High-level Construal as a Novel Pathway for Affect Regulation and Cancer Control
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 300
- 试验地点
- 4
- 主要终点
- Aim 1: Neural similarity at baseline among the proposed psychological mechanisms
研究概览
简要总结
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).
详细描述
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:
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Basic Science
- 盲法
- Triple (Participant, Care Provider, Outcomes Assessor)
入排标准
- 年龄范围
- 25 Years 至 55 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Persistent smokers: cigarette smokers (at least 10 cigarettes/day for at least 1 year)
- •Want to quit but have tried and failed at least once
- •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
排除标准
- •Metal implants (e.g., braces, permanent retainers, pins)
- •Metal fragments, pacemakers or other electronic medical implants
- •Claustrophobia
- •Weight ˃ 550 lbs.
- •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.
研究组 & 干预措施
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").
干预措施: Down-regulation of craving for cigarettes (Behavioral)
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).
干预措施: High-level construal (Behavioral)
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.
干预措施: Up-regulation of goal energization (Behavioral)
结局指标
主要结局
Aim 1: Neural similarity at baseline among the proposed psychological mechanisms
时间窗: 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).
Aim 1: Neural similarity in pre-post change among the proposed psychological mechanisms
时间窗: 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).
Aim 2: Degree of prediction success of change in smoking from surveys
时间窗: 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.
Aim 2: Correlation of pattern representation of high-level construal with survey measure
时间窗: 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).
Aim 2: Prediction success of change in smoking from task data
时间窗: 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.
Aim 2: Prediction of craving ratings from multivariate representations of high-level construal
时间窗: 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.
次要结局
- Aim 3: Effect size of high-level construal on smoking at endpoint(56 days after the baseline session)
- Aim 3: Time-series of the effect size of high-level construal on smoking across the training period(Inclusive of days 1-56 of the training period)
研究者
Elliot Berkman
Associate Professor of Psychology, Associate Director of the Center for Translational Neuroscience
University of Oregon