Comparative Effectiveness of an Internet-Based Smoking Cessation Intervention Versus Clinic-Based Specialty Care for Veterans

Quality of Care in Veterans Affairs Health Care System In-Person and National TeleOncology Service-Delivered Care

PURPOSE

The Veterans Affairs Health Administration (VA) has experience using telehealth (TH) to deliver care to 10 million enrolled Veterans for many clinical care needs. The VA National TeleOncology Service (NTO) was established in 2020 to provide specialized cancer services regardless of geography. We sought to compare quality in TH-delivered cancer services with traditional (TR) in-person VA care.

METHODS

Using electronic health record data, we identified patients with an International Classification of Diseases-10 diagnostic code for an incident malignancy from December 2016 to March 2021 at early adopting sites providing both TR and TH care. We classified patients as TH users if they received TH services at least once for their cancer care. We gathered demographic, clinical, and treatment characteristics to calculate 25 Quality Oncology Practice Initiative (QOPI) measures in the symptoms and toxicity management (two), end of life and palliative care (10), and core measure domains (13). We report disease-specific measures, QOPI measures descriptively, and performed chi-square tests to compare TH and TR.

RESULTS

We identified 972 patients with lymphoma, prostate, lung, or colorectal cancer. In all, 427 (44%) were TH users. Patients were predominately White (n = 819, 84.3%) men (n = 930, 95.7%). Across 25 QOPI measures, TH users received better (n = 12), worse (n = 10), the same (n = 2), and unevaluable (n = 1) descriptive performance. Appropriate tobacco cessation support within the previous year was higher in TH (85.3% v 76.2%, P = .002). TH and TR rates were similar for the other QOPI measures.

CONCLUSION

VA is a leader in TH cancer care because of both its volume and quality. VA-provided TH cancer care quality is similar to or better than that of TR in-person care. NTO specifically, and VA teleoncology broadly, provides another option to Veterans for cancer care.

Efficacy of e-health interventions for smoking cessation management in smokers: a systematic review and meta-analysis

Background

Smoking is one of the major risk factors for shortened lifespan and disability, while smoking cessation is currently the only guaranteed method to reduce the harm caused by smoking. E-health is a field that utilizes information and communication technology to support the health status of its users. The emergence of this digital health approach has provided a new way of smoking cessation support for smokers seeking help, and an increasing number of researchers are attempting to use e-health for a wide range of effective smoking cessation interventions. We conducted a systematic review and meta-analysis of studies that used e-health as a smoking cessation support tool.

Methods

This systematic review and meta-analysis searched the PubMed, Embase, and Cochrane Library databases until December 2022. The included studies were randomized controlled trials (RCTs) comparing the use of e-health interventions and traditional offline smoking cessation care interventions. The primary outcome of the studies was the point smoking cessation rate (7-day and 30-day), and the secondary outcome was sustained smoking cessation rates. Studies were excluded if there was no clear e-health intervention described or if standard-compliant cessation outcomes were not clearly reported. Fixed-effects meta-analysis and meta-regression analyses were performed on the included study data to evaluate the effectiveness of the interventions. The meta-analysis outcome was the risk ratio (RR) and a 95% confidence interval. The study was registered with PROSPERO, CRD42023388667.

Findings

We collectively screened 2408 articles, and ultimately included 39 articles with a total of 17,351 eligible participants, of which 44 studies were included in the meta-analysis. The meta-analysis revealed that compared to traditional smoking cessation interventions, e-health interventions can increase point quit rates (RR 1.86, 95% CI 1.69-2.04) as well as sustained quit rates in the long-term (RR 1.79, 95% CI 1.60-2.00) among smokers. Subgroup analysis showed that text and telephone interventions in e-health significantly improved short-term quit rates for up to 7 days (RR 2.10, 95% CI 1.77-2.48). Website and app interventions also had a positive impact on improving short-term quit rates for up to 7 days (RR 1.74, 95% CI 1.56-1.94). The heterogeneity of the study results was low, demonstrating the significant smoking cessation advantages of e-health interventions.

Interpretation

We have found that personalized e-health interventions can effectively help smokers quit smoking. The diverse remote intervention methods of e-health can provide more convenient options for further customization. Additionally, further follow-up research is needed to evaluate the sustained effectiveness of interventions on smokers' continuous abstinence over a longer period (greater than one year). In the future, e-health can further optimize smoking cessation strategies.

Funding

No funding.

Digital interventions for smoking abstinence: a systematic review and meta-analysis of randomized control trials

OBJECTIVES

Technological advancements have improved patients' health and clinical care through digital interventions. This study investigated the effects of digital interventions on smoking abstinence.

METHODS

PubMed, the Cochrane Library, and Scopus were systematically searched from inception until December 2021. Meta-analysis was carried out using a random-effects model. The degree of heterogeneity, quality, and publication bias of the selected studies was further evaluated.

RESULTS

A total of 43 randomized control trial studies were eligible for this study. 38,814 participants from 18 countries were included in the analysis. Digital interventions on seven-day point prevalence abstinence (1 month) showed increased smoking abstinence. The odds ratio was 2.02 and confidence interval (CI) was 1.67-2.43; p < 0.001; I2 = 55.1%) . The result for a 30-day point prevalence abstinence (1 month) was 1.63 (CI 1.09-2.46; p = 0.018; I2 = 0%). Digital intervention also had a significant effect on continuous abstinence (odds ratio = 1.68; CI 1.29-2.18; p < 0.001; I2 = 70.1%) and prolonged abstinence (odds ratio = 1.60; CI 1.19-2.15; p = 0.002; I2 = 53.6%). There was evidence of heterogeneity and publication bias.

CONCLUSIONS

Digital interventions led to increased smoking abstinence and can be a valuable tool in smoking cessation. Further research is required to evaluate the long-term impact of digital interventions on outcomes related to smoking cessation.

Computerized Psychological Interventions in Veterans and Service Members: Systematic Review of Randomized Controlled Trials

BACKGROUND

Computerized psychological interventions can overcome logistical and psychosocial barriers to the use of mental health care in the Veterans Affairs and Department of Defense settings.

OBJECTIVE

In this systematic review, we aim to outline the existing literature, with the goal of describing: the scope and quality of the available literature, intervention characteristics, study methods, study efficacy, and study limitations and potential directions for future research.

METHODS

Systematic searches of two databases (PsycINFO and PubMed) using PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines were conducted from inception until November 15, 2020. The following inclusion criteria were used: the study was published in an English language peer-reviewed journal, participants were randomly allocated to a computerized psychological intervention or a control group (non-computerized psychological intervention active treatment or nonactive control group), an intervention in at least one treatment arm was primarily delivered through the computer or internet with or without additional support, participants were veterans or service members, and the study used validated measures to examine the effect of treatment on psychological outcomes.

RESULTS

This review included 23 studies that met the predefined inclusion criteria. Most studies were at a high risk of bias. Targeted outcomes, participant characteristics, type of support delivered, adherence, and participant satisfaction were described. Most of the examined interventions (19/24, 79%) yielded positive results. Study limitations included participant characteristics limiting study inference, high rates of attrition, and an overreliance on self-reported outcomes.

CONCLUSIONS

Relatively few high-quality studies were identified, and more rigorous investigations are needed. Several recommendations for future research are discussed, including the adoption of methods that minimize attrition, optimize use, and allow for personalization of treatment.

Evaluating the effectiveness of bupropion and varenicline for smoking cessation using an internet-based delivery system: A pragmatic randomized controlled trial (MATCH study)

BACKGROUND

Traditional randomized controlled trials have demonstrated the efficacy of pharmacotherapy for smoking cessation. However, accessibility to treatments remains a barrier, necessitating the remote delivery of evidence-based cessation interventions. The aim of this study was to evaluate the effectiveness of an online treatment that included first-line prescription medications using a pragmatic randomized controlled trial design.

METHODS

This study was a two-group, parallel block randomized, open label, controlled trial, and conducted exclusively online. Participants were randomised (1:1) to either bupropion (150 mg) or varenicline (1 mg) for twelve weeks. Medication was couriered to participants. The primary outcome was 7-day point prevalence abstinence (PPA; defined as 0 cigarette puffs in the last 7 days) at 12 weeks. Secondary outcomes were 7-day PPA at 4-, 8-, 26-, and 52-weeks follow-up. Adverse events were evaluated at each follow-up session during treatment.

RESULTS

The varenicline group (n = 499) had significantly higher 7-day PPA (30.3%) compared to the bupropion group (n = 465; 19.6%) at end of treatment (OR=2.08, 95% CI: 1.49-2.90, p < 0.001). Seven-day PPA was also higher for the varenicline group at 4-weeks (OR=1.71, 95% CI: 1.23-2.40 p = 0.0001), and 8-weeks follow-up (OR=1.95, 95% CI: 1.43-2.67 p < 0.0001), but not at post-treatment follow-up. More adverse events were reported in the varenicline group, compared to bupropion.

CONCLUSIONS

This internet-based pharmacotherapy intervention was a feasible and effective method of treatment delivery for smoking cessation. This method can be used to increase the accessibility and availability of cessation interventions, decreasing the burden of smoking-related diseases.

TRIAL REGISTRATION

This trial was registered with clinical trials.gov under NCT02146911. Registered 26 May 2014, https://clinicaltrials.gov/ct2/show/NCT02146911.

Digital interventions in mental health: evidence syntheses and economic modelling

BACKGROUND

Economic evaluations provide evidence on whether or not digital interventions offer value for money, based on their costs and outcomes relative to the costs and outcomes of alternatives.

OBJECTIVES

(1) Evaluate and summarise published economic studies about digital interventions across different technologies, therapies, comparators and mental health conditions; (2) synthesise clinical evidence about digital interventions for an exemplar mental health condition; (3) construct an economic model for the same exemplar mental health condition using the previously synthesised clinical evidence; and (4) consult with stakeholders about how they understand and assess the value of digital interventions.

METHODS

We completed four work packages: (1) a systematic review and quality assessment of economic studies about digital interventions; (2) a systematic review and network meta-analysis of randomised controlled trials on digital interventions for generalised anxiety disorder; (3) an economic model and value-of-information analysis on digital interventions for generalised anxiety disorder; and (4) a series of knowledge exchange face-to-face and digital seminars with stakeholders.

RESULTS

In work package 1, we reviewed 76 economic evaluations: 11 economic models and 65 within-trial analyses. Although the results of the studies are not directly comparable because they used different methods, the overall picture suggests that digital interventions are likely to be cost-effective, compared with no intervention and non-therapeutic controls, whereas the value of digital interventions compared with face-to-face therapy or printed manuals is unclear. In work package 2, we carried out two network meta-analyses of 20 randomised controlled trials of digital interventions for generalised anxiety disorder with a total of 2350 participants. The results were used to inform our economic model, but when considered on their own they were inconclusive because of the very wide confidence intervals. In work package 3, our decision-analytic model found that digital interventions for generalised anxiety disorder were associated with lower net monetary benefit than medication and face-to-face therapy, but greater net monetary benefit than non-therapeutic controls and no intervention. Value for money was driven by clinical outcomes rather than by intervention costs, and a value-of-information analysis suggested that uncertainty in the treatment effect had the greatest value (£12.9B). In work package 4, stakeholders identified several areas of benefits and costs of digital interventions that are important to them, including safety, sustainability and reducing waiting times. Four factors may influence their decisions to use digital interventions, other than costs and outcomes: increasing patient choice, reaching underserved populations, enabling continuous care and accepting the 'inevitability of going digital'.

LIMITATIONS

There was substantial uncertainty around effect estimates of digital interventions compared with alternatives. This uncertainty was driven by the small number of studies informing most comparisons, the small samples in some of these studies and the studies' high risk of bias.

CONCLUSIONS

Digital interventions may offer good value for money as an alternative to 'doing nothing' or 'doing something non-therapeutic' (e.g. monitoring or having a general discussion), but their added value compared with medication, face-to-face therapy and printed manuals is uncertain. Clinical outcomes rather than intervention costs drive 'value for money'.

FUTURE WORK

There is a need to develop digital interventions that are more effective, rather than just cheaper, than their alternatives.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42018105837.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 1. See the NIHR Journals Library website for further project information.

The effectiveness of technology-based interventions for smoking cessation: An umbrella review and quality assessment of systematic reviews

ISSUES

With the advancement and rapid increase in the public's interest in utilisation of Internet and mobile phones, technology-based interventions are being implemented across a range of health conditions to improve patient outcomes. The aim of this review was to summarise findings from systematic reviews that evaluated the effectiveness of technology-based smoking cessation interventions and to critically appraise their methodological qualities.

APPROACH

An umbrella review was conducted using studies identified from a comprehensive literature search of six databases and grey literature. All included systematic reviews were checked for eligibility criteria and quality using the Assessment of Multiple Systematic Reviews tool. The level of evidence for each intervention category was assessed, citation matrices were generated and corrected covered area was calculated.

KEY FINDINGS

Five systematic reviews with a total of 212 randomised controlled trials and 237 760 participants were included. Fourteen intervention approaches were identified and classified into three categories: stand-alone web-based; stand-alone mobile phone-based and multicomponent interventions. Incorporating web and/or mobile-based interventions with face-to-face approach improved the rate of smoking cessation. However, there was no consistent evidence regarding the effectiveness of stand-alone Internet or mobile-based interventions.

IMPLICATIONS

Policymakers are recommended to develop strategies that enable health professionals to integrate these approaches with face-to-face smoking cessation support. Health professionals are recommended to be trained and equipped for online and mobile-based interventions.

CONCLUSION

Adding technology-based intervention to face-to-face smoking cessation support improves smoking cessation. Further research is needed to evaluate stand-alone web-based and mobile phone-based interventions.

Effectiveness of the Internet-Based Versus Face-to-Face Interaction on Reduction of Tobacco Use Among Adults: A Meta-Analysis

Literature reported the effectiveness of internet-based interventions over face-to-face interaction on tobacco quitting; however, limited sample size reinforces to integrate and analyze these studies' findings to reach a single conclusion. Therefore, we evaluated the effectiveness of the internet as an intervention approach versus face-to-face interaction on reducing tobacco use among adults. A systematic search was performed through various electronic databases such as Medline, PsychInfo, PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), ResearchGate, Google Scholar, and Academia. Reference lists of the eligible articles were also screened. Full-text articles were included as per eligibility criteria (PICO framework). No ethnicity restriction was applied. A total of 13 studies were selected for meta-analysis, with 3852 and 3908 participants in intervention and control groups, respectively. Forest plot favours the intervention group at one month follow up for tobacco quitting (OR: 2.37, CI: 1.86-3.02, P=0.00001, I2=0%), at three months (OR: 1.88, CI: 1.48-2.40, P=0.00001, I2=42%) at six months (OR: 2.02, CI: 1.64-2.50, P=0.00001, I2=38%) and at one year of follow-up (OR: 1.43, CI: 1.18-1.74, P=0.00001, I2=36%) comparing to control group. Conclusively, internet and web-based interventions are highly useful in tobacco quitting at one month, three months, six months, and one year of follow-up compared to face-to-face interaction or no intervention, although the level of evidence was moderate. Additionally, limited trials in developing countries, arising need for research on internet use for tobacco control in developing countries.

A systematic review of trial-based economic evaluations of internet- and mobile-based interventions for substance use disorders

BACKGROUND

Substance use disorders (SUDs) contribute significantly to global rates of morbidity and mortality. Internet- and mobile-based interventions (IMIs) have been suggested as an adjunct to face-to-face health services. However, the evidence for the cost-effectiveness of IMIs for SUDs is scant.

METHODS

A comprehensive literature search in PubMed, PsycINFO, the Cochrane Central Register of Controlled Trials, NHS Economic Evaluations Database, NHS Health Technology Assessment Database, Office of Health Economics Evaluations Database and EconLit was conducted. We included economic evaluations alongside randomized controlled trials of IMIs for SUDs compared with a control group.

RESULTS

Of 1687 abstracts identified, 11 studies met the inclusion criteria. Targeted conditions were alcohol use disorder (four studies) and tobacco smoking (five studies) whereas two studies included any SUD. Cost-effectiveness results demonstrated that IMIs had a firm probability of being more cost-effective than TAU (e.g. less costs per additional abstinent person). Compared with (online) psycho-education, evidence towards an additional benefit of IMIs was less clear. Regarding cost-utility (e.g. costs per quality-adjusted life year gained), except for one study, results suggested that TAU and online psycho-education would probably be more preferable than IMIs. Quality of study reporting was at least adequate.

CONCLUSIONS

The likelihood of IMIs being more cost-effective than TAU looks promising but more economic evaluations are needed in order to determine the economic merit of IMIs. With an increasing pressure on health care budgets, strategies to disseminate effective interventions at affordable costs are required. This review suggests that IMIs might carry that promise and have potential as a cost-effective strategy to scale-up existing evidence-based treatments for SUDs.

SYSTEMATIC REVIEW REGISTRATION

The systematic review has been registered in the PROSPERO database (no. CRD42018099486).

Cost evaluation of tobacco control interventions in clinical settings: A systematic review

Elucidating the cost implications of tobacco control interventions is a prerequisite to their adoption in clinical settings. This review fills a knowledge gap in characterizing the extent to which cost is measured in tobacco control studies. A search of English literature was conducted in the following electronic databases: MEDLINE, EconLit, PsychINFO, and CINAHL using MeSH terms from 2009 to 2018. Studies were reviewed by two independent reviewers and included if they were conducted in U.S. inpatient or outpatient facilities and reported costs associated with a tobacco control intervention. They were categorized according to evaluation type, clinical setting, target population, cost measures, and stakeholder perspective. Bias risk was evaluated for RCTs. Seventeen publications were included, representing counseling interventions (n = 8) and combination (i.e., counseling and pharmacotherapy) interventions (n = 9). Studies were categorized by evaluation type: cost-effectiveness analysis (n = 10), cost utility analysis (n = 3) and cost identification (n = 4). The selected studies targeted the following populations: general adults (n = 6), hospitalized/inpatient (n = 4), military/veterans (n = 4), individuals with low socioeconomic status (n = 4), mental health or medical comorbidities (n = 2), and pregnant women (n = 2). Intervention costs included personnel, medication, education material, technology, and overhead costs. Stakeholder perspectives included: healthcare organization (n = 10), payer (n = 8), patient (n = 2), and societal (n = 1). Few studies have reported the cost of tobacco control interventions in clinical settings. Cost is a critical outcome that should be consistently measured in evaluations of tobacco control interventions to promote their uptake in clinical settings.

Behavioural interventions for smoking cessation: an overview and network meta-analysis

BACKGROUND

Smoking is a leading cause of disease and death worldwide. In people who smoke, quitting smoking can reverse much of the damage. Many people use behavioural interventions to help them quit smoking; these interventions can vary substantially in their content and effectiveness.

OBJECTIVES

To summarise the evidence from Cochrane Reviews that assessed the effect of behavioural interventions designed to support smoking cessation attempts and to conduct a network meta-analysis to determine how modes of delivery; person delivering the intervention; and the nature, focus, and intensity of behavioural interventions for smoking cessation influence the likelihood of achieving abstinence six months after attempting to stop smoking; and whether the effects of behavioural interventions depend upon other characteristics, including population, setting, and the provision of pharmacotherapy. To summarise the availability and principal findings of economic evaluations of behavioural interventions for smoking cessation, in terms of comparative costs and cost-effectiveness, in the form of a brief economic commentary.

METHODS

This work comprises two main elements. 1. We conducted a Cochrane Overview of reviews following standard Cochrane methods. We identified Cochrane Reviews of behavioural interventions (including all non-pharmacological interventions, e.g. counselling, exercise, hypnotherapy, self-help materials) for smoking cessation by searching the Cochrane Library in July 2020. We evaluated the methodological quality of reviews using AMSTAR 2 and synthesised data from the reviews narratively. 2. We used the included reviews to identify randomised controlled trials of behavioural interventions for smoking cessation compared with other behavioural interventions or no intervention for smoking cessation. To be included, studies had to include adult smokers and measure smoking abstinence at six months or longer. Screening, data extraction, and risk of bias assessment followed standard Cochrane methods. We synthesised data using Bayesian component network meta-analysis (CNMA), examining the effects of 38 different components compared to minimal intervention. Components included behavioural and motivational elements, intervention providers, delivery modes, nature, focus, and intensity of the behavioural intervention. We used component network meta-regression (CNMR) to evaluate the influence of population characteristics, provision of pharmacotherapy, and intervention intensity on the component effects. We evaluated certainty of the evidence using GRADE domains. We assumed an additive effect for individual components.

MAIN RESULTS

We included 33 Cochrane Reviews, from which 312 randomised controlled trials, representing 250,563 participants and 845 distinct study arms, met the criteria for inclusion in our component network meta-analysis. This represented 437 different combinations of components. Of the 33 reviews, confidence in review findings was high in four reviews and moderate in nine reviews, as measured by the AMSTAR 2 critical appraisal tool. The remaining 20 reviews were low or critically low due to one or more critical weaknesses, most commonly inadequate investigation or discussion (or both) of the impact of publication bias. Of note, the critical weaknesses identified did not affect the searching, screening, or data extraction elements of the review process, which have direct bearing on our CNMA. Of the included studies, 125/312 were at low risk of bias overall, 50 were at high risk of bias, and the remainder were at unclear risk. Analyses from the contributing reviews and from our CNMA showed behavioural interventions for smoking cessation can increase quit rates, but effectiveness varies on characteristics of the support provided. There was high-certainty evidence of benefit for the provision of counselling (odds ratio (OR) 1.44, 95% credibility interval (CrI) 1.22 to 1.70, 194 studies, n = 72,273) and guaranteed financial incentives (OR 1.46, 95% CrI 1.15 to 1.85, 19 studies, n = 8877). Evidence of benefit remained when removing studies at high risk of bias. These findings were consistent with pair-wise meta-analyses from contributing reviews. There was moderate-certainty evidence of benefit for interventions delivered via text message (downgraded due to unexplained statistical heterogeneity in pair-wise comparison), and for the following components where point estimates suggested benefit but CrIs incorporated no clinically significant difference: individual tailoring; intervention content including motivational components; intervention content focused on how to quit. The remaining intervention components had low-to very low-certainty evidence, with the main issues being imprecision and risk of bias. There was no evidence to suggest an increase in harms in groups receiving behavioural support for smoking cessation. Intervention effects were not changed by adjusting for population characteristics, but data were limited. Increasing intensity of behavioural support, as measured through the number of contacts, duration of each contact, and programme length, had point estimates associated with modestly increased chances of quitting, but CrIs included no difference. The effect of behavioural support for smoking cessation appeared slightly less pronounced when people were already receiving smoking cessation pharmacotherapies.

AUTHORS' CONCLUSIONS

Behavioural support for smoking cessation can increase quit rates at six months or longer, with no evidence that support increases harms. This is the case whether or not smoking cessation pharmacotherapy is also provided, but the effect is slightly more pronounced in the absence of pharmacotherapy. Evidence of benefit is strongest for the provision of any form of counselling, and guaranteed financial incentives. Evidence suggested possible benefit but the need of further studies to evaluate: individual tailoring; delivery via text message, email, and audio recording; delivery by lay health advisor; and intervention content with motivational components and a focus on how to quit. We identified 23 economic evaluations; evidence did not consistently suggest one type of behavioural intervention for smoking cessation was more cost-effective than another. Future reviews should fully consider publication bias. Tools to investigate publication bias and to evaluate certainty in CNMA are needed.

Real-time video counselling for smoking cessation

BACKGROUND

Real-time video communication software such as Skype and FaceTime transmits live video and audio over the Internet, allowing counsellors to provide support to help people quit smoking. There are more than four billion Internet users worldwide, and Internet users can download free video communication software, rendering a video counselling approach both feasible and scalable for helping people to quit smoking.

OBJECTIVES

To assess the effectiveness of real-time video counselling delivered individually or to a group in increasing smoking cessation, quit attempts, intervention adherence, satisfaction and therapeutic alliance, and to provide an economic evaluation regarding real-time video counselling.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group Specialised Register, CENTRAL, MEDLINE, PubMed, PsycINFO and Embase to identify eligible studies on 13 August 2019. We searched the World Health Organization International Clinical Trials Registry Platform and ClinicalTrials.gov to identify ongoing trials registered by 13 August 2019. We checked the reference lists of included articles and contacted smoking cessation researchers for any additional studies.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), randomised trials, cluster RCTs or cluster randomised trials of real-time video counselling for current tobacco smokers from any setting that measured smoking cessation at least six months following baseline. The real-time video counselling intervention could be compared with a no intervention control group or another smoking cessation intervention, or both.

DATA COLLECTION AND ANALYSIS

Two authors independently extracted data from included trials, assessed the risk of bias and rated the certainty of the evidence using the GRADE approach. We performed a random-effects meta-analysis for the primary outcome of smoking cessation, using the most stringent measure of smoking cessation measured at the longest follow-up. Analysis was based on the intention-to-treat principle. We considered participants with missing data at follow-up for the primary outcome of smoking cessation to be smokers.

MAIN RESULTS

We included two randomised trials with 615 participants. Both studies delivered real-time video counselling for smoking cessation individually, compared with telephone counselling. We judged one study at unclear risk of bias and one study at high risk of bias. There was no statistically significant treatment effect for smoking cessation (using the strictest definition and longest follow-up) across the two included studies when real-time video counselling was compared to telephone counselling (risk ratio (RR) 2.15, 95% confidence interval (CI) 0.38 to 12.04; 2 studies, 608 participants; I2 = 66%). We judged the overall certainty of the evidence for smoking cessation as very low due to methodological limitations, imprecision in the effect estimate reflected by the wide 95% CIs and inconsistency of cessation rates. There were no significant differences between real-time video counselling and telephone counselling reported for number of quit attempts among people who continued to smoke (mean difference (MD) 0.50, 95% CI -0.60 to 1.60; 1 study, 499 participants), mean number of counselling sessions completed (MD -0.20, 95% CI -0.45 to 0.05; 1 study, 566 participants), completion of all sessions (RR 1.13, 95% CI 0.71 to 1.79; 1 study, 43 participants) or therapeutic alliance (MD 1.13, 95% CI -0.24 to 2.50; 1 study, 398 participants). Participants in the video counselling arm were more likely than their telephone counselling counterparts to recommend the programme to a friend or family member (RR 1.06, 95% CI 1.01 to 1.11; 1 study, 398 participants); however, there were no between-group differences on satisfaction score (MD 0.70, 95% CI -1.16 to 2.56; 1 study, 29 participants).

AUTHORS' CONCLUSIONS

There is very little evidence about the effectiveness of real-time video counselling for smoking cessation. The existing research does not suggest a difference between video counselling and telephone counselling for assisting people to quit smoking. However, given the very low GRADE rating due to methodological limitations in the design, imprecision of the effect estimate and inconsistency of cessation rates, the smoking cessation results should be interpreted cautiously. High-quality randomised trials comparing real-time video counselling to telephone counselling are needed to increase the confidence of the effect estimate. Furthermore, there is currently no evidence comparing real-time video counselling to a control group. Such research is needed to determine whether video counselling increases smoking cessation.

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

OBJECTIVES

Current 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 MATERIALS

We 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.

RESULTS

Three 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.

CONCLUSIONS

Several categories of cessation interventions are promising for implementation in the LCS setting.

Which eHealth interventions are most effective for smoking cessation? A systematic review

PURPOSE

To synthesize evidence of the effects and potential effect modifiers of different electronic health (eHealth) interventions to help people quit smoking.

METHODS

Four databases (MEDLINE, PsycINFO, Embase, and The Cochrane Library) were searched in March 2017 using terms that included "smoking cessation", "eHealth/mHealth" and "electronic technology" to find relevant studies. Meta-analysis and meta-regression analyses were performed using Mantel-Haenszel test for fixed-effect risk ratio (RR) and restricted maximum-likelihood technique, respectively. Protocol Registration Number: CRD42017072560.

RESULTS

The review included 108 studies and 110,372 participants. Compared to nonactive control groups (eg, usual care), smoking cessation interventions using web-based and mobile health (mHealth) platform resulted in significantly greater smoking abstinence, RR 2.03 (95% CI 1.7-2.03), and RR 1.71 (95% CI 1.35-2.16), respectively. Similarly, smoking cessation trials using tailored text messages (RR 1.80, 95% CI 1.54-2.10) and web-based information and conjunctive nicotine replacement therapy (RR 1.29, 95% CI 1.17-1.43) may also increase cessation. In contrast, little or no benefit for smoking abstinence was found for computer-assisted interventions (RR 1.31, 95% CI 1.11-1.53). The magnitude of effect sizes from mHealth smoking cessation interventions was likely to be greater if the trial was conducted in the USA or Europe and when the intervention included individually tailored text messages. In contrast, high frequency of texts (daily) was less effective than weekly texts.

CONCLUSIONS

There was consistent evidence that web-based and mHealth smoking cessation interventions may increase abstinence moderately. Methodologic quality of trials and the intervention characteristics (tailored vs untailored) are critical effect modifiers among eHealth smoking cessation interventions, especially for web-based and text messaging trials. Future smoking cessation intervention should take advantages of web-based and mHealth engagement to improve prolonged abstinence.

Internet-based interventions for smoking cessation

BACKGROUND

Tobacco use is estimated to kill 7 million people a year. Nicotine is highly addictive, but surveys indicate that almost 70% of US and UK smokers would like to stop smoking. Although many smokers attempt to give up on their own, advice from a health professional increases the chances of quitting. As of 2016 there were 3.5 billion Internet users worldwide, making the Internet a potential platform to help people quit smoking.

OBJECTIVES

To determine the effectiveness of Internet-based interventions for smoking cessation, whether intervention effectiveness is altered by tailoring or interactive features, and if there is a difference in effectiveness between adolescents, young adults, and adults.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group Specialised Register, which included searches of MEDLINE, Embase and PsycINFO (through OVID). There were no restrictions placed on language, publication status or publication date. The most recent search was conducted in August 2016.

SELECTION CRITERIA

We included randomised controlled trials (RCTs). Participants were people who smoked, with no exclusions based on age, gender, ethnicity, language or health status. Any type of Internet intervention was eligible. The comparison condition could be a no-intervention control, a different Internet intervention, or a non-Internet intervention. To be included, studies must have measured smoking cessation at four weeks or longer.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed and extracted data. We extracted and, where appropriate, pooled smoking cessation outcomes of six-month follow-up or more, reporting short-term outcomes narratively where longer-term outcomes were not available. We reported study effects as a risk ratio (RR) with a 95% confidence interval (CI).We grouped studies according to whether they (1) compared an Internet intervention with a non-active control arm (e.g. printed self-help guides), (2) compared an Internet intervention with an active control arm (e.g. face-to-face counselling), (3) evaluated the addition of behavioural support to an Internet programme, or (4) compared one Internet intervention with another. Where appropriate we grouped studies by age.

MAIN RESULTS

We identified 67 RCTs, including data from over 110,000 participants. We pooled data from 35,969 participants.There were only four RCTs conducted in adolescence or young adults that were eligible for meta-analysis.Results for trials in adults: Eight trials compared a tailored and interactive Internet intervention to a non-active control. Pooled results demonstrated an effect in favour of the intervention (RR 1.15, 95% CI 1.01 to 1.30, n = 6786). However, statistical heterogeneity was high (I2 = 58%) and was unexplained, and the overall quality of evidence was low according to GRADE. Five trials compared an Internet intervention to an active control. The pooled effect estimate favoured the control group, but crossed the null (RR 0.92, 95% CI 0.78 to 1.09, n = 3806, I2 = 0%); GRADE quality rating was moderate. Five studies evaluated an Internet programme plus behavioural support compared to a non-active control (n = 2334). Pooled, these studies indicated a positive effect of the intervention (RR 1.69, 95% CI 1.30 to 2.18). Although statistical heterogeneity was substantial (I2 = 60%) and was unexplained, the GRADE rating was moderate. Four studies evaluated the Internet plus behavioural support compared to active control. None of the studies detected a difference between trial arms (RR 1.00, 95% CI 0.84 to 1.18, n = 2769, I2 = 0%); GRADE rating was moderate. Seven studies compared an interactive or tailored Internet intervention, or both, to an Internet intervention that was not tailored/interactive. Pooled results favoured the interactive or tailored programme, but the estimate crossed the null (RR 1.10, 95% CI 0.99 to 1.22, n = 14,623, I2 = 0%); GRADE rating was moderate. Three studies compared tailored with non-tailored Internet-based messages, compared to non-tailored messages. The tailored messages produced higher cessation rates compared to control, but the estimate was not precise (RR 1.17, 95% CI 0.97 to 1.41, n = 4040), and there was evidence of unexplained substantial statistical heterogeneity (I2 = 57%); GRADE rating was low.Results should be interpreted with caution as we judged some of the included studies to be at high risk of bias.

AUTHORS' CONCLUSIONS

The evidence from trials in adults suggests that interactive and tailored Internet-based interventions with or without additional behavioural support are moderately more effective than non-active controls at six months or longer, but there was no evidence that these interventions were better than other active smoking treatments. However some of the studies were at high risk of bias, and there was evidence of substantial statistical heterogeneity. Treatment effectiveness in younger people is unknown.

Racial and Sociodemographic Disparities in Internet Access and eHealth Intervention Utilization Among Veteran Smokers

INTRODUCTION

Access to the internet at home may be an important barrier to electronic health (eHealth) smoking cessation interventions. The current study explored possible sociodemographic disparities in access to the internet at home among veteran smokers.

METHODS

Data from participants proactively recruited and enrolled in a randomized smoking cessation effectiveness trial (N = 408) that compared a web-based smoking cessation intervention to Veterans Affairs (VA) usual care were used to examine the demographic attributes of smokers with and without internet access at home. Multivariable logistic regression was used to examine associations between demographic factors and home internet access. Data from patients randomized to the internet arm of the study (N = 205) were used to ascertain correlates of utilization of the intervention website.

RESULTS

While the majority of the sample (82 %) endorsed access to the internet at home, veterans who were African-American, older, and not married were significantly less likely to have home internet access. Veterans who were African-American, older, less educated, had longer travel times to the nearest VA facility, and increased nicotine dependence were less likely to access the internet on a daily basis. While several sociodemographic variables (e.g., age, race, education, employment) were related to utilization of a free membership to a commercial, web-based smoking cessation intervention in bivariate analyses, only access to the internet at home was related to use of the smoking cessation site in adjusted results.

CONCLUSION

These results highlight gaps in internet access and use among veterans and additionally underscore the importance of improving accessibility of eHealth interventions for low-income, minority, and socially disadvantaged veteran populations.