Trial Of Smoking Cessation Intervention Research Articles

A Pilot Randomized Controlled Trial of a Multicomponent Smoking Cessation Intervention for Adults with Serious Mental Illness.

Background: Combined behavioral- and pharmacological-based tobacco cessation interventions are effective for adults with serious mental illness; yet, they continue to smoke at alarming rates. Materials and Methods: A pilot two-arm randomized controlled trial of the program consisted of 50 minutes of game-based groups 3×/week, for 12 weeks alongside counseling and pharmacotherapy. The intervention group engaged in game-based group physical activity (PA), while the control group engaged in sedentary games. Results: Multilevel regression analyses were used to examine the primary aims. Mean number of cigarettes smoked per week (cig/wk) for the sample (n = 48) at enrollment was 56.3 cigarettes. The linear change was significant at 2.9 fewer cig/wk. The average psychiatric symptom score at enrollment for the PA group was 41.5 points with a significant predicted linear decrease in scores. Conclusion: Both study groups showed a significant reduction in cig/wk. The combination of counseling and pharmacotherapy offered in groups may have aided with cessation and cigarette reduction while also providing a benefit to mental health.

Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project

Background Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods Researchers manually annotated 70 items of information (‘entities’) in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the ‘FLAIR’ framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results The F1 evaluation score, derived from the false positive and false negative rates (range 0–1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05–0.88) compared with an average human annotator’s score of 0.75 (SD=0.15, range 0.38–1.00). The algorithm for assigning entities to study arms (e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper 1 ).

A pilot pragmatic randomized controlled trial of a nicotine metabolite ratio-guided smoking cessation intervention in a lung health and screening program

IntroductionPatients with pulmonary nodules detected through lung cancer screening or as incidental findings are often followed in lung health and screening programs. The use of personalized pharmacotherapy for smoking cessation informed by the nicotine metabolite ratio (NMR), a measure of nicotine metabolism, has not yet been evaluated in this setting. This pilot randomized controlled trial (RCT) evaluated the feasibility of conducting a larger trial. MethodsThrough a pragmatic RCT design, participants were recruited from a Mid-Atlantic lung health and screening program. Eligible participants smoked >5 cigarettes per day and completed a blood draw to determine NMR before being randomized to standard or NMR-guided care treatment arms. Standard care participants were offered nicotine replacement therapy (NRT) or varenicline and a referral to phone-based smoking cessation counseling. NMR-guided participants received standard care except they were provided a personalized medication recommendation based on their NMR. Study outcomes included measures of feasibility, medication uptake, and treatment matching (i.e., uptake of the optimal medication). ResultsMore than 80 % of 205 screened patients were eligible. However, only 37 (22 %) of these patients enrolled in the study, with a mean age of 65 years, 43 % female, and 25 % Black. Nearly all patients who declined cited a disinterest in smoking cessation. Participants in both treatment arms had high rates of medication uptake (68 %), with NMR-guided participants showing a trend towards greater treatment matching (55 % vs. 29 %). ConclusionsThe results of this pilot study provide support for conducting a larger RCT of an NMR-guided smoking cessation intervention in a lung health and screening setting. Consideration should be given to augmenting the intervention to address barriers to study entry.

A qualitative study of using nicotine products for smoking cessation after discharge from residential drug and alcohol treatment in Australia.

Tobacco smoking is highly prevalent among alcohol and other drugs (AOD) service clients and, despite interest in quitting, abstinence is rarely sustained. Nicotine products may assist after discharge from residential treatment services, but little is known about client receptivity to them. This study examined AOD withdrawal service clients' experiences of two types of nicotine products for smoking cessation post-discharge, combination nicotine replacement therapy (cNRT) and nicotine vaping products (NVP). We held semi-structured telephone interviews with 31 Australian AOD service clients in a clinical trial of a 12-week smoking cessation intervention using Quitline support plus cNRT or NVP delivered post-discharge from a smoke-free residential service. We asked about health and social factors, nicotine cravings, Quitline experience, and barriers and facilitators to cNRT or NVP, then thematically analysed data. cNRT and NVP were described by participants as feasible and acceptable for smoking cessation. For most participants, cost limited cNRT access post study, as did difficulty navigating NVP prescription access. Quitline support was valued, but not consistently used, with participants noting low assistance with NVP-facilitated cessation. Participants considered both cessation methods acceptable and socially supported, and sought information on decreasing nicotine use via NVP. AOD service clients highly valued receiving cNRT or NVP with behavioural support for smoking reduction or abstinence. Both interventions were acceptable to service clients. Findings suggest a potential need to examine both whether NVP use should be permitted in this context, and guidance on the individual suitability of cNRT or NVP.

Social networks of people with serious mental illness who smoke: potential role in a smoking cessation intervention

Background Smoking is a major contributor to morbidity and mortality among individuals with serious mental illness (SMI) and social networks may play an important role in smoking behaviors. Aims Our objectives were to (1) describe the network characteristics of adults with SMI who smoke tobacco (2) explore whether network attributes were associated with nicotine dependence. Methods We performed a secondary analysis of baseline data from a tobacco smoking cessation intervention trial among 192 participants with SMI. A subgroup (n = 75) completed questions on the characteristics of their social network members. The network characteristics included network composition (e.g. proportion who smoke) and network structure (e.g. density of connections between members). We used multilevel models to examine associations with nicotine dependence. Results Participant characteristics included: a mean age 50 years, 49% women, 48% Black, and 41% primary diagnosis of schizophrenia/schizoaffective disorder. The median personal network proportion of active smokers was 22%, active quitters 0%, and non-smokers 53%. The density of ties between actively smoking network members was greater than between non-smoking members (55% vs 43%, p = .02). Proportion of network smokers was not associated with nicotine dependence. Conclusions We identified potential social network challenges and assets to smoking cessation and implications for network interventions among individuals with SMI.

Predicting outcomes of smoking cessation interventions in novel scenarios using ontology-informed, interpretable machine learning

Background Systematic reviews of effectiveness estimate the relative average effects of interventions and comparators in a set of existing studies e.g., using rate ratios. However, policymakers, planners and practitioners require predictions about outcomes in novel scenarios where aspects of the interventions, populations or settings may differ. This study aimed to develop and evaluate an ontology-informed, interpretable machine learning algorithm to predict smoking cessation outcomes using detailed information about interventions, their contexts and evaluation study methods. This is the second of two linked papers on the use of machine learning in the Human Behaviour-Change Project. Methods The study used a corpus of 405 reports of randomised trials of smoking cessation interventions from the Cochrane Library database. These were annotated using the Behaviour Change Intervention Ontology to classify, for each of 971 study arms, 82 features representing details of intervention content and delivery, population, setting, outcome, and study methodology. The annotated data was used to train a novel machine learning algorithm based on a set of interpretable rules organised according to the ontology. The algorithm was evaluated for predictive accuracy by performance in five-fold 80:20 cross-validation, and compared with other approaches. Results The machine learning algorithm produced a mean absolute error in prediction percentage cessation rates of 9.15% in cross-validation, outperforming other approaches including an uninterpretable ‘black-box’ deep neural network (9.42%), a linear regression model (10.55%) and a decision tree-based approach (9.53%). The rules generated by the algorithm were synthesised into a consensus rule set to create a publicly available predictive tool to provide outcome predictions and explanations in the form of rules expressed in terms of predictive features and their combinations. Conclusions An ontologically-informed, interpretable machine learning algorithm, using information about intervention scenarios from reports of smoking cessation trials, can predict outcomes in new smoking cessation intervention scenarios with moderate accuracy.

Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project.

Background Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the 'FLAIR' framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator's score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms ( e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper 3).

Validation and Psychometric Properties of the Tobacco Urge Management Scale (TUMS).

During quit attempts, smokers must overcome smoking urges triggered by environmental cues and nicotine withdrawal symptoms. This study investigates the psychometric properties of the 12-item Tobacco Urge Management Scale (TUMS), a new measure of smoking urge management behaviors. We analyzed secondary data (n = 327) from a behavioral smoking cessation intervention trial, Kids Safe and Smokefree (KiSS). Confirmatory factor analysis of the TUMS indicated that a one-factor model and a correlated two-factor model had similar model fit indices, and a Chi-square difference test supported the one-factor model. Further study of the parsimonious one-factor scale provided evidence of reliability and construct validity. Known group validity was evidenced by significantly higher TUMS scores in the KiSS intervention arm receiving urge management skills training than in the control arm (p < 0.001). Concurrent validity was evidenced by TUMS's inverse association with cigarettes smoked per day and positive associations with nonsmoking days, 7-day abstinence, and self-efficacy to control smoking behaviors (p's < 0.05). The TUMS is a reliable, valid measure of smoking urge management behaviors. The measure can support theory-driven research on smoking-specific coping mechanisms, inform clinical practice by identifying coping strategies that might be under-utilized in treatment-seeking smokers, and function as a measure of treatment adherence in cessation trials that target urge management behaviors.

A Cost-Effectiveness Analysis of Comprehensive Smoking-Cessation Interventions Based on the Community and Hospital Collaboration.

BackgroundThe prevalence of cigarette smoking in China is high and the utilization of smoking cessation clinics is very low. Multicomponent smoking cessation interventions involving community and hospital collaboration have the potential to increase the smoking cessation rate. However, the cost-effectiveness of this intervention model is unknown.MethodsWe conducted a smoking cessation intervention trial in 19 community health service centers in Beijing, China. A cost-effectiveness analysis was performed from a societal perspective to compare three strategies of smoking cessation: no intervention (NI), pharmacological intervention (PI), and comprehensive intervention (CI) (PI plus online health promotion). A Markov model, with a time horizon of 20 years, was used to simulate the natural progression of estimated 10,000 male smokers. A cross-sectional survey was conducted to obtain data on costs and quality-adjusted life years (QALYs) by using the five-level EuroQol-5-dimension (EQ-5D-5L) questionnaire. Probabilistic sensitivity analysis was performed to explore parameters of uncertainty in the model.ResultsA total of 680 participants were included in this study, including 283 in the PI group and 397 in the CI group. After 6 months of follow-up, the smoking cessation rate reached 30.0% in the CI group and 21.2% in the PI group. Using the Markov model, compared with the NI group, the intervention strategies of the PI group and the CI group were found to be cost-effective, with an incremental cost-effectiveness ratio (ICER) of $535.62/QALY and $366.19/QALY, respectively. The probabilistic sensitivity analysis indicated that the CI strategy was always the most cost-effective intervention.ConclusionCI for smoking cessation, based in hospital and community in China, is more cost-effective than PI alone. Therefore, this smoking cessation model should be considered to be implemented in healthcare settings.

Longitudinal Electronic Cigarette Use Among Patients Recently Diagnosed With Cancer Enrolled in a Smoking Cessation Trial.

Many cancer patients who smoke report concurrent e-cigarette use. Using a mixed-methods approach, we aimed to (1) describe longitudinal e-cigarette use over 6 months after a cancer diagnosis and (2) assess the association between e-cigarette use and smoking cessation, among cancer patients in a smoking cessation trial. Data were from a 2-site randomized controlled trial of Standard (brief counseling) versus Intensive treatment (sustained counseling plus smoking cessation medication) in individuals who smoke recently diagnosed with cancer. Participants (n = 303) reported e-cigarette use at baseline, 3 months, and 6 months. Biochemically-verified past 7-day cigarette abstinence was collected at 6 months. Qualitative interviews at 6 months explored factors related to e-cigarette use. E-cigarette use prevalence was highest between baseline and 3 months (16%) and declined over time. Participants using e-cigarettes at follow-up had higher baseline cigarette dependence and smoked more heavily. Multivariable analyses found no significant association between follow-up e-cigarette use and 6-month cigarette abstinence. E-cigarette use at follow-up was higher in the Standard versus Intensive treatment group (p = .003 and .001 at 3 and 6 mo, respectively). Smoking cessation and health concerns were primary reasons for using e-cigarettes. Among individuals who smoke recently diagnosed with cancer and enrolled in a smoking cessation intervention trial, e-cigarette use during trial participation was not associated with smoking abstinence. Individuals who chose to use e-cigarettes were less likely to be receiving intensive cessation support as part of the trial. Further studies are needed to evaluate the association between e-cigarette use and smoking cessation in cancer patients. E-cigarette use was not associated with cigarette abstinence at 6 months among adults who smoke recently diagnosed with cancer enrolled in a smoking cessation trial. Individuals with easier access to evidence-based smoking cessation treatment may be less likely to use e-cigarettes.

An application of PRECIS-2 to evaluate trial design in a pilot cluster randomised controlled trial of a community-based smoking cessation intervention for women living in disadvantaged areas of Ireland

Background“We Can Quit2” (WCQ2) was a pilot cluster randomised controlled trial with an embedded process evaluation assessing the feasibility and acceptability of ‘We Can Quit’ (WCQ, a peer-delivered community-based stop-smoking programme for women in disadvantaged communities. The control group comprised ‘enhanced usual care’ offered by the Irish Health Service Executive (HSE). The PRagmatic Explanatory Continuum Indicator Summary (PRECIS-2) is a tool to assess whether a trial design is more explanatory (working under ideal conditions) or pragmatic (working under ‘real-world’ conditions). The aim of this paper was to retrospectively evaluate the WCQ2 pilot trial using PRECIS-2 to inform the decision-making process on progression to a future definitive trial (DT).MethodsThe WCQ2 trial protocol and HSE standard stop-smoking service were described across the nine PRECIS-2 domains: eligibility, recruitment, setting, organisation, flexibility-delivery, flexibility-adherence, follow-up and primary outcome. Team members scored the domains as pragmatic or explanatory for each arm in a half-day workshop.ResultsSeven team members (practitioners and researchers) assessed the overall trial design as more explanatory than pragmatic. Important differences emerged between the two arms. WCQ targeted adult women from disadvantaged communities whereas HSE run a limited enhanced service for all quitters. Trial recruitment was challenging, intense efforts were needed as the trial proceeded. WCQ was delivered in a non-clinical community setting, HSE services in a clinical setting. WCQ organisation was co-designed with community partners and comprises peer-to-peer group support delivered by trained lay community facilitators, whereas HSE one-to-one support is delivered by Smoking Cessation Officers with a clinical background. Only WCQ allowed flexibility in delivery and adherence. Follow-up was more intensive in WCQ. Greater efforts to improve participant retention will be required in a future DT.ConclusionsPRECIS-2 allowed the reflection of practitioners and researchers on similarities and differences between intervention and control arms. Results will inform the decision on progression to an effectiveness DT, which will require more a pragmatic and less explanatory design. This novel use of PRECIS-2 to retrospectively evaluate a complex community-based pilot trial in advance of a full DT will also support learning for those undertaking hybrid trials of implementation and effectiveness.Trial registrationThis trial is registered with the ISRCTN registry (No. 74721694).

Internalized stigma among cancer patients enrolled in a smoking cessation trial: The role of cancer type and associations with psychological distress.

Cancer patients who smoke may experience significant stigma due both to their disease, and negative attitudes and beliefs regarding smoking. We investigated whether internalized stigma differed between currently smoking cancer patients diagnosed with lung or head and neck cancers, other smoking related cancers, and non smoking-related cancers, and whether internalized stigma was associated with psychological distress. This cross-sectional analysis used baseline data on 293 participants enrolled in a multi-site randomized smoking cessation intervention trial of patients with recently diagnosed cancer. Internalized stigma was assessed using five Internalized Shame items from the Social Impact of Disease Scale. Smoking-related cancers included lung, head and neck, esophageal, bladder, kidney, liver, pancreatic, colorectal, anal, small intestinal, gastric, and cervical. We used multivariable linear regression to examine whether mean internalized stigma levels differed between individuals with lung and head and neck cancers, other smoking-related cancers, and non smoking-related cancers, adjusting for potential confounders. We further examined the association of internalized stigma with depression, anxiety, and perceived stress, overall and among cancer type groups. Thirty-nine percent of participants were diagnosed with lung or head and neck cancer, 21% with another smoking-related cancer, and 40% with a non smoking-related cancer. In multivariable-adjusted models, participants with lung or head and neck cancers (11.6, 95% confidence intervals (CI)=10.8-12.2; p<0.0001) or other smoking-related cancers (10.7, 95% CI=9.8-11.7; p=0.03) had higher mean internalized stigma scores compared to those non-smoking-related cancers (9.3, 95% CI=8.6-10.0). We observed similar positive associations between internalized stigma and depressive symptoms, anxiety, and perceived stress among participants with smoking-related and non smoking-related cancers. Among smokers, those with smoking-related cancers experienced the highest levels of internalized stigma, and greater internalized stigma was associated with greater psychological distress across cancer types. Providers should assess patients for internalized and other forms of stigma, refer patients for appropriate psychosocial support services, and address stigma in smoking cessation programs.

Daily use of nicotine replacement medications is related to daily smoking status: An ecological momentary assessment study

BackgroundFew studies have examined how daily adherence to nicotine replacement therapy (NRT) impacts daily smoking abstinence. MethodsData from a pilot randomized controlled trial of a smartphone-based smoking cessation intervention were used. Separate, generalized linear mixed models examined the association between ecological momentary assessments of NRT use and same-day and next day smoking status. Separate models examined the relationship between daily smoking status and (1) any use of NRT, (2) quantity of nicotine gum used, and (3) nicotine patch wear time. Reasons for medication non-adherence were also examined. ResultsParticipants (n = 77) were predominantly White (66.2%) and female (50.6%), 50.4 years old (SD=11.6) on average, and they smoked an average of 21.8 cigarettes per day (SD=11.0) at baseline. Daily NRT use was significantly associated with a lower likelihood of smoking both within that same day and the following day. While using the gum and patch together, and using the patch alone were associated with reduced odds of same-day and next-day smoking, using the gum alone was not significantly associated with reduced odds of smoking. The most commonly cited reasons for not using the patch or gum was “other” (43.3%), followed by “side effects” (27.1%), and “forgot” (18.9%). ConclusionDaily use of the patch or both the patch and gum was associated with a lower risk of daily smoking. Low levels of nicotine gum use alone may not be an effective cessation strategy. Future studies should further explore reasons for NRT non-compliance, and ways to increase NRT adherence.

Six-Month Outcomes from the NEXit Junior Trial of a Text Messaging Smoking Cessation Intervention for High School Students: Randomized Controlled Trial With Bayesian Analysis.

BackgroundThe prevalence of daily or occasional smoking among high school students in Sweden was approximately 20% in 2019, which is problematic since lifestyle behaviors are established in adolescence and track into adulthood. The Nicotine Exit (NEXit) Junior trial was conducted in response to a lack of evidence for the effects of text message smoking cessation interventions among high school students in Sweden.ObjectiveThe aim of this study was to estimate the 3- and 6-month effects of a text messaging intervention among high school students in Sweden on smoking cessation outcomes.MethodsA 2-arm, single-blind randomized controlled trial was employed to estimate the effects of the intervention on smoking cessation in comparison to treatment as usual. Participants were recruited from high schools in Sweden using advertising and promotion by school staff from January 10, 2018, to January 10, 2019. Weekly or daily smokers who were willing to make a quit attempt were eligible for inclusion. Prolonged abstinence and point prevalence of smoking cessation were measured at 3 and 6 months after randomization.ResultsComplete case analysis was possible on 57.9% (310/535) of the participants at 6 months, with no observed statistically significant effect on 5-month prolonged abstinence (odds ratio [OR] 1.27, 95% CI 0.73-2.20; P=.39) or 4-week smoking cessation (OR 1.42; 95% CI 0.83-2.46; P=.20). Sensitivity analyses using imputation yielded similar findings. Unplanned Bayesian analyses showed that the effects of the intervention were in the anticipated direction. The findings were limited by the risk of bias induced by high attrition (42.1%). The trial recruited high school students in a pragmatic setting and included both weekly and daily smokers; thus, generalization to the target population is more direct compared with findings obtained under more strict study procedures.ConclusionsHigher than expected attrition rates to follow-up 6 months after randomization led to null hypothesis tests being underpowered; however, unplanned Bayesian analyses found that the effects of the intervention were in the anticipated direction. Future trials of smoking cessation interventions targeting high school students should aim to prepare strategies for increasing retention to mid- and long-term follow-up.Trial RegistrationIRCTN Registry ISRCTN15396225; https://www.isrctn.com/ISRCTN15396225International Registered Report Identifier (IRRID)RR2-10.1186/s13063-018-3028-2

Predictors of recruitment and retention in randomized controlled trials of behavioural smoking cessation interventions: a systematic review and meta-regression analysis.

To investigate predictors of participant eligibility, recruitment and retention in behavioural randomized controlled trials (RCTs) for smoking cessation. Systematic review and pre-specified meta-regression analysis of behavioural RCTs for smoking cessation including adult (≥18-year-old) smokers. The pre-specified predictors were identified through a literature review and experts' consultation and included participant, trial and intervention characteristics and recruitment and retention strategies. Outcome measures included eligibility rates (proportion of people eligible for the trials), recruitment rates, retention rates and differential retention rates. A total of 172 RCTs with 89 639 participants. Eligibility [median 57.6%; interquartile range (IQR)=34.7-83.7], recruitment (median 66.4%; IQR=42.7-85.2) and retention rates (median 80.5%; IQR=68.5-89.5) varied considerably across studies. For eligibility rates, the recruitment strategy appeared not to be associated with eligibility rates. For recruitment rates, use of indirect recruitment strategies (e.g. public announcements) [odds ratio (OR)=0.30, 95% confidence interval (CI)=0.11-0.82] and self-help interventions (OR=0.14, 95% CI=0.03-0.67) were associated with lower recruitment rates. For retention rates, higher retention was seen if the sample had ongoing physical health condition/s (OR=1.66, 95% CI=1.04-2.63), whereas lower retention was seen amongst primarily female samples (OR=0.83, 95% CI=0.71-0.98) and those motivated to quit smoking (OR=0.74, 95% CI=0.55-0.99) when indirect recruitment methods were used (OR=0.60, 95% CI=0.38-0.97) and at longer follow-up assessments (OR=0.83, 95% CI=0.79-0.87). For differential retention, higher retention in the intervention group occurred when the intervention but not comparator group received financial incentives for smoking cessation (OR=1.35, 95% CI=1.02-1.77). In randomized controlled trials of behavioural smoking cessation interventions, recruitment and retention rates appear to be higher for smoking cessation interventions that include a person-to-person rather than at-a-distance contact; male participants, smokers with chronic conditions, smokers not initially motivated to quit and shorter follow-up assessments seems to be associated with improved retention; financial incentive interventions improve retention in groups receiving them relative to comparison groups.

Effects of a Text Messaging Smoking Cessation Intervention Among Online Help Seekers and Primary Health Care Visitors in Sweden: Protocol for a Randomized Controlled Trial Using a Bayesian Group Sequential Design.

BackgroundA steady decline of the smoking prevalence in Sweden has been recorded over the past decade; however, people still start and continue to smoke. There is a need for effective smoking cessation interventions that can scale to a national level and that are designed to reach individuals requiring smoking cessation support in the general population.ObjectivePrevious randomized controlled trials of smoking cessation interventions among high school and university students in Sweden have found consistent evidence that text messaging interventions are effective in helping students quit smoking. However, there are no studies that investigate the effects of text messaging interventions in a more general population. The objective of this study is to estimate the effects of a text messaging intervention on individuals seeking help to quit online and individuals visiting primary health care units.MethodsA 2-arm, parallel-group (1:1), randomized controlled trial will be employed to address the study objectives. The trial will follow a Bayesian group sequential design. Recruitment will be conducted using online advertisement (Google, Bing, and Facebook) and through health care professionals at primary health care units. All participants will receive treatment as usual; however, participants who are allocated to the intervention arm will also be given access to a 12-week text message smoking cessation intervention. Primary outcomes are 8-week prolonged abstinence and 4-week point prevalence, measured 3 months and 6 months postrandomization. Mediator variables (self-efficacy, importance, and know-how) will be measured to estimate causal mediation models.ResultsRecruitment commenced in September 2020 and will not exceed 24 months. This means that a complete dataset will be available at the latest towards the end of 2022. We expect to publish the findings from this trial by June 2023.ConclusionsThis trial will further our understanding of the effects of text messaging interventions among a more general population than has previously been studied. We also aim to learn about differential effects between those who seek support online and those who are given facilitated support at primary health care units. Trial recruitment is limited to the Swedish population; however, a strength of this study is the pragmatic way in which participants are recruited. Through online advertisements, individuals are recruited in reaction to their own interest in seeking help to quit. At primary health care units, individuals who were not necessarily looking for smoking cessation support are given information about the trial. This closely mimics the way the intervention would be disseminated in a real-world setting and may therefore strengthen the argument of generalizability of findings.Trial RegistrationISRCTN 13455271; http://www.isrctn.com/ISRCTN13455271.International Registered Report Identifier (IRRID)PRR1-10.2196/23677

Comparing Methods of Recruiting Spanish-Preferring Smokers in the United States: Findings from a Randomized Controlled Trial.

BackgroundThere is a pressing need to address the unacceptable disparities and underrepresentation of racial and ethnic minority groups, including Hispanics or Latinxs, in smoking cessation trials.ObjectiveGiven the lack of research on recruitment strategies for this population, this study aims to assess effective recruitment methods based on enrollment and cost.MethodsRecruitment and enrollment data were collected from a nationwide randomized controlled trial (RCT) of a Spanish-language smoking cessation intervention (N=1417). The effectiveness of each recruitment strategy was evaluated by computing the cost per participant (CPP), which is the ratio of direct cost over the number enrolled. More effective strategies yielded lower CPPs. Demographic and smoking-related characteristics of participants recruited via the two most effective strategies were also compared (n=1307).ResultsFacebook was the most effective method (CPP=US $74.12), followed by TV advertisements (CPP=US $191.31), whereas public bus interior card advertising was the least effective method (CPP=US $642.50). Participants recruited via Facebook had lower average age (P=.008) and had spent fewer years in the United States (P<.001). Among the participants recruited via Facebook, a greater percentage of individuals had at least a high school education (P<.001) and an annual income above US $10,000 (P<.001). In addition, a greater percentage of individuals were employed (P<.001) and foreign born (P=.003). In terms of subethnicity, among the subjects recruited via Facebook, a lower percentage of individuals were of Mexican origin (P<.001) and a greater percentage of individuals were of Central American (P=.02), South American (P=.01), and Cuban (P<.001) origin.ConclusionsFacebook was the most effective method for recruiting Hispanic or Latinx smokers in the United States for this RCT. However, using multiple methods was necessary to recruit a more diverse sample of Spanish-preferring Hispanic or Latinx smokers.

Underreporting of the active content of behavioural interventions: a systematic review and meta-analysis of randomised trials of smoking cessation interventions

ABSTRACT Despite its importance, underreporting of the active content of experimental and comparator interventions in published literature has not been previously examined for behavioural trials. We assessed completeness and variability in reporting in 142 randomised controlled trials of behavioural interventions for smoking cessation published between 1/1996 and 11/2015. Two coders reliably identified the potential active components of experimental and comparator interventions (activities targeting behaviours key to smoking cessation and qualifying as behaviour change techniques, BCTs) in published, and in unpublished materials obtained from study authors directly. Unpublished materials were obtained for 129/204 (63%) experimental and 93/142 (65%) comparator groups. For those, only 35% (1200/3403) of experimental and 26% (491/1891) of comparator BCTs could be identified in published materials. Reporting quality (#published BCTs/#total BCTs) varied considerably between trials and between groups within trials. Experimental (vs. comparator) interventions were better reported (B(SE) = 0.34 (0.11), p < .001). Unpublished materials were more often obtained for recent studies (B(SE) = 0.093 (0.03), p = .003) published in behavioural (vs. medical) journals (B(SE) = 1.03 (0.41), p = .012). This high variability in underreporting of active content compromises reader's ability to interpret the effects of individual trials, compare and explain intervention effects in evidence syntheses, and estimate the additional benefit of an experimental intervention in other settings.

Smoking cessation, metabolic risk behaviors, and stress management over time in a sample of young adult smokers.

Smoking cessation may support changes in metabolic risk behaviors (e.g., high-fat diet, physical inactivity, poor sleep, low fruit and vegetable consumption [FVC]). We examined the association between smoking cessation and metabolic risk behavior profiles, mediated by readiness to change risk behaviors and moderated by stress management. Participants were young adult smokers in a randomized controlled trial of a Facebook smoking cessation intervention. Measures included stage of change for five metabolic risk behaviors: FVC, diet, physical activity, sleep hygiene, and stress management. Moderated mediation was used to examine relationships between smoking cessation at T1 (predictor), readiness to change metabolic risk behaviors at T2 (mediators), stress management at T3 (moderator), and metabolic risk behavior profile at T3 (outcome) over 9 months. T1 smoking abstinence was associated with greater readiness to increase FVC at T2, which predicted lower likelihood of T3 metabolic risk (β = -0.22, 95% confidence interval [CI] [-0.53, -0.03]). This indirect effect was moderated by stress management such that greater readiness to increase FVC at T2 was associated with lower T3 metabolic risk for participants with unmanaged stress (β = -0.90, 95% CI [-1.32, -0.49], p < .001), but not for participants with well-managed stress (β = -.22, 95% CI [-0.48, 0.04], p = .096). Young adults who quit smoking subsequently had lower metabolic risk behaviors. Among participants with unmanaged stress, those who quit smoking had greater readiness to increase FVC and lower likelihood of subsequent metabolic risk. Smoking cessation interventions could aim to teach broadly applicable behavior change skills and build confidence for decreasing metabolic risk.

Smoking cessation interventions for potential use in the lung cancer screening setting: A systematic review and meta-analysis

ObjectivesCurrent guidelines recommend delivery of smoking cessation interventions with lung cancer screening (LCS). Unfortunately, there are limited data to guide clinicians and policy-makers in choosing cessation interventions in this setting. Several trials are underway to fill this evidence gap, but results are not expected for several years. Methods and materialsWe conducted a systematic review and meta-analysis of current literature on the efficacy of smoking cessation interventions among populations eligible for LCS. We searched PubMed, Medline, and PsycINFO for randomized controlled trials of smoking cessation interventions published from 2010–2017. Trials were eligible for inclusion if they sampled individuals likely to be eligible for LCS based on age and smoking history, had sample sizes >100, follow-up of 6- or 12-months, and were based in North America, Western Europe, Australia, or New Zealand. ResultsThree investigators independently screened 3,813 abstracts and identified 332 for full-text review. Of these, 85 trials were included and grouped into categories based on the primary intervention: electronic/web-based, in-person counseling, pharmacotherapy, and telephone counseling. At 6-month follow-up, electronic/web-based (odds ratio [OR] 1.14, 95% CI 1.03–1.25), in-person counseling (OR 1.46, 95% CI 1.25–1.70), and pharmacotherapy (OR 1.53, 95% CI 1.33–1.77) interventions significantly increased the odds of abstinence. Telephone counseling increased the odds but did not reach statistical significance (OR 1.21, 95% CI 0.98–1.50). At 12-months, in-person counseling (OR 1.28 95% CI 1.10–1.50) and pharmacotherapy (OR 1.46, 95% CI 1.17–1.84) remained efficacious, although the decrement in efficacy was of similar magnitude across all intervention categories. ConclusionsSeveral categories of cessation interventions are promising for implementation in the LCS setting.