Withdrawal Symptom, Treatment Mechanism, and/or Side Effect? Developing an Explicit Measurement Model for Smoking Cessation Research.
Withdrawal Symptom, Treatment Mechanism, and/or Side Effect? Developing an Explicit Measurement Model for Smoking Cessation Research.
复制标题
戒断症状、治疗机制和/或副作用？
DOI：
10.1093/ntr/nty262
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Hawk,LarryW
中科院分区：
文献类型：
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作者：
Tonkin,SarahS;Williams,TrevorF;Simms,LeonardJ;Tiffany,StephenT;Mahoney,MartinC;Schnoll,RobertA;Cinciripini,PaulM;Hawk,LarryW
戒断症状、治疗机制和副作用的评估对于理解和改善戒烟干预措施至关重要。虽然每个领域通常都是单独评估的，但广泛使用的调查问卷可以单独评估每个领域（例如，明尼苏达尼古丁戒断量表=戒断;吸烟欲望问卷-简短=渴望;积极和消极情感量表=情感;症状检查表=副作用），这种隐含的“一个问卷等于一个结构”测量模型存在实质性问题，包括问卷中的项目重叠。本研究旨在澄清戒烟过程中评估的结构的数量和性质，通过开发一个明确的测量model.MethodsTwo子样本随机创建1246吸烟者在临床试验。进行探索性和验证性因素分析，以确定和选择最能代表数据的模型。测量不变性进行了评估，以确定是否因素及其内容是一致的之前和期间退出。在此模型内的结构重叠的改进进行了比较，对隐式测量模型使用correlational analysis.ResultsA 5因素测量模型组成的负面影响，躯体症状，睡眠问题，积极的影响，和渴望，以及戒烟前和戒烟过程中的数据。除躯体症状外，所有因子内容均随时间一致。相关性分析表明，5-因素模型衰减构造重叠相比，隐式model.ConclusionsThe模型产生的数据驱动的方法（如，5-因素模型）减少重叠，更好地代表这些措施的结构。这种方法创建了独特的，稳定的结构，跨越措施的副作用和潜在的治疗mechanism.ImplicationsThis研究表明，措施评估治疗机制，戒断症状，副作用包含有问题的重叠，降低了这些关键结构的清晰度。使用数据驱动的方法表明，这些措施不映射到他们的假设潜在的结构（例如，明尼苏达尼古丁戒断量表不产生戒断因子）。相反，这些措施形成了独特的基本过程，可能代表了未来戒烟和治疗研究的更有意义的结构。旨在单独检查这些过程的评估可能会改善治疗机制的研究。
IntroductionAssessment of withdrawal symptoms, treatment mechanisms, and side effects is central to understanding and improving smoking cessation interventions. Though each domain is typically assessed separately with widely used questionnaires to separately assess each domain (eg, Minnesota Nicotine Withdrawal Scale = withdrawal; Questionnaire of Smoking Urges-Brief = craving; Positive and Negative Affect Schedule = affect; symptom checklist = side effects), there are substantial problems with this implicit “one questionnaire equals one construct” measurement model, including item overlap across questionnaires. This study sought to clarify the number and nature of constructs assessed during smoking cessation by developing an explicit measurement model.MethodsTwo subsamples were randomly created from 1246 smokers in a clinical trial. Exploratory and confirmatory factor analyses were conducted to identify and select a model that best represented the data. Measurement invariance was assessed to determine if the factors and their content were consistent prior to and during the quit. Improvement in construct overlap within this model was compared against the implicit measurement model using correlational analyses.ResultsA 5-factor measurement model composed of negative affect, somatic symptoms, sleep problems, positive affect, and craving fits the data well prior to and during quitting. All factor content except somatic symptoms was consistent over time. Correlational analyses indicated that the 5-factor model attenuated construct overlap compared to the implicit model.ConclusionsThe models generated from data-driven approaches (eg, the 5-factor model) reduced overlap and better represented the constructs underlying these measures. This approach created distinct, stable constructs that span over measures of side effects and potential treatment mechanisms.ImplicationsThis study demonstrated that measures assessing treatment mechanisms, withdrawal symptoms, and side effects contain problematic overlap that reduces the clarity of these key constructs. The use of data-driven approaches showed that these measures do not map on to their posited latent constructs (eg, the Minnesota Nicotine Withdrawal Scale does not yield a withdrawal factor). Rather, these measures form distinct, basic processes that may represent more meaningful constructs for future research on cessation and treatment. Assessments designed to individually examine these processes may improve the study of treatment mechanisms.