Is Action Planning Helpful for Smoking Cessation? Assessing the Effects of Action Planning in a Web-Based Computer-Tailored Intervention

Effectiveness of the use of implementation intentions on reduction of substance use: A meta-analysis

OBJECTIVE

Background: Substance use, such as alcohol drinking, tobacco smoking and illicit drug use, have been associated with severe health conditions and an annual estimated 12 % of all deaths worldwide. Implementation intentions are self-regulatory processes which help achieve health-related behaviour change.

OBJECTIVES

To investigate the effectiveness of forming implementation intentions to reduce substance use.

DESIGN

Data sources: PsycINFO, MEDLINE, Psychology and Behavioural Science Collection, clinicaltrials.gov, UK Clinical Trials Gateway, Reference lists.

INCLUSION CRITERIA

RCT of substance users forming implementation intentions to reduce consumption (active or passive control condition present).

STUDY APPRAISAL AND SYNTHESIS METHODS

the SIGN checklist for RCT quality was used for quality appraisal, data was extracted by two reviewers.

RESULTS

Twenty-one studies were included in the meta-analysis. The overall effect size for alcohol use was g = 0.31 (95 % CI: 0.21, 0.42), p < .001; for tobacco smoking g = 0.31 (CI: 0.12, 0.5), p = .002; no studies were retrieved for the use of implementation intentions on illicit drug use.

CONCLUSION

This review suggests that implementation intention interventions are effective in reducing some forms of substance use (alcohol use and tobacco smoking), albeit revealing small effect sizes, among the general population and students in secondary and higher education. Review registration number: CRD42018116170.

Towards a better understanding of factors affecting smoking uptake among Saudi male adolescents: A qualitative study

INTRODUCTION

An increased smoking uptake by Saudi male adolescents and a lack of data about its determinants emphasize the need for a better understanding of factors leading to the onset of smoking and identifying ways to prevent it. The aim of this qualitative study is to explore adolescents' views on smoking and their opinions about a smoking prevention program.

METHODS

A total of 103 school-going adolescents, aged 12-16 years, were purposely selected from grades seven, eight and nine from nine schools in Taif in Saudi Arabia. They were interviewed in 11 focus group discussions; five groups were held for smokers and six for non-smokers. An interview scheme was developed based on the I-Change Model, a model used for understanding smoking onset and prevention. We used QDA Lite version 2:0 software for data analysis.

RESULTS

Most of the participants agreed on the importance of social influences as determining factors to start smoking. The presence of smoking friend(s) and family member(s), especially the father, were mentioned. Factors such as having extra pocket money, absence of alternatives, showing off, to be seen as western, to be seen as an adult and the good taste of cigarettes were also mentioned as beliefs associated with smoking. Adolescents indicated to have low confidence not to smoke under peer pressure, suggesting self-efficacy problems. Intentions to smoke were also often mentioned. Almost all participants agreed that an interactive approach is optimal for an effective smoking prevention program.

CONCLUSIONS

Determinants of smoking seem to be very similar to those outlined by previous studies. A smoking prevention program for Saudi adolescents should address how to cope with social pressure to smoke, the advantages connected with smoking, and how to increase self-efficacy. Information should be presented in an interactive rather than static way.

Prevalence and correlates of skin self-examination practices among cutaneous malignant melanoma survivors

Melanoma patients are at elevated risk for recurrence of the primary cancer as well as second primary melanomas. Regular skin self-examination (SSE) is recommended as part of follow-up surveillance. In this study, we examined SSE performance and comprehensiveness as well as knowledge and attitudinal correlates of SSE performance and comprehensiveness. Four hundred forty-one melanoma survivors completed measures of SSE performance as well as knowledge and attitudes about SSE and melanoma. Approximately two-thirds of the sample reported having conducted an SSE in the past two months; the average number of body parts examined was 10.64 (out of 15 maximum). Only 7.5% of the sample checked all 15 body parts. Greater worry about recurrence, fewer barriers to SSE, more planning for when to conduct SSE, and more confidence in the ability to conduct SSE and recognize a suspicious growth were associated with both SSE performance and greater SSE comprehensiveness. Physician influence was positively associated with SSE performance. Survivor education efforts may benefit from reminding survivors to check hard-to-see and sensitive areas, develop a plan for how to ask for assistance in conducting exams, as well as use mirrors to see hard-to-reach areas. Addressing perceived SSE barriers, fostering SSE planning, and improving SSE self-efficacy may be important foci for intervention efforts to enhance SSE performance and comprehensiveness.

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)

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.

Long-term effects of a web-based cancer aftercare intervention on moderate physical activity and vegetable consumption among early cancer survivors: a randomized controlled trial

BACKGROUND

The number of cancer survivors is growing. Negative physical and psychosocial consequences of cancer treatment can occur during survivorship. Following healthy lifestyle recommendations is beneficial to increase quality of life and to reduce the risk of cancer recurrence and comorbidities. To meet individual needs, web-based interventions can supply a large population of cancer survivors with easily accessible and personalized information. Evidence concerning the long-term effects of web-based cancer aftercare interventions on lifestyle outcomes is limited. The present study evaluates the 12-month effects of a fully automated web-based cancer aftercare intervention. We investigated whether the previously determined 6-month effects on moderate physical activity and vegetable intake were maintained over 12 months. Possible moderator effects of using specific intervention modules, gender, age, and education were also explored.

METHOD

A two-armed randomized controlled trial was conducted using online self-report questionnaires among survivors of various types of cancer (N = 462). The intervention group had access to the online intervention for 6 months, and the control group received access after 12-months. Multilevel linear regression analyses (complete cases and intention-to-treat) were conducted to explore 12- month effects.

RESULTS

A significant intervention effect after 12 months was found for moderate physical activity (complete cases: B = 128.475, p = .010, d = .35; intention-to-treat: B = 129.473, p = .011). Age was the only significant moderator (p = .010), with the intervention being effective among participants aged younger than 57 years (B = 256.549, p = .000, d = .59). No significant intervention effect remained for vegetable consumption after 12 months (complete cases: B = 5.860, p = .121; intention-to-treat: B = 5.560, p = .132).

CONCLUSION

The online cancer after care intervention is effective in increasing and maintaining moderate physical activity in the long term among early cancer survivors younger than 57 years. Short-term increases in vegetable consumption were not sustained in the long term. These findings indicate the value and potential of eHealth interventions for cancer survivors. Based on the study results, web-based self-management interventions could be recommended for younger cancer survivors (<57 years of age) as a possible method to increase physical activity.

TRIAL REGISTRATION

Dutch Trial Register NTR3375 . Registered 29 March 2012.

Smoking cessation in cardiac patients: the influence of action plans, coping plans and self-efficacy on quitting smoking

Smoking cessation is the most effective action for cardiac patients who smoke to improve their prognosis, yet more than one-half of cardiac patients continue to smoke after hospital admission. This study examined the influence of action plans, coping plans and self-efficacy on intention to quit and smoking cessation in cardiac patients. Cardiac patients completed a baseline questionnaire (N = 245) assessing demographic characteristics, smoking behavior, intention, self-efficacy, relapse self-efficacy and action and coping plans. Six months later (N = 184) continued abstinence from smoking was assessed. Self-efficacy predicted intention to quit smoking and was an indirect predictor of continued abstinence, through intention. Intention to quit smoking and making action plans both directly influenced continued abstinence. Future interventions to facilitate smoking cessation in cardiac patients should put strong emphasis on enhancing self-efficacy and on making specific action plans to increase the likelihood of smoking cessation.

Lifestyle-related effects of the web-based Kanker Nazorg Wijzer (Cancer Aftercare Guide) intervention for cancer survivors: a randomized controlled trial

Purpose

The web-based Kanker Nazorg Wijzer (Cancer Aftercare Guide) responds to the needs of cancer survivors and oncology care providers to improve the counseling related to self-management of lifestyle and psychosocial challenges. In present study, overall intervention effects and the effects of using specific components were evaluated on vegetable, fruit, whole grain bread, and fish consumption, physical activity (PA), and smoking behavior.

Methods

Cancer survivors from 21 Dutch hospitals were recruited for a randomized controlled trial (N = 432). Intervention effects after 6 months were evaluated using multilevel linear regression analysis (complete cases and intention-to-treat). By conducting moderation analyses, additional effects of following the behavior-related modules were explored. The false discovery rate correction was applied to account for multiple testing.

Results

After 6 months, 409 participants completed follow-up (dropout = 11.5 %). Indications were found that access to the intervention may result in increases of moderate PA and vegetable intake. The moderate PA increase was meaningful: 74.74 min p/w higher increase in the intervention condition. Effect sizes of moderate PA (d = .25) and vegetable (d = .37) consumption were comparable to prior effective interventions. Visiting behavior-related modules affected moderate PA, fruit, and fish consumption. However, after correction for multiple testing, significances expired. No significant intervention effect was found on smoking behavior due to low numbers of smokers.

Implications for Cancer Survivors

Although the effectiveness was only shown only to a limited extend, this study provided several indications that this theory-based, comprehensive, and personalized eHealth intervention provides valuable content to complement usual cancer aftercare.