Indices of pain variability: a paradigm shift.
Indices of pain variability: a paradigm shift.
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疼痛变异性指数：范式转变。
DOI：
10.1097/j.pain.0000000000001627
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发表时间：
2019
期刊：
影响因子：
7.4
通讯作者：
Keefe,FrancisJ
中科院分区：
文献类型：
--
作者：
Winger,JosephG;PlumbVilardaga,JenniferC;Keefe,FrancisJ
疼痛研究人员和科学家承认疼痛是动态的，可以有很大的变化。然而，我们对疼痛的了解大多来自于在单个时间点评估疼痛的研究。Mun等人的研究之所以重要，有几个原因。首先，它将注意力集中在评估疼痛作为一个动态过程的优势上。其次，它提供了关于密集的纵向数据方法和相关统计方法的科学状况的全面分析。沿着这些思路，他们强调了某些可变性统计指标（例如，急性变化的概率）的效用，这些指标可以为疼痛现象（例如，镰状细胞病、类风湿性关节炎或生命末期患者可能出现的急性疼痛发作）提供新的见解。最后，他们就如何选择测量方法、分析数据和以更有效和有意义的方式解释疼痛的可变性提供了明确和实用的指导。Mun等人的研究代表了大多数疼痛科学家的范式转变。许多疼痛科学要么依赖于单一的疼痛评估，要么依赖于有限的疼痛评估。不幸的是，在临床样本中，这些通常是回顾性的、自我报告的测量，容易受到回忆和其他报告偏差的影响。这种传统的方法不能很好地满足疼痛科学的最终目标，即理解、预测和影响作为动态现象的疼痛。技术为研究疼痛的变化提供了重要的新机会。智能手机应用程序可用或可以编程，使研究人员能够轻松地定期取样（例如，时间偶然抽样）或更接近重要事件的时间（例如，事件偶然抽样）。我们不再需要依赖纸和铅笔的方法，因为它们更繁琐，而且远离瞬间的经验，这两种方法都可能增加偏见。反复评估疼痛对临床医生和疼痛患者的好处是潜在的巨大的。首先，这些方法可以帮助临床医生更好地理解和验证患者的日常经历。其次，反复的疼痛评估是一种自我监控的形式，可以为患者提供有意义模式的视角，并且获得的见解可能有助于激励行为改变。第三，正如Mun等人所指出的8，每天密集的疼痛记录可能揭示预期和意外的治疗效果。5,7最后，这些方法通过改善治疗结果的预测，与日益强调的精准医学相适应。4
Pain researchers and scientists acknowledge that pain is dynamic and can vary substantially. Yet, much of what we know about pain comes from studies that assess pain at single time points. The study by Mun et al. 8 is important for several reasons. First, it focuses attention on the advantages of assessing pain as a dynamic process. Second, it provides a comprehensive analysis of the state of the science with regard to intensive longitudinal data methods and related statistical approaches. Along these lines, they highlight the utility of certain statistical indices of variability (eg, the probability of acute change) that can provide new insights into pain phenomena (eg, acute pain flares that may occur in patients with sickle cell disease, rheumatoid arthritis, or at the end of life). Finally, they offer clear and practical guidance on how to choose measurement approaches, analyze data, and interpret variability in pain in a more valid and meaningful fashion. The study by Mun et al. 8 represents a paradigm shift for most pain scientists. Much of pain science relies on either single or a limited set of pain assessments. Unfortunately, in clinical samples, these are often retrospective, self-report measures subject to recall and other reporting biases. 12 This traditional approach does a poor job of meeting the ultimate goal of pain science, ie, to understand, predict, and influence pain as dynamic phenomenon.Technology provides important and new opportunities to study variations in pain. Smartphone apps are available or can be programmed in ways that enable researchers to easily sample pain at regular intervals (eg, time contingent sampling) or much closer in time to events of importance (eg, event contingent sampling). 10 No longer do we need to rely on paper and pencil methods that are more burdensome and further removed from momentary experience, both of which may increase bias. The benefits of repeated assessments of pain for clinicians and patients with pain are potentially enormous. First, these approaches can help the clinician better understand and validate patients’ daily experiences. Second, repeated pain assessments are a form of self-monitoring that can provide patients with perspective on meaningful patterns and the insights gained may serve to motivate behavior change. 1 Third, as pointed out by Mun et al., 8 intensive daily recordings of pain may reveal both expected and unexpected treatment effects. 5, 7 Finally, these approaches fit well with the growing emphasis on precision medicine by improving the prediction of treatment outcomes. 4