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 food insecurity and other social risk factors among U.S. adults

BACKGROUND

Food insecurity (FI) often co-exists with other social risk factors, which makes addressing it particularly challenging. The degree of association between FI and other social risk factors across different levels of income and before and during the COVID-19 pandemic is currently unknown, impeding the ability to design effective interventions for addressing these co-existing social risk factors.

OBJECTIVE

To determine the association between FI and other social risk factors overall and across different levels of income-poverty ratios and before (2019) and during (2020-2021) the pandemic.

DESIGN

We used nationally representative data from the 2019-2021 National Health Interview Survey for our cross-sectional analysis. Social risk factors available in NHIS included difficulties paying for medical bills, difficulties paying for medications, receiving income assistance, receiving rental assistance, and "not working last week".

SUBJECTS

93,047 adults (≥18 years old).

KEY RESULTS

Individuals with other social risk factors (except receiving income assistance) were more likely to report FI, even after adjusting for income and education inequalities. While poverty leads to a higher prevalence of FI, associations between FI and other social risk factors were stronger among people with higher incomes, which may be related to their ineligibility for social safety net programs. Associations were similar before and during the pandemic, perhaps due to the extensive provision of social safety net programs during the pandemic.

CONCLUSIONS

Future research should explore how access to a variety of social safety net programs may impact the association between social risk factors. With the expiration of most pandemic-related social supports, further research and monitoring are also needed to examine FI in the context of increasing food and housing costs. Our findings may also have implications for the expansion of income-based program eligibility criteria and screening for social risk factors across all patients and not only low-income people.

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.

Integrating Intensive Longitudinal Data (ILD) to Inform the Development of Dynamic Theories of Behavior Change and Intervention Design: a Case Study of Scientific and Practical Considerations

The increasing sophistication of mobile and sensing technology has enabled the collection of intensive longitudinal data (ILD) concerning dynamic changes in an individual's state and context. ILD can be used to develop dynamic theories of behavior change which, in turn, can be used to provide a conceptual framework for the development of just-in-time adaptive interventions (JITAIs) that leverage advances in mobile and sensing technology to determine when and how to intervene. As such, JITAIs hold tremendous potential in addressing major public health concerns such as cigarette smoking, which can recur and arise unexpectedly. In tandem, a growing number of studies have utilized multiple methods to collect data on a particular dynamic construct of interest from the same individual. This approach holds promise in providing investigators with a significantly more detailed view of how a behavior change processes unfold within the same individual than ever before. However, nuanced challenges relating to coarse data, noisy data, and incoherence among data sources are introduced. In this manuscript, we use a mobile health (mHealth) study on smokers motivated to quit (Break Free; R01MD010362) to illustrate these challenges. Practical approaches to integrate multiple data sources are discussed within the greater scientific context of developing dynamic theories of behavior change and JITAIs.

Socio-economic status moderates within-person associations of risk factors and smoking lapse in daily life

BACKGROUND AND AIMS

Individuals of lower socio-economic status (SES) display a higher prevalence of smoking and have more diffxiculty quitting than higher SES groups. The current study investigates whether the within-person associations of key risk (e.g. stress) and protective (self-efficacy) factors with smoking lapse varies by facets of SES.

DESIGN AND SETTING

Observational study using ecological momentary assessment to collect data for a 28-day period following a smoking quit attempt. Multi-level mixed models (i.e. generalized linear mixed models) examined cross-level interactions between lapse risk and protective factors and indicators of SES on smoking lapse.

PARTICIPANTS

A diverse sample of 330 adult US smokers who completed a larger study examining the effects of race/ethnicity and social/environmental influences on smoking cessation.

MEASUREMENTS

Risk factors included momentary urge, negative affect, stress; protective factors included positive affect, motivation, abstinence self-efficacy; SES measures: baseline measures of income and financial strain; the primary outcome was self-reported lapse.

FINDINGS

Participants provided 43 297 post-quit observations. Mixed models suggested that income and financial strain moderated the effect of some risk factors on smoking lapse. The within-person association of negative [odds ratio (OR) = 0.967, 95% CI= 0.945, 0.990, P < 0.01] and positive affect (OR = 1.023, 95% CI = 1.003, 1.044, P < 0.05) and abstinence self-efficacy (OR = 1.020, 95% CI = 1.003, 1.038, P < 0.05) on lapse varied with financial strain. The within-person association of negative affect (OR = 1.005, 95% CI = 1.002, 1.008, P < 0.01), motivation (OR = 0.995, 95% CI = 0.991, 0.999, P < 0.05) and abstinence self-efficacy (OR = 0.996, 95% CI = 0.993, 0.999, P < 0.01) on lapse varied by income. The positive association of negative affect with lapse was stronger among individuals with higher income and lower financial strain. The negative association between positive affect and abstinence self-efficacy with lapse was stronger among individuals with lower financial strain, and the negative association between motivation and abstinence self-efficacy with lapse was stronger among those with higher income. The data were insensitive to detect statistically significant moderating effects of income and financial strain on the association of urge or stress with lapse.

CONCLUSION

Some risk factors (e.g. momentary negative affect) exert a weaker influence on smoking lapse among lower compared to higher socio-economic status groups.

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.

Barriers to and solutions for representative inclusion across the lifespan and in life course research: The need for structural competency highlighted by the COVID-19 pandemic

Exclusion of special populations (older adults; pregnant women, children, and adolescents; individuals of lower socioeconomic status and/or who live in rural communities; people from racial and ethnic minority groups; individuals from sexual or gender minority groups; and individuals with disabilities) in research is a pervasive problem, despite efforts and policy changes by the National Institutes of Health and other organizations. These populations are adversely impacted by social determinants of health (SDOH) that reduce access and ability to participate in biomedical research. In March 2020, the Northwestern University Clinical and Translational Sciences Institute hosted the "Lifespan and Life Course Research: integrating strategies" "Un-Meeting" to discuss barriers and solutions to underrepresentation of special populations in biomedical research. The COVID-19 pandemic highlighted how exclusion of representative populations in research can increase health inequities. We applied findings of this meeting to perform a literature review of barriers and solutions to recruitment and retention of representative populations in research and to discuss how findings are important to research conducted during the ongoing COVID-19 pandemic. We highlight the role of SDOH, review barriers and solutions to underrepresentation, and discuss the importance of a structural competency framework to improve research participation and retention among special populations.

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.

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.

Trajectories of motivation and self-efficacy during a smoking quit attempt: An ecological momentary assessment study

OBJECTIVE

Conceptual models of addiction highlight self-efficacy and motivation as key variables important during a smoking quit attempt; however, recent research has primarily focused on self-efficacy. Given the importance of motivation in these models, a clearer understanding of the effects of motivation during a smoking quit attempt is needed.

METHOD

This study utilized ecological momentary assessment (EMA) to investigate the dynamic effects of motivation and self-efficacy early in a quit attempt. Participants were 356 smokers (45% male; 34% African American, 33% non-Hispanic White, 33% Latino). Participants completed EMAs of motivation, self-efficacy, and smoking for 4 days prequit through 1 week postquit, and returned for a follow-up assessment at 4 weeks postquit. Trajectory parameters of motivation and self-efficacy (mean, slope, and volatility) were analyzed in separate and combined regression models to predict smoking outcomes.

RESULTS

Prequit results showed that parameters of motivation and self-efficacy were not associated with smoking on quit day. Postquit analyses revealed that participants with lower mean levels of motivation and self-efficacy were more likely to smoke at the end of Week 1. Moreover, individuals with decreasing levels of motivation over the first week of their quit attempt were more likely to be smoking at the end of Week 4.

CONCLUSIONS

These findings highlight the need to incorporate dynamic measures of motivation in smoking research. Furthermore, the results underscore the value of utilizing EMA methods and trajectory parameters to gain a more nuanced understanding of the dynamic effects that key mechanisms have on smoking during a quit attempt. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Intersectionality and Smoking Cessation: Exploring Various Approaches for Understanding Health Inequities

OBJECTIVE

Many marginalized groups smoke at higher rates and have greater difficulty quitting than less marginalized groups. Most research on smoking cessation inequities has focused on a single sociodemographic attribute (eg, race or socioeconomic status), yet individuals possess multiple attributes that may increase risk. The current study used an intersectionality framework to examine how the interplay between multiple marginalized attributes may impact smoking cessation outcomes.

METHODS

A diverse sample of 344 adults enrolled in a smoking cessation program and reported on sociodemographic attributes (eg, race/ethnicity, gender, income) and continuous smoking abstinence on their quit date and at 1, 2, and 4 weeks postquit date. A Cox proportional hazard regression model was used to estimate whether intersectional links among race/ethnicity, gender, and income were related to smoking cessation outcomes.

RESULTS

Lower household income may be related to higher risk of smoking cessation failure. There were no significant interactions among race/ethnicity, gender, and income in predicting relapse. Pairwise intersectional group differences suggested some groups may be at higher risk of relapse. Number of marginalized sociodemographic attributes did not predict relapse.

CONCLUSIONS

Intersectionality may be a promising framework for addressing health inequities, and may help elucidate how to best design and target intervention efforts for individuals characterized by sociodemographic intersections that concur particularly high risk for poor tobacco cessation outcomes.

IMPLICATIONS

Despite an overall decline in smoking rates, socioeconomic inequities in smoking prevalence and cancer mortality are widening. Efforts targeting tobacco cessation should incorporate new theory to capture the complex set of factors that may account for tobacco cessation inequities (eg, multiple aspects of identity that may influence access to tobacco cessation treatment and exposure to certain stressors that impede cessation efforts). Intersectionality may be a promising framework for addressing health inequities in tobacco use and cessation and may help elucidate how to best design and target intervention efforts for individuals that concur particularly high risk for poor tobacco cessation outcomes.

Evaluation of Revised US Preventive Services Task Force Lung Cancer Screening Guideline Among Women and Racial/Ethnic Minority Populations

Importance

Lung cancer incidence and mortality disproportionately affect women and racial/ethnic minority populations, yet screening guidelines for the past several years were derived from clinical trials of predominantly White men. To reflect current evidence, the US Preventive Services Task Force (USPSTF) has revised the eligibility criteria, which may help to ameliorate sex- and race/ethnicity-related disparities in lung cancer screening.

Objective

To determine the changes associated with the revised USPSTF guideline for lung cancer screening eligibility among female, Black, and Hispanic populations using a large nationwide survey.

Design, Setting, and Participants

This cross-sectional study included respondents to the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System who were 50 to 80 years of age with a smoking history in 19 states that used the optional lung cancer screening module. The change in eligibility among female, male, Black, Hispanic, and White participants was examined. Eligibility by sex and race/ethnicity was compared with a reference population. Data were collected from January 1, 2017, to December 31, 2018, and analyzed from May 8 to June 11, 2020.

Exposures

Self-reported sex, race/ethnicity, age, and smoking history.

Main Outcomes and Measures

Lung cancer screening eligibility using the revised USPSTF criteria. The previous criteria included current or past smokers (within 15 years) who were 55 to 80 years of age and had a smoking history of more than 30 pack-years. In the revised criteria, age was modified to 50 to 80 years; smoking history, to 20 pack-years.

Results

Among 40 869 respondents aged 50 to 80 years with a smoking history, 21 265 (52.0%) were women, 3430 (8.4%) were Black, and 1226 (30.0%) were Hispanic (mean [SD] age, 65.6 [7.9] years). The revised criteria increased eligibility for the following populations: men (29.4% to 38.3% [8.9% difference]; P < .001), women (25.9% to 36.4% [10.5% difference]; P < .001), White individuals (31.1% to 40.9% [9.8% difference]; P < .001), Black individuals (16.3% to 28.8% [12.5% difference]; P < .001), and Hispanic individuals (10.5% to 18.7% [8.2% difference]; P < .001). The odds of eligibility were lower for women compared with men (adjusted odds ratio [AOR], 0.88; 95% CI, 0.79-0.99; P = .04) and for Black (AOR, 0.43; 95% CI, 0.33-0.56; P < .001) and Hispanic populations (AOR, 0.70; 95% CI, 0.62-0.80; P < .001) compared with the White population.

Conclusions and Relevance

The revised USPSTF guideline may likely increase lung cancer screening rates for female, Black, and Hispanic populations. However, despite these potential improvements, lung cancer screening inequities may persist without tailored eligibility criteria.

A time-varying model of the dynamics of smoking lapse

OBJECTIVE

The majority of smokers who make a quit attempt experience their first lapse within the first week of quitting, yet limited research to date has examined how the strength and direction of the relationship between smoking risk factors and lapse may change over longer periods of time. Time-varying effect modeling (TVEM) was used to address this gap.

METHOD

A diverse sample (N = 325) of adult smokers completed ecological momentary assessments of risk factors for lapse for 28 days after quitting. TVEM was used to examine the relationship between risk factors (abstinence self-efficacy, positive affect, positive coping expectancies, smoking expectancies, motivation, negative affect, stress, and urge) and lapse for 28 days postquit.

RESULTS

Some associations were stable (e.g., negative affect, motivation), whereas others varied over time. Abstinence self-efficacy, positive affect, and positive coping expectancies were most strongly associated with lapse between Days 3 and 8 postquit. The association of urge with lapse was strongest between Days 4 and 10, as well as near the end of the quit attempt. Stress was also most strongly associated with lapse near the beginning and end of the postquit period and was the only predictor associated with lapse on quit date. The strength of the association between smoking expectancies and lapse increased over time.

CONCLUSION

There may be periods during a quit attempt when certain risk factors are more strongly related to lapse. This work has relevance for tailoring interventions designed to deliver intervention components in particular contexts or times of need. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Biopsychosocial correlates of discrimination in daily life: A review

The bulk of the literature on the relationship of discrimination to health has relied on retrospective reports of perceived exposure to discrimination. Much less is known known about the real-time within-person association between discrimination and biopsychosocial processes. Intensive longitudinal methods, including ecological momentary assessment (EMA) and daily diary studies, are well suited for studying the dynamic nature of discriminatory experiences in daily life. This review examines research utilizing EMA and daily diaries to study discrimination within individuals in real-time and natural settings. We identified studies of discrimination related to race, sex, weight, and sexual orientation, and examined the prevalence and frequency of discrimination in daily life, the psychosocial correlates of discrimination, contextual factors related to discriminatory experiences, and moderators of the effects of discrimination. We highlight the benefits of using EMA and daily diaries in the study of discrimination and biopsychosocial processes, and provide suggestions for future work on these associations. EMA data suggests that discrimination occurs more frequently than might be clear from survey research and is associated with poor mental health in daily life. Further, discrimination may be enacted through various means (e.g., verbal expressions, behavior), by a variety of perpetrators (e.g., spouses, friends), and in various settings (e.g., in the home, in public places) in daily life. This methodology has potential to facilitate our understanding of the dynamic temporal relationship between cognitive, behavioral, and emotional responses to discrimination and poor health outcomes in daily life.