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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...

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Autores principales: Do, Huyen Phuc, Tran, Bach Xuan, Le Pham, Quyen, Nguyen, Long Hoang, Tran, Tung Thanh, Latkin, Carl A, Dunne, Michael P, Baker, Philip RA
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2018
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.
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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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