Cancer Clinical Trial
Official title:
High-level Construal as a Novel Pathway for Affect Regulation and Cancer Control
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).
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. |
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 |
Lead Sponsor | Collaborator |
---|---|
University of Oregon | Ohio State University |
United States,
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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 |
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