The Use of Web-Based Support Groups Versus Usual Quit-Smoking Care for Men and Women Aged 21-59 Years: Protocol for a Randomized Controlled Trial (Preprint)

BACKGROUND Existing smoking cessation treatments are challenged by low engagement and high relapse rates, suggesting the need for more innovative, accessible, and interactive treatment strategies. Twitter is a Web-based platform that allows people to communicate with each other throughout the day using their phone. OBJECTIVE This study aims to leverage the social media platform of Twitter for fostering peer-to-peer support to decrease relapse with quitting smoking. Furthermore, the study will compare the effects of coed versus women-only groups on women’s success with quitting smoking. METHODS The study design is a Web-based, three-arm randomized controlled trial with two treatment arms (a coed or women-only Twitter support group) and a control arm. Participants are recruited online and are randomized to one of the conditions. All participants will receive 8 weeks of combination nicotine replacement therapy (patches plus their choice of gum or lozenges), serial emails with links to Smokefree.gov quit guides, and instructions to record their quit date online (and to quit smoking on that date) on a date falling within a week of initiation of the study. Participants randomized to a treatment arm are placed in a fully automated Twitter support group (coed or women-only), paired with a buddy (matched on age, gender, location, and education), and encouraged to communicate with the group and buddy via daily tweeted discussion topics and daily automated feedback texts (a positive tweet if they tweet and an encouraging tweet if they miss tweeting). Recruited online from across the continental United States, the sample consists of 215 male and 745 female current cigarette smokers wanting to quit, aged between 21 and 59 years. Self-assessed follow-up surveys are completed online at 1, 3, and 6 months after the date they selected to quit smoking, with salivary cotinine validation at 3 and 6 months. The primary outcome is sustained biochemically confirmed abstinence at the 6-month follow-up. RESULTS From November 2016 to September 2018, 960 participants in 36 groups were recruited for the randomized controlled trial, in addition to 20 participants in an initial pilot group. Data analysis will commence soon for the randomized controlled trial based on data from 896 of the 960 participants (93.3%), with 56 participants lost to follow-up and 8 dropouts. CONCLUSIONS This study combines the mobile platform of Twitter with a support group for quitting smoking. Findings will inform the efficacy of virtual peer-to-peer support groups for quitting smoking and potentially elucidate gender differences in quit rates found in prior research. CLINICALTRIAL ClinicalTrials.gov NCT02823028; https://clinicaltrials.gov/ct2/show/NCT0282302

Association of Medicaid Expansion and Health Insurance with Receipt of Smoking Cessation Services and Smoking Behaviors in Substance Use Disorder Treatment.

This study examined whether living in a Medicaid-expanded state or having health insurance was associated with receipt of smoking cessation services or smoking behaviors among substance use disorder (SUD) treatment clients. In 2015 and 2016, 1702 SUD clients in 14 states were surveyed for health insurance status, smoking cessation services received in their treatment program, and smoking behaviors. Services and behaviors were then compared by state Medicaid expansion and health insurance status independently. Clients in Medicaid-expanded states were more likely to be insured (89.9% vs. 54.4%, p < 0.001) and to have quit smoking during treatment (AOR = 3.77, 95% CI = 2.47, 5.76). Insured clients had higher odds of being screened for smoking status in their treatment program and making quit attempts in the past year. Medicaid expansion supports greater health insurance coverage of individuals in SUD treatment and may enhance smoking cessation

Tobacco use among substance use disorder (SUD) treatment staff is associated with tobacco-related services received by clients

Background: Despite disproportionately high rates of smoking among people in residential substance use disorder (SUD) treatment, few receive tobacco cessation services. Little is known about how smoking among treatment staff may impact this disparity. We explored the relationship between staff tobacco use and client tobacco use. Additionally, we examined the relationship between staff tobacco use and tobacco-related services reported by staff and clients. Methods: Staff (n = 363) and clients (n = 639) in 24 California publicly-funded residential SUD treatment programs were surveyed in 2019-20. Staff self-reported current tobacco use, as well as their beliefs, self-efficacy, and practices regarding smoking cessation. Clients reported their tobacco use and they services received while in treatment. Regression analyses examined the adjusted and unadjusted associations between staff and client tobacco use and other outcomes. Results: Use of any tobacco product by staff ranged from 0% to 100% by program, with an average of 32% across programs. Adjusted analyses found that higher rates of staff tobacco use were associated with higher rates of client tobacco use, and with fewer clients receiving tobacco-related counseling. In programs that had higher rates of staff tobacco use, staff were less likely to believe that clients should quit smoking in treatment and had lower self-efficacy to address smoking. Conclusion: Higher rates of tobacco use among staff are associated with higher rates of client tobacco use and fewer clients receiving cessation counseling. Efforts to reduce tobacco use among SUD clients should be supported by efforts to reduce tobacco use among staff. SUD treatment programs, and agencies that fund and regulate those programs, should aim to reduce the use of tobacco products among staff

Tobacco Cessation Services in Addiction Treatment: What Do Clients Say?

ObjectiveSpecialty addiction programs treat people who are addicted to alcohol, opioids, stimulants, and other drugs. This study identified the proportion of addiction program clients who received tobacco-related services and factors associated with receipt of such services.MethodsIn 2015 and 2016, clients (N=2,119) in 24 programs were surveyed for receipt of services aligning with three of the five As of tobacco cessation: ask, advise, assist. Multivariate analyses examined factors associated with receipt of each service.ResultsMost clients (76%) were asked about smoking. Among smokers (N=1,630), 53% were advised to quit, 41% received counseling, 26% received cessation medication, and 17% received counseling and medication. Clients were more likely to receive tobacco-related services if they wanted help quitting smoking or were enrolled in programs with tobacco-free grounds.ConclusionsThese correlational findings suggest that increasing client motivation to quit and implementing tobacco-free policies on the grounds of treatment centers may increase tobacco-related services in addiction treatment

Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials

BACKGROUND: Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected. METHODS: Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable. FINDINGS: No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms. INTERPRETATION: Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation. FUNDING: NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC)

A test of the DSM-5 severity specifier for bulimia nervosa in adolescents: Can we anticipate clinical treatment outcomes?

OBJECTIVE:This study tested clinical utility of the DSM-5 severity specifier for bulimia nervosa (BN) in predicting treatment response among adolescents (N = 110) within a randomized clinical trial of two psychosocial treatments. METHOD:Analyses grouped individuals meeting criteria for BN diagnosis by baseline severity, per DSM-5. Associations among baseline severity classification and BN behavior (i.e., binge eating and compensatory behavior) and eating disorder examination (EDE) Global scores at end-of-treatment (EOT), 6- and 12-month follow-up were examined. RESULTS:Associations between severity categories with BN symptoms were not significant at EOT, or follow-up. Test for linear trend in BN behavior was significant at EOT, F = 5.23, p = 0.02, without demonstrating a linear pattern. Relation between severity categories with EDE Global scores was significant at 6-month follow-up, F = 3.76, p = 0.01. Tests for linear trend in EDE Global scores were significant at EOT, F = 5.40, p = 0.02, and at 6 months, F = 10.73, p = 0.002, with the expected linear pattern. DISCUSSION:Findings suggest the DSM-5 BN severity specifier holds questionable utility in anticipating outpatient treatment response in adolescents with BN. The specifier may have improved ability to predict attitudinal rather than behavioral treatment outcomes

An International Systematic Review of Smoking Prevalence in Addiction Treatment

Aims: Smoking prevalence is higher among people enrolled in addiction treatment compared with the general population, and very high rates of smoking are associated with opiate drug use and receipt of opiate replacement therapy (ORT). We assessed whether these findings are observed internationally. Methods: PubMed, PsycINFO and the Alcohol and Alcohol Problems Science Database were searched for papers reporting smoking prevalence among addiction treatment samples, published in English, from 1987 to 2013. Search terms included tobacco use, cessation and substance use disorders using and/or Boolean connectors. For 4549 papers identified, abstracts were reviewed by multiple raters; 239 abstracts met inclusion criteria and these full papers were reviewed for exclusion. Fifty-four studies, collectively comprising 37364 participants, were included. For each paper we extracted country, author, year, sample size and gender, treatment modality, primary drug treated and smoking prevalence. Results: The random-effect pooled estimate of smoking across people in addiction treatment was 84% [confidence interval (CI)=79, 88%], while the pooled estimate of smoking prevalence across matched population samples was 31% (CI=29, 33%). The difference in the pooled estimates was 52% (CI=48%, 57%, P<.0001). Smoking rates were higher in programs treating opiate use compared with alcohol use [odds ratio (OR)=2.52, CI=2.00, 3.17], and higher in ORT compared to out-patient programs (OR=1.42, CI=1.19, 1.68). Conclusions: Smoking rates among people in addiction treatment are more than double those of people with similar demographic characteristics. Smoking rates are also higher in people being treated for opiate dependence compared with people being treated for alcohol use disorder

Expectancies regarding the interaction between smoking and substance use in alcohol-dependent smokers in early recovery.

The purpose of this study was to investigate expectancies regarding the interaction between cigarette smoking and use of alcohol among alcohol-dependent smokers in early recovery, using the Nicotine and Other Substances Interaction Expectancies Questionnaire (NOSIE). Participants were 162 veterans, 97% male, with a mean age of 50 years, enrolled in a clinical trial aimed at determining the efficacy of an intensive smoking cessation intervention versus usual care. At baseline, participants were assessed on measures of smoking behavior, abstinence thoughts about alcohol and tobacco use, symptoms of depression, and smoking-substance use interaction expectancies. In addition, biologically verified abstinence from tobacco and alcohol was assessed at 26 weeks. Participants reported that they expected smoking to have less of an impact on substance use than substance use has on smoking (p &lt; .001). Severity of depressive symptoms was significantly associated with the expectancy that smoking provides a way of coping with the urge to use other substances (p &lt; .01). The expectation that smoking increases substance urges/use was predictive of prospectively measured and biologically verified abstinence from smoking at 26 weeks (p &lt; .03). The results add to our knowledge of smoking-substance use interaction expectancies among alcohol-dependent smokers in early recovery and will inform the development of more effective counseling interventions for concurrent alcohol and tobacco use disorders

Global Reach of an Internet Smoking Cessation Intervention among Spanish- and English-Speaking Smokers from 157 Countries

This investigation is a secondary analysis of demographic, smoking, and depression information in a global sample of Spanish- and English-speaking smokers who participated in a series of randomized controlled smoking cessation trials conducted via the Internet. The final sample consisted of 17,579 smokers from 157 countries. Smoking profiles were similar across languages and world regions and consistent with characteristics of participants in traditional smoking cessation studies. Participants were predominantly Spanish-speakers, evenly divided between men and women and relatively few indicated using traditional smoking cessation methods (e.g., groups or medication). This study demonstrates that substantial numbers of smokers from numerous countries seek Web-based smoking cessation resources and adds to the growing support for Web-assisted tobacco interventions as an additional tool to address the need for global smoking cessation efforts