Demographic variables, smoking variables, and outcome across five studies
Demographic variables, smoking variables, and outcome across five studies
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DOI：
10.1037/0278-6133.26.3.278
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发表时间：
2007-05-01
影响因子：
4.2
通讯作者：
Prochaska, James O.
中科院分区：
文献类型：
--
作者：
Velicer, Wayne F.;Redding, Colleen A.;Prochaska, James O.
目的：干预效果可能受到不同人口亚组成员（种族、民族、性别、年龄和教育水平）或吸烟行为变量（第一次吸烟的时间、以前最长的戒烟尝试、过去一年的尝试次数、香烟数量和改变阶段）的潜在影响。之前对这两组变量的研究产生了不同的结果。设计：该二次数据分析结合了5项有效性试验的数据（随机数字拨号样本[N = 1358]， HMO成员[N = 207]，学校研究招募的学生家长[N = 347]，保险公司名单中的患者[N = 535]和员工[N = 175]），其中吸烟者都是从确定的人群中主动招募的，并且都接受了相同的专家系统干预。干预在5项研究中产生了一致的22%至26%的点患病率戒烟率。主要结局指标：主要结局指标为24小时点患病率、7天点患病率、30天延长禁欲和6个月延长禁欲。结果：结果在性别、种族和民族亚组之间没有显著差异。年龄和教育亚组有显著差异和较小的效应量。所有5个吸烟行为变量均存在显著差异和较大的效应量。讨论：人口统计变量是静态变量，而吸烟变量是动态的，也就是说，是可以改变的。考虑到吸烟变量的动态性质和大的效应量，针对吸烟变量的干预措施应该更成功。
Objective: Intervention effectiveness can potentially be affected by membership in different demographic subgroups (race, ethnicity, gender, age, and education level) or smoking behavior variables (time to first cigarette, longest previous quit attempt, number of attempts in the past year, number of cigarettes, and stage of change). Previous research on these 2 sets of variables has produced mixed results. Design: This secondary data analysis combined data from 5 effectiveness trials (a random-digit-dial sample [N = 1,358], members of an HMO [N = 207], parents of students recruited for a school-based study [N = 347], patients from an insurance provider list [N = 535], and employees [N = 175]) in which smokers were all proactively recruited from a defined population and all received the same expert system intervention. The intervention produced a consistent 22% to 26% point prevalence cessation rate across the 5 studies. Main Outcome Measures: The main outcome measures were 24-hr point prevalence, 7-day point prevalence, 30-day prolonged abstinence, and, 6-month prolonged abstinence. Results: There were no significant differences in outcome across gender, race, and ethnicity subgroups. There were significant differences and small effect sizes for age and education subgroups. There were significant differences and large effect sizes for all 5 smoking behavior variables. Discussion: Demographic variables are static variables, whereas the smoking variables are more dynamic, that is, open to change. Given the dynamic nature of the smoking variables and the large effect sizes, interventions tailored on the smoking variables should be more successful.