Unaffiliated.
Health Expect. 2014 Dec;17(6):840-51. doi: 10.1111/j.1369-7625.2012.00811.x. Epub 2012 Sep 20.
Decision making in knee osteoarthritis, with many treatment options, challenges patients and physicians alike. Unfortunately, physicians cannot describe in detail each treatment's benefits and risks. One promising adjunct to decision making in osteoarthritis is adaptive conjoint analysis (ACA).
To obtain insight into the experiences of elderly patients who use adaptive conjoint analysis to explore treatment options for their osteoarthritis.
Participants, all 65 and older, completed an ACA decision aid exploring their preferences with regard to the underlying attributes of osteoarthritis interventions. We used focus groups to obtain insight into their experiences using this software.
Content analysis distributed our participants' concerns into five areas. The predicted preferred treatment usually agreed with the individual's preference, but our participants experienced difficulty in four other domains: the choices presented by the software were sometimes confusing, the treatments presented were not the treatments of most interest, the researchers' claims about treatment characteristics were unpersuasive and cumulative overload sometimes developed.
Adaptive conjoint analysis presented special challenges to our elderly participants; we believe that their relatively low level of computer comfort was a significant contributor to these problems. We suggest that other researchers choose the software's treatments and present the treatment attributes with care. The next and equally vital step is to educate participants about what to expect, including the limitations in choice and apparent arbitrariness of the trade-offs presented by the software. Providing participants with a sample ACA task before undertaking the study task may further improve participant understanding and engagement.
在膝关节骨关节炎的治疗方案选择中，有许多治疗选择，这给患者和医生都带来了挑战。不幸的是，医生无法详细描述每种治疗的益处和风险。一种有前途的骨关节炎决策辅助方法是自适应联合分析（ACA）。
深入了解使用自适应联合分析探索骨关节炎治疗选择的老年患者的体验。
所有参与者均为 65 岁及以上的老年人，他们使用 ACA 决策辅助工具来探索他们对骨关节炎干预措施的潜在属性的偏好。我们使用焦点小组来深入了解他们使用该软件的体验。
内容分析将我们参与者的关注点分为五个领域。预测的首选治疗方案通常与个人的偏好一致，但我们的参与者在其他四个领域遇到了困难：软件呈现的选择有时令人困惑，呈现的治疗方案不是最感兴趣的治疗方案，研究人员对治疗特征的说法没有说服力，有时会出现累积过载。
自适应联合分析给我们的老年参与者带来了特殊的挑战；我们认为他们相对较低的计算机舒适度是这些问题的一个重要原因。我们建议其他研究人员谨慎选择软件的治疗方案并呈现治疗属性。下一个同样重要的步骤是教育参与者预期什么，包括对软件呈现的选择限制和明显的权衡的局限性。在进行研究任务之前，为参与者提供一个示例 ACA 任务可能会进一步提高参与者的理解和参与度。
