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Identifying Distinct Quitting Trajectories after an Unassisted Smoking Cessation Attempt: An Ecological Momentary Assessment Study

机译:自主戒烟尝试后识别不同的戒烟轨迹:一项生态矩评估研究

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Objectives: This study aimed at identifying distinct quitting trajectories over 29 days after an unassisted smoking ces- sation attempt by ecological momentary assessment (EMA). In order to validate these trajectories we tested if they predict smoking frequency up to six months later. Methods: EMA via mobile phones was used to collect real time data on smoking (yeso) after an unassisted quit attempt over 29 days. Smoking frequency one, three and six months after the quit attempt was assessed with online questionnaires. Latent class growth modeling was used to analyze the data of 230 self-quitters. Results: Four different quitting trajectories emerged: quitter (43.9%), late quitter (11.3%), returner (17%) and persistent smoker (27.8%). The quitting trajectories predicted smoking frequency one, three and six months after the quit attempt (all p < 0.001). Conclusions: Outcome after a smoking cessation attempt is better described by four distinct trajectories instead of a binary variable for abstinence or relapse. In line with the relapse model by Marlatt and Gordon, late quitter may have learned how to cope with lapses during one month after the quitting attempt. This group would have been allocated to the relapse group in traditional outcome studies.
机译:目的:本研究旨在通过生态瞬时评估(EMA)在无辅助吸烟尝试后的29天内识别出不同的戒烟轨迹。为了验证这些轨迹,我们测试了它们是否可以预测六个月后的吸烟频率。方法:在超过29天的无戒烟尝试后,通过手机EMA收集了实时吸烟数据(是/否)。尝试在线戒烟后1、3和6个月的吸烟频率进行了评估。潜伏类增长模型用于分析230个自发戒烟者的数据。结果:出现了四种不同的戒烟轨迹:戒烟者(43.9%),晚期戒烟者(11.3%),返回者(17%)和持续吸烟者(27.8%)。戒烟轨迹预测了戒烟尝试后一个月,三个月和六个月的吸烟频率(所有p <0.001)。结论:戒烟尝试后的结果可以通过四个不同的轨迹更好地描述,而不是戒酒或复发的二元变量。与Marlatt和Gordon的复发模型相一致,晚期戒烟者可能已经学会了如何应对戒烟后一个月内的失误。在传统结果研究中,该组将被分配到复发组。

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