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基于网络的自适应选择式联合分析技术在诱发关节炎治疗偏好中的作用。

The Role of Web-Based Adaptive Choice-Based Conjoint Analysis Technology in Eliciting Patients' Preferences for Osteoarthritis Treatment.

机构信息

Department of Epidemiology and Population Health, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates.

Faculty of Health and Life Sciences, The University of Northumbria, Benton, Newcastle upon Tyne NE7 7XA, UK.

出版信息

Int J Environ Res Public Health. 2023 Feb 14;20(4):3364. doi: 10.3390/ijerph20043364.

DOI:10.3390/ijerph20043364
PMID:36834057
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9959784/
Abstract

OBJECTIVE

To assess the feasibility of using adaptive choice-based conjoint (ACBC) analysis to elicit patients' preferences for pharmacological treatment of osteoarthritis (OA), patients' satisfaction with completing the ACBC questionnaire, and factors associated with questionnaire completion time.

METHODS

Adult patients aged 18 years and older with a medical diagnosis of OA, experiencing joint pain in the past 12 months, and living in the Northeast of England participated in the study. The participants completed a web-based ACBC questionnaire about their preferences regarding pharmaceutical treatment for OA using a touchscreen laptop independently, and accordingly, the questionnaire completion time was measured. Moreover, the participants completed a pen-and-paper feedback form about their experience in completing the ACBC questionnaire.

RESULTS

Twenty participants aged 40 years and older, 65% females, 75% had knee OA, and suffering from OA for more than 5 years participated in the study. About 60% of participants reported completing a computerized questionnaire in the past. About 85% of participants believed that the ACBC task helped them in making decisions regarding their OA medications, and 95% agreed or strongly agreed that they would be happy to complete a similar ACBC questionnaire in the future. The average questionnaire completion time was 16 min (range 10-24 min). The main factors associated with longer questionnaire completion time were older age, never using a computer in the past, and no previous experience in completing a questionnaire.

CONCLUSIONS

The ACBC analysis is a feasible and efficient method to elicit patients' preferences for pharmacological treatment of OA, which could be used in clinical settings to facilitate shared decision-making and patient-centered care. The ACBC questionnaire completion consumes a significantly longer time for elderly participants, who never used a computer, and never completed any questionnaire previously. Therefore, the contribution of patients and public involvement (PPI) group in the development of the ACBC questionnaire could facilitate participants' understanding and satisfaction with the task. Future research including patients with different chronic conditions may provide more useful information about the efficiency of ACBC analysis in eliciting patients' preferences for osteoarthritis treatment.

摘要

目的

评估自适应选择联盟分析(ACBC)在诱发患者对骨关节炎(OA)药物治疗偏好、患者对完成 ACBC 问卷的满意度以及与问卷完成时间相关的因素方面的可行性。

方法

研究纳入了年龄在 18 岁及以上、经医学诊断患有 OA、过去 12 个月内有过关节疼痛且居住在英格兰东北部的成年患者。参与者通过触摸屏幕笔记本电脑独立完成了关于 OA 药物治疗偏好的基于网络的 ACBC 问卷,同时测量了问卷完成时间。此外,参与者还填写了一份纸质的反馈表,用于报告他们完成 ACBC 问卷的体验。

结果

共有 20 名年龄在 40 岁及以上的参与者参与了研究,其中 65%为女性,75%患有膝关节炎,且 OA 患病时间超过 5 年。约 60%的参与者报告过去曾完成过计算机化问卷。约 85%的参与者认为 ACBC 任务有助于他们做出 OA 药物治疗决策,95%的参与者同意或强烈同意他们将来愿意完成类似的 ACBC 问卷。平均问卷完成时间为 16 分钟(范围为 10-24 分钟)。与更长的问卷完成时间相关的主要因素是年龄较大、过去从未使用过计算机以及从未完成过问卷。

结论

ACBC 分析是一种可行且高效的方法,可以用来诱发患者对 OA 药物治疗的偏好,这可以在临床环境中用于促进共同决策和以患者为中心的护理。对于年龄较大、从未使用过计算机以及从未完成过任何问卷的参与者,ACBC 问卷的完成时间明显较长。因此,患者和公众参与(PPI)小组在 ACBC 问卷的开发过程中的贡献可以促进参与者对任务的理解和满意度。包括患有不同慢性疾病的患者的未来研究可能会提供有关 ACBC 分析在诱发患者对 OA 治疗偏好方面的效率的更有用信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31d0/9959784/85a3a8b4474d/ijerph-20-03364-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31d0/9959784/a8cf6c9333c7/ijerph-20-03364-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31d0/9959784/85a3a8b4474d/ijerph-20-03364-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31d0/9959784/a8cf6c9333c7/ijerph-20-03364-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31d0/9959784/85a3a8b4474d/ijerph-20-03364-g001.jpg

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