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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 “smoki...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6188156/ https://www.ncbi.nlm.nih.gov/pubmed/30349201 http://dx.doi.org/10.2147/PPA.S169397 |
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author | Do, Huyen Phuc Tran, Bach Xuan Le Pham, Quyen Nguyen, Long Hoang Tran, Tung Thanh Latkin, Carl A Dunne, Michael P Baker, Philip RA |
author_facet | Do, Huyen Phuc Tran, Bach Xuan Le Pham, Quyen Nguyen, Long Hoang Tran, Tung Thanh Latkin, Carl A Dunne, Michael P Baker, Philip RA |
author_sort | Do, Huyen Phuc |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6188156 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-61881562018-10-22 Which eHealth interventions are most effective for smoking cessation? A systematic review Do, Huyen Phuc Tran, Bach Xuan Le Pham, Quyen Nguyen, Long Hoang Tran, Tung Thanh Latkin, Carl A Dunne, Michael P Baker, Philip RA Patient Prefer Adherence 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. Dove Medical Press 2018-10-08 /pmc/articles/PMC6188156/ /pubmed/30349201 http://dx.doi.org/10.2147/PPA.S169397 Text en © 2018 Do et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. |
spellingShingle | Review Do, Huyen Phuc Tran, Bach Xuan Le Pham, Quyen Nguyen, Long Hoang Tran, Tung Thanh Latkin, Carl A Dunne, Michael P Baker, Philip RA Which eHealth interventions are most effective for smoking cessation? A systematic review |
title | Which eHealth interventions are most effective for smoking cessation? A systematic review |
title_full | Which eHealth interventions are most effective for smoking cessation? A systematic review |
title_fullStr | Which eHealth interventions are most effective for smoking cessation? A systematic review |
title_full_unstemmed | Which eHealth interventions are most effective for smoking cessation? A systematic review |
title_short | Which eHealth interventions are most effective for smoking cessation? A systematic review |
title_sort | which ehealth interventions are most effective for smoking cessation? a systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6188156/ https://www.ncbi.nlm.nih.gov/pubmed/30349201 http://dx.doi.org/10.2147/PPA.S169397 |
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