In a recent study published in eClinicalMedicine, researchers conducted a systematic review and meta-analysis of studies that compared newly emerged e-health interventions with conventional smoking cessation support interventions.
They pursued evidence of its effectiveness on a large scale to inform the future development of more targeted e-health applications for smoking cessation.
Study: Efficacy of e-health interventions for smoking cessation management 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 system diseases.
Smoking is an exceedingly important global public health issue, with ~60,000 people dying each year due to smoking-related complications. It also reduces life expectancy and the quality of life.
Quitting smoking at any point in time is beneficial, but due to their physiological dependence on nicotine, smokers find it challenging.
Both pharmacological and behavioral interventions, used alone or in combination, effectively increase smoking cessation rates in adults.
Thus, convenient, secure, low-cost, and widely applicable smoking cessation intervention methods are need of the hour.
E-health, encompassing mobile health (m-health) and telemedicine, extends smoking cessation counseling beyond hospitals and professional care facilities using information and communication technologies.
Despite evidence of its effectiveness, e-health as a smoking cessation intervention has not been systematically evaluated and compared with traditional approaches.
About the study
In the current systematic review and meta-analysis, researchers thoroughly searched PubMed, Embase, and Cochrane Library from inception until December 2022, retrieving 2,408 published randomized controlled trials (RCTs) comparing e-health and usual offline care methods of smoking cessation.
The primary outcome was the point smoking quit rates at seven days and 30 days, and the secondary outcome was sustained smoking quit rates.
The Cochrane risk of bias tool assessed the risk of bias in each included study and helped the researchers grade the quality of evidence as very low, low, moderate, or high.
The team used the fixed-effects meta-analysis and meta-regression analyses of data from all included RCTs to evaluate the effectiveness and impact of different e-health interventions. Finally, risk ratios (RRs) and their confidence intervals (95% CI) were calculated for different interventions.
Results
The final analysis sample set of the current systematic review comprised 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 e-health interventions in various forms, such as 17 studies used text messaging and phone calls, while 27 studies used websites and mobile applications.
The intervention duration varied from 21 days to 12 months across all studies. Only three articles reported the 30-day point quit rate, so combining the results for two periods demonstrated that the e-health intervention group had a higher smoking cessation rate than the control group (RR 1.86, 95% CI 1.69–2.04).
The e-health 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.
Interventions through SMS and telephone showed more promising results than websites and mobile applications.
As a m-health intervention, the RR value for e-health interventions was 2.10. Even though the RR value of telemedicine was 1.74, it was still remarkably effective, demonstrating that the differences in definitions don't diminish the recommended use of e-health in smoking cessation.
With advancements in automatic telephone reminder methods, more research in this area is anticipated.
However, participants browsing websites or apps more times had higher smoking cessation rates; for instance, Villanti et al. showed that for each additional registration completed, the 7-day smoking cessation rate and the 30-day point prevalence abstinence increased by 7% and 9%, respectively.
Multiple studies have also demonstrated that successful quitters tend to spend more time using websites and apps with higher levels of engagement.
Conclusions
Overall, e-health interventions for smoking cessation were effective for smokers in different countries. It increased the smoking cessation rate increased by 1.86 times.
However, they had slightly lower sustained quit rates than point quit rates. Thus, more research trials are needed to improve the validity of the evidence of sustained cessation rates, i.e., over a year via e-health interventions.
Additionally, a cost-effectiveness analysis of e-health interventions and how to integrate them effectively into a daily workflow is necessary.
Nonetheless, the new digital health smoking cessation interventions based on widely used electronic products are attractive for several reasons. First, they are simple, convenient, and easy to disseminate.
Second, they offer the possibility of addressing the global issue of smoking in areas with disproportionate medical resources.
Third, they may be tailored to meet the needs of smoking populations with special needs, such as pregnant women, HIV-infected people, and those with depression.
Finally, despite the lack of a standardized methodological approach in assessing compliance across included studies, e-health remained a method with higher compliance and satisfaction than standard care in this study.
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