Smoking Study

It is a well known fact that use of tobacco, especially in the form of cigarette smoking, causes cancer in different organs throughout the body, leads to cardiovascular and respiratory diseases, and harms reproduction. Given the adverse impact of smoking on human health, significant research is conducted on development of smoking interventions. However, most smoking cessation programs achieve low success rate (i.e., less than 10%) and one of the main reasons behind is that they do not have mechanisms to intervene at the right moment.

In smoking research one of the important goals is to identify the antecedents and precipitants (i.e., high risk situations, such as stress) of smoking lapses. Scientific user studies resort to observing and recording user’s context while smoking occurs. These studies must have some mechanism of detecting when smoking occurs, so that physical, physiological, psychological, behavioral, social, and environmental contexts before, during, and after a smoking session can be identified.

Most current studies on smoking behavior rely on various self-reporting techniques, where subjects are asked to self-report each smoking episode. These methods range from basic pen-paper methods and retrospective recalls, to electronic diary keeping and ecological momentary assessments (EMA). These methods have the limitation of introducing biases when recalling events, forgetting to report, among several others.

In order to address these limitations, we aim to develop a reliable and automated smoking detector. Our previous work on physiological signals (e.g. ECG, Respiration) obtained from body worn sensors (Autosense) in controlled settings indicate that respiratory pattern during smoking contains potential information from which a detector can be built. However, confounding factors (e.g, physical movement, conversations) in the natural environment significantly alter the respiratory pattern during smoking. In this study, we therefore aim to use Autosense along with a portable smoking topography device (CReSS Pocket) and several new sensors (Accelerometers, Gyroscopes, and Nicotine sensors) in order to collect physiological, motion and smoking related data from smokers in their natural environment.

Approximately 15 men and 15 women will be recruited from the full and part-time student population at the University of Memphis. We aim to recruit an equal number of men and women so that we can study gender effects with respect to our hypotheses. Given the student population, we anticipate most participants will be between the ages of 21 and 30.

Data Collection Equipment and Tools

AutoSense: Participants will wear AutoSense - a body-area, wireless sensor network that continuously measures several physiological signals. AutoSense consists of two unobtrusive, flexible bands worn about the chest and upper arm, respectively. Several sensors are embedded in the bands, providing the following physiological and activity signals from the wearer: ECG, 3-axis acceleration, temperature (ambient), respiration, and galvanic skin response.

CReSS Pocket: Participants will be asked to smoke through the CReSS Pocket that collects the timing of these smoking events as well as the puff characteristics (e.g., puff duration, inter-puff duration, and puff volume).

Wrist Band: The subjects will wear a wristband that includes a three-axis accelerometer, a 3-axis gyroscope, a 3-axis magnetometer, and two ambient-light sensors and a temperature sensor. The accelerometer (ADXL335), gyroscope (ST Microelectronics A3G4250D), and magnetometer (Honeywell HMC5883L) will be sampled at 10 Hz at each axis to capture fine-level wrist motion, helping us improve automated detection of smoking by capturing smoking gestures of smoking arms. The two light sensors (Hamamatsu S1087 and S1087-1) will capture light energy in different parts of the visible spectrum (320-730nm and 320-1100nm), allowing us to differentiate between outdoor (natural light) and indoor (artificial light) environments.

Nicotine Sensor: The sensor will record nicotine readings, as well as temperature, humidity, and accelerometer readings. The Nicotine sensor is to be attached to the participant’s clothing near the collar with a clip.

Mobile Phone: During the field studies, participants will also carry a mobile smart phone customized to communicate with the AutoSense sensor suite. The mobile phone has three roles. First, it will use signals captured by AutoSense, the wrist band sensors, the Nicotine sensor and sensors on the phone (phone signals: 3-axis acceleration, names of nearby Bluetooth and WiFi devices, GPS traces, battery levels, and user interaction) to infer participant’s smoking events as well as the timing and characteristics of situational factors associated with smoking. These factors include stress, conversation episodes, exposure to smoking hotspots, physical activity levels, posture, places visited, and commuting episodes. Second, participants will use the phone to complete questionnaires in the field (see Field Questionnaire section below). Third, participants will indicate the beginning smoking episodes in the field by pushing a button on the mobile phone.

In the field Study, physiological and self-report data will be collected from the participant in his/her natural environment for three consecutive days. These data will be used to test the initial smoking detector built from the data collected in our previous study (Autosense Field Study) and build an automated smoking detector.