Results of the PAS Study: A Randomized Controlled Trial Evaluating the Effectiveness of a Web-Based Multiple Tailored Smoking Cessation Program Combined With Tailored Counseling by Practice Nurses

The role of general practice nurses in supporting people to quit smoking: A qualitative study

PURPOSE

Encounters with General Practitioners (GPs) have previously been identified as opportune for the delivery of smoking cessation care however the role of nurses in general practice settings is unclear. This study aimed to understand how nurses are providing smoking cessation care in general practice.

METHODS

Participants were registered nurses currently working in a general practice setting in Australia, who participated in one-off interviews over Zoom. Interviews were recorded and a thematic analysis was conducted.

RESULTS

Fourteen nurses participated of which 13 (93%) were female. Three themes were evident in the data: 1) Nurses' current practices in supporting people to quit smoking, 2) The influence of the general practice setting on smoking cessation discussions and 3) The challenges experienced by nurses in providing optimal smoking cessation care. Theme one describes the strategies currently employed by nurses to deliver smoking cessation care such as identifying appropriate clinical scenarios to have smoking cessation conversations with patients. Theme two explores the impact of diversity in the systems, processes, and structures across Australian general practice settings on the support offered by nurses, such as opportunities for ongoing relationships with patients Theme three focuses on ambiguity in nurses' roles within the practice setting including a lack of clarity for nurses in their roles in delivering smoking cessation care in the general practice setting.

CONCLUSIONS

General practice nurses recognise the importance of their role in providing smoking cessation care and consider that general practice settings are ideally positioned to deliver that care. Smoking cessation care provided by nurses varies according to systems and processes within general practice clinics and relationships with general practitioners. Vaping is an emerging issue and nurses are seeking information on how to address this with patients. There is opportunity to support nurses to provide improved smoking cessation care.

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.

The Usability of an Online Tool to Promote the Use of Evidence-Based Smoking Cessation Interventions

To increase usage of evidence-based smoking cessation interventions (EBSCIs) among smokers, an online decision aid (DA) was developed. The aims of this study were (1) to conduct a usability evaluation; (2) to conduct a program evaluation and evaluate decisional conflict after using the DA and (3) to determine the possible change in the intention to use EBSCIs before and directly after reviewing the DA. A cross-sectional study was carried out in September 2020 by recruiting smokers via the Internet (n = 497). Chi-squared tests and t-tests were conducted to test the differences between smokers who differed in the perceived usability of the DA on the program evaluation and in decisional conflict. The possible changes in intention to use EBSCIs during a cessation attempt before and after reviewing the DA were tested using t-tests, McNemar's test and χ2 analysis. The participants evaluated the usability of the DA as moderate (MU; n = 393, 79.1%) or good (GU; n = 104, 20.9%). GU smokers rated higher on all the elements of the program evaluation and experienced less decisional conflict, but also displayed a higher intention to quit. After reviewing the DA, the participants on average had a significantly higher intention to use more EBSCIs, in particular in the form of eHealth. Recommendations to make the DA more usable could include tailoring, using video-based information and including value clarification methods. Furthermore, a hybrid variant in which smokers can use the DA independently and with the guidance of a primary care professional could aid both groups in choosing a fitting EBSCI option.

A smoker's choice? Identifying the most autonomy-supportive message frame in an online computer-tailored smoking cessation intervention

OBJECTIVE

To test the effect of autonomy-supportive message framing on people's perceived autonomy-support while considering the individual need for autonomy as a moderator. Also, to test whether autonomy-supportive message frames - through increased perceived autonomy-support - lead to more self-determined motivation, and increased intention to quit smoking.

DESIGN

An online 2(autonomy-supportive; controlling language) × 2(choice; no choice) between-subjects design with control condition (generic advice) with adult smokers intending to quit (N = 626).

MAIN OUTCOME

Intention to quit smoking (Theory of Planned Behaviour).

MEASURES

Perceived autonomy-support (Virtual Climate Care Questionnaire), need for autonomy (Health Causality Orientations Scale), self-determined motivation (Treatment Self-Regulation Questionnaire), attitudes, social influence, self-efficacy (I-Change Model).

RESULTS

Structural equation modelling revealed no significant effect of autonomy-supportive-message frames on perceived autonomy-support or self-determined motivation, neither did the need for autonomy moderate these effects. Self-determined motivation had a positive, significant effect on intention to quit, mediated by attitudes, social influence, and self-efficacy.

CONCLUSION

Although message frames did not affect perceived autonomy-support or self-determined motivation, higher self-determined motivation increased intention to quit via attitudes, social influence, and self-efficacy. Before drawing the conclusion that message framing has no effect, we recommend to replicate this study in a real-life setting with smokers more likely to read and process the message frames more attentively.

Effectiveness of Message Frame-Tailoring in a Web-Based Smoking Cessation Program: Randomized Controlled Trial

Background

The content of online computer-tailored interventions is often determined to match an individual’s characteristics, beliefs, and behavioral factors. These content-tailored interventions lead to better message processing and a higher likelihood of behavior change such as smoking cessation. However, a meta-analysis of online computer-tailored interventions showed that effect sizes, albeit positive, remain small, suggesting room for improvement. A promising strategy to enhance the effectiveness of online computer-tailored interventions is to tailor the message frame (ie, how a message is communicated) based on the preferred communication style of the user in addition to content-tailoring. One factor that determines an individual’s communication style preference is the need for autonomy; some individuals prefer an autonomy-supportive communication style (offering choice and use of suggestive language), whereas others might prefer a directive communication style, which is replete with imperatives and does not provide choice. Tailoring how messages are presented (eg, based on the need for autonomy) is called message frame-tailoring.

Objective

The aim of the present study was to test the effectiveness of message frame-tailoring based on the need for autonomy, in isolation and in combination with content-tailoring, within the context of an online computer-tailored smoking cessation intervention. The primary outcome measure was the 7-day point-prevalence of smoking abstinence. Secondary outcomes were perceived message relevance, self-determined motivation to quit smoking, and sociocognitive beliefs.

Methods

A randomized controlled trial with a 2 (message frame-tailoring vs no message frame-tailoring) by 2 (content-tailoring vs no content-tailoring) design was conducted among adult smokers intending to quit smoking (N=273).

Results

Structural equation modeling revealed that the content-tailored condition increased smoking abstinence rates 1 month after the start of the intervention (beta=.57, P=.02). However, neither message frame-tailoring nor its interaction with content-tailoring significantly predicted smoking abstinence. In our model, message frame-tailoring, content-tailoring, as well as their interaction significantly predicted perceived relevance of the smoking cessation messages, which consequently predicted self-determined motivation. In turn, self-determined motivation positively affected attitudes and self-efficacy for smoking cessation, but only self-efficacy consequently predicted smoking abstinence. Participants in the control condition perceived the highest level of message relevance (mean 4.78, SD 1.27). However, messages that were frame-tailored for individuals with a high need for autonomy in combination with content-tailored messages led to significantly higher levels of perceived message relevance (mean 4.83, SD 1.03) compared to those receiving content-tailored messages only (mean 4.24, SD 1.05, P=.003).

Conclusions

Message frame-tailoring based on the need for autonomy seems to be an effective addition to conventional content-tailoring techniques in online smoking cessation interventions for people with a high need for autonomy; however, this is not effective in its current form for people with a low need for autonomy.

Trial Registration

Dutch Trial Register (NL6512/NRT-6700); https://www.trialregister.nl/trial/6512

Comparing the Efficacy of an Identical, Tailored Smoking Cessation Intervention Delivered by Mobile Text Messaging Versus Email: Randomized Controlled Trial

Background

There is a need to deliver smoking cessation support at a population level, both in developed and developing countries. Studies on internet-based and mobile phone–based smoking cessation interventions have shown that these methods can be as effective as other methods of support, and they can have a wider reach at a lower cost.

Objective

This randomized controlled trial (RCT) aimed to compare, on a population level, the efficacy of an identical, tailored smoking cessation intervention delivered by mobile text messaging versus email.

Methods

We conducted a nationwide 2-arm, double-blinded, fully automated RCT, close to a real-world setting, in Norway. We did not offer incentives to increase participation and adherence or to decrease loss to follow-up. We recruited users of the website, slutta.no, an open, free, multi-component Norwegian internet-based smoking cessation program, from May 2010 until October 2012. Enrolled smokers were considered as having completed a time point regardless of their response status if it was 1, 3, 6, or 12 months post cessation. We assessed 7315 participants using the following inclusion criteria: knowledge of the Norwegian language, age 16 years or older, ownership of a Norwegian cell phone, having an email account, current cigarette smoker, willingness to set a cessation date within 14 days (mandatory), and completion of a baseline questionnaire for tailoring algorithms. Altogether, 6137 participants were eligible for the study and 4378 participants (71.33%) provided informed consent to participate in the smoking cessation trial. We calculated the response rates for participants at the completed 1, 3, 6, and 12 months post cessation. For each arm, we conducted an intention-to-treat (ITT) analysis for each completed time point. The main outcome was 7-day self-reported point prevalence abstinence (PPA) at the completed 6 months post cessation. We calculated effect size of the 7-day self-reported PPA in the text message arm compared with the email arm as odds ratios (ORs) with 95% CIs for the 4 time points post cessation.

Results

At 6 months follow-up, 21.06% (384/1823) of participants in the text message arm and 18.62% (333/1788) in the email arm responded (P=.07) to the surveys. In the ITT analysis, 11.46% (209/1823) of participants in the text message arm compared with 10.96% (196/1788) in the email arm (OR 1.05, 95% CI 0.86-1.30) reported to have achieved 7 days PPA.

Conclusions

This nationwide, double-blinded, large, fully automated RCT found that 1 in 9 enrolled smokers reported 7-day PPA in both arms, 6 months post cessation. Our study found that identical smoking cessation interventions delivered by mobile text messaging and email may be equally successful at a population level.

Trial Registration

ClinicalTrials.gov NCT01103427; https://clinicaltrials.gov/ct2/show/NCT01103427

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.

The Effectiveness of a Computer-Tailored E-Learning Program for Practice Nurses to Improve Their Adherence to Smoking Cessation Counseling Guidelines: Randomized Controlled Trial

Background

Improving practice nurses’ (PN) adherence to smoking cessation counseling guidelines will benefit the quality of smoking cessation care and will potentially lead to higher smoking abstinence rates. However, support programs to aid PNs in improving their guideline uptake and adherence do not exist yet.

Objective

The aim of this study was to assess the effects of a novel computer-tailored electronic learning (e-learning) program on PNs’ smoking cessation guideline adherence.

Methods

A Web-based randomized controlled trial (RCT) was conducted in which an intervention group (N=147) with full access to the e-learning program for 6 months was compared with a control group (N=122) without access. Data collection was fully automated at baseline and 6-month follow-up via online questionnaires, assessing PNs’ demographics, work-related factors, potential behavioral predictors based on the I-Change model, and guideline adherence. PNs also completed counseling checklists to retrieve self-reported counseling activities for each consultation with a smoker (N=1175). To assess the program’s effectiveness in improving PNs’ guideline adherence (ie, overall adherence and adherence to individual counseling guideline steps), mixed linear and logistic regression analyses were conducted, thus accommodating for the smokers being nested within PNs. Potential effect moderation by work-related factors and behavioral predictors was also examined.

Results

After 6 months, 121 PNs in the intervention group (82.3%, 121/147) and 103 in the control group (84.4%, 103/122) completed the follow-up questionnaire. Mixed linear regression analysis revealed that counseling experience moderated the program’s effect on PNs’ overall guideline adherence (beta=.589; 95% CI 0.111-1.068; P_{Holm-Bonferroni} =.048), indicating a positive program effect on adherence for PNs with a more than average level of counseling experience. Mixed logistic regression analyses regarding adherence to individual guideline steps revealed a trend toward moderating effects of baseline levels of behavioral predictors and counseling experience. More specifically, for PNs with less favorable scores on behavioral predictors (eg, low baseline self-efficacy) and high levels of counseling experience, the program significantly increased adherence.

Conclusions

Results from our RCT showed that among PNs with more than average counseling experience, the e-learning program resulted in significantly better smoking cessation guideline adherence. Experienced PNs might have been better able to translate the content of our e-learning program into practically applicable counseling strategies compared with less experienced colleagues. Less favorable baseline levels of behavioral predictors among PNs possibly contributed to this effect, as there was more room for improvement by consulting the tailored content of the e-learning program. To further substantiate the effectiveness of e-learning programs on guideline adherence by health care professionals (HCPs), it is important to assess how to support a wider range of HCPs.

Trial Registration

Netherlands Trial Register NTR4436; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4436 (Archived by WebCite at http://www.webcitation.org/6zJQuSRq0)

Subgroups Among Smokers in Preparation: A Cluster Analysis Using the I-Change Model

BACKGROUND

Investigating potential sub-stages of change could provide important information that could be used to improve the tailoring of smoking cessation interventions to individual smokers' profiles. Smokers in the preparation stage may be most interesting, as they are most likely to participate in smoking cessation interventions.

OBJECTIVE

To examine whether Dutch adult smokers in the preparation stage of change, i.e. motivated to quit smoking within one month, can be organized into subgroups.

METHODS

Data from 753 smokers who participated in an effectiveness trial of a web-based, computer-tailored smoking cessation programme were subjected to secondary analysis. Cluster analyses were based on respondents' baseline responses to items on pros and cons of quitting and quitting self-efficacy. Chi-squared tests and ANOVA were used to compare the baseline characteristics of the resulting clusters. Logistic and multinomial regression were used for longitudinal comparisons of clusters with respect to smoking abstinence and stage transition at six-week and six-month follow-ups.

RESULTS

Four clusters were identified; Classic, Unprepared, Progressing and Disengaged Preparers. Cross-sectional and longitudinal analyses validated these clusters: they differed with respect to the clustering variables, gender, cigarette dependence and educational level. Disengaged Preparers were less likely than Progressing Preparers to report smoking abstinence at six months (OR = 0.28; p < .05).

CONCLUSIONS

These results suggest that smoking cessation interventions tailored to the preparation stage of change, i.e. the set of cognitions usually present in preparers, are only appropriate for the subgroup we defined as Classic Preparers. The other clusters might need different interventions as they display different cognition sets.

Nursing interventions for smoking cessation

BACKGROUND

Healthcare professionals, including nurses, frequently advise people to improve their health by stopping smoking. Such advice may be brief, or part of more intensive interventions.

OBJECTIVES

To determine the effectiveness of nursing-delivered smoking cessation interventions in adults. To establish whether nursing-delivered smoking cessation interventions are more effective than no intervention; are more effective if the intervention is more intensive; differ in effectiveness with health state and setting of the participants; are more effective if they include follow-ups; are more effective if they include aids that demonstrate the pathophysiological effect of smoking.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group Specialized Register and CINAHL in January 2017.

SELECTION CRITERIA

Randomized trials of smoking cessation interventions delivered by nurses or health visitors with follow-up of at least six months.

DATA COLLECTION AND ANALYSIS

Two review authors extracted data independently. The main outcome measure was abstinence from smoking after at least six months of follow-up. We used the most rigorous definition of abstinence for each trial, and biochemically-validated rates if available. Where statistically and clinically appropriate, we pooled studies using a Mantel-Haenszel fixed-effect model and reported the outcome as a risk ratio (RR) with a 95% confidence interval (CI).

MAIN RESULTS

Fifty-eight studies met the inclusion criteria, nine of which are new for this update. Pooling 44 studies (over 20,000 participants) comparing a nursing intervention to a control or to usual care, we found the intervention increased the likelihood of quitting (RR 1.29, 95% CI 1.21 to 1.38); however, statistical heterogeneity was moderate (I² = 50%) and not explained by subgroup analysis. Because of this, we judged the quality of evidence to be moderate. Despite most studies being at unclear risk of bias in at least one domain, we did not downgrade the quality of evidence further, as restricting the main analysis to only those studies at low risk of bias did not significantly alter the effect estimate. Subgroup analyses found no evidence that high-intensity interventions, interventions with additional follow-up or interventions including aids that demonstrate the pathophysiological effect of smoking are more effective than lower intensity interventions, or interventions without additional follow-up or aids. There was no evidence that the effect of support differed by patient group or across healthcare settings.

AUTHORS' CONCLUSIONS

There is moderate quality evidence that behavioural support to motivate and sustain smoking cessation delivered by nurses can lead to a modest increase in the number of people who achieve prolonged abstinence. There is insufficient evidence to assess whether more intensive interventions, those incorporating additional follow-up, or those incorporating pathophysiological feedback are more effective than one-off support. There was no evidence that the effect of support differed by patient group or across healthcare settings.

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.

A Review of the Theoretical Basis, Effects, and Cost Effectiveness of Online Smoking Cessation Interventions in the Netherlands: A Mixed-Methods Approach

BACKGROUND

Tobacco smoking is a worldwide public health problem. In 2015, 26.3% of the Dutch population aged 18 years and older smoked, 74.4% of them daily. More and more people have access to the Internet worldwide; approximately 94% of the Dutch population have online access. Internet-based smoking cessation interventions (online cessation interventions) provide an opportunity to tackle the scourge of tobacco.

OBJECTIVE

The goal of this paper was to provide an overview of online cessation interventions in the Netherlands, while exploring their effectivity, cost effectiveness, and theoretical basis.

METHODS

A mixed-methods approach was used to identify Dutch online cessation interventions, using (1) a scientific literature search, (2) a grey literature search, and (3) expert input. For the scientific literature, the Cochrane review was used and updated by two independent researchers (n=651 identified studies), screening titles, abstracts, and then full-text studies between 2013 and 2016 (CENTRAL, MEDLINE, and EMBASE). For the grey literature, the researchers conducted a Google search (n=100 websites), screening for titles and first pages. Including expert input, this resulted in six interventions identified in the scientific literature and 39 interventions via the grey literature. Extracted data included effectiveness, cost effectiveness, theoretical factors, and behavior change techniques used.

RESULTS

Overall, many interventions (45 identified) were offered. Of the 45 that we identified, only six that were included in trials provided data on effectiveness. Four of these were shown to be effective and cost effective. In the scientific literature, 83% (5/6) of these interventions included changing attitudes, providing social support, increasing self-efficacy, motivating smokers to make concrete action plans to prepare their attempts to quit and to cope with challenges, supporting identity change and advising on changing routines, coping, and medication use. In all, 50% (3/6) of the interventions included a reward for abstinence. Interventions identified in the grey literature were less consistent, with inclusion of each theoretical factor ranging from 31% to 67% and of each behavior change technique ranging from 28% to 54%.

CONCLUSIONS

Although the Internet may provide the opportunity to offer various smoking cessation programs, the user is left bewildered as far as efficacy is concerned, as most of these data are not available nor offered to the smokers. Clear regulations about the effectiveness of these interventions need to be devised to avoid disappointment and failed quitting attempts. Thus, there is a need for policy regulations to regulate the proliferation of these interventions and to foster their quality in the Netherlands.