Mohammadpoorasl Asghar, Nedjat Saharnaz, Yazdani Kamran, Fakhari Ali, Foroushani Abbas Rahimi, Fotouhi Akbar
Department of Public Health, School of Health, Qazvin University of Medical Sciences, Qazvin, Iran.
Department of Epidemiology and Biostatistics, Knowledge Utilization Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Int J Prev Med. 2013 Nov;4(11):1304-11.
Notwithstanding the importance of smoking stages evaluation in adolescents, there is not an appropriate instrument for its measurement. This study aims to introduce an appropriate instrument for measurement of smoking stages in adolescents and to examine its validity using latent class analysis (LCA) model.
We designed an algorithm to measure the smoking stages. The relevancy and clarity of the algorithm was examined by experts and lay experts. We assessed the reliability of our algorithm using test-retest method. Moreover, using the LCA, we studied the validity of the stages measured by the designed algorithm in 4903 students (ages 14-19), who were randomly selected from grade 10 high school students in Tabriz (North-West of Iran).
The algorithm content validity indicates high relevancy and clarity percentages. Intra-class correlation of 0.929 was found in the assessment of the reliability of smoking stages (9 stages) in 154 students within a two-week interval. The LCA model revealed nine interpretable classes (G(2) = 0.051, df = 1, P = 0.821) for the measurement of smoking stages. Examination of the smoking cessation stages in a sample of 218 students in the cessation stage demonstrated that the results for five classes could be interpreted (G(2) = 0.001, df = 1, P = 0.975).
The results suggested that this algorithm is clear, valid, and reliable.
尽管青少年吸烟阶段评估很重要,但尚无合适的测量工具。本研究旨在引入一种适合测量青少年吸烟阶段的工具,并使用潜在类别分析(LCA)模型检验其有效性。
我们设计了一种算法来测量吸烟阶段。专家和非专业专家对该算法的相关性和清晰度进行了检验。我们使用重测法评估了算法的可靠性。此外,我们使用LCA研究了从大不里士(伊朗西北部)10年级高中生中随机抽取的4903名学生(年龄14 - 19岁)中,由设计算法测量的阶段的有效性。
算法内容效度显示出较高的相关性和清晰度百分比。在对154名学生吸烟阶段(9个阶段)的可靠性评估中,两周间隔内组内相关性为0.929。LCA模型揭示了用于测量吸烟阶段的九个可解释类别(G(2) = 0.051,自由度 = 1,P = 0.821)。对处于戒烟阶段的218名学生样本中的戒烟阶段进行检查表明,五个类别的结果可以解释(G(2) = 0.001,自由度 = 1,P = 0.975)。
结果表明该算法清晰、有效且可靠。