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The psychophysiology of tobacco use and craving

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This research provides evidence that cravings are associated with individual patterns of arousal and are expressed differently in smokers and non-smokers. Patterns of physiologic arousal were ...

This research provides evidence that cravings are associated with individual patterns of arousal and are expressed differently in smokers and non-smokers. Patterns of physiologic arousal were identified for craving and tobacco use. This research supports the development of individual algorithms to predict tobacco use for tobacco cessation treatment.

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    The psychophysiology of tobacco use and craving The psychophysiology of tobacco use and craving Presentation Transcript

    • The Psychophysiology of Tobacco Use and Craving Leigh W. Jerome, Ph.D. The Institute for Triple Helix Innovation Becky Rodericks, M.Sc. Pacific Telehealth & Technology Hui Funding provided by the USAMRMC (TATRC) (W81XWH-07-2-0086)
    • INTRODUCTION: SMOKING
      • Smoking is the leading cause of preventable disease, disability, and death in the US [1]
      • 85% of lung cancers; 30% of all cancer deaths [2]
      • Approximately 440,000 deaths each year [2]
      • $157 billion in annual health-related economic losses [3]
    • CRAVING
      • No formally agreed upon definition of craving - it is an intrapersonal and subjective response
      • Generally speaking, a craving is a construct of factors associated with arousal patterns, behaviors and relapse [4]
      • For every physiological reaction, there is a psychological reaction, and vice versa. Cravings can be thought of as the psychological construct, while arousal patterns are the physiologic correlate.
      • Nicotine is a highly addictive psychoactive drug that induces both physiologic and psychological effects that reinforce the continued use of tobacco products [5]
    • CRAVING
      • Physiological changes associated with tobacco deprivation
      • ● As early as 6 hours after cessation, physiological changes
      • are observed [5,6]
      • ● Decreases in heart rate, decreases in cortical
      • arousal associated with drowsiness, hypersensitive
      • visual stimuli, reduction in auditory evoked response,
      • decreases in blood pressure, respiration rate, and
      • increases in skin temperature [7]
      • Distinct patterns of nicotine withdrawal as specified in the DSM-IV [8]
      • ● Depressed mood, insomnia, irritability, anxiety, difficulty
      • concentrating, restlessness, and increased appetite.
    • CESSATION
      • 70% of all smokers report that they want to quit [3]
      • While a number of evidence-based pharmacological and behavioral interventions have proven to be effective in smoking cessation, 70-80% of smokers relapse after a single quit attempt and require additional attempts before becoming smoke-free [9,10]
      • Only about 4% of smokers who try to quit smoking each year succeed [3]
    • INTERVENTIONS
      • Interventions are underutilized, and do not serve the needs of a majority of smokers
      • Individually tailored interventions prove more successful than non-tailored or no interventions [11]
      • Emergent technologies, such as biosensors, offer new opportunities for delivering individually tailored behavioral interventions [12]
      • Non-invasive wearable sensors offer improved methods for data collection through their portability, innovation, and wireless connectivity
    • OVERVIEW
      • The relationship between tobacco smoking and stress has long been an area of interest [5], The literature supports that arousal patterns associated with the substrate of stress are comparable to arousal patterns associated with tobacco smoking and craving.
      • Physiologic arousal patterns associated with cravings have not been a primary focus of previous research.
      • Sensors have the ability to dynamically capture physiological data.
      • If we can identify physiologic patterns associated with craving, it may be possible to intervene prior to the onset of an addiction-behavior spiral.
    • AIMS AND OBJECTIVES
      • This study uses principles of cue exposure and non-invasive sensors to investigate the biometric signature associated with elicited arousal and tobacco craving.
      • Cue exposure involves presenting an individual with standardized stimuli that is used to cue or prompt an event, reaction, or feeling (e.g. showing a beer bottle to an alcoholic).
      • Cue exposure techniques were used to stimulate craving in participants under a various conditions. One question that was explored, can physiological responses to cue exposure be used to predict smoking behavior?
      • It is hypothesized that comparing responses to elicited arousal and stress reactivity will differentiate physiological patterns between non-smokers and smokers.
      • A long-term goal is to generate statistical algorithms that identify and predict the arousal patterns associated with craving and tobacco use behavior. It is hoped that this knowledge may be used to create new medical technologies to augment smoking cessation interventions.
    • ● Current smokers, non-smokers, and former smokers ● Smokers : minimum of 10 cigarettes a day on average ● Former smokers : quit at least 6 months ago ● Self-described “good” health – no major medical conditions ● Not undergoing any form of nicotine replacement therapy ● Fluent in English ● 18 years + Eligibility
      • Started in mid-April 2008
      • Flyers on bulletin boards
      • Mass emails
      • Classrooms
      • Word of mouth
      • Ad in campus newspaper
      • PSA radio broadcast
      • Bars, bus stops, and various
      • Areas where smokers congregate
      • Participant stipend $100
      Recruitment
    • PARTICIPANTS
      • 4 Semi-Randomized Groups
        • Non-smokers = 15
        • Former smokers = 23
        • Current smokers = 14
        • Deprived smokers = 15
      • [refrain from smoking for 6 hours in Phase 3]
      • Final sample size = 75 participants
    • PARTICIPANTS
      • Demographics
      • Gender = 56% female
      • Mean BMI = 24.26 (SD=3.71)
      • Mean Age = 34.03 (SD=12.65)
      • Mean Years of Education = 16.04 (SD=2.1)
      • Marital Status : 20% Married
      • Ethnicity : 54.7% White, 16.0% Japanese, 8.0% Chinese, 6.7% Asian Indian, 14.6% Other
      • General Health : 81.3% rated their health as good or very good. This increased to 97.3% when including those who rated their health as excellent.
      • Group Differences: There were no significant demographic differences between the groups, except for age (F(3,73)=4.53, p <.05), where former smokers were found to be significantly older ( M FS =41.1) than non-smokers and smokers ( M NS =30.6; M S =30.7).
    • SMOKING HISTORY
      • Began smoking in teen years: mean age 17.7 (SD=4.83)
      • Average length of time smoked: 11.64 years (SD=9.93)
      • Reported little readiness to change and self-selected into the precontemplation (51.7%), contemplation (34.5%) or preparation (13.8%) stage of change for smoking cessation
      • Rated a moderate level of nicotine dependence, with a mean nicotine dependence score of 5.69 (SD=1.07)
    • STUDY DESIGN
      • Procedure
      • Phase 1: Orientation Session (30-45 minutes)
      • Phase 2: Naturalistic Data Collection (3 days)
      • Phase 3: Experimental Lab Session (75 minutes)
      • What is the Armband?
      • BodyMedia SenseWear ® Pro 2 Armband
      • A multi-sensor wearable body monitor
      • Collects physiological and lifestyle data
      • Can timestamp specific events
      Phase 2
      • Requirements
      • Worn for 3 days
      • Smokers: press “timestamp button”
      • every time they have a cigarette
      • What information is collected
      • from the armband?
      • movement
      • heat flux
      • skin temperature
      • galvanic skin response
      • sleep/wake cycles
      • energy expenditure
      • (e.g. calories burned)
      • levels of physical activity
      • (e.g. # of steps taken in a day)
      Participant Summary Phase 2
    • TIMESTAMP Sleep Cycles
      • Physiological Sensors
      • - BodyMedia SenseWear ® Pro 2
        • - Thought Technology ProComp Infiniti
      • - Biofeedback sensors
            • EKG
            • Respiration
            • Blood Volume Pulse
      • Lab Activities
      • - Resting phase
      • - Elicited stress activity (speech)
      • - Mediated stress activity (films)
      Phase 3
    • In the last 10 years, Hawaii has gone from being ranked the number 1 healthiest state in the nation to the 9th healthiest state (Morgan Quitno, 2007). You will have 60 seconds to prepare a 3-minute speech explaining why you think this decline has taken place. “ 50% of the participants will be randomly selected to deliver their speech in front of a video camera (webcam). Your speech will be evaluated by the research team at a later date.” Elicited Stress Activity
      • 25 short film clips (44 minutes total)
      • - (4) films elicit positive stress (i.e. amusement)
      • - (4) films elicit negative stress (i.e. fear)
      • - (4) film scenes that introduce cravings (i.e. scenes with smoking)
      • - (13) “neutral” clips
      • 6 conditions of elicited arousal:
      • - resting, speech/stress, amusement, fear, craving, and neutral
      • After each clip, participants described:
      • - emotional experience;
      • - level of arousal; and
      • - valence (positive or negative)
      Mediated Stress Exposure
    • Laboratory Setting Phase 3
      • FILM CLIPS
    • Film clips: smoking scenes
    •  
    •  
    •  
    •  
    • Films eliciting fear
    • Films eliciting amusement
    • Neutral film clip images
    •  
      • Play examples of film clips
    • ANALYSES
      • Data analyses include physiological sensor data (InnerView and Biograph Infiniti) and self-report data (SPSS)
      • Began by focusing on normative arousal patterns, followed by individualized patterns of craving
      • The goal of the analyses is to generate statistical algorithms that can accurately predict tobacco use behavior
    • ANALYSES
      • A 4 (group) x 6 (emotion) factorial MANOVA compared each group’s physiologic profile across each of the arousal conditions for all biometric parameters.
      • Main effects were found for both group assignment and type of film clip (Wilks’ λ=.53, p<.05 and Wilks’ λ=.78, p<.05 respectively) as well as a significant interaction effect (Wilks’ λ=.95, p=.91 ).
      • A series of six follow-up univariate ANOVAs (using Bonferroni correction) were employed to provide a means plot for each physiologic variable with standardized T-scores ( M =50, SD =10) on the y-axis and arousal condition on the x-axis.
    • RESULTS
      • Physiologic variables that were monitored:
        • ● 10 physiologic data channels :
        • Galvanic skin response (GSR), respiration rate (RR), skin temperature (SkT), blood volume pulse (BVP), heat flux (HF), heart rate (HR), electrocardiograph (EKG), energy expenditure (EE), and movement (transverse accelerometer and longitudinal accelerometer).
        • ● Significant mean differences were found for the interactions on 8 out of the 10 physiologic variables ( p <.01, η2 range=.01 to .26), with only EKG and EE being non-significant.
    • KEY RESULTS
      • Physiological Data
      • Non-smokers were significantly different from all other groups in their physiologic arousal to fear, amusement, and craving, with SkT, movement, and BVP significantly different across all 3 conditions.
      • Former smokers behaved more similarly to smokers than to non-smokers, with SkT, movement, BVP and RR significantly different from never smokers across the 3 conditions.
    • BLOOD VOLUME PRESSURE (HRV)
    • SKIN TEMPERATURE
    • HEAT FLUX
    • KEY RESULTS
      • Physiological Data
      • Early analyses have detected patterns of distinction between the two smoking groups.
      • In the next two Figures, smokers were significantly different from deprived smokers in their physiologic arousal to fear, amusement, and craving, with GSR and RR significantly different across all 3 conditions.
    • RESPIRATION RATE
    • GALVANIC SKIN RESPONSE
    • KEY RESULTS
      • Self-Report Data
      • Self-report responses found that most participants matched their emotion to the intent of the film clip:
      • - 86.7% rated the neutral clips as calming or neutral
      • - 91% rated the amusement clips as funny
      • - 75% the fear clips as scary or anxiety provoking
      • - Among smokers, 45% indicated craving a cigarette
      • after watching a smoking clip
    • Both deprived and non-deprived smokers reported similar levels of intensity after watching smoking clips; however deprived smokers were more likely to rate it an unpleasant experience (lower valence).
    • Following each of the four smoking clips, deprived smokers reported craving a cigarette more often than non-deprived smokers.
    • DISCUSSION
      • Initial findings support cue exposure techniques as effective in eliciting arousal.
      • Findings indicate that physiology is expressed differently in smokers and non-smokers, even once cessation is successful.
      • - Arousal patterns for former smokers are more closely related to smokers
      • - Non-smokers are significantly different than all other groups
      • - Deprived smokers & non-deprived smokers also differ in their physiology
      • This research indicates that physiologic arousal patterns can be identified for craving and tobacco use behavior.
      • These early findings are being used to generate statistical algorithms that will help predict patterns associated with tobacco use behavior.
    • NEXT STEP
      • The next step is to shift from normative arousal patterns to individual aspects of craving.
      • Future work would allow for the algorithm to be downloaded to the SenseWear armband for continuous collection and adjustment of the algorithm.
      • It is anticipated that the algorithms will contribute to determining the optimal timeframe to deliver just-in-time interventions to smokers.
    • PILOT STUDY
      • This project builds on analyses that have been completed for a pilot study. The pilot study explored the potential of using statistical algorithms to assist in identifying and predicting arousal patterns. 9 participants wore the armband for 7 days.
      • In our current study, the sample size has been increased from 9 participants to 75. Four groups of smokers and non-smokers have been included, and lab activities were created to elicit arousal and craving.
      • An algorithm framework was written for the pilot study, and a subset of the data set was fed into an application for generation of algorithms to predict tobacco cravings.
      • Time lagging capabilities were dynamically set (e.g. 10 min, 20 min)
    • PILOT STUDY
      • Results
      • Algorithms were inconsistent at predicting craving within a 10-minute interval, however, predictive ability increased by an average of 62% when increased to 15 minutes.
      • While it is possible to predict smoking events based on physiology alone, prediction improves when using both physiologic and psychological data.
      • - 4 of 6 physiologic predictors had a correct classification rate of 64%.
      • - 6 of 6 psychological predictors had a correct classification rate of 69.4%.
      • - 7 of 10 combined physiologic and psychological predictors were able to
      • correctly predict 68.9% of the binary smoking events in the data.
    • FUTURE APPLICATIONS
      • Incorporate algorithms into commercially available products.
      • Develop interventions according to individual styles, patterns, and readiness for change.
      • Specifically tailored interventions may be useful for treating addictions of all levels, including those who are resistant to change, those already in treatment, and those who need help preventing relapse.
      • It is envisioned that this research will also serve as a foundation for addressing additional behavioral addictions such as alcohol, drugs, and disordered eating.
    • REFERENCES
      • 1 Centers for Disease Control and Prevention, (2005a). Annual smoking – attributable mortality, years of potential life
      • lost, and productivity losses – United States, 1997-2001. Morbidity and Mortality Weekly Report, 54, 625-628.
      • 2 Centers for Disease Control and Prevention, (2005b). Declines in lung cancer rates – California, 1988 to 1997. Morbidity and Mortality Weekly Report, Highlights.
      • 3 Centers for Disease Control and Prevention, (2002). Cigarette smoking among adults – United States, 2000. Morbidity and Mortality Weekly Report, 51, 642-645.
      • 4 Smith, J.G., (2003). Addictive behaviours in relation to tobacco usage: A review of literature.
      • 5 Kelly, J.T., Barrett, S., Pihl, R.O., & Dagher, A., (2004). Level of abstinence mediates heart-rate response to cue-elicited craving in smokers. McGill Journal of Medicine, 8, 50-57.
      • 6 Kecklund, G., & Akerstedt, T., (2004). Sensation (507231): Report on methods and classification of stress, inattention and emotional states. Stockholm, Sweden: Karolinska Instituet.
      • 7 Centers for Disease Control and Prevention, (1988). Tobacco Information and Prevention Source (TIPS); The health consequences of smoking, nicotine addiction, a report of the Surgeon General 1988, pp 241-376.
      • 8 American Psychiatric Association, (1994). Diagnostic and statistical manual of mental disorders (4 th ed.). Washington, DC: American Psychiatric Association.
      • 9 Schacter, S., (1992). Recidivism and self-cure of smoking and obesity. American Psychologist, 37, 436-444.
      • 10 Schwartz, J.L., (1987). Review and evaluation of smoking cessation methods: United States and Canada, 1978-1985. U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health, NIH Publication No. 87-2940.
      • 11 Strecher, V.J., Wang, C., Derry, H., Wildenhaus, K., & Johnson, C. (2002). Tailored interventions for multiple risk behaviors. Health Education Research, 17, 619-626.
      • 12 Walters, S., Wright, J., & Shegog, R., (2006). A review of computer and Internet-based interventions for smoking behavior. Addictive Behaviors, 31, 264-277.
    • For information contact: [email_address]