Chakraborty Bibhas, Maiti Raju, Strecher Victor J
Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore.
J Med Internet Res. 2018 Jun 20;20(6):e213. doi: 10.2196/jmir.9555.
Project Quit was a randomized Web-based smoking cessation trial designed and conducted by researchers from the University of Michigan, where its primary outcome was the 7-day point prevalence. One drawback of such an outcome is that it only focuses on smoking behavior over a very short duration, rather than the quitting process over the entire study period.
The aim of this study was to consider the number of quit attempts during the 6-month study period as an alternative outcome, which would better reflect the quitting process. We aimed to find out whether tailored interventions (high vs low) are better in reducing the number of quit attempts for specific subgroups of smokers.
To identify interactions between intervention components of smoking cessation and individual smoker characteristics, we employed Poisson regression to analyze the number of quit attempts. This approach allowed us to construct data-driven, personalized interventions.
A negative effect of the number of cigarettes smoked per day (P=.03) and a positive effect of education (P=.03) on the number of quit attempts were detected from the baseline covariates (n=792). Thus, for every 10 extra cigarettes smoked per day, there was a 5.84% decrease in the expected number of quit attempts. Highly educated participants had a 15.49% increase in their expected number of quit attempts compared with their low-educated counterparts. A negative interaction between intervention component story and smoker's education was also detected (P=.03), suggesting that a high-tailored story given to highly educated people results in 13.50% decrease in the number of quit attempts compared with a low-tailored story.
A highly individually tailored story is significantly more effective for smokers with a low level of education. This is consistent with prior findings from Project Quit based on the 7-day point prevalence.
“戒烟计划”是一项基于网络的随机戒烟试验，由密歇根大学的研究人员设计并开展，其主要结局是7天的时点患病率。这样一个结局的一个缺点是，它只关注非常短时期内的吸烟行为，而不是整个研究期间的戒烟过程。
本研究的目的是将6个月研究期间的戒烟尝试次数作为替代结局进行考量，这将能更好地反映戒烟过程。我们旨在查明针对特定亚组吸烟者，定制干预措施（高强度与低强度）在减少戒烟尝试次数方面是否更有效。
为了确定戒烟干预措施各组成部分与吸烟者个体特征之间的相互作用，我们采用泊松回归分析戒烟尝试次数。这种方法使我们能够构建数据驱动的个性化干预措施。
从基线协变量（n = 792）中检测到，每天吸烟支数对戒烟尝试次数有负面影响（P = 0.03），而教育程度对戒烟尝试次数有正面影响（P = 0.03）。因此，每天多吸10支烟，预期戒烟尝试次数就会减少5.84%。与低学历参与者相比，高学历参与者的预期戒烟尝试次数增加了15.49%。还检测到干预措施组成部分“故事”与吸烟者教育程度之间存在负向相互作用（P = 0.03），这表明与低定制化故事相比，给予高学历人群的高定制化故事可使戒烟尝试次数减少13.50%。
高度个性化定制的故事对低学历吸烟者的效果显著更佳。这与基于7天时点患病率的“戒烟计划”先前研究结果一致。
