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Working Memory Training on Delay Discounting Among Cigarette Smokers

Remember to Abstain: Assessment of Working Memory Training on Delay Discounting in Low-SES Cigarette Smokers

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT05210608
Acronym
RTA
Enrollment
13
Registered
2022-01-27
Start date
2021-11-30
Completion date
2022-06-30
Last updated
2025-02-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Tobacco Use Disorder/Cigarette Smoking

Brief summary

Despite widespread awareness of significant negative health consequences, cigarette smoking remains the leading cause of preventable morbidity and mortality in the US (Creamer et al., 2019; Jamal, 2018). Moreover, the highest rate of smoking and heaviest burden of smoking-related illness occurs among low-socioeconomic status (SES) individuals relative to higher SES groups (Businelle et al., 2010; Clegg et al., 2009). Low SES individuals are also 40% less likely to succeed in quitting smoking when they attempt to do so (National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health, 2014). One potential explanation for the disparity in rate of smoking and successful quit attempts may be differences in individual rates of delay discounting (DD), i.e., the degree to which rewards loses their value as the delays to their receipt increase (Odum, 2011). A proposed way to reduce steep DD and, potentially, substance use has been computer training for working memory, which has shown favorable results in a sample of individuals with stimulant dependence (Bickel et al., 2011) and substance use broadly (Felton et al., 2019), with the latter even showing decreases in cigarette smoking in a subset of the sample.

Detailed description

The highest rate of smoking and heaviest burden of smoking-related illness occurs among low-SES individuals (Businelle et al., 2010; Clegg et al., 2009). One explanation for this disparity may be differences in individual rates of DD, which have been showed to be reduced with working memory training. (Bickel et al., 2011; Felton et al., 2019). Given the low cost of administering working memory training, such an intervention may be favorable for low-SES populations to improve smoking cessation outcomes. DD has significant associations with: Cigarette smoking (smokers tend to have higher rates of DD compared to non-smokers; Bickel et al., 1999); Smoking treatment outcome (individuals who remained smoke free after a smoking cessation intervention had lower DD compared to those who didn't; González-Roz et al., 2019; Krishnan-Sarin et al., 2007; MacKillop & Kahler, 2009; Yoon et al., 2007); SES (individuals with lower education and income have higher DD rates compared to those who are more educated and affluent; de Wit et al., 2007; Reimers et al., 2009). An innovative way to reduce DD that has been proposed is via working memory (WM) training. WM refers to one's capacity to hold information while engaging in complex mental tasks, including reasoning, comprehension, and learning (Baddeley, 2010). Previous research has shown that DD and WM correlate negatively (Shamosh et al., 2008), that individuals with higher DD rates show neural deficits in WM (Herting et al., 2010), and that acute nicotine abstinence is associated with WM deficits (Mendrek et al., 2006; Patterson et al., 2010). Furthermore, previous studies targeting WM to reduce DD have shown favorable results in a sample of individuals with stimulant dependence (Bickel et al., 2011) and substance use broadly (Felton et al., 2019), with the latter even showing decreases in cigarette smoking in a subset of the sample. Although previous research has shown WM training to reduce DD (which would support H3), and cigarette use in a small subsample, the hypotheses of this study are largely exploratory. However, given the theoretical connections between DD, SES, and WM, it is expected that the hypotheses of this project will be supported. The performance of this project may advance our knowledge of the relevant clinical targets for smoking cessation in low-SES individuals. In particular, this project is expected to shed light on DD as the putative mechanism in smoking for low-SES individuals and the durability of reductions in smoking as a result of reductions in DD through WM training. Despite the evidence for some successful techniques for reducing DD, little of this work has been translated into intervention approaches to target clinical outcomes. This application seeks to capitalize on the emerging literatures indicating (1) WM training may be an effective and efficient way to reduce DD, and (2) DD is associated with SES, cigarette smoking, and treatment outcomes. Though WM training has been successfully implemented in laboratory-controlled experiments to reduce DD, we are not aware of any interventions for clinical disorders that specifically seek to do so and potentially enhance treatment outcomes. The development of effective, theoretically coherent interventions addressing cigarette smoking is imperative, particularly interventions that would be feasible, efficacious, and acceptable in low-SES individuals. The proposed research is an innovative approach that capitalizes on previous findings showing reductions in delay discounting and even cigarette smoking. If working memory training is found to improve smoking cessation outcomes as a function of reductions in delay discounting, the project results could be helpful in future development of low-cost interventions for cigarette smoking.

Interventions

BEHAVIORALWorking Memory Training + Behavioral Intervention

Participants will be randomized to complete 10 sessions of a Working Memory Training. All participants will receive behavioral activation (a behavioral intervention for smoking cessation) and nicotine patches.

Sponsors

National Center for Advancing Translational Sciences (NCATS)
CollaboratorNIH
University of Kansas Medical Center
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
TREATMENT
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

To be included in this study, a participant must be 18 years of age or older, who have smoked at least four cigarettes per day for at least 6 months, are interested in quitting cigarette smoking, are at or below the federal poverty line based on persons in family/household and annual household income: * 1 -- $12,880 * 2 -- $17,420 * 3 -- $21,960 * 4 - $26,500 * 5 -- $31,040 * 6 -- $35,580 * 7 -- $40,120 * 8 -- $44,660 * 9 - add $4,540 for each additional person, OR they or their child(ren) utilize a federal program for low-income individuals, and are willing to participate in a 5-week working memory training program as a pretreatment adjunct to behavioral group + nicotine replacement therapy (NRT; via nicotine patches).

Exclusion criteria

Participants must not indicate a severe substance use disorder according to the DSM-V with any substance other than tobacco or have any significant medical or psychiatric condition. Such conditions could include traumatic brain injury, dementia, significant learning disability, or psychotic symptoms. Participants must be at least at a 5th-grade reading level. In case that participants are excluded, they will be provided with resources in the community and provided with contact information for the Kansas Tobacco Quitline.

Design outcomes

Primary

MeasureTime frameDescription
Delay DiscountingBaseline, Post-treatment, 1 month follow upDelay Discounting (DD) was measured via an established computerized binary choice task in which participants choose between an amount of money available immediately and larger amount of money available after a specified delay (1 day to 25 years). A computerized algorithm adjusts the immediately available reward across seven trials to determine an indifference point (k) for each amount/delay pairing. Indifference points are then used to calculate a rate of delay discounting for a $50, $200, $1,000 larger later sum. Larger scores mean greater delay discounting. While there is no strict minimum or maximum k-value, but in practical research settings, typical k-values often range from close to 0 for individuals who discount delayed rewards very slowly to values above 1 for those who heavily discount delayed rewards. There is no strict lower or upper bound, but values can be extremely high (above 1) if an individual very strongly prefers immediate rewards.
Timeline Follow-Back (TLFB): Number of Total Cigarettes Smoked Per WeekBaseline, Post-treatment, 1 month follow upThe Timeline Follow-Back (TLFB) for cigarette smoking is a self-report method used to assess an individual's smoking behavior over a specified period and specified as one week for this study. In this method, individuals are guided to recall their daily cigarette use by referencing events, routines, and cues that help them accurately track their smoking patterns. They are asked to document the number of cigarettes smoked each day, which provides a detailed, day-by-day account of their smoking habits. This data was then be summed to give a weekly total cigarettes smoked per week. The TLFB approach is valued for its reliability and ability to capture fluctuations in smoking behavior over time.
Carbon Monoxide LevelsBaseline, Post-treatment, 1 month follow upParticipant reports of abstinence will be verified by expired carbon monoxide (\< 6 ppm cutoff for stated abstinence). CO levels are collected via a CO monitor.
Working MemoryBaseline, Post-treatment, 1 month follow upWorking memory was assessed by adding the scores of 3 different working memory measures: 1) the total achievement score in the Tower of Hanoi, 2) the total recall score of the Hopkins Verbal Learning Test- Revised and 3) the total scaled score of the Letter Number Sequencing. These measures are commonly used to assess working memory. In this study, the composite score of all measures ranged between 36 and 89 with higher scores representing greater working memory,

Countries

United States

Participant flow

Participants by arm

ArmCount
Working Memory Training+ Behavioral Intervention
Participants will complete 10 sessions of a Working Memory Training. All participants will receive behavioral activation (a behavioral intervention for smoking cessation) and nicotine patches.
13
Total13

Baseline characteristics

CharacteristicWorking Memory Training+ Behavioral Intervention
Age, Categorical
<=18 years
0 Participants
Age, Categorical
>=65 years
0 Participants
Age, Categorical
Between 18 and 65 years
13 Participants
Age, Continuous55.37 years
STANDARD_DEVIATION 10.52
Delay Discounting (DD) Measure-5.21 k-value
Race (NIH/OMB)
American Indian or Alaska Native
0 Participants
Race (NIH/OMB)
Asian
0 Participants
Race (NIH/OMB)
Black or African American
10 Participants
Race (NIH/OMB)
More than one race
0 Participants
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
0 Participants
Race (NIH/OMB)
Unknown or Not Reported
0 Participants
Race (NIH/OMB)
White
3 Participants
Region of Enrollment
United States
13 Participants
Sex: Female, Male
Female
8 Participants
Sex: Female, Male
Male
5 Participants

Adverse events

Event typeEG000
affected / at risk
deaths
Total, all-cause mortality
0 / 0
other
Total, other adverse events
0 / 0
serious
Total, serious adverse events
0 / 0

Outcome results

Primary

Carbon Monoxide Levels

Participant reports of abstinence will be verified by expired carbon monoxide (\< 6 ppm cutoff for stated abstinence). CO levels are collected via a CO monitor.

Time frame: Baseline, Post-treatment, 1 month follow up

ArmMeasureGroupValue (MEAN)Dispersion
Working Memory Training + Behavioral InterventionCarbon Monoxide LevelsBaseline17.70 ppm (parts per million)Standard Deviation 11.65
Working Memory Training + Behavioral InterventionCarbon Monoxide LevelsPost-treatment17.67 ppm (parts per million)Standard Deviation 9.44
Working Memory Training + Behavioral InterventionCarbon Monoxide Levels1 month follow up30 ppm (parts per million)Standard Deviation 17.45
Comparison: We analyzed whether there were significant differences in carbon monoxide ppm from baseline to Post-treatment.p-value: 0.995% CI: [-9.86, 8.86]t-test, 2 sided
Comparison: We analyzed whether there were significant difference in carbon monoxide ppm from baseline to the 1 month follow-up.p-value: 0.3295% CI: [-49.42, 22.42]t-test, 2 sided
Primary

Delay Discounting

Delay Discounting (DD) was measured via an established computerized binary choice task in which participants choose between an amount of money available immediately and larger amount of money available after a specified delay (1 day to 25 years). A computerized algorithm adjusts the immediately available reward across seven trials to determine an indifference point (k) for each amount/delay pairing. Indifference points are then used to calculate a rate of delay discounting for a $50, $200, $1,000 larger later sum. Larger scores mean greater delay discounting. While there is no strict minimum or maximum k-value, but in practical research settings, typical k-values often range from close to 0 for individuals who discount delayed rewards very slowly to values above 1 for those who heavily discount delayed rewards. There is no strict lower or upper bound, but values can be extremely high (above 1) if an individual very strongly prefers immediate rewards.

Time frame: Baseline, Post-treatment, 1 month follow up

ArmMeasureGroupValue (MEAN)
Working Memory Training + Behavioral InterventionDelay DiscountingBaseline-5.21 k-value
Working Memory Training + Behavioral InterventionDelay DiscountingPost-treatment-6.57 k-value
Working Memory Training + Behavioral InterventionDelay Discounting1 month follow up-7.13 k-value
Comparison: We analyzed whether significant changes occurred between baseline and the post-treatment assessment.p-value: 0.2995% CI: [-0.85, 2.18]t-test, 2 sided
Comparison: We analyzed whether significant changes occurred between baseline and the 1 month follow upp-value: 0.4895% CI: [-3.45, 5.79]t-test, 2 sided
Primary

Timeline Follow-Back (TLFB): Number of Total Cigarettes Smoked Per Week

The Timeline Follow-Back (TLFB) for cigarette smoking is a self-report method used to assess an individual's smoking behavior over a specified period and specified as one week for this study. In this method, individuals are guided to recall their daily cigarette use by referencing events, routines, and cues that help them accurately track their smoking patterns. They are asked to document the number of cigarettes smoked each day, which provides a detailed, day-by-day account of their smoking habits. This data was then be summed to give a weekly total cigarettes smoked per week. The TLFB approach is valued for its reliability and ability to capture fluctuations in smoking behavior over time.

Time frame: Baseline, Post-treatment, 1 month follow up

ArmMeasureGroupValue (MEAN)Dispersion
Working Memory Training + Behavioral InterventionTimeline Follow-Back (TLFB): Number of Total Cigarettes Smoked Per WeekBaseline76.53 number of total cigarettes/weekStandard Deviation 60.7
Working Memory Training + Behavioral InterventionTimeline Follow-Back (TLFB): Number of Total Cigarettes Smoked Per WeekPost-treatment38.83 number of total cigarettes/weekStandard Deviation 40.15
Working Memory Training + Behavioral InterventionTimeline Follow-Back (TLFB): Number of Total Cigarettes Smoked Per Week1 month follow up59.50 number of total cigarettes/weekStandard Deviation 27.55
Comparison: We analyzed whether there were significant differences in number of total cigarettes smoked per week from baseline to Post-treatment.p-value: =0.01595% CI: [19.9, 102.1]t-test, 2 sided
Comparison: We analyzed whether there were significant differences in number of total cigarettes smoked per week from baseline to 1 month follow-up.p-value: 0.0895% CI: [-14.57, 142.57]t-test, 2 sided
Primary

Working Memory

Working memory was assessed by adding the scores of 3 different working memory measures: 1) the total achievement score in the Tower of Hanoi, 2) the total recall score of the Hopkins Verbal Learning Test- Revised and 3) the total scaled score of the Letter Number Sequencing. These measures are commonly used to assess working memory. In this study, the composite score of all measures ranged between 36 and 89 with higher scores representing greater working memory,

Time frame: Baseline, Post-treatment, 1 month follow up

ArmMeasureGroupValue (MEAN)Dispersion
Working Memory Training + Behavioral InterventionWorking MemoryBaseline58.61 Scores on a scaleStandard Deviation 14.43
Working Memory Training + Behavioral InterventionWorking MemoryPost-treatment61.33 Scores on a scaleStandard Deviation 11.13
Working Memory Training + Behavioral InterventionWorking Memory1 month follow up71.25 Scores on a scaleStandard Deviation 13.42
Comparison: We analyzed whether there were significant differences in working memory scores from baseline to post-treatment.p-value: 0.1895% CI: [-5.1, 19.1]t-test, 2 sided
Comparison: We analyzed whether there were significant differences in working memory scores from baseline to the 1 month follow up.p-value: 0.1895% CI: [-25.38, 7.38]t-test, 2 sided

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026