Wen Si, Wiers Reinout W, Boffo Marilisa, Grasman Raoul P P P, Pronk Thomas, Larsen Helle
Addiction Development and Psychopathology (ADAPT)-lab, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands.
Center for Urban Mental Health, University of Amsterdam, Amsterdam, the Netherlands.
Internet Interv. 2021 Oct 18;26:100473. doi: 10.1016/j.invent.2021.100473. eCollection 2021 Dec.
Web-based smoking interventions hold potential for smoking cessation; however, many of them report low intervention usage (i.e., high levels of non-usage attrition). One strategy to counter this issue is to tailor such interventions to user subtypes if these can be identified and related to non-usage attrition outcomes. The aim of this study was two-fold: (1) to identify and describe a smoker typology in participants of a web-based smoking cessation program and (2) to explore subtypes of smokers who are at a higher risk for non-usage attrition (i.e., early dropout times).
We conducted secondary analyses of data from a large randomized controlled trial (RCT) that investigated effects of a web-based Cognitive Bias Modification intervention in adult smokers. First, we conducted a two-step cluster analysis to identify subtypes of smokers based on participants' baseline characteristics (including demographics, psychological and smoking-related variables, = 749). Next, we conducted a discrete-time survival analysis to investigate the predictive value of the subtypes on time until dropout.
We found three distinct clusters of smokers: Cluster 1 (25.2%, = 189) was characterized by participants being relatively young, highly educated, unmarried, light-to-moderate smokers, poly-substance users, and relatively high scores on sensation seeking and impulsivity; Cluster 2 (41.0%, = 307) was characterized by participants being older, with a relatively high socio-economic status (SES), moderate-to-heavy smokers and regular drinkers; Cluster 3 (33.8%, = 253) contained mostly females of older age, and participants were further characterized by a relatively low SES, heavy smoking, and relatively high scores on hopelessness, anxiety sensitivity, impulsivity, depression, and alcohol use. Additionally, Cluster 1 was more likely to drop out at the early stage of the intervention compared to Cluster 2 (adjusted Hazard Ratio ( ) = 1.51, = [1.25, 1.83]) and Cluster 3 ( = 1.52, = [1.25, 1.86]).
We identified three clusters of smokers that differed on a broad range of characteristics and on intervention non-usage attrition patterns. This highlights the heterogeneity of participants in a web-based smoking cessation program. Also, it supports the idea that such interventions could be tailored to these subtypes to prevent non-usage attrition. The subtypes of smokers identified in this study need to be replicated in the field of e-health outside the context of RCT; based on the smoker subtypes identified in this study, we provided suggestions for developing tailored web-based smoking cessation intervention programs in future research.
基于网络的吸烟干预措施在戒烟方面具有潜力；然而，其中许多措施报告的干预使用率较低（即非使用损耗率较高）。应对这一问题的一种策略是，如果能够识别出用户亚型并将其与非使用损耗结果相关联，就针对这些亚型调整此类干预措施。本研究的目的有两个：（1）识别并描述基于网络的戒烟项目参与者中的吸烟者类型；（2）探索非使用损耗风险较高（即早期退出时间）的吸烟者亚型。
我们对一项大型随机对照试验（RCT）的数据进行了二次分析，该试验调查了基于网络的认知偏差修正干预对成年吸烟者的影响。首先，我们进行了两步聚类分析，以根据参与者的基线特征（包括人口统计学、心理和吸烟相关变量，n = 749）识别吸烟者亚型。接下来，我们进行了离散时间生存分析，以研究这些亚型对直至退出的时间的预测价值。
我们发现了三类不同的吸烟者：第1组（25.2%，n = 189）的特点是参与者相对年轻、受过高等教育、未婚、轻度至中度吸烟者、多物质使用者，以及在寻求刺激和冲动性方面得分相对较高；第2组（41.0%，n = 307）的特点是参与者年龄较大，社会经济地位（SES）相对较高，中度至重度吸烟者且经常饮酒；第3组（33.8%，n = 253）主要由年龄较大的女性组成，参与者的进一步特征是SES相对较低、重度吸烟，以及在绝望感、焦虑敏感性、冲动性、抑郁和酒精使用方面得分相对较高。此外，与第2组（调整后的风险比（HR）= 1.51，95%置信区间（CI）= [1.25, 1.83]）和第3组（HR = 1.52，95%CI = [1.25, 1.86]）相比，第1组在干预早期更有可能退出。
我们识别出了三类吸烟者，他们在广泛的特征和干预非使用损耗模式上存在差异。这凸显了基于网络的戒烟项目参与者的异质性。此外，这支持了这样一种观点，即可以针对这些亚型调整此类干预措施以防止非使用损耗。本研究中识别出的吸烟者亚型需要在RCT背景之外的电子健康领域进行重复验证；基于本研究中识别出的吸烟者亚型，我们为未来研究中开发针对性的基于网络的戒烟干预项目提供了建议。
