医学
骨关节炎
物理疗法
多项式logistic回归
队列
逻辑回归
膝关节痛
潜在类模型
体质指数
观察研究
物理医学与康复
内科学
替代医学
统计
机器学习
病理
计算机科学
数学
作者
Ali Kiadaliri,Helena Hörder,Stefan Lohmander,Leif Dahlberg
出处
期刊:Pain Medicine
[Oxford University Press]
日期:2023-12-21
卷期号:25 (4): 291-299
摘要
Abstract Objective Digital self-management programs are increasingly used in the management of osteoarthritis (OA). Little is known about heterogeneous patterns in response to these programs. We describe weekly pain trajectories of people with knee or hip OA over up to 52-week participation in a digital self-management program. Methods Observational cohort study among participants enrolled between January 2019 and September 2021 who participated at least 4 and up to 52 weeks in the program (n = 16 274). We measured pain using Numeric Rating Scale (NRS 0–10) and applied latent class growth analysis to identify classes with similar trajectories. Associations between baseline characteristics and trajectory classes were examined using multinomial logistic regression and dominance analysis. Results We identified 4 pain trajectory classes: “mild-largely improved” (30%), “low moderate-largely improved” (34%), “upper moderate-improved” (24%), and “severe-persistent” (12%). For classes with decreasing pain, the most pain reduction occurred during first 20 weeks and was stable thereafter. Male sex, older age, lower body mass index (BMI), better physical function, lower activity impairment, less anxiety/depression, higher education, knee OA, no walking difficulties, no wish for surgery and higher physical activity, all measured at enrolment, were associated with greater probabilities of membership in “mild-largely improved” class than other classes. Dominance analysis suggested that activity impairment followed by wish for surgery and walking difficulties were the most important predictors of trajectory class membership. Conclusions Our results highlight the importance of reaching people with OA for first-line treatment prior to developing severe pain, poor health status and a wish for surgery.
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