Effectiveness of the Offer of the Smoke Free Smartphone App Compared With No Intervention for Smoking Cessation: Pragmatic Randomized Controlled Trial.

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Sarah Jackson, Dimitra Kale, Emma Beard, Olga Perski, Robert West, Jamie Brown
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引用次数: 0

Abstract

Background: Digital technologies offer the potential for low-cost, scalable delivery of interventions to promote smoking cessation.

Objective: We aimed to evaluate the effectiveness of the offer of Smoke Free-an evidence-informed, widely used app-for smoking cessation versus no support.

Methods: In this 2-arm randomized controlled trial, 3143 motivated adult smokers were recruited online between August 2020 and April 2021 and randomized to receive an offer of the Smoke Free app plus follow-up (intervention arm) versus follow-up only (comparator arm). Both groups were shown a brief message at the end of the baseline questionnaire encouraging them to make a quit attempt. The primary outcome was self-reported 6-month continuous abstinence assessed 7 months after randomization. Secondary outcomes included quit attempts in the first month post randomization, 3-month continuous abstinence assessed at 4 months, and 6-month continuous abstinence at 7 months among those who made a quit attempt. The primary analysis was performed on an intention-to-treat (ITT) analysis basis. Sensitivity analyses included (1) restricting the intervention group to those who took up the offer of the app, (2) using complete cases, and (3) using multiple imputation.

Results: The effective follow-up rate for 7 months was 41.9%. The primary analysis showed no evidence of a benefit of the intervention on rates of 6-month continuous abstinence (intervention 6.8% vs comparator 7.0%; relative risk 0.97, 95% CI 0.75-1.26). Analyses of all secondary outcomes also showed no evidence of a benefit. Similar results were observed on complete cases and using multiple imputation. When the intervention group was restricted to those who took up the offer of the app (n=395, 25.3%), participants in the intervention group were 80% more likely to report 6-month continuous abstinence (12.7% vs 7.0%; relative risk 1.80, 95% CI 1.30-2.45). Equivalent subgroup analyses produced similar results on the secondary outcomes. These differences persisted after adjustment for key baseline characteristics.

Conclusions: Among motivated smokers provided with very brief advice to quit, the offer of the Smoke Free app did not have a detectable benefit for cessation compared with follow-up only. However, the app increased quit rates when smokers randomized to receive the app downloaded it.

Trial registration: ISRCTN ISRCTN85785540; https://www.isrctn.com/ISRCTN85785540.

International registered report identifier (irrid): RR2-https://onlinelibrary.wiley.com/doi/full/10.1111/add.14652.

提供 "无烟 "智能手机应用程序与不采取任何戒烟干预措施相比的效果:务实的随机对照试验。
背景数字技术为低成本、可扩展的戒烟干预提供了可能性:我们旨在评估提供 "Smoke Free"--一款循证、广泛使用的戒烟应用程序--与不提供戒烟支持的效果:在这项两臂随机对照试验中,我们在 2020 年 8 月至 2021 年 4 月间在线招募了 3143 名有戒烟动机的成年吸烟者,并随机分配他们接受 "无烟 "应用程序和随访(干预组)与仅随访(对比组)。在基线问卷调查结束时,两组受试者都会收到一条简短信息,鼓励他们尝试戒烟。主要结果是在随机分组 7 个月后对自我报告的 6 个月连续戒烟情况进行评估。次要结果包括随机分组后第一个月的尝试戒烟情况、4 个月时评估的 3 个月连续戒烟情况以及尝试戒烟者 7 个月时评估的 6 个月连续戒烟情况。主要分析以意向治疗(ITT)分析为基础。敏感性分析包括:(1) 将干预组限制为那些接受了该应用程序的人,(2) 使用完整病例,(3) 使用多重估算:7 个月的有效随访率为 41.9%。主要分析结果显示,没有证据表明干预对连续戒烟 6 个月的比率有任何益处(干预组 6.8% 对比对照组 7.0%;相对风险 0.97,95% CI 0.75-1.26)。对所有次要结果的分析也没有显示出干预的益处。在完整病例和使用多重归因法时也观察到了类似的结果。当干预组仅限于接受了该应用程序的参与者(人数=395,占 25.3%)时,干预组参与者报告连续戒断 6 个月的可能性增加了 80%(12.7% vs 7.0%;相对风险 1.80,95% CI 1.30-2.45)。在次要结果方面,等效亚组分析得出了相似的结果。在对主要基线特征进行调整后,这些差异依然存在:在接受了非常简短的戒烟建议的有戒烟意愿的吸烟者中,提供无烟应用与仅进行随访相比,在戒烟方面没有可检测到的益处。然而,当随机接受该应用的吸烟者下载该应用后,戒烟率有所提高:ISRCTN ISRCTN85785540; https://www.isrctn.com/ISRCTN85785540.International 注册报告标识符 (irrid):RR2-https://onlinelibrary.wiley.com/doi/full/10.1111/add.14652。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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