Do eHealth interventions work for managing smoking cessation?

In a recent study published in eClinicalMedicineresearchers conducted a systematic review and meta-analysis of studies that compared emerging eHealth interventions with conventional interventions to support smoking cessation.

They pursued evidence of its effectiveness on a large scale to inform future development of more targeted eHealth applications for smoking cessation.

study: Effectiveness of eHealth interventions to manage smoking cessation in smokers: a systematic review and meta-analysis. Image credit: a-image/Shutterstock.com

Background

Smoking cessation is currently the only guaranteed method of reducing the risks associated with smoking, such as cancer, cardiovascular and respiratory diseases.

Smoking is an extremely important global public health problem, with approximately 60,000 people dying each year due to smoking-related complications. It also reduces life expectancy and quality of life.

Quitting cigarettes at any time is helpful, but because of their physiological dependence on nicotine, smokers find it challenging.

Both pharmacological and behavioral interventions, used alone or in combination, are effective in increasing smoking cessation rates in adults.

Thus, convenient, safe, inexpensive, and widely applicable smoking cessation intervention methods are an urgent need.

E-health, including mobile health (m-health) and telemedicine, extends smoking cessation counseling beyond hospitals and professional medical facilities using information and communication technologies.

Despite evidence of its effectiveness, eHealth as a smoking cessation intervention has not been systematically evaluated and compared with traditional approaches.

About the research

In the current systematic review and meta-analysis, researchers thoroughly searched PubMed, Embase, and the Cochrane Library from inception to December 2022, retrieving 2,408 published randomized controlled trials (RCTs) comparing eHealth and usual offline care methods for smoking cessation.

The primary outcome was the point quit rate at seven days and 30 days, and the secondary outcome was sustained quit rate.

The Cochrane Risk of Bias Tool assessed the risk of bias in each included study and helped researchers rate the quality of the evidence as very low, low, moderate or high.

The team used fixed-effects meta-analysis and meta-regression analyzes of data from all included RCTs to assess the effectiveness and impact of different eHealth interventions. Finally, risk ratios (RR) and their confidence intervals (95% CI) were calculated for different interventions.

Results

The final sample set for analysis of the current systematic review consisted of 44 articles, of which 15 were conducted in the United States, 17 in Europe, eight in Asia, two in Brazil, one in Argentina, and one in Australia.

All used eHealth interventions in various forms, with 17 studies using text messages and phone calls, while 27 studies used websites and mobile applications.

The duration of the intervention ranged from 21 days to 12 months in all studies. Only three articles reported the 30-day point quit rate, so combining the results of the two periods showed that the eHealth intervention group had a higher quit rate than the control group (RR 1.86, 95% CI 1.69–2.04).

The eHealth intervention group had better sustained quit rates for two months or more (RR 1.79, 95% CI 1.60–2.00) than the control group.

SMS and phone interventions showed more promising results than websites and mobile apps.

As a mHealth intervention, the RR value for eHealth interventions was 2.10. Although the RR value of telemedicine was 1.74, it was still remarkably effective, demonstrating that differences in definitions do not reduce the recommended use of eHealth in smoking cessation.

With advances in automated telephone reminder methods, more research is expected in this area.

However, participants browsing websites or apps more times had higher rates of smoking cessation; for example, Villanti et al. showed that for each additional registration completed, the 7-day quit rate and 30-day abstinence from distribution increased by 7% and 9%, respectively.

Multiple studies also show that successful quitters tend to spend more time on websites and apps with higher levels of engagement.

Conclusions

Overall, eHealth interventions for smoking cessation are effective for smokers in different countries. It increased the smoking cessation rate increased by 1.86 times.

However, they had slightly lower sustained churn rates than point churn rates. Thus, more research trials are needed to improve the validity of the evidence for sustainable cessation rates, ie. for one year through eHealth interventions.

In addition, an analysis of the cost-effectiveness of eHealth interventions and how to integrate them effectively into the daily workflow is needed.

Nevertheless, new digital health interventions for smoking cessation based on widely used electronic products are attractive for several reasons. First, they are simple, convenient and easy to distribute.

Second, they offer an opportunity to address the global smoking problem in areas with disproportionate medical resources.

Third, they can be tailored to meet the needs of smokers with special needs, such as pregnant women, people infected with HIV, and people with depression.

Finally, despite the lack of a standardized methodological approach in assessing compliance in the included studies, eHealth remained a modality with higher compliance and satisfaction than standard care in this study.

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