Which eHealth interventions are most effective for smoking cessation? A systematic review
Received 27 March 2018
Accepted for publication 21 July 2018
Published 8 October 2018 Volume 2018:12 Pages 2065—2084
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Johnny Chen
Huyen Phuc Do,1,2 Bach Xuan Tran,3,4 Quyen Le Pham,5 Long Hoang Nguyen,6,7 Tung Thanh Tran,2 Carl A Latkin,3 Michael P Dunne,1,8 Philip RA Baker1
1School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia; 2Institute for Global Health Innovations, Duy Tan University, Danang, Vietnam; 3Department of Health, Behaviours and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 4Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam; 5Department of Internal Medicine, Hanoi Medical University, Hanoi, Vietnam; 6Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden; 7Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam; 8Institute for Community Health Research, Hue University, Hue, Vietnam
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.
Keywords: effectiveness, eHealth, smoking cessation intervention, mHealth, website, computer
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