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Do HP, Tran BX, Le Pham Q, Nguyen LH, Tran TT, Latkin CA, Dunne MP, Baker PR. Which eHealth interventions are most effective for smoking cessation? A systematic review. Patient Prefer Adherence 2018; 12:2065-2084. [PMID: 30349201 PMCID: PMC6188156 DOI: 10.2147/ppa.s169397] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
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.
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Affiliation(s)
- Huyen Phuc Do
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia,
- Institute for Global Health Innovations, Duy Tan University, Danang, Vietnam,
| | - Bach Xuan Tran
- Department of Health, Behaviours and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
| | - Quyen Le Pham
- Department of Internal Medicine, Hanoi Medical University, Hanoi, Vietnam
| | - Long Hoang Nguyen
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
- Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
| | - Tung Thanh Tran
- Institute for Global Health Innovations, Duy Tan University, Danang, Vietnam,
| | - Carl A Latkin
- Department of Health, Behaviours and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Michael P Dunne
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia,
- Institute for Community Health Research, Hue University, Hue, Vietnam
| | - Philip Ra Baker
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia,
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