van der Veer Sabine N, Ali S Mustafa, Yu Ziqiao, McBeth John, Chiarotto Alessandro, James Ben, Dixon William G
Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Pain Rep. 2024 Feb 16;9(2):e1131. doi: 10.1097/PR9.0000000000001131. eCollection 2024 Apr.
Many people worldwide suffer from chronic pain. Improving our knowledge on chronic pain prevalence and management requires methods to collect pain self-reports in large populations. Smartphone-based tools could aid data collection by allowing people to use their own device, but the measurement properties of such tools are largely unknown.
To assess the reliability, validity, and responsiveness of a smartphone-based manikin to support pain self-reporting.
We recruited people with fibromyalgia, rheumatoid arthritis, and/or osteoarthritis and access to a smartphone and the internet. Data collection included the Global Pain Scale at baseline and follow-up, and 30 daily pain drawings completed on a 2-dimensional, gender-neutral manikin. After deriving participants' pain extent from their manikin drawings, we evaluated convergent and discriminative validity, test-retest reliability, and responsiveness and assessed findings against internationally agreed criteria for good measurement properties.
We recruited 131 people; 104 were included in the full sample, submitting 2185 unique pain drawings. Manikin-derived pain extent had excellent test-retest reliability (intraclass correlation coefficient, 0.94), moderate convergent validity (ρ, 0.46), and an ability to distinguish fibromyalgia and osteoarthritis from rheumatoid arthritis (F statistics, 30.41 and 14.36, respectively; < 0.001). Responsiveness was poor (ρ, 0.2; , 0.06) and did not meet the respective criterion for good measurement properties.
Our findings suggest that smartphone-based manikins can be a reliable and valid method for pain self-reporting, but that further research is warranted to explore, enhance, and confirm the ability of such manikins to detect a change in pain over time.
全球许多人都患有慢性疼痛。提高我们对慢性疼痛患病率和管理的认识需要在大量人群中收集疼痛自我报告的方法。基于智能手机的工具可以让人们使用自己的设备来辅助数据收集，但此类工具的测量特性在很大程度上尚不清楚。
评估一种基于智能手机的人体模型在支持疼痛自我报告方面的可靠性、有效性和反应性。
我们招募了患有纤维肌痛、类风湿性关节炎和/或骨关节炎且能使用智能手机和互联网的人群。数据收集包括基线和随访时的全球疼痛量表，以及在一个二维、无性别差异的人体模型上完成的30份每日疼痛绘图。从参与者的人体模型绘图中得出其疼痛程度后，我们评估了收敛效度和区分效度、重测信度和反应性，并根据国际公认的良好测量特性标准评估结果。
我们招募了131人；104人纳入全样本，提交了2185份独特的疼痛绘图。通过人体模型得出的疼痛程度具有出色的重测信度（组内相关系数，0.94）、中等收敛效度（ρ，0.46），并且能够区分纤维肌痛和骨关节炎与类风湿性关节炎（F统计量分别为30.41和14.36；<0.001）。反应性较差（ρ，0.2；，0.06），未达到良好测量特性的相应标准。
我们 的研究结果表明，基于智能手机的人体模型可以是一种可靠且有效的疼痛自我报告方法，但需要进一步研究来探索、增强并确认此类人体模型检测疼痛随时间变化的能力。
