A time-varying model of the dynamics of smoking lapse

Examining Links Between Distinct Affective States and Tobacco Lapse During a Cessation Attempt Among African Americans: A Cohort Study

BACKGROUND

Affect states are posited to play a pivotal role in addiction-related processes, including tobacco lapse (i.e., smoking during a quit attempt), and distinct affective states (e.g., joy vs. happiness) may differentially influence lapse likelihood. However, few studies have examined the influence of distinct affective states on tobacco lapse.

PURPOSE

This study examines the influence of 23 distinct affect states on tobacco lapse among a sample of tobacco users attempting to quit.

METHODS

Participants were 220 adults who identified as African American (50% female, ages 18-74). Ecological momentary assessment was used to assess affect and lapse in real-time. Between and within-person associations testing links between distinct affect states and lapse were examined with multilevel modeling for binary outcomes.

RESULTS

After adjusting for previous time's lapse and for all other positive or negative affect items, results suggested that at the between-person level, joy was associated with lower odds of lapse, and at the within-person level, attentiveness was associated with lower odds of lapse. Results also suggested that at the between-person level, guilt and nervous were associated with higher odds of lapse, and at the within-person level, shame was associated with higher odds of lapse.

CONCLUSIONS

The present study uses real-time, real-world data to demonstrate the role of distinct positive and negative affects on momentary tobacco lapse. This work helps elucidate specific affective experiences that facilitate or hinder the ability to abstain from tobacco use during a quit attempt.

Associations between morning affect and later-day smoking urges and behavior

OBJECTIVE

Affective experiences are associated with smoking urges and behavior. Few studies have examined the temporal nature of these associations within a day, such as whether positive and negative affect in the morning are associated with smoking urges and behavior later in the day.

METHOD

Participants (N = 63; MAge = 50 years, 48% female; 60% White) were randomized into one of three smoking cessation interventions and answered up to five daily ecological momentary assessments for 28 days during a quit attempt (M = 21.0 days, SD = 7.1). Before analysis, scores for morning positive and negative affect and later-day smoking urges and behavior were calculated.

RESULTS

On days when individuals' morning positive affect was higher than usual, later-day smoking urges tended to be lower than usual. In contrast, on days when individuals' morning negative affect was higher than usual, later-day smoking urges tended to be higher than usual, and smoking was more likely. Further, individuals who had higher characteristic morning positive affect tended to have less intense later-day smoking urges, whereas those who tended to have higher characteristic morning negative affect tended to have more intense later-day smoking urges.

CONCLUSIONS

Morning positive and negative affect were associated with later-day smoking urges, and morning negative affect was related to later-day smoking behavior. Future research should examine whether interventions that boost positive affect on mornings when it is lower than usual and attenuate negative affect on mornings when it is higher than usual, may reduce the intensity of smoking urges and the likelihood of smoking later in the day. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studies

Objective

Insufficient engagement is a critical barrier impacting the utility of digital interventions and mobile health assessments. As a result, engagement itself is increasingly becoming a target of studies and interventions. The purpose of this study is to investigate the dynamics of engagement in mobile health data collection by exploring whether, how, and why response to digital self-report prompts change over time in smoking cessation studies.

Method

Data from two ecological momentary assessment (EMA) studies of smoking cessation among diverse smokers attempting to quit (N = 573) with a total of 65,974 digital self-report prompts. We operationalize engagement with self-reporting in term of prompts delivered and prompt response to capture both broad and more granular engagement in self-reporting, respectively. The data were analyzed to describe trends in prompt delivered and prompt response over time. Time-varying effect modeling (TVEM) was employed to investigate the time-varying effects of response to previous prompt and the average response rate on the likelihood of current prompt response.

Results

Although prompt response rates were relatively stable over days in both studies, the proportion of participants with prompts delivered declined steadily over time in one of the studies, indicating that over time, fewer participants charged the device and kept it turned on (necessary to receive at least one prompt per day). Among those who did receive prompts, response rates were relatively stable. In both studies, there is a significant, positive and stable relationship between response to previous prompt and the likelihood of response to current prompt throughout all days of the study. The relationship between the average response rate prior to current prompt and the likelihood of responding to the current prompt was also positive, and increasing with time.

Conclusion

Our study highlights the importance of integrating various indicators to measure engagement in digital self-reporting. Both average response rate and response to previous prompt were highly predictive of response to the next prompt across days in the study. Dynamic patterns of engagement in digital self-reporting can inform the design of new strategies to promote and optimize engagement in digital interventions and mobile health studies.

Acceptance and Commitment Therapy-Based Smartphone Applications for Cessation of Tobacco Use among Adults with High Nicotine Dependence: Results from the iCanQuit Randomized Trial

Background: With 1 in 2 adult tobacco users being highly dependent on nicotine, population-based interventions specifically designed for this group are urgently needed. This study used data from a randomized trial to evaluate whether (1) Acceptance and Commitment Therapy (ACT) delivered via a smartphone application (iCanQuit) would be more efficacious for cessation of nicotine-containing tobacco products than the US Clinical Practice Guidelines (USCPG)-based application (QuitGuide) among highly nicotine-dependent adults, (2) the effect of treatment on cessation was mediated by increases in acceptance of cravings to smoke, and (3) treatment utilization and satisfaction differed by arm. Methods: A total of 1452 highly nicotine-dependent adults received the iCanQuit or QuitGuide application for 12-months. Cessation outcomes were self-reported complete-case 30-day abstinence of nicotine-containing tobacco products (e.g., combustible cigarettes, e-cigarettes, chewing tobacco, snus, hookahs, cigars, cigarillos, tobacco pipes, and kreteks) at 3, 6, and 12-month post-randomization timepoints, missing-as-smoking, and multiple imputation analyses. Acceptance of cues to smoke and satisfaction with the applications was also reported. Results: Participants who received iCanQuit were significantly more likely to report 30-day abstinence of nicotine-containing tobacco products than those who received QuitGuide at 12-months (24% vs. 17%; OR = 1.47 95% CI: 1.11, 1.95). iCanQuit participants utilized their application more frequently and reported greater satisfaction than those who received QuitGuide. Increases in participants' acceptance of cues to smoke mediated the intervention effect on cessation of nicotine-containing tobacco products. Conclusions: Among nicotine-dependent adults, an application-delivered ACT-based intervention was more engaging and efficacious than a USCPG-based intervention for cessation of nicotine-containing tobacco products.

A Just-In-Time Adaptive intervention (JITAI) for smoking cessation: Feasibility and acceptability findings

Smoking cessation treatments that are easily accessible and deliver intervention content at vulnerable moments (e.g., high negative affect) have great potential to impact tobacco abstinence. The current study examined the feasibility and acceptability of a multi-component Just-In-Time Adaptive Intervention (JITAI) for smoking cessation. Daily smokers interested in quitting were consented to participate in a 6-week cessation study. Visit 1 occurred 4 days pre-quit, Visit 2 was on the quit day, Visit 3 occurred 3 days post-quit, Visit 4 was 10 days post-quit, and Visit 5 was 28 days post-quit. During the first 2 weeks (Visits 1-4), the JITAI delivered brief mindfulness/motivational strategies via smartphone in real-time based on negative affect or smoking behavior detected by wearable sensors. Participants also attended 5 in-person visits, where brief cessation counseling (Visits 1-4) and nicotine replacement therapy (Visits 2-5) were provided. Outcomes were feasibility and acceptability; biochemically-confirmed abstinence was also measured. Participants (N = 43) were 58.1 % female (AgeMean = 49.1, mean cigarettes per day = 15.4). Retention through follow-up was high (83.7 %). For participants with available data (n = 38), 24 (63 %) met the benchmark for sensor wearing, among whom 16 (67 %) completed at least 60 % of strategies. Perceived ease of wearing sensors (Mean = 5.1 out of 6) and treatment satisfaction (Mean = 3.6 out of 4) were high. Biochemically-confirmed abstinence was 34 % at Visit 4 and 21 % at Visit 5. Overall, the feasibility of this novel multi-component intervention for smoking cessation was mixed but acceptability was high. Future studies with improved technology will decrease participant burden and better detect key intervention moments.

An ecological momentary assessment study of outcome expectancies and smoking lapse in daily life

OBJECTIVE

Outcome expectancies have been identified as key components of behavior change. Expectancies related to affect control are hypothesized to play an important role in smoking cessation, such that smokers may be more likely to lapse if they believe they can control their affect by smoking and less likely if they believe they can control their affect by means other than smoking. However, little is known about whether real-time, real-world changes in affect control expectancies influence smoking lapse during a quit attempt.

METHODS

A diverse sample (N = 369) of adult smokers completed ecological momentary assessment of smoking expectancies and lapse for 28 days following a quit attempt. Multilevel logistic regression was used to examine whether the difference score of positive smoking outcome expectancies (the belief that smoking would improve mood) minus positive coping outcome expectancies (the belief that something other than smoking would improve mood) was related to smoking lapse in daily life.

RESULTS

There was a significant within-person association between the expectancies difference score and lapse likelihood. When the difference score was 1 unit above a person's typical level, odds of lapse increased by 18.65 % (β = 0.174, SE = 0.024, p < .0001, OR = 1.189, 95 % CI [1.135, 1.247]).

CONCLUSION

Smokers undergoing a quit attempt were more likely to lapse in moments when the difference in the belief that smoking would improve their mood minus the belief that something other than smoking would improve their mood was larger. This work has relevance for tailoring interventions to both cultivate positive coping outcome expectancies and reduce smoking outcome expectancies, and informs theoretical models about the dynamic nature of outcome expectancies.

Quitting Smoking before and after Pregnancy: Study Methods and Baseline Data from a Prospective Cohort Study

Smoking during pregnancy and postpartum remains an important public health problem. No known prior study has prospectively examined mutual changes in risk factors and women's smoking trajectory across pregnancy and postpartum. The objective of this study was to report methods used to implement a prospective cohort (Msgs4Moms), present participant baseline characteristics, and compare our sample characteristics to pregnant women from national birth record data. The cohort study was designed to investigate smoking patterns, variables related to tobacco use and abstinence, and tobacco treatment quality across pregnancy through 1-year postpartum. Current smokers or recent quitters were recruited from obstetrics clinics. Analyses included Chi-square and independent sample t-tests using Cohen's d. A total of 62 participants (41 smokers and 21 quitters) were enrolled. Participants were Black (45.2%), White (35.5%), and multiracial (19.3%); 46.8% had post-secondary education; and most were Medicaid-insured (64.5%). Compared with quitters, fewer smokers were employed (65.9 vs 90.5%, Cohen's d = 0.88) and more reported financial strain (61.1% vs 28.6%; Cohen's d = 0.75). Women who continue to smoke during pregnancy cope with multiple social determinants of health. Longitudinal data from this cohort provide intensive data to identify treatment gaps, critical time points, and potential psychosocial variables warranting intervention.

The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol

Smoking is the leading preventable cause of death and disability in the U.S. Empirical evidence suggests that engaging in evidence-based self-regulatory strategies (e.g., behavioral substitution, mindful attention) can improve smokers' ability to resist craving and build self-regulatory skills. However, poor engagement represents a major barrier to maximizing the impact of self-regulatory strategies. This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) - a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies. The study will employ a 10-day Micro-Randomized Trial (MRT) enrolling 112 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either: (a) no intervention prompt; (b) a prompt recommending brief (low effort) cognitive and/or behavioral self-regulatory strategies; or (c) a prompt recommending more effortful cognitive or mindfulness-based strategies. Prompts will be delivered via push notifications from the MARS mobile app. The goal is to investigate whether, what type of, and under what conditions prompting the individual to engage in self-regulatory strategies increases engagement. The results will build the empirical foundation necessary to develop a mHealth intervention that effectively utilizes intensive longitudinal self-report and sensor-based assessments of emotions, context and other factors to engage an individual in the type of self-regulatory activity that would be most beneficial given their real-time, real-world circumstances. This type of mHealth intervention holds enormous potential to expand the reach and impact of smoking cessation treatments.