在城市的年轻哮喘患者中,对计算机设计的哮喘管理计划治疗无效的相关因素

2011/01/27

    摘要
    背景:在任何干预治疗的早期,鉴别可能对干预措施无效的参与者,有助于提醒治疗者制定更有效的措施,帮助行为改变和改善治疗计划的有效性。
    目的:本试验旨在城市青少年中研究与哮喘管理干预措施无效有关(如:缺乏行为变化)的因素。干预措施针对多个行为,包括用药依从性、随身携带急救吸入器和吸烟。
    方法:对来自一项研究干预措施的随机试验的数据进行判别分析。参与者为主诉有医生诊断的哮喘患者,基线状态下完成问卷调查,随机分为治疗组(完成4个教育主题中的至少2个,并完成3个随访问卷调查中的至少2个)。90名满足入选标准的学生纳入该亚组进行分析。
    结果:分析用药依从的logistic回归模型显示,干预无效与基线状态下哮喘自我调节较低相关,优势比=3.6(95%CI=1.3~9.5)。随身携带急救吸入器的模型分析显示,干预无效与基线状态下自我调节较低和治疗逆反心理相关(分别为OR 4.7[1.6-13.2]和5.6[1.7-18.0])。吸烟的模型分析显示,干预无效与治疗逆反心理、情感支持较低以及宗教信仰低相关(分别为:7.6[1.8-32.3], 9.5[1.4-63.5]和6.6[1.5-29.8])。
    结论:对于城市非洲裔美国青少年哮喘患者,某些变量能鉴别干预措施是否有效。这些变量可在干预早期用于鉴别干预无效的患者,从而可采取针对性干预措施。这些类型的分析有助于行为干预。
 
(刘国梁 审校)
J Asthma. 2010 Aug;47(6):667-73.
 
 
 
Factors associated with nonresponse to a computer-tailored asthma management program for urban adolescents with asthma.
 
Joseph CL, Havstad SL, Johnson D, Saltzgaber J, Peterson EL, Resnicow K, Ownby DR, Baptist AP, Johnson CC, Strecher VJ.
Department of Biostatistics and Research Epidemiology, Henry Ford Health System, Detroit, Michigan 48202, USA. cjoseph1@hfhs.org
 
Abstract
BACKGROUND: The ability to identify potentially resistant participants early in the course of an intervention could inform development of strategies for behavior change and improve program effectiveness.
OBJECTIVE: The objective of this analysis was to identify factors related to nonresponse (i.e., lack of behavior change) to an asthma management intervention for urban teenagers. The intervention targeted several behaviors, including medication adherence, having a rescue inhaler nearby, and smoking.
METHODS: A discriminate analysis was conducted using data from a randomized trial of the intervention. Included in this analysis are participants who reported a physician diagnosis of asthma, completed a baseline questionnaire, were randomized to the treatment group, completed >or=2 of 4 educational sessions, and completed >or=2 of 3 follow-up questionnaires. Ninety students met criteria for inclusion in this subgroup analysis.
RESULTS: In logistic regression models for medication adherence, nonresponse was related to low baseline asthma self-regulation, odds ratio = 3.6 (95% confidence interval = 1.3-9.5). In models for having an inhaler nearby, nonresponse was related to low baseline self-regulation and to rebelliousness, OR = 4.7 (1.6-13.2) and 5.6 (1.7-18.0), respectively. Nonresponse to smoking messages was related to rebelliousness, low emotional support, and low religiosity, ORs = 7.6 (1.8-32.3), 9.5 (1.4-63.5), and 6.6 (1.5-29.8) respectively.
CONCLUSIONS: Certain variables had the ability to discriminate the likelihood of response from that of nonresponse to an asthma program for urban, African American adolescents with asthma. These variables can be used to identify resistant subgroups early in the intervention, allowing the application of specialized strategies through tailoring. These types of analyses can inform behavioral interventions.


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