discussed with respect to Figure 1 (e.g., device 102, 104, or 106) or Figure 2 (e.g., device 202 or 204) may be used to perform one or more of the steps in Figure 15.

FIG16 depicts an illustrative flow chart 1600 for a single measurement of a patient's SpCO level using a PPG sensor. For example, a wearable device (e.g., device 102 or 202) is applied to a patient and receives a sample for a single measurement of the patient's SpCO level. At step 1602, a processor in the wearable device receives a PPG measurement of the patient's SpCO level and any other suitable data, such as time, location, SpO2, heart rate, respiratory rate, blood pressure, body temperature, perspiration, heart rate variability, electrical rhythm, pulse velocity, galvanic skin response, pupil size, geographic location, environment, ambient temperature, stressors, 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 exceeding a certain threshold may indicate that the patient has recently smoked.

At 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 SpCO curve's shape, starting point, upslope, slope, peak, increment, downslope, upslope, time of change, area under the curve, and other appropriate factors may indicate a smoking event. One or more of these factors may assist in quantifying smoking events. For example, the total number of peaks on a given day may indicate the number of cigarettes smoked, while the gradient shape and magnitude of each peak, as well as other characteristics, may indicate the intensity and volume of each cigarette smoked. If the processor determines that the SpCO level indicates that no smoking event has occurred, then at step 1608, the processor returns a denial message indicating that the patient has not had a recent smoking event. The patient's physician may find this information useful in evaluating the patient's smoking behavior. If the processor determines that the SpCO level indicates that a smoking event has occurred, then at step 1610, the processor returns a confirmation message indicating that the patient has indeed had a recent smoking event. In this case, as described above, the collected data can be used to establish a smoking cessation program for the patient.

After step 1608 or 1610, at step 1612, the processor updates the patient database to record this information. At step 1614, the processor terminates the SpCO level assessment for the patient. The patient may be provided with a wearable device to wear as an outpatient for a period of time, such as a day, a week, or another suitable period of time. A longer wear time may provide greater sensitivity in detecting smoking behavior and greater accuracy in quantifying variables related to smoking behavior.

It is contemplated that the steps or descriptions of Figure 16 may be used in conjunction with any other embodiment of the present disclosure. Additionally, the steps and descriptions described with respect to Figure 16 may be performed in an alternate order or in parallel to facilitate the purposes of the present disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously, as appropriate, to reduce delay or improve the speed of the system or method. Additionally, it should be noted that any one of the devices or equipment discussed with respect to Figure 1 (e.g., devices 102, 104, or 106) or the devices or equipment discussed with respect to Figure 2 (e.g., devices 202 or 204) may be used to perform one or more of the steps in Figure 16.

In some embodiments, data from one or more devices such as devices 102 and 104 or device 202 associated with the patient are received at a central location such as server 106 or 204. The patient device records multiple biometric and situational variables in real time or near real time. For example, biometric variables may include CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse speed, skin galvanic response, pupil size and other suitable biometric variables. For example, situational variables may include GPS location, patient activity (e.g., sports, fitness, shopping or another suitable patient activity), patient environment (e.g., at work, at home, in a car, in a bar or another suitable patient environment), stressors, life events and other suitable situational variables. The collected data may also include personal observations of the patient's smoking behavior. A spouse or friend or partner may be able to input data on their patient's smoking and associate the data with the SpCO reading to determine accuracy.

Server 106 includes a processor for receiving data for multiple patients within a time period, and for trend analysis data occurring near the time of an actual smoking event. Based on trend, the processor determines the diagnosis and/or detection test for a smoking event. The test can include one or more algorithms applied to data determined by the processor. For example, the processor can analyze the spike in the patient's CO level. Detecting a spike can include determining that the CO level is higher than a certain specified level. Detecting a spike can include a relative increase in the CO level of the patient according to a previously measured baseline detection. The processor can detect a spike as the change in the slope of the patient's CO trend within a time period. For example, a CO trend moving from a negative slope to a positive slope can indicate a spike in CO level. In another example, the processor can apply one or more algorithms to the change in heart rate variation, increased heart rate variability, blood pressure variation or other suitable data to detect a smoking event.

Figure 17 depicts an illustrative flowchart 1700 for detecting a smoking event as described above. A processor (e.g., in server 106 or 204) can determine a diagnostic and/or detection test for a smoking event according to flowchart 1700. At step 1702, the processor receives current patient data. At step 1704, the processor retrieves previously stored patient data from a database (e.g., a patient database stored at server 106 or 204). At step 1706, the processor compares current and previous patient data to detect a smoking event. For example, the processor can analyze a spike in the patient's CO level. Detecting a spike can include detecting a relative increase in the patient's CO level based on a previously measured baseline. The processor can detect a spike as a change in the slope of the patient's CO trend over a period of time. For example, a CO trend that moves from a negative slope to a positive slope can indicate smoking behavior. In another example, the processor can apply one or more algorithms to changes in heart rate, increased heart rate variability, blood pressure, or other suitable data to detect a smoking event. At step 1708, the processor determines whether a puffing event occurred, as described, based on, for example, a spike in the patient's CO level. If no puffing event was detected, then at step 1710, the processor returns a message indicating that no puffing event occurred. If a puffing event was detected, then at step 1712, the processor returns a message indicating that a puffing event occurred. At step 1714, the processor updates the patient database with the results from either step 1710 or 1712.

It is contemplated that the steps or descriptions of Figure 17 may be used in conjunction with any other embodiment of the present disclosure. Additionally, the steps and descriptions described with respect to Figure 17 may be performed in an alternate order or in parallel to facilitate the purposes of the present disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously, as appropriate, to reduce delay or improve the speed of the system or method. Additionally, it should be noted that any one of the devices or equipment discussed with respect to Figure 1 (e.g., devices 102, 104, or 106) or the devices or equipment discussed with respect to Figure 2 (e.g., devices 202 or 204) may be used to perform one or more of the steps in Figure 17.

In some embodiments, the processor analyzes the initially received data to measure when people smoke and associates an algorithm with a variable that triggers the algorithm for diagnosing and/or detecting smoking events. As additional patient data is received, the processor continues to analyze other variables. The processor can determine another variable that changes when the patient smokes and use that variable to trigger the algorithm instead. For example, the processor can choose to use another variable because it is less invasive or easier to measure than the initially selected variable.

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

In some embodiments, the algorithm for detecting smoking events has high specificity. Specificity is defined as the ability of a test to not make false positive calls for smoking events (i.e., a positive test where no smoking events are present). If a sensor and algorithm does not make any false positive calls in a single day, it has 100% specificity.

In another example, if a patient smokes 20 times and the algorithm identifies 18 of the 20 actual smoking events and indicates 20 other false smoking events, it has a sensitivity of 90% (i.e., detects 90% of the smoking events) and a specificity of 50% (i.e., estimates the number of smoking events to be more than 2 times).

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

In certain embodiments, the patient data that processor analyzes is received is to predict the possibility of smoking incident before smoking incident occurs.Processor can analyze the patient data that is received in a time period (for example, five minutes, 10 minutes, 15 minutes, 20 minutes or another suitable time interval) before smoking incident, to determine one or more triggers.For example, some smoking incidents can be before situation trigger (for example, in bar, before eating, during or after, before sexual behavior, during or after or another suitable situation trigger).In another example, some smoking incidents can be before the change of biometric variable such as heart rate or another suitable biometric variable.The variable determined may overlap with the variable selected for diagnosis and detection, therefore also can be used for prediction.Alternatively, the variable determined may not overlap with the variable selected for diagnosis.

The processor can notify the patient of the possibility of a smoking event and trigger (e.g., as discussed with respect to Figures 14 and 15) a preventive program to prevent smoking-changing behavior. The processor detects smoking events for the patient inputted in, for example, a smoking cessation program and tracks and analyzes the trend of received patient data. The processor can determine the patient's goals and reward them when he reaches the set goals (e.g., as discussed with respect to Figure 13). The processor can predict when the patient will smoke and intervene in time by suggesting a call to a peer group or physician or by administering nicotine pills (e.g., as discussed with respect to Figure 12).

In one example, the processor predicts a patient's smoking events during diagnosis based on 75% of the patient's smoking events being preceded by an increased heart rate (or a suitable change in another variable). During a smoking cessation program, the processor may apply one or more algorithms to received patient data to predict smoking events and initiate a prevention program. For example, a prevention program may involve the patient in a timely manner by contacting a supporter such as a doctor, counselor, companion, team member, nurse, spouse, friend, robot, or another suitable supporter. In some embodiments, the processor applies an algorithm to each patient based on their five-day run-in diagnostic period to adjust settings such as baseline, threshold, sensitivity, and other suitable settings. The processor may then use these customized algorithms for a specific patient's smoking cessation program. The described combination of technologies for changing a patient's smoking behavior may be referred to as a digital drug.

In some embodiments, the processor detects smoking with a positive or negative indication in a binary manner. The processor initially uses observational studies and SpCO measurements from the patient to detect smoking behavior. For example, the processor receives data about true positives for smoking events from the observational data of the patient's smoking behavior. The processor determines whether the detection based on the SpCO measurement matches the true positive of the smoking event. If there is a match, the processor applies the 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 speed, skin galvanic response, pupil size, geographical location, environment, ambient temperature, stressor, life events and other suitable parameters. The processor determines whether any pattern in the non-SpCO variable data also indicates a smoking event. These variables can be used for algorithms for non-SpCO devices (such as wearable smart watches or heart rate monitoring belts or other devices) to detect smoking events.

When smoking behavior is detected based on the patient data received, the processor can quantify the smoking behavior. For example, the processor analyzes the SpCO data trend to indicate the intensity of each cigarette smoked by the patient, how many cigarettes the patient smokes in a day, how much each cigarette smokes and/or how long each cigarette smokes. The processor can also use other biometric features or situational variables for indication. The processor uses the received patient data to predict the possibility of a smoking event occurring in the near future, for example, within the next 10 minutes. The processor can analyze the received patient data in a previous time period (for example, five minutes, 10 minutes, 15 minutes, 20 minutes or another suitable time interval) before the smoking event to determine one or more triggers.

In certain embodiments, system and method as herein described provide for assessing the smoking behavior of patient.During the five-day test period, patient performance is as he usually does.Device 102,104 and/or 106 or device 202 and/or server 204 receives the patient data relevant to the patient's smoking behavior.Due to the purpose of the test period is to observe the patient's smoking pattern, the patient hardly participates.If necessary, the test period can be extended to a second five-day period.Alternatively, the first and second periods can be shorter (for example, two or three days) or longer (for example, one week or longer).Before the second test period, the processor determines the model of how the patient smokes.

In the second test phase, the processor applies a series of disturbances to the model to see if smoking behavior changes. There may be several types of disturbances, each with several dimensions. For example, a disturbance may be whether sending a text message before or during a smoking episode causes the smoking episode to be avoided or shortened. The dimensions within the disturbance may be different senders, different timings, and/or different content for the text message. In another example, a disturbance may be whether a phone call at a certain time of day or before or during a smoking episode causes the smoking episode to be avoided or shortened. The dimensions within the disturbance may be different callers, different timings, and/or different content for the phone call. In yet another example, a disturbance may be a warning to the patient to check their smoking behavior at several points in the day to see if they will avoid smoking within a period of time thereafter. The dimensions may include determining whether and when to eliminate this avoidance. In other examples, the disturbance may be a reward, team action, or other suitable trigger to avoid or shorten the patient's smoking episode.

In some embodiments, the processor uses a machine learning process to deliver perturbations to the patient's smoking model. The machine learning process delivers the perturbations, tests the results, and adjusts the perturbations accordingly. The processor determines the most effective way to achieve the identified behavioral changes by trying options through the machine learning process. The machine learning process can be applied as a minor perturbation during the second testing phase. The machine learning process can also be applied as a significant perturbation during the patient's smoking cessation phase to increase the patient's efforts to quit or continue to quit smoking.

FIG18 depicts an illustrative flowchart 1800 for applying one or more perturbations to a patient's smoking model during a second testing phase. At step 1802, a processor in the wearable device 102 or 202, mobile device 104, or server 106 or 204 receives patient data related to the patient's smoking behavior during a first testing phase. At step 1804, the processor analyzes the received patient data to determine a model for the patient's smoking behavior. At step 1806, the processor applies one or more perturbations to the model to determine whether the smoking behavior changes. Perturbations can be applied to the model using a machine learning process. Several types of perturbations are possible, each with several dimensions. For example, a perturbation might be whether sending a text message before or during a smoking episode resulted in the smoking episode being avoided or shortened. Dimensions within a perturbation might include different senders, different timing, and/or different content of the text messages.

At step 1808, the processor determines whether the perturbation changed the patient's smoking behavior. For example, the processor determines whether receiving a text message before or during a smoking episode caused the patient to quit or shorten their smoking. If the perturbation caused a change in the patient's smoking behavior, then at step 1810, the processor updates the model of the patient's smoking behavior to reflect the positive results of the applied perturbation. The processor then proceeds to step 1812. Otherwise, the processor proceeds directly from step 1808 to step 1812 and determines whether to apply additional perturbations or changes to the dimensions of the current perturbation. The processor can use a machine learning process to determine whether to apply additional perturbations to the model. If no further perturbations are needed, then at step 1814, the processor ends the process of applying perturbations.

If more perturbations need to be applied, the processor determines another perturbation to apply to the model at step 1816. For example, the processor can adjust the current perturbation 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 perturbation by initiating a phone call to the patient before or during a smoking event. The processor returns to step 1806 to apply the perturbation to the model. The processor can use a machine learning process to pass the perturbation, test the results, and adjust the perturbation or another selected perturbation accordingly. In this way, the processor determines the most effective way to achieve the identified behavioral change for the patient by trying different options through the machine learning process.

It is contemplated that the steps or descriptions of Figure 18 may be used in conjunction with any other embodiment of the present disclosure. Additionally, the steps and descriptions described with respect to Figure 18 may be performed in an alternate order or in parallel to facilitate the purposes of the present disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously, as appropriate, to reduce delay or improve the speed of the system or method. Additionally, it should be noted that any one of the devices or equipment discussed with respect to Figure 1 (e.g., devices 102, 104, or 106) or the devices or equipment discussed with respect to Figure 2 (e.g., devices 202 or 204) may be used to perform one or more of the steps in Figure 18.

In an illustrative example, a 52-year-old male patient is motivated by his employer to screen for smoking behavior. The patient entered the assessment project on June 1, 2015. The patient reported smoking 20 cigarettes a day. A project coordinator (such as a physician or consultant) loads the application onto the patient's smartphone (e.g., mobile device 104) and provides the patient with a connected sensor (e.g., wearable device 102 or 202). The coordinator notifies the patient of normal smoking and performance during a five-day test period, and responds to the prompt from the application when the prompt appears. After the five-day period ends, the coordinator allows the patient to enter a supplementary test period, in which the application prompts more frequently (e.g., to apply a disturbance). The coordinator notifies the patient that it is up to him to decide whether to respond at this moment, although he hopes the patient will respond. The coordinator sets up a target date on June 10, 2015 to include a 10-day test.

After the five-day testing period, the coordinator receives a report (e.g., the five-day report card discussed about Figure 8). The report indicates 150 smoking events detected using CO compared to 100 smoking events based on the patient's estimate. The report indicates that the associated situational variables include alcohol, location, stress, and other suitable data. The report indicates that the associated biometric variables include an increase in heart rate in the absence of exercise, as occurred in 50% of the smoking events. The report indicates that a reminder for stress level shows an increase in stress in 20% of the smoking events.

During the supplementary five-day test period, a processor in a mobile device (e.g., device 104), a wearable device (e.g., device 102 or 202), or a remote server (e.g., server 106 or 204) applies perturbations via a machine learning process. For example, the mobile device prompts the patient four times a day with a display that includes the number of cigarettes smoked, the intensity of smoking, and the time of day. As the day progresses, the prompts cause the patient to reduce smoking over a longer period of time. The effective effect is that the patient smokes fewer cigarettes in the second half of the day compared to the first half of the day. In another example, the mobile device prompts the patient at 10 a.m. every day with a display that includes the number of cigarettes smoked the previous day. The effective 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 to change the perturbation dimension as needed.

In another example, the processor applies a perturbation in the form of text messages sent to the patient during a smoking episode via a machine learning process. The machine learning process varies the dimension of the perturbation by having different senders, different timing, sending before or during a smoking episode, different content of the message, different images in the message, and/or different rewards for quitting. In another example, the processor applies a perturbation in the form of phone calls during a smoking episode via a machine learning process. The machine learning process varies the dimension of the perturbation by having different callers, different timing, calling before or during a smoking episode, different call content, different tones in the call, and/or different rewards for quitting.

In another example, the processor applies a perturbation via a machine learning process in the form of a prompt on the patient's mobile device for a specific activity. The prompt indicates that the patient is currently smoking but should consider smoking only half a cigarette before going out. During the long periods between smoking events, or when an event is predicted, the machine learning process applies a perturbation to attempt to avoid the smoking event altogether. For example, the mobile device displays a prompt notifying the patient that they are in a high-risk area and should consider alternative activities or locations or call a friend.

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 an algorithm to the data. The processor uses all data from the first and second test periods to customize the algorithm and initiate a program and smoking cessation program intervention for the specific patient. The diagnostic and detection algorithms can use one or more biometric variables of the patient (such as SpCO) to detect smoking behavior. The smoking cessation program includes a nicotine regimen as part of nicotine replacement therapy starting from day one. Nicotine can be delivered via a transdermal patch or transdermal transfer from a nicotine reservoir stored in a wearable device provided to the patient. The processor applies an algorithm to the received patient data to determine the most effective intervention. The processor applies the intervention and further adjusts it as needed. The processor can set a target event count and determine which method is most effective for changing the patient's smoking behavior. The processor can call multiple personalized interventions from stakeholders as perturbations via a machine learning process and test which is most effective for changing the patient's smoking behavior. The perturbation that has the greatest impact on the patient's smoking model may be retained, while perturbations with little or no impact may not be used further.

While the exemplary embodiments of the systems and methods described above focus on smoking behaviors, examples include, but are not limited to, tobacco smoking via cigarettes, pipes, cigars, and hookahs, as well as the use of illicit products (such as marijuana, cocaine, heroin), and alcohol-related behaviors, it will be apparent to those skilled in the art that the teachings of the present invention are equally applicable to any number of other undesirable behaviors. These other examples include: oral administration of certain substances, with specific examples including, but not limited to, placing chewing tobacco and snuff in the mouth, transdermal absorption of certain substances, with specific examples including, but not limited to, applying certain creams, ointments, gels, patches, or other products containing drugs of abuse (such as narcotics and LSD) to the skin, and nasal inhalation of drugs or substances of abuse, including, but not limited to, nasal inhalation of cocaine.

Generally, the basic configuration of devices 102 and 104 or device 202 and the associated steps and methods disclosed herein will be similar between the different behaviors being addressed. Devices may differ in design to account for different target substances required for testing or different testing methods necessitated by different markers associated with a particular undesirable behavior.

Those skilled in the art will also recognize that patients participating in formal smoking cessation programs can utilize the systems and methods disclosed herein as an aid to smoking cessation programs. It will also be recognized that patients can independently motivate themselves and therefore advantageously use the systems and methods for unilaterally quitting undesirable behaviors outside of formal smoking cessation programs.

In further exemplary embodiments, the systems and methods disclosed herein can be readily adapted for data collection, and in particular, for collecting reliable and verifiable data for studies relating to undesirable behaviors that the present invention is well suited to testing. Such studies can be accomplished without substantial modification to the underlying apparatus or methods, except that where treatment is not included, the need to update test or treatment protocols based on user input will not be necessary.

Figure 19 shows another variation of a system and/or method for influencing an individual's smoking behavior and further quantifying an individual's exposure to cigarette smoke using the multiple aspects described herein. In the illustrated example, samples of multiple biometric data are obtained from an individual and analyzed to quantify the individual's exposure to cigarette smoke, so that the quantified information can be forwarded to the individual, a medical caregiver, and/or other parties with an interest in the individual's health. The example discussed below uses a portable device 1900 that obtains multiple exhaled breath samples from an individual and has a commonly available sensor that measures the amount of carbon monoxide (also known as exhaled carbon monoxide or ECO) within the exhaled breath samples. However, quantification and information transfer are not limited to exposure based on exhaled smoke. As described above, there are many sampling devices for obtaining an individual's smoking exposure. The methods and devices described in this example can be combined with or supplemented by such sampling devices where possible, while still maintaining the scope of the present invention. In addition, although this example discusses the use of a portable sampling unit, the methods and processes described herein can be used with dedicated or non-portable sampling units.

It is known that the measurement of exhaled CO levels is used as an immediate, non-invasive method to assess an individual's smoking status. See, for example, "The Measurement of Exhaled Carbon Monoxide in Healthy Smokers and Non-smokers," published by S. Erhan Devecia et al., Department of Public Health, Faculty of Medicine, Firat University, Elazig, Turkey, in 2003, and "Comparison of Tests Used to Distinguish Smokers from Nonsmokers," published by M.J. Jarvis et al., American Journal of Public Health, Vol. 77, No. 11, November 1987. These articles discuss that the exhaled CO ("eCO") level for non-smokers can be between 3.61 ppm and 5.6 ppm. In one example, a cutoff level for eCO is above 8-10 ppm to identify smokers.

Returning to Figure 19, as shown, portable or personal sampling unit 1900 communicates with personal electronic device 110 or computer 112. Personal electronic device 110 includes but is not limited to smart phones, ordinary phones, cellular phones or other personal transmission devices specially designed for receiving data from personal sampling unit 1900. Similarly, computer 112 is intended to include personal computers, local servers or remote servers. Data transmission 114 from personal sampling unit 1900 can occur to both or any one of personal electronic device 110 and/or computer 112. In addition, synchronization 116 between personal electronic device 110 and computer 112 is optional. As described herein, any one of personal electronic device 110, computer 112 and/or personal sampling unit 1900 can transmit data to a remote server for data analysis. Alternatively, data analysis can be performed completely or partially in 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, a caretaker or other individual, as shown in Figure 19.

In the depicted example of FIG19 , a personal sampling unit 1900 receives a sample 108 of exhaled breath from an individual via a collection tube 1902. The hardware within the personal sampling unit 1900 includes any commercially available electrochemical gas sensor for detecting carbon monoxide (CO) gas within the exhaled breath sample, and commercially available transmission hardware for transmitting data 114 (e.g., via Bluetooth, cellular, or other radio waves that provide data transmission). The transmitted data and associated measurements and quantifications are then displayed on either (or both) a computer display 112 or a personal electronic device 110. Alternatively, or in combination, any information can be selectively displayed on the portable sampling unit 1900.

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

The personal sampling unit 1900 may also include any number of input triggers (such as switches or sensors) 1904, 1906. As described below, the input triggers 1904, 1906 allow an individual to prepare the device 1900 in advance for delivering a breath sample 108 or recording other information about a cigarette (such as the amount, intensity, etc. of smoking). In addition, variations of the personal sampling unit 1900 also associate the timestamp of any input to the device 1900. For example, the personal sampling unit 1900 may associate the time at which the sample is provided and provide the measured or input data and the measurement time when transmitting data 114. Alternatively, the personal sampling device 1900 may use an alternative device to identify the time at which the sample is obtained. For example, considering a series of samples, instead of recording the timestamp for each sample, the time period between each sample in the series may be recorded. Therefore, the identification of the timestamp of any one sample allows the timestamp for each sample in the series to be determined.

In certain variations, the personal sampling unit 1900 is designed so that it has a minimal profile and can be easily carried by an individual with minimal effort. Thus, the input trigger 1904 can include a low-profile tactile switch, an optical switch, a capacitive touch switch, or any commonly used switch or sensor. The portable sampling unit 1900 can also provide feedback or information to the user using any number of well-known techniques. For example, as shown, the portable sampling unit 1900 can include a screen 1908 that displays selection information as discussed below. Alternatively or in addition, feedback can take 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, which can serve as a reminder to provide a sample and/or provide feedback related to the measurement of smoking behavior. In addition, the feedback component can provide an alert to the individual on a recurring basis in an effort to remind the individual to provide periodic samples of exhaled breath, thereby extending the period during which the system captures biometrics (such as eCO, CO levels, etc.) and other behavioral data (such as location, number of cigarettes, or other triggers entered manually or via a GPS component coupled to the unit). In some cases, reminders can be triggered more frequently during an initial project or data capture. Once sufficient data is available, the reminder frequency can be reduced.

Figure 20A shows a visual representation of the data that can be collected using a variation of the system shown in Figure 19. As discussed above, an individual provides a breath sample using a portable sampling unit. Depending on the nature of the treatment or intervention project, the individual may be reminded periodically or at random intervals. Each sample is evaluated by one or more sensors within the portable sampling unit to measure the amount of CO. The CO measurements typically correspond to inflection points 410 on the graph of Figure 20A. Each CO measurement 410 corresponds to a timestamp as shown on the horizontal axis. The data accumulated via the portable sampling unit allows collection of a data set comprising at least the CO measurements and time of the sample, which can be plotted to obtain an eCO curve indicating the amount of CO attributable to the smoking behavior of the individual over the course of the time period.

As described herein, an individual can further track additional information such as the number of cigarettes smoked. The smoking of a cigarette can be associated with its own timestamp as shown in column 414. In a variation of the methods and systems of the present disclosure, an individual can use an input trigger on a portable sampling unit to input the number of cigarettes smoked or a portion thereof. For example, each actuation of the input trigger can be associated with a small number of cigarettes (e.g., 1/2, 1/3, 1/4, etc.).

FIG20B shows a portion of a graphical representation of the data collected as described above. However, in this variation, the quantification of an individual's smoking behavior can use behavioral data to better approximate the CO values between eCO readings. For example, in some variations, a linear approximation between two points can be used to approximate the eCO measurement between any two points 410. However, it is known that CO levels decay within the bloodstream without exposure to new CO. This decay can be approximated using a standard rate, a rate based on the patient's biometric information (weight, heart rate, activity, etc.). As shown in FIG20B , when the patient is between cigarettes 414, the calculated CO level can follow a decay rate 440. Once an individual records a cigarette 414, the CO increase 442 can be approximated again using a standard rate or a value calculated using the biometric data discussed above, or based on the intensity, duration, and amount of smoking. Therefore, the methods and systems described herein can optionally use an improved (or approximate) eCO curve 438 utilizing the behavioral data discussed above. This improved eCO rate can also be used to determine an improved eCO curve 438 when the individual is sleeping. This improved eCO curve can then provide an improved eCO load as described herein.The biometric information used to determine the decay rate can be measured by a portable sampling device or by an external biometric measurement device in communication with the system.

This approximate or improved eCO curve 438 can be displayed to the individual (or to a third party) as a means of assisting in behavior change, as the individual can view the real-time approximate CO level (i.e., the rate of decrease when not smoking and the rate of increase when smoking). Additional information can also be displayed, for example, the system can also calculate the CO increase for each cigarette based on its starting CO value.

Figure 21 shows an example of a data set for determining an eCO curve 412 over a period of time, wherein the eCO attributable to an individual's smoking behavior can be quantified within different time intervals to determine the eCO burden or eCO load for each interval. As shown, the time period extends along the horizontal axis and includes historical and ongoing data captured/transmitted by a portable sampling unit. In order to provide more effective feedback about its smoking behavior to an individual, the eCO curve 412 during a specific time interval can be quantified. In the example shown, the time interval between time 416 and 418 includes an interval of 24 hours. The subsequent 24-hour interval is defined between time 418 and 420. The interval of time or time interval can include any time between two points in the time period spanned by the data set. In most cases, the time interval will be compared with other time intervals with the same duration (that is, each interval can include 24 minutes, 3 hours, 4 days, etc.).

One method of quantifying eCO burden/load over time intervals is to use the dataset to obtain the area defined by or below the eCO curve 412 between given time intervals (e.g., 416 to 418, 418 to 420, etc.), as shown in the graph of FIG21 . In the example shown, the eCO burden/load 422 for the first interval (416 to 418) includes 41 (measured in CO ppm*t), while the eCO burden 422 for the second interval (418 to 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 a timestamp for each cigarette. For any given time interval, this cigarette data can also be summarized as 426 along with the eCO burden/load 422. In the example shown, the eCO burden/load is a daily load, which allows individuals to track their CO exposure. Determining CO load is a more accurate reflection of total smoke exposure than simply counting cigarettes, as smokers vary in their smoking patterns. In one embodiment, the present invention relates to a method for the treatment of CO2 in the presence of a substance which is present in the body. The method comprises the steps of: a) quantifying the CO load of a patient and b) determining the CO load of the patient; b) determining the CO load of the patient and b) determining the CO load of the patient. The method comprises the steps of: a) quantifying the CO load of a patient and b) determining the CO load of the patient. The method comprises the steps of: b) quantifying the CO load of a patient and b) determining the CO load of the patient. The method comprises the steps of: c) quantifying the CO load of a patient and b) determining the CO load of the patient. The method comprises the steps of: e) quantifying the CO load of a patient and b) determining the CO load of the patient. The method comprises the steps of: e) quantifying the CO load of a patient and b) determining the CO load of the patient. The method comprises the steps of: e) quantifying the CO load of a patient and b) determining the CO load of the patient.

The data shown in FIG21 is for illustrative purposes only, and the duration of a time period for a given data set depends on the amount of time an individual uses the portable sampling unit to capture biometric and behavioral data. Quantifying exhaled carbon monoxide exposure involves using the data set to correlate exhaled carbon monoxide as a function of time over a time period and obtaining the area under the eCO curve 412. In variations of the methods and systems, the eCO curve can be calculated or approximated.

FIG22 shows an example of displaying biometric data and various other information for the benefit of a user, caregiver, or other party when assessing an individual's smoking behavior. The data shown in FIG22 is for illustrative purposes and can be displayed on a portable electronic device (e.g., see 110 in FIG19 ) or one or more computers. In addition, any of the biometric data or other data can be displayed on the portable sampling unit 1900.

FIG22 shows a "dashboard" view 118 of an individual's smoking behavior data, which includes a graphical output 120 of an eCO curve 412 over a period of time and a cigarette count for any given time interval during the period of time. The graphical output 120 may also provide a measured or calculated nicotine trend 424. This nicotine trend 424 may be determined based on the number of puffs 426, rather than a direct measurement of nicotine.

FIG22 also shows a second graphical output display 122 of an eCO curve 412 over an alternative time period. In this example, the first graphical display 120 displays the eCO curve 412 over a seven-day period, while the second display 122 displays data over a three-day period. The dashboard view 118 may also include additional information, including the latest eCO burden/load 124 (or the latest eCO reading from the latest sample), the number of cigarettes 126 within a defined period, such as the current day, and the amount of nicotine 128. Furthermore, the dashboard 118 may also include a count of the number of samples 130 provided by the individual within the defined period (such as a daily to monthly count).

The dashboard 118 can also display information that can help individuals reduce or stop smoking. For example, FIG22 also shows cigarette spending 132 using a count of the fraction of cigarettes inhaled by individuals 126 or 426. The dashboard can also display social connections 146, 142, 140 that can help individuals stop smoking. For example, the dashboard can display a doctor or counselor 140 that can be directly messaged. Additionally, information can be displayed for social acquaintances 142 who are also trying to reduce their own smoking behavior.

As described above, the dashboard 118 may also display information about smoking triggers 134 to serve as a reminder to the individual to avoid the triggers. The dashboard may also provide the user with additional behavioral information, including but not limited to the results of a behavioral questionnaire 136 that the individual previously completed with his/her doctor or counselor.

Dashboard 118 can also selectively display any information discussed herein based on the analysis of individual. For example, individual smoking behavior can be characterized, and such behavior can be associated with some means that are effective in helping individual to reduce or stop smoking. In these cases, individual behavior allows classification in one or more phenotypes (individual observable qualities allow classification within one or more groups). Dashboard can display information that is found to be effective for this particular phenotype. In addition, the user can selectively adjust the information on the dashboard to allow individual discovery of customization that is effective as a non-smoking incentive.

FIG23 shows another variation of a dashboard 118 displaying similar information to that shown in FIG22 . As described above, the information displayed is customizable. For example, this variation illustrates a graphical display of eCO load 140 showing historical data (yesterday's load), current eCO burden or load, and a target level of load for a non-smoker. As shown in FIG22 and FIG23 , an individual's previous attempts at quitting smoking 138 can be displayed. In addition, a graphical representation 120 of an eCO trend 412 can be displayed using individual eCO readings (for a corresponding sample), wherein information regarding puffing times 426 and a graphical representation showing the time or duration of puffing can be displayed (as shown by circles of varying diameters). As described above, such information can be input by a portable sampling unit and displayed in additional formats, such as 126 and 127 , which show historical and current data regarding the number of puffs and the number of whole cigarettes smoked, respectively.

Figure 24 A to Figure 24 C shows another variation of a data set, and this data set comprises exhaled carbon monoxide, collection time and cigarette data, and it is quantified and displayed to be useful for the individuality of trying to understand its smoking behavior. Figure 24 A shows the example that wherein patient collects breath sample in the process of many days. The example data shown in Figure 24 A to Figure 24 C have shown the data displayed in 21 days, but any time range is all within the scope of system and method described herein.

As shown in FIG24A , a time period 432 is shown along the horizontal axis, where the time interval is each day within the time period. Although not shown, during the early stages of sample collection, the time period itself may include one or more days, where the time interval is a multiple of hours or minutes. Obviously, the longer the time period, the greater the ability of the project to select meaningful time intervals within the time period.

FIG24A shows a variation of the dashboard 118 in which smoking data (including the total number of cigarettes 428 and the associated curve 430) is superimposed on a graph showing an eCO curve 412. As described above, individuals provide breath samples on a regular or random basis. In certain variations, a portable sampling unit (not shown) prompts individuals to provide samples for measuring CO. The portable sampling unit allows samples to be associated with timestamps and transmits other user-generated data as discussed above. The CO data is then quantified to provide exposure values for CO (eCO of exhaled CO) at time intervals (e.g., daily as shown in FIG24A).

24A also illustrates the ability to display historical data alongside current data. For example, CO load data 140 shows the CO load for the previous day, along with the highest CO reading, lowest CO reading, and average CO reading. Similar history is shown for cigarette data and smoking cessation questionnaire results 136.

Figures 24B and 24C show a data set in graphical form as an individual reduces his/her smoking behavior. As shown in Figure 24C, as the individual continues to provide samples for measuring CO, the graphical representation of the data set shows the individual's self-report of smoking less, which is verified by the reduced value of CO load 124.

System and method as herein described (that is, the quantification and display of smoking behavior and other behavioral data) can utilize the effective project that is designed to reduce the influence of cigarette smoke for health-care professionals and provide foundation.For example, system and method as herein described can be used for identifying smoker colony only from general group.Once this colony is determined, just can set up data set for this individual specific smoking behavior before attempting to register individual in smoking cessation program.As mentioned above, the quantification of smoking burden (or CO burden) together with the time data of smoking activity can be combined with other behavioral data to identify the smoking trigger that is unique to this individual.Therefore, before selecting smoking cessation program, health-care professionals can understand individual smoking behavior well.In addition, system and method as herein described is easily applicable to once individual enters smoking cessation program and just detects this individual behavior, and once they stop smoking, just can monitor individual, to guarantee that smoking cessation program remains effective, and individual suppresses smoking.

Furthermore, the above-described systems and methods for quantifying smoking behavior can be used to build, update, and improve the models of smoking behavior discussed above, and provide perturbations to help ultimately reduce an individual's smoking behavior.

Many embodiments of the present invention have been described. However, it will be understood that various modifications may be made without departing from the spirit and scope of the present invention. Combinations of aspects of the variations discussed above, as well as combinations of the variations themselves, are intended to be within the scope of this disclosure.

Without departing from the true spirit and scope of the present invention, various changes can be made to the described present invention, and equivalents (for the sake of brevity, whether or not this paper records or is not included) are replaced. In addition, any optional features of the modification of the present invention can be independently set forth and advocated, or combined with any one or more features of the features described herein. Therefore, where possible, the present invention contemplates the combination of the various aspects of the embodiment or the combination of the embodiment itself. Reference to a single project includes the possibility of the plurality of identical projects. More specifically, as used in this article and in the appended claims, unless the context clearly stipulates otherwise, the singular form "a (a)", "and (and)", "said (said)" and "the (the)" include plural references.

Claims (26)

1. A method for quantifying an individual's smoking behavior, the method comprising: Multiple exhaled breath samples were obtained from the individuals within a period prior to the smoking cessation program, and the collection time associated with each exhaled breath sample was recorded; The amount of exhaled carbon monoxide was measured for each of the plurality of exhaled breath samples; Compile a dataset including the amount of exhaled carbon monoxide and the collection time for each of the plurality of exhaled breath samples; Using the dataset, exhaled carbon monoxide exposure at time intervals within the given time period is quantified, and exhaled carbon monoxide load is assigned to the time intervals; wherein the exhaled carbon monoxide load is a quantification of the individual's carbon monoxide exposure at the given time intervals; and This shows the exhaled carbon monoxide load. 2. The method according to claim 1, wherein obtaining multiple exhaled breath samples from the individual within a time period and recording the collection time of each exhaled breath sample comprises obtaining the multiple exhaled breath samples sequentially. 3. The method of claim 1, wherein quantifying exhaled carbon monoxide exposure comprises using the dataset to correlate exhaled carbon monoxide as a function of time within the time period. 4. The method of claim 3, further comprising obtaining the area of exhaled carbon monoxide and time under a curve defined by the function within the time interval. 5. The method of claim 1, further comprising generating a signal to the individual to provide at least one sample of exhaled air. 6. The method of claim 1, further comprising repeatedly alerting the individual to provide samples of the plurality of exhaled breaths during the time period. 7. The method of claim 1 further includes receiving input data from the individual and recording the time of input. 8. The method of claim 7, wherein the input data includes at least a count of a portion of the cigarettes smoked by the individual. 9. The method of claim 8, further comprising visually displaying the sum of the counts of portions of the cigarette smoked by the individual. 10. The method of claim 8, further comprising using the count of portions of a cigarette smoked by the individual to determine and display the cigarette expenses. 11. The method of claim 8, further comprising using the count of portions of a cigarette smoked by the individual to determine and display a nicotine estimate. 12. The method of claim 7, wherein the input data includes behavioral information of the individual. 13. The method of claim 1, further comprising providing a visual display of the exhaled carbon monoxide load associated with a visual display time interval during the said time period. 14. The method of claim 1, further comprising providing a visual display of a count of the number of samples of the plurality of exhaled breaths. 15. The method of claim 1, further comprising determining a series of exhaled carbon monoxide loads over a series of time intervals within the said time period. 16. The method of claim 15, further comprising displaying the series of exhaled carbon monoxide loads for the series of time intervals. 17. The method of claim 1, wherein measuring the amount of exhaled carbon monoxide includes using a portable sensor. 18. The method of claim 17, further comprising transmitting the amount of exhaled carbon monoxide associated with each sample of exhaled breath and the collection time from the portable sensor to an electronic device. 19. The method of claim 1, further comprising obtaining the location of the individual when at least one of the plurality of exhaled breath samples is obtained. 20. The method of claim 1, further comprising obtaining the results of a behavioral questionnaire from the individual and displaying the results of the behavioral questionnaire in relation to the exhaled carbon monoxide load. 21. The method of claim 1, wherein quantifying the exposure to exhaled carbon monoxide over time intervals within the time period includes estimating the rate of carbon monoxide decay. 22. The method of claim 1, wherein quantifying the exposure to exhaled carbon monoxide over time intervals within the time period includes estimating the rate of increase in carbon monoxide levels when the individual smokes a cigarette. 23. A method for quantifying an individual's smoking behavior, the method comprising: Multiple carbon monoxide samples were obtained from the individual within a time period, and the collection time associated with each carbon monoxide sample was recorded; Compile a dataset that includes the collection time and amount of carbon monoxide for each sample; Using the dataset, carbon monoxide exposure over time intervals within the given time period is quantified, and carbon monoxide trends are assigned to the time intervals; and This shows the carbon monoxide trend. 24. The method of claim 23, wherein obtaining the plurality of carbon monoxide samples comprises obtaining a plurality of exhaled breath samples from the individual over time, and measuring the amount of exhaled carbon monoxide for each of the exhaled breath samples, and wherein recording the collection time associated with each carbon monoxide sample comprises recording the collection time associated with each exhaled breath sample. 25. The method of claim 23, wherein the carbon monoxide trend includes carbon monoxide load. 26. An apparatus for acquiring data to quantify an individual's smoking behavior, the apparatus comprising: A portable breathing unit configured to receive multiple exhaled breath samples from the individual over a time period and configured to record the collection time associated with each exhaled breath sample; A sensor located within the unit and configured to measure the amount of exhaled carbon monoxide in each sample of the exhaled air. At least one input switch, the at least one input switch being configured to record input data from the individual; A storage unit configured to store at least the amount of exhaled carbon monoxide and the collection time; A transmitter configured to transmit the amount of exhaled carbon monoxide, the collection time, and input data to an external electronic device configured to quantify exhaled carbon monoxide exposure over time intervals within a given time period; assign exhaled carbon monoxide load to the time intervals using a dataset; and display the exhaled carbon monoxide load; and An alarm unit is configured to provide an alarm to the user in response to the submission of samples of the plurality of exhaled breaths.