Construal Level as a Novel Pathway for Affect Regulation and Cancer Control

Cancer Clinical Trial

Official title:

High-level Construal as a Novel Pathway for Affect Regulation and Cancer Control

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04620915
• Other study ID #: EPCS27942
• Status: Recruiting
• Phase: N/A
• Start date: October 11, 2021
• Est. completion date: September 30, 2025

Study information

• Verified date: February 2024
• Source: University of Oregon
• Contact: Elliot T Berkman, Ph.D.
• Phone: 541-346-4909
• Email: berkman[@]uoregon.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The objective of the proposed research is to conduct a longitudinal experiment on the neurocognitive pathways and individual differences in high-level construal for affect regulation and smoking cessation. The population is adult smokers aged 25-55 who have tried and failed to quit and who are experiencing poverty. The primary endpoints are (a) the similarity in neural representation of high-level construal to one of two candidate pathways, (b) the presence of meaningful individual differences in the neural representation of high-level construal, and (c) as a secondary endpoint, the effect size of the high-level construal condition on smoking as measured by cigarettes per day. Each of these endpoints corresponds to a specific null hypothesis. The null hypothesis for the first endpoint is that high-level construal is not significantly different in its neural representation from down-regulation of craving, which would suggest that high-level construal does not operate through distinct mechanisms from traditional treatments. The null hypothesis for the second endpoint is that the between-subjects variability in the neural representation of construal level does not significantly relate to relevant individual differences measures (e.g., traits, task behavior), which would suggest that individual differences are not meaningfully related to outcomes. Finally, the null hypothesis for the secondary endpoint is that the magnitude of the effect of high-level construal on smoking as measured by reductions in average cigarettes per day is not significantly greater than in the other conditions, which would suggest that the efficacy of the high-level construal condition is not significantly greater than a standard text-messaging intervention. The primary endpoints will be assessed at baseline and change from pre-to-post training (8 weeks).

Description:

OVERVIEW: The proposed work will achieve the three specific aims (two confirmatory, one exploratory) in the context of a 3-arm translational experiment. Assessments of neurocognitive mechanisms of high-level construal, as well as of two candidate pathways, will be completed at baseline and endpoint sessions. The 300 participants enrolled in the study will complete a multimodal battery that will assess neural, behavioral, and self-report indices relating to the three processes of interest - construal level, down-regulation of craving, and up-regulation of goal energization - and then are randomized to one of three experimental conditions related to those processes. This design is highly advantageous because it allows for the establishment of the mechanisms of the construal-level intervention, to compare them with the mechanisms of the other two processes, and to test whether, and to what extent, our potentially novel affect regulation strategy engages and alters those mechanisms (Aim 1); and to identify individual differences in the effects of that novel strategy (high-level construal) on patterns of brain activation, affect regulation, and cessation outcomes (Aim 2). Though the translational experiment design is not intended to be an intervention per se (because the evidence base does not yet support a full-scale trial and materials for such an intervention still need to be developed), the investigators will nonetheless quantify the effect size of high-level construal on changes in smoking so future RCTs have that information and can be adequately powered to detect an effect. ASSIGNMENT OF PARTICIPANTS TO CONDITION: Participants will be randomly assigned to a condition using the randomizer function in the RedCap participant tracking and management software. This assignment will happen only after participants are screened, consented, enrolled, and complete the baseline session. In other words, all 300 participants will be treated in exactly the same way through the end of the baseline session, and only at that point will RedCap be used to assign participants to their condition. All participants will have equal probability of being assigned to the conditions (i.e., 33.33% chance of assignment to each). The study coordinator will know which participant is enrolled in each condition, but researchers involved in data analysis (i.e., Drs. Berkman, Fujita, Chavez, and Weston, as well as the graduate students) as well as the research assistants who interact with the participants will be blind to condition during data gathering and analysis. METHODS FOR SAMPLE SIZE CALCULATION AND DATA ANALYSIS: The method for selecting the sample size was based on a power analysis of the least sensitive test - Aim 2. The key statistical tests for the three aims are as follows. For Aim 1, the test of neural similarity between conditions is essentially a paired-samples t-test on similarity scores (e.g., pattern correlations calculated within subjects). This is a high-powered comparison because the covariance within subjects of the scores is high. For Aim 2, the individual difference analyses are between-subjects correlations and regression (or partial regression or logistic regression) coefficients relating neural similarity to affect regulation scores, responses to trait and individual difference surveys, and smoking behavior as measured by cigarettes per day. This family of test has relatively low power because it is entirely between subjects. For Exploratory Aim 3, the key test is the condition-by-time interaction (i.e., differences among the groups in change in smoking behavior from pre-to-post training). This test is a within-between (sometimes called "mixed") ANOVA analysis that is more powerful that the individual difference analyses for Aim 2 but less powerful than the fully within-subjects tests for Aim 1. INTERVENTION CONTENT: The content for all three training arms is a set of brief (<160 character) messages designed to enable participants to practice one specific affect regulation strategy (experimental arms). In brief, all messages in the experimental arms are generated by a large group of mTurk workers who are smokers; the process of message generation and validation has already begun. During the award period the investigators will add even more messages to the corpus and validate them. In the high-level construal condition, workers compose messages that encourage thinking about purpose of quitting and future goals (e.g., "What would quitting mean to you and your future?"). In the down-regulation of craving condition, workers compose messages that encourage effortful inhibitory or attentional control of cravings (e.g., "When you feel an urge to smoke, think about the health consequences"). And in the up-regulation of goal energization condition, workers compose messages that tie one specific core value (which the mTurk worker ranked as within his or her top three core values) to the goal of quitting (e.g., for the core value of "family", a message might be, "Quitting will help you model a healthy lifestyle for your family"). The content for the up-regulation of goal energization condition is matched to participants' own top three core values. INTERVENTION DELIVERY: All participants receive text messages and complete biweekly online "booster" sessions for 8 weeks. Beginning on their quit date, participants will be sent messages via SMS text messaging 5 times each day for 28 consecutive days, then 4 times each day for the following 28 days. The investigators chose to increase the messaging frequency in the first month of cessation given the higher likelihood of relapse during this period. The order of the messages will be pseudo-randomized such that each message will be seen no more than three times across the 8-week training period, separated by at least 10 days. The timing of the messages will be adjusted for each participant to be evenly spaced starting at wake-up and ending 15 minutes before bedtime and adjusting for weekday/weekend differences. Participants will reply to each message and use a 5-point scale to rate its perceived helpfulness. Participants can also text "SOS" to the system at any point to initiate support via text if they feel tempted to smoke. Daily text messaging is an ideal delivery format for this training because it allows for in vivo participant contact at moments when smoking decisions are being made. Text messaging has very high adoption rates in the United States even among underserved communities that are typically difficult to sample densely for extended durations, and low SES users are comfortable receiving texts throughout the day. The investigators have used text messaging for experience sampling and for theory-based intervention in a community sample (see Preliminary Studies 5 and 6). Participants will provide their mobile phone number at the baseline session; those without a text message-enabled phone or those who do not wish to use their own, will be provided a prepaid phone at the end of the baseline session. In addition to the text messaging, participants will complete biweekly online "booster" sessions using Qualtrics with a custom, personalized link sent to the participant via email and text. The booster sessions are designed to reinforce the active interventions (high-level construal, goal energization, down-regulation of craving) and provide opportunities to practice the assigned affect regulation strategy. In the sessions, participants will be reminded of their assigned strategy and write brief responses to specific prompts (e.g., "What are two specific ways that quitting now will change your future life?", "Why is quitting important to you today?", and "What are two ways you can change how you think to reduce your craving for cigarettes?"). Participants will also generate triggers to smoking (situations or cues) and practice using down-regulation of craving when they see those triggers. This exercise helps form the habit of deploying the strategy in everyday life. Not all participants have computer access, so, as in previous studies, participants will be provided access in the lab and the research team will work with the local library system to ensure all study sites are accessible from their computers. BOOSTER COMPLETION AND RETENTION: Dr. Berkman is currently using similar "booster" sessions, which are programmed in and are delivered via Qualtrics, in a current translational experiment on healthy eating (R01 CA211224). The automated reminders and links, which can be sent via email and text messaging depending on participant preference, greatly increase completion of the boosters. Together with the reminders, graded incentives (e.g., a monetary bonus for completing >90% of the boosters during the study) have greatly increased completion of the at-home booster sessions.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 300
• Est. completion date: September 30, 2025
• Est. primary completion date: April 30, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 25 Years to 55 Years

Eligibility

Inclusion Criteria:

1. Low-SES

2. Persistent smokers: cigarette smokers (at least 10 cigarettes/day for at least 1 year)

3. Want to quit but have tried and failed at least once

4. Income-to-needs ratio (INR) is less than 2.0, meaning that their household income adjusted for household size is below 200% of the federal poverty line

5. Ages 25-55

Scan Exclusion Criteria:

1. Metal implants (e.g., braces, permanent retainers, pins)

2. Metal fragments, pacemakers or other electronic medical implants

3. Claustrophobia

4. Weight ? 550 lbs.

5. Women who are pregnant or believe they might be pregnant People in this population are likely to have some comorbid psychiatric, substance use, and/or other health disorders that might pose a challenge to retention and intervention compliance. Such comorbidities are inherent to the population of interest (persistent smokers) so they will not be exclusionary criteria; instead, we will gather information about psychiatric, substance use, and medical comorbidities on intake so that we can monitor and report any associations with attrition, compliance, and effects of the experimental conditions. E-cigarette use is acceptable - it is not an exclusionary criterion - but it will be recorded and covaried as appropriate in the analyses. To increase the homogeneity of the sample in terms of cessation aids, we require that all participants use pharmacological cessation aids such as nicotine replacement therapy (NRT). This inclusion criterion also more realistically models how cessation happens in vivo, as medical care providers often recommend adding pharmacological assistance such as NRT to quit programs. We will provide patches or gum (e.g., Nicoderm) to participants who cannot afford. Participants who want or are able to provide their own NRT will be included as long as they agree to continue using NRT for the duration of the training period. We will monitor NRT use weekly to ensure compliance with this inclusion criterion. No exclusions will be made on gender, race, or ethnicity, so the sample will reflect the demographic profile of the United States. Eligible participants will be scheduled for the Zoom pre-session.

Related Conditions & MeSH terms

• Cancer
• Smoking Cessation
• Smoking Reduction

Intervention

Behavioral:
• High-level construal
In the high-level construal condition, participants will be sent messages asking them to consider why they are quitting ("What are your main reasons for quitting?") and to imagine what their lives will look like in the future if they succeed ("What would quitting mean to you and your family's future?"; Yeager et al., 2014). The corpus for this condition is 100 messages composed by a large independent sample of mTurk workers who are smokers and validated by a team of RAs trained to 0.8 reliability on ratings of high-level construal. To meet criteria for inclusion, a message must be rated as significantly closer to high-level (vs. low-level) on a rating scale of construal level. In addition to the texting, participants will complete biweekly online "booster" sessions using Qualtrics with a custom, personalized link sent to the participant via email and text.
• Down-regulation of craving for cigarettes
In the down-regulation of craving condition, participants will be sent messages that encourage inhibitory control of cravings for cigarettes (e.g., using cognitive reappraisal or attentional control) and that provide strategies to do so (e.g., "When you feel an urge to smoke, think about the health consequences"). The corpus for this condition is 100 messages composed by a large, independent sample of mTurk smokers and validated by a team of RAs trained to 0.8 reliability on ratings of plausibility AND effortful cognitive inhibition or control. In addition to the texting, participants will complete biweekly online "booster" sessions using Qualtrics with a custom, personalized link sent to the participant via email and text.
• Up-regulation of goal energization
In the up-regulation of goal energization condition, participants will be sent messages that encourage them to consider the core values that drive their desire to quit smoking. These messages will name a specific core value that the participant rated in the top three (of 19) during the baseline session, and will draw a connection between quitting and the core value. For example, a message for a person who nominated "family" as one of her top three core values might read, "Quitting will help you model a healthy lifestyle for your family." This intervention is grounded in robust theory and evidence supporting Self-Affirmation Theory. The corpus for this condition is 100 messages composed by a large, independent sample of mTurk smokers and validated by a team of RAs trained to 0.8 reliability in correctly identifying to which core value the message is tied. In addition to the texting, participants will complete biweekly online "booster" sessions using Qualtrics.

Locations

Country	Name	City	State
United States	University of Oregon, Lewis Integrative Sciences Building	Eugene	Oregon
United States	University of Oregon, Social and Affective Neuroscience Laboratory	Eugene	Oregon

Sponsors (2)

Lead Sponsor	Collaborator
University of Oregon	Ohio State University

Country where clinical trial is conducted

United States, 

References & Publications (97)

Armitage CJ. Efficacy of a brief worksite intervention to reduce smoking: the roles of behavioral and implementation intentions. J Occup Health Psychol. 2007 Oct;12(4):376-90. doi: 10.1037/1076-8998.12.4.376. — View Citation

Bakermans-Kranenburg MJ, van IJzendoorn MH. The hidden efficacy of interventions: genexenvironment experiments from a differential susceptibility perspective. Annu Rev Psychol. 2015 Jan 3;66:381-409. doi: 10.1146/annurev-psych-010814-015407. Epub 2014 Aug 11. — View Citation

Barratt, E.S. (1994). Impulsiveness and Aggression. In Monahan, J., and H.J. Steadman (Eds.), Violence and Mental Disorder: Developments in Risk Assessment (pp. 61-79). University of Chicago Press, Chicago, IL.

Benowitz NL, Kuyt F, Jacob P 3rd. Influence of nicotine on cardiovascular and hormonal effects of cigarette smoking. Clin Pharmacol Ther. 1984 Jul;36(1):74-81. doi: 10.1038/clpt.1984.142. — View Citation

Berkman ET, Dickenson J, Falk EB, Lieberman MD. Using SMS text messaging to assess moderators of smoking reduction: Validating a new tool for ecological measurement of health behaviors. Health Psychol. 2011 Mar;30(2):186-94. doi: 10.1037/a0022201. — View Citation

Berkman ET, Falk EB, Lieberman MD. In the trenches of real-world self-control: neural correlates of breaking the link between craving and smoking. Psychol Sci. 2011 Apr;22(4):498-506. doi: 10.1177/0956797611400918. Epub 2011 Mar 4. — View Citation

Berkman ET, Falk EB. Beyond Brain Mapping: Using Neural Measures to Predict Real-World Outcomes. Curr Dir Psychol Sci. 2013 Feb;22(1):45-50. doi: 10.1177/0963721412469394. — View Citation

Berkman ET, Kahn LE, Merchant JS. Training-induced changes in inhibitory control network activity. J Neurosci. 2014 Jan 1;34(1):149-57. doi: 10.1523/JNEUROSCI.3564-13.2014. — View Citation

Berkman ET, Lieberman MD. Using Neuroscience to Broaden Emotion Regulation: Theoretical and Methodological Considerations. Soc Personal Psychol Compass. 2009 Jul 1;3(4):475-493. doi: 10.1111/j.1751-9004.2009.00186.x. — View Citation

Berkman ET, Livingston JL, Kahn LE. Finding the "self" in self-regulation: The identity-value model. Psychol Inq. 2017;28(2-3):77-98. doi: 10.1080/1047840X.2017.1323463. Epub 2017 Aug 18. — View Citation

Berridge KC, Robinson TE. Parsing reward. Trends Neurosci. 2003 Sep;26(9):507-13. doi: 10.1016/S0166-2236(03)00233-9. Erratum In: Trends Neurosci. 2003 Nov;26(11):581. — View Citation

Brewer JA, Elwafi HM, Davis JH. Craving to quit: psychological models and neurobiological mechanisms of mindfulness training as treatment for addictions. Psychol Addict Behav. 2013 Jun;27(2):366-79. doi: 10.1037/a0028490. Epub 2012 May 28. — View Citation

Buhle JT, Silvers JA, Wager TD, Lopez R, Onyemekwu C, Kober H, Weber J, Ochsner KN. Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cereb Cortex. 2014 Nov;24(11):2981-90. doi: 10.1093/cercor/bht154. Epub 2013 Jun 13. — View Citation

Cacioppo JT, Petty RE, Kao CF. The efficient assessment of need for cognition. J Pers Assess. 1984 Jun;48(3):306-7. doi: 10.1207/s15327752jpa4803_13. — View Citation

Cascio CN, O'Donnell MB, Tinney FJ, Lieberman MD, Taylor SE, Strecher VJ, Falk EB. Self-affirmation activates brain systems associated with self-related processing and reward and is reinforced by future orientation. Soc Cogn Affect Neurosci. 2016 Apr;11(4):621-9. doi: 10.1093/scan/nsv136. Epub 2015 Nov 5. — View Citation

Chiou WB, Wu WH, Chang MH. Think abstractly, smoke less: a brief construal-level intervention can promote self-control, leading to reduced cigarette consumption among current smokers. Addiction. 2013 May;108(5):985-92. doi: 10.1111/add.12100. Epub 2013 Jan 30. — View Citation

Cox LS, Tiffany ST, Christen AG. Evaluation of the brief questionnaire of smoking urges (QSU-brief) in laboratory and clinical settings. Nicotine Tob Res. 2001 Feb;3(1):7-16. doi: 10.1080/14622200020032051. — View Citation

Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996 Jun;29(3):162-73. doi: 10.1006/cbmr.1996.0014. — View Citation

Curry S, Wagner EH, Grothaus LC. Intrinsic and extrinsic motivation for smoking cessation. J Consult Clin Psychol. 1990 Jun;58(3):310-6. doi: 10.1037//0022-006x.58.3.310. — View Citation

Daly MC, Duncan GJ, McDonough P, Williams DR. Optimal indicators of socioeconomic status for health research. Am J Public Health. 2002 Jul;92(7):1151-7. doi: 10.2105/ajph.92.7.1151. Erratum In: Am J Public Health 2002 Aug;92(8):1212. — View Citation

Danziger S, Montal R, Barkan R. Idealistic advice and pragmatic choice: a psychological distance account. J Pers Soc Psychol. 2012 Jun;102(6):1105-17. doi: 10.1037/a0027013. Epub 2012 Jan 23. — View Citation

David SP, Munafo MR. Genetic variation in the dopamine pathway and smoking cessation. Pharmacogenomics. 2008 Sep;9(9):1307-21. doi: 10.2217/14622416.9.9.1307. — View Citation

Dusthimer, N., Fujita, K., & Berkman, E.T. (unpublished). A feasibility study of people's ability to generate text messages to motivate eating restraint. The Ohio State University Data Archives.

Enders CK. The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychol Methods. 2001 Dec;6(4):352-70. — View Citation

Falk EB, O'Donnell MB, Cascio CN, Tinney F, Kang Y, Lieberman MD, Taylor SE, An L, Resnicow K, Strecher VJ. Self-affirmation alters the brain's response to health messages and subsequent behavior change. Proc Natl Acad Sci U S A. 2015 Feb 17;112(7):1977-82. doi: 10.1073/pnas.1500247112. Epub 2015 Feb 2. — View Citation

Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009 Nov;41(4):1149-60. doi: 10.3758/BRM.41.4.1149. — View Citation

Free C, Whittaker R, Knight R, Abramsky T, Rodgers A, Roberts IG. Txt2stop: a pilot randomised controlled trial of mobile phone-based smoking cessation support. Tob Control. 2009 Apr;18(2):88-91. doi: 10.1136/tc.2008.026146. — View Citation

Freitas, A. L., Gollwitzer, P., & Trope, Y. (2004). The influence of abstract and concrete mindsets on anticipating and guiding others' self-regulatory efforts. Journal of Experimental Social Psychology, 40(6), 739-752. http://doi.org/10.1016/j.jesp.2004.04.003

Freund, A. M., & Hennecke, M. (2015). On means and ends: The role of goal focus in successful goal pursuit. Current Directions in Psychological Science, 24(2), 149-153. http://doi.org/10.1177/0963721414559774

Fujita K, Han HA. Moving beyond deliberative control of impulses: the effect of construal levels on evaluative associations in self-control conflicts. Psychol Sci. 2009 Jul;20(7):799-804. doi: 10.1111/j.1467-9280.2009.02372.x. Epub 2009 Jun 1. — View Citation

Fujita K, Trope Y, Liberman N, Levin-Sagi M. Construal levels and self-control. J Pers Soc Psychol. 2006 Mar;90(3):351-67. doi: 10.1037/0022-3514.90.3.351. — View Citation

Fujita K. On conceptualizing self-control as more than the effortful inhibition of impulses. Pers Soc Psychol Rev. 2011 Nov;15(4):352-66. doi: 10.1177/1088868311411165. Epub 2011 Jun 17. — View Citation

Fujita, K. (2008). Seeing the forest beyond the trees: A construal-level approach to self-control. Social and Personality Psychology Compass, 2(3), 1475-1496. http://doi.org/10.1111/j.1751- 9004.2008.00118.x

Fujita, K., & Carnevale, J. J. (2012). Transcending temptation through abstraction: The role of construal level in self-control. Current Directions in Psychological Science, 21(4), 248-252. http://doi.org/10.1177/0963721412449169

Fujita, K., & Sasota, J. A. (2011). The effects of construal levels on asymmetric temptation-goal cognitive associations. Social Cognition, 29(2), 125-146.

Giuliani NR, Berkman ET. Craving is an Affective State and Its Regulation Can Be Understood in Terms of the Extended Process Model of Emotion Regulation. Psychol Inq. 2015;26(1):48-53. doi: 10.1080/1047840X.2015.955072. No abstract available. — View Citation

Giuliani NR, Mann T, Tomiyama AJ, Berkman ET. Neural systems underlying the reappraisal of personally craved foods. J Cogn Neurosci. 2014 Jul;26(7):1390-402. doi: 10.1162/jocn_a_00563. Epub 2014 Jan 6. — View Citation

Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69-119.

Gross JJ, John OP. Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. J Pers Soc Psychol. 2003 Aug;85(2):348-62. doi: 10.1037/0022-3514.85.2.348. — View Citation

Gross, J.J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26(1), 1-26. https://doi.org/10.1080/1047840X.2014.940781

Hartwell KJ, Johnson KA, Li X, Myrick H, LeMatty T, George MS, Brady KT. Neural correlates of craving and resisting craving for tobacco in nicotine dependent smokers. Addict Biol. 2011 Oct;16(4):654-66. doi: 10.1111/j.1369-1600.2011.00340.x. Epub 2011 Jul 25. — View Citation

Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science. 2001 Sep 28;293(5539):2425-30. doi: 10.1126/science.1063736. — View Citation

Heatherton TF, Wagner DD. Cognitive neuroscience of self-regulation failure. Trends Cogn Sci. 2011 Mar;15(3):132-9. doi: 10.1016/j.tics.2010.12.005. Epub 2011 Jan 26. — View Citation

Hettema JE, Hendricks PS. Motivational interviewing for smoking cessation: a meta-analytic review. J Consult Clin Psychol. 2010 Dec;78(6):868-84. doi: 10.1037/a0021498. — View Citation

Hollis JF, McAfee TA, Fellows JL, Zbikowski SM, Stark M, Riedlinger K. The effectiveness and cost effectiveness of telephone counselling and the nicotine patch in a state tobacco quitline. Tob Control. 2007 Dec;16 Suppl 1(Suppl 1):i53-9. doi: 10.1136/tc.2006.019794. — View Citation

Hughes JR, Keely JP, Niaura RS, Ossip-Klein DJ, Richmond RL, Swan GE. Measures of abstinence in clinical trials: issues and recommendations. Nicotine Tob Res. 2003 Feb;5(1):13-25. Erratum In: Nicotine Tob Res. 2003 Aug;5(4):603. — View Citation

Illes J, Kirschen MP, Karetsky K, Kelly M, Saha A, Desmond JE, Raffin TA, Glover GH, Atlas SW. Discovery and disclosure of incidental findings in neuroimaging research. J Magn Reson Imaging. 2004 Nov;20(5):743-7. doi: 10.1002/jmri.20180. — View Citation

Insel, T. (2014, February 27). A New Approach to Clinical Trials. Retrieved May 1, 2015, from http://www.nimh.nih.gov/about/director/2014/a-new-approach-to-clinical-trials.shtml

Jamal A, Phillips E, Gentzke AS, Homa DM, Babb SD, King BA, Neff LJ. Current Cigarette Smoking Among Adults - United States, 2016. MMWR Morb Mortal Wkly Rep. 2018 Jan 19;67(2):53-59. doi: 10.15585/mmwr.mm6702a1. — View Citation

John, O. P., & Gross, J. J. (2007). Individual differences in emotion regulation. In J. J. Gross (Ed.), Handbook of emotion regulation (pp. 351-372). New York, NY: Guilford Press.

John, O.P., & Srivastava, S. (1999). The Big-Five trait taxonomy: History, measurement, and theoretical perspectives. In L.A. Pervin & O.P. John (Eds.), Handbook of personality: Theory and research (Vol. 2, pp. 102-138). New York: Guilford Press.

Kable JW, Glimcher PW. The neural correlates of subjective value during intertemporal choice. Nat Neurosci. 2007 Dec;10(12):1625-33. doi: 10.1038/nn2007. Epub 2007 Nov 4. — View Citation

Khoury MJ, Rich EC, Randhawa G, Teutsch SM, Niederhuber J. Comparative effectiveness research and genomic medicine: an evolving partnership for 21st century medicine. Genet Med. 2009 Oct;11(10):707-11. doi: 10.1097/GIM.0b013e3181b99b90. — View Citation

Kober H, Mende-Siedlecki P, Kross EF, Weber J, Mischel W, Hart CL, Ochsner KN. Prefrontal-striatal pathway underlies cognitive regulation of craving. Proc Natl Acad Sci U S A. 2010 Aug 17;107(33):14811-6. doi: 10.1073/pnas.1007779107. Epub 2010 Aug 2. — View Citation

Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2010 Jan;35(1):217-38. doi: 10.1038/npp.2009.110. Erratum In: Neuropsychopharmacology. 2010 Mar;35(4):1051. — View Citation

Kruglanski AW, Thompson EP, Higgins ET, Atash MN, Pierro A, Shah JY, Spiegel S. To "do the right thing" or to "just do it": locomotion and assessment as distinct self-regulatory imperatives. J Pers Soc Psychol. 2000 Nov;79(5):793-815. doi: 10.1037//0022-3514.79.5.793. — View Citation

Libby LK, Shaeffer EM, Eibach RP. Seeing meaning in action: a bidirectional link between visual perspective and action identification level. J Exp Psychol Gen. 2009 Nov;138(4):503-16. doi: 10.1037/a0016795. — View Citation

Liberman N, Trope Y. The psychology of transcending the here and now. Science. 2008 Nov 21;322(5905):1201-5. doi: 10.1126/science.1161958. — View Citation

Liberman N, Trope Y. Traversing psychological distance. Trends Cogn Sci. 2014 Jul;18(7):364-9. doi: 10.1016/j.tics.2014.03.001. Epub 2014 Apr 10. — View Citation

Liberman, N., & Trope, Y. (1998). The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. Journal of Personality and Social Psychology, 75(1), 5-18. http://doi.org/10.1037/0022-3514.75.1.5

Ludwig RM, Flournoy JC, Berkman ET. Inequality in personality and temporal discounting across socioeconomic status? Assessing the evidence. J Res Pers. 2019 Aug;81:79-87. doi: 10.1016/j.jrp.2019.05.003. Epub 2019 May 18. — View Citation

Ludwig, R., Srivastava, S., & Berkman, E.L. (2017). Planfulness: A process-focused construct of individual differences in goal achievement. http://doi.org/10.17605.OSF.IO/YUQMA

Mann T, de Ridder D, Fujita K. Self-regulation of health behavior: social psychological approaches to goal setting and goal striving. Health Psychol. 2013 May;32(5):487-98. doi: 10.1037/a0028533. — View Citation

Meyer ML, Spunt RP, Berkman ET, Taylor SE, Lieberman MD. Evidence for social working memory from a parametric functional MRI study. Proc Natl Acad Sci U S A. 2012 Feb 7;109(6):1883-8. doi: 10.1073/pnas.1121077109. Epub 2012 Jan 23. — View Citation

Mumford JA. A power calculation guide for fMRI studies. Soc Cogn Affect Neurosci. 2012 Aug;7(6):738-42. doi: 10.1093/scan/nss059. Epub 2012 May 28. — View Citation

National Cancer Institute (2013). SmokefreeTXT: Quitting on your phone, on your terms. Retrieved from http://smokefree.gov/smokefreetxt.

National Centre for Smoking Cessation and Training. (2012). Smoking Reduction (No. 2). (A. McEwen, Ed.) (pp. 1-7).

Nguyen, T., Fujita, K., & Berkman, E.T. (unpublished). A feasibility study of smokers' ability to generate text messages to motivate smoking cessation. The Ohio State University Data Archives.

Nisbett, R. E., Caputo, C., Legant, P., & Marecek, J. (1973). Behavior as seen by the actor and as seen by the observer. Journal of personality and Social Psychology, 27(2), 154-164.

Noori HR, Cosa Linan A, Spanagel R. Largely overlapping neuronal substrates of reactivity to drug, gambling, food and sexual cues: A comprehensive meta-analysis. Eur Neuropsychopharmacol. 2016 Sep;26(9):1419-1430. doi: 10.1016/j.euroneuro.2016.06.013. Epub 2016 Jul 7. — View Citation

Petry NM. Contingency management treatments: controversies and challenges. Addiction. 2010 Sep;105(9):1507-9. doi: 10.1111/j.1360-0443.2009.02879.x. — View Citation

Rabois D, Haaga DA. The influence of cognitive coping and mood on smokers' self-efficacy and temptation. Addict Behav. 2003 Apr;28(3):561-73. doi: 10.1016/s0306-4603(01)00249-0. — View Citation

Rollnick S, Heather N, Gold R, Hall W. Development of a short 'readiness to change' questionnaire for use in brief, opportunistic interventions among excessive drinkers. Br J Addict. 1992 May;87(5):743-54. doi: 10.1111/j.1360-0443.1992.tb02720.x. — View Citation

Ryan, R.M. (1982). Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43, 450-461.

Schwartz, S. H. (1994). Are there universal aspects in the structure and contents of human values? Journal of Social Issues, 50(4), 19-45.

Sherman, D. K., & Cohen, G. L. (2006). The psychology of self-defense: Self-affirmation theory. Advances in experimental social psychology, 38, 183-242.

Shiffman S, Engberg JB, Paty JA, Perz WG, Gnys M, Kassel JD, Hickcox M. A day at a time: predicting smoking lapse from daily urge. J Abnorm Psychol. 1997 Feb;106(1):104-16. doi: 10.1037//0021-843x.106.1.104. — View Citation

Shiffman S. Coping with temptations to smoke. J Consult Clin Psychol. 1984 Apr;52(2):261-7. doi: 10.1037//0022-006x.52.2.261. No abstract available. — View Citation

Spunt RP, Adolphs R. Validating the Why/How contrast for functional MRI studies of Theory of Mind. Neuroimage. 2014 Oct 1;99:301-11. doi: 10.1016/j.neuroimage.2014.05.023. Epub 2014 May 17. — View Citation

Spunt RP, Falk EB, Lieberman MD. Dissociable neural systems support retrieval of how and why action knowledge. Psychol Sci. 2010 Nov;21(11):1593-8. doi: 10.1177/0956797610386618. Epub 2010 Oct 19. — View Citation

Spunt RP, Satpute AB, Lieberman MD. Identifying the what, why, and how of an observed action: an fMRI study of mentalizing and mechanizing during action observation. J Cogn Neurosci. 2011 Jan;23(1):63-74. doi: 10.1162/jocn.2010.21446. — View Citation

SRNT Subcommittee on Biochemical Verification. Biochemical verification of tobacco use and cessation. Nicotine Tob Res. 2002 May;4(2):149-59. doi: 10.1080/14622200210123581. No abstract available. — View Citation

Sweeney, A. M., & Freitas, A. L. (2014). Relating action to abstract goals increases physical activity reported a week later. Psychology of Sport and Exercise, 15(4), 364-373. http://doi.org/10.1016/j.psychsport.2014.03.009

Tangney JP, Baumeister RF, Boone AL. High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J Pers. 2004 Apr;72(2):271-324. doi: 10.1111/j.0022-3506.2004.00263.x. — View Citation

Trope Y, Liberman N. Temporal construal. Psychol Rev. 2003 Jul;110(3):403-21. doi: 10.1037/0033-295x.110.3.403. — View Citation

Vallacher RR, Wegner DM, Somoza MP. That's easy for you to say: action identification and speech fluency. J Pers Soc Psychol. 1989 Feb;56(2):199-208. doi: 10.1037//0022-3514.56.2.199. — View Citation

Vallacher, R. R., & Wegner, D. M. (1987). What do people thing they're doing? Action identification and human behavior. Psychological Review, 94(1), 3-15.

Velicer WF, Prochaska JO, Rossi JS, Snow MG. Assessing outcome in smoking cessation studies. Psychol Bull. 1992 Jan;111(1):23-41. doi: 10.1037/0033-2909.111.1.23. — View Citation

Webb TL, Miles E, Sheeran P. Dealing with feeling: a meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. Psychol Bull. 2012 Jul;138(4):775-808. doi: 10.1037/a0027600. Epub 2012 May 14. — View Citation

Webb TL, Sheeran P. Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychol Bull. 2006 Mar;132(2):249-68. doi: 10.1037/0033-2909.132.2.249. — View Citation

Wewers ME, Shoben A, Conroy S, Curry E, Ferketich AK, Murray DM, Nemeth J, Wermert A. Effectiveness of Two Community Health Worker Models of Tobacco Dependence Treatment Among Community Residents of Ohio Appalachia. Nicotine Tob Res. 2017 Nov 7;19(12):1499-1507. doi: 10.1093/ntr/ntw265. — View Citation

Whittaker R, Borland R, Bullen C, Lin RB, McRobbie H, Rodgers A. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev. 2009 Oct 7;(4):CD006611. doi: 10.1002/14651858.CD006611.pub2. — View Citation

Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev. 2016 Apr 10;4(4):CD006611. doi: 10.1002/14651858.CD006611.pub4. — View Citation

Wilson SJ, Sayette MA, Fiez JA. Self-control, negative affect and neural activity during effortful cognition in deprived smokers. Soc Cogn Affect Neurosci. 2014 Jun;9(6):887-94. doi: 10.1093/scan/nst065. Epub 2013 Apr 24. — View Citation

Xu X, Bishop EE, Kennedy SM, Simpson SA, Pechacek TF. Annual healthcare spending attributable to cigarette smoking: an update. Am J Prev Med. 2015 Mar;48(3):326-33. doi: 10.1016/j.amepre.2014.10.012. Epub 2014 Dec 10. — View Citation

Yeager DS, Henderson MD, Paunesku D, Walton GM, D'Mello S, Spitzer BJ, Duckworth AL. Boring but important: a self-transcendent purpose for learning fosters academic self-regulation. J Pers Soc Psychol. 2014 Oct;107(4):559-80. doi: 10.1037/a0037637. — View Citation

Yong LC, Luckhaupt SE, Li J, Calvert GM. Quit interest, quit attempt and recent cigarette smoking cessation in the US working population, 2010. Occup Environ Med. 2014 Jun;71(6):405-14. doi: 10.1136/oemed-2013-101852. Epub 2014 Feb 4. — View Citation

* Note: There are 97 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Aim 1: Neural similarity at baseline among the proposed psychological mechanisms	Neural similarity as indexed by Pearson's correlations derived from the similarity matrices produced by Representational Similarity Analysis. The correlation is among the vectorized 3D images representing the patterns of BASELINE functional neural activity related to (a) high-level construal, (b) down-regulation, and (c) up-regulation of goal energization. There will be 3 correlations in total (a with b, a with c, and b with c).	At baseline
Primary	Aim 1: Neural similarity in pre-post change among the proposed psychological mechanisms	Neural similarity as indexed by Pearson's correlations derived from the similarity matrices produced by Representational Similarity Analysis. The correlation is among the vectorized 3D images representing the patterns of PRE-TO-POST CHANGE in the functional neural activity related to (a) high-level construal, (b) down-regulation, and (c) up-regulation of goal energization. There will be 3 correlations in total (a with b, a with c, and b with c).	56 days after the baseline session
Primary	Aim 2: Correlation of pattern representation of high-level construal with survey measure	Correlation between the similarity matrices produced by Representational Similarity Analysis and the self-report measures assessed at baseline. The measure is the Pearson's correlation between (a) the vectorized 3D image representing the patterns of baseline functional neural activity related to high-level construal and (b) the Levels of Personal Agency Questionnaire. The Outcome is the Pearson's r between (a) and (b).	Within two weeks of enrollment
Primary	Aim 2: Degree of prediction success of change in smoking from surveys	Cross-validated machine learning (ML) prediction of endpoint (56-day) smoking quantity in terms of cigarettes per day based on responses to baseline responses to the Levels of Personal Agency Questionnaire. Degree of prediction will be expressed in Pearson's r correlation between (a) actual # of cigarettes per day at endpoint and (b) ML-predicted # of cigarettes per day.	56 days after the baseline session
Primary	Aim 2: Prediction success of change in smoking from task data	Cross-validated machine learning prediction of endpoint (56-day) smoking quantity in terms of cigarettes per day based on responses to behavioral performance on the Construal Level Task as measured by the difference in response time in milliseconds between in the high- and low-level conditions. Degree of prediction will be expressed in Pearson's r correlation units.	56 days after the baseline session
Primary	Aim 2: Prediction of craving ratings from multivariate representations of high-level construal	Cross-validated machine learning prediction of baseline craving ratings during reactivity to personalized cigarette smoking cues based on multivariate neural representation of high-level construal. Ratings are on a 1 to 5 scale from "no craving" to "extreme craving". Degree of prediction will be expressed in Pearson's r units. Higher r values indicate better prediction of craving ratings.	Within two weeks of enrollment
Secondary	Aim 3: Effect size of high-level construal on smoking at endpoint	Difference in baseline-to-endpoint change in cigarettes per day among the three conditions.	56 days after the baseline session
Secondary	Aim 3: Time-series of the effect size of high-level construal on smoking across the training period	Trajectory of the group difference of change in cigarettes per day from the baseline assessment.	Inclusive of days 1-56 of the training period