Although there has been a socioeconomic gradient in smoking prevalence, cessation, and disease burden for decades, these disparities have become even more pronounced over time. The aim of the current study was to develop and test a conceptual model of the mechanisms linking socioeconomic status (SES) to smoking cessation. The conceptual model was evaluated using a latent variable modeling approach in a sample of 424 smokers seeking treatment (34% African American; 33% Latino; 33% White). Hypothesized mechanisms included social support, neighborhood disadvantage, negative affect/stress, agency, and craving. The primary outcome was Week 4 smoking status. As was hypothesized, SES had significant direct and indirect effects on cessation. Specifically, neighborhood disadvantage, social support, negative affect/stress, and agency mediated the relation between SES and smoking cessation. A multiple group analysis indicated that the model was a good fit across racial/ethnic groups. The present study yielded one of the more comprehensive models illuminating the specific mechanisms that link SES and smoking cessation. Policy, community, and individual-level interventions that target low SES smokers and address the specific pathways identified in the current model could potentially attenuate the impact of SES on cessation.

Background and Aims: Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi‐factorial. Just‐in‐time adaptive interventions (JITAIs) aim to deliver tailored support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology‐mediated JITAIs for reducing harmful substance use.Methods: Systematic review of empirical studies of any design with a narrative synthesis. We searched Ovid MEDLINE, Embase, PsycINFO, Web of Science, the ACM Digital Library, the IEEE Digital Library, ClinicalTrials.gov, the ISRCTN register and dblp using terms related to substance use/mHealth/JITAIs. Outcomes were user engagement and intervention effectiveness. Study quality was assessed with the mHealth Evidence Reporting and Assessment checklist.Findings: We included 17 reports of 14 unique studies, including two randomized controlled trials. JITAIs targeted alcohol (S = 7, n = 120 520), tobacco (S = 4, n = 187), cannabis (S = 2, n = 97) and a combination of alcohol and illicit substance use (S = 1, n = 63), and primarily relied on active measurement and static (i.e. time‐invariant) decision rules to deliver support tailored to micro‐scale changes in mood or urges. Two studies used data from prior participants and four drew upon theory to devise decision rules. Engagement with available JITAIs was moderate‐to‐high and evidence of effectiveness was mixed. Due to substantial heterogeneity in study designs and outcome variables assessed, no meta‐analysis was performed. Many studies reported insufficient detail on JITAI infrastructure, content, development costs and data security.Conclusions: Current implementations of just‐in‐time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro‐scale changes in mood or urges. Studies on JITAI effectiveness are lacking.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: Smartphone apps for smoking cessation could offer easily accessible, highly tailored, intensive interventions at a fraction of the cost of traditional counseling. Although there are hundreds of publicly available smoking cessation apps, few have been empirically evaluated using a randomized controlled trial (RCT) design. The Smart-Treatment (Smart-T2) app is a just-in-time adaptive intervention that uses ecological momentary assessments (EMAs) to assess the risk for imminent smoking lapse and tailors treatment messages based on the risk of lapse and reported symptoms. Objective: This 3-armed pilot RCT aimed to determine the feasibility and preliminary efficacy of an automated smartphone-based smoking cessation intervention (Smart-T2) relative to standard in-person smoking cessation clinic care and the National Cancer Institute’s free smoking cessation app, QuitGuide. Methods: Adult smokers who attended a clinic-based tobacco cessation program were randomized into groups and followed for 13 weeks (1 week prequitting through 12 weeks postquitting). All study participants received nicotine patches and gum and were asked to complete EMAs five times a day on study-provided smartphones for 5 weeks. Participants in the Smart-T2 group received tailored treatment messages after the completion of each EMA. Both Smart-T2 and QuitGuide apps offer on-demand smoking cessation treatment. Results: Of 81 participants, 41 (50%) were women and 55 (68%) were white. On average, participants were aged 49.6 years and smoked 22.4 cigarettes per day at baseline. A total of 17% (14/81) of participants were biochemically confirmed 7-day point prevalence abstinent at 12 weeks postquitting (Smart-T2: 6/27, 22%, QuitGuide: 4/27, 15%, and usual care: 4/27, 15%), with no significant differences across groups (P>.05). Participants in the Smart-T2 group rated the app positively, with most participants agreeing that they can rely on the app to help them quit smoking, and endorsed the belief that the app would help them stay quit, and these responses were not significantly different from the ratings given by participants in the usual care group. Conclusions: Dynamic smartphone apps that tailor intervention content in real time may increase user engagement and exposure to treatment-related materials. The results of this pilot RCT suggest that smartphone-based smoking cessation treatments may be capable of providing similar outcomes to traditional, in-person counseling. Trial Registration: ClinicalTrials.gov NCT02930200; https://clinicaltrials.gov/show/NCT02930200

Objectives. We evaluated the influence of financial strain on smoking cessation among Latino, African American, and Caucasian smokers of predominantly low socioeconomic status. Methods. Smokers enrolled in a smoking cessation study (N = 424) were followed from 1 week prequit through 26 weeks postquit. We conducted a logistic regression analysis to evaluate the association between baseline financial strain and smoking abstinence at 26 weeks postquit after control for age, gender, race/ethnicity, educational level, annual household income, marital status, number of cigarettes smoked per day, and time to first cigarette of the day. Results. Greater financial strain at baseline was significantly associated with reduced odds of abstinence at 26 weeks postquit among those who completed the study (odds ratio [OR] = 0.77; 95% confidence interval [CI] = 0.62, 0.94; P = .01). There was a significant association as well in analyses that included those who completed the study in addition to those lost to follow-up who were categorized as smokers (OR = 0.78; 95% CI = 0.64, 0.96; P = .02). Conclusions. Greater financial strain predicted lower cessation rates among racially/ethnically diverse smokers. Our findings highlight the impact of economic concerns on smoking cessation and the need to address financial strain in smoking cessation interventions.

The purpose of this study was to prospectively examine the relationships between socioeconomic status (SES), demographic variables and mental health and to determine whether number of life stressors mediated these relationships. Wave 1 (2001-02) and 2 (2004-05) data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; n = 34,459) were used in the current study. Controlling for wave 1 mental health, a mediation model was tested to determine the relative impact of socioeconomic and demographic variables (all measured at wave 1) on mental health 3 years later (wave 2). The number of life stressors experienced in the 12 months prior to wave 1, assessed at wave 1, was evaluated as the mediator. Findings indicated that SES, age, race/ethnicity, gender and marital status independently predicted changes in mental health ratings at wave 2. In addition, the number of life stressors mediated the relation between socioeconomic and demographic variables and mental health. Exposure to life stressors helps to explain commonly reported socioeconomic and demographic disparities in mental health. Findings may suggest that reducing exposure to stressors and/or improving coping with life stressors may both improve mental health and reduce health disparities.