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

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ClinicalTrials.gov Identifier: NCT04620915
Recruitment Status : Not yet recruiting
First Posted : November 9, 2020
Last Update Posted : April 28, 2021
Sponsor:
Collaborator:
Ohio State University
Information provided by (Responsible Party):
Elliot Berkman, University of Oregon

Brief 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 multiple times 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 (relative to treatment-as-usual) 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 TAU, 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).


Condition or disease Intervention/treatment Phase
Smoking Cessation Smoking Reduction Cancer Behavioral: High-level construal Behavioral: Down-regulation of craving for cigarettes Behavioral: Up-regulation of goal energization Behavioral: Treatment-as-usual control Not Applicable

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 240 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Triple (Participant, Care Provider, Outcomes Assessor)
Primary Purpose: Basic Science
Official Title: High-level Construal as a Novel Pathway for Affect Regulation and Cancer Control
Estimated Study Start Date : May 2, 2021
Estimated Primary Completion Date : June 2024
Estimated Study Completion Date : June 2024

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Smoking

Arm Intervention/treatment
Experimental: High-level construal
Participants will be sent messages asking them 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).
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.


Experimental: Effortful down-regulation of craving for cigarettes
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").
Behavioral: 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.


Experimental: Up-regulation of goal energization
Participants will be sent messages that encourage them to consider the core values that drive their desire to quit smoking.
Behavioral: 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.


Placebo Comparator: Treatment-as-usual control
Participants will be sent generic messages through NCI's text messaging cessation program, SmokefreeTXT (National Cancer Institute, 2013).
Behavioral: Treatment-as-usual control

In the treatment-as-usual (TAU) control condition, participants will be sent generic messages through NCI's text messaging cessation program, SmokefreeTXT (National Cancer Institute, 2013). An example message is, "SmokefreeTXT: Cravings are tough, but you can do this. Avoid big triggers for now & focus on beating smaller ones. Practice makes perfect." The control messages provide affect regulation strategies and information, and do not overlap with the messages from the other three arms of the study.

In addition to the text messaging, participants in the TAU condition will be invited to visit a website with information about the dangers of smoking and benefits of quitting. Twice per week, they will be sent a custom, personalized, and trackable link to the website via email and text messaging.





Primary Outcome Measures :
  1. Aim 1: Neural similarity at baseline among the proposed psychological mechanisms [ Time Frame: At baseline ]
    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).

  2. Aim 1: Neural similarity in pre-post change among the proposed psychological mechanisms [ Time Frame: 56 days after the baseline session ]
    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).

  3. Aim 2: Correlation of pattern representation of high-level construal with survey measure [ Time Frame: Within two weeks of enrollment ]
    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).

  4. Aim 2: Degree of prediction success of change in smoking from surveys [ Time Frame: 56 days after the baseline session ]
    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.

  5. Aim 2: Prediction success of change in smoking from task data [ Time Frame: 56 days after the baseline session ]
    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.

  6. Aim 2: Prediction of craving ratings from multivariate representations of high-level construal [ Time Frame: Within two weeks of enrollment ]
    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.


Secondary Outcome Measures :
  1. Aim 3: Effect size of high-level construal on smoking at endpoint [ Time Frame: 56 days after the baseline session ]
    Difference in baseline-to-endpoint change in cigarettes per day between the high-level construal and TAU conditions.

  2. Aim 3: Time-series of the effect size of high-level construal on smoking across the training period [ Time Frame: Inclusive of days 1-56 of the training period ]
    Trajectory of the group difference (high-level construal vs TAU) of change in cigarettes per day from the baseline assessment.



Information from the National Library of Medicine

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Ages Eligible for Study:   25 Years to 55 Years   (Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  1. Low-SES
  2. Persistent smokers: cigarette smokers (at least 10 cigarettes/day for at least 2 years)
  3. Want to quit but have tried and failed at least three times
  4. Income-to-needs ratio (INR) is less than 1.0, meaning that their household income adjusted for household size is below the federal poverty line
  5. Ages 25-55

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
  6. Screen for handedness

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 name-brand patch 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 Lane County, Oregon. Eligible participants will be scheduled for the in-lab pre-session.


Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04620915


Contacts
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Contact: Elliot T Berkman, Ph.D. 541-346-4909 berkman@uoregon.edu

Sponsors and Collaborators
University of Oregon
Ohio State University
Investigators
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Principal Investigator: Elliot T Berkman, Ph.D. University of Oregon
Publications:
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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, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8(3), 430-457.
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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.
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, J.J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26(1), 1-26. https://doi.org/10.1080/1047840X.2014.940781
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
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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.
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, 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
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).
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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.
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.
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
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Responsible Party: Elliot Berkman, Associate Professor of Psychology, Associate Director of the Center for Translational Neuroscience, University of Oregon
ClinicalTrials.gov Identifier: NCT04620915    
Other Study ID Numbers: EPCS27942
First Posted: November 9, 2020    Key Record Dates
Last Update Posted: April 28, 2021
Last Verified: April 2021

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Elliot Berkman, University of Oregon:
Smoking
Construal level
Cigarettes
Quitting