Treatment effect modifiers for return-to-work in patients with musculoskeletal disorders

The Journal of Pain(2024)

引用 0|浏览0
暂无评分
摘要
Investigating how individual characteristics modify treatment effects can improve understanding, interpretation, and translation of trial findings. The purpose of this secondary analysis was to identify treatment effect modifiers of the MI-NAV trial, a three arm, parallel randomized controlled trial which compared motivational interviewing and stratified vocational advice intervention in addition to usual case management, to usual case management alone. This study included (n=514) participants with musculoskeletal disorders on sick leave for at least 50% of their contracted work hours for at least 7 consecutive weeks with the Norwegian Labour and Welfare Administration. Sickness absence days was the primary outcome, measured from baseline assessment date until the six-month follow-up. Potential treatment effect modifiers, identified a priori and informed by expert consultation and literature, were evaluated using linear regression models and statistical interaction tests. For motivational interviewing versus usual case management, age (mean difference -0.7, 95% confidence interval -1.5 to 0.2; P=0.13) and self-perceived health status (mean difference -0.3, 95% confidence interval -0.7 to 0.1; P=0.19) were identified as potential effect modifiers (p ≤ 0.2). For stratified vocational advice intervention versus usual case management, analgesic medication use (MD -26.2, 95% CI -45.7 to -6.7; P=0.009) was identified as a treatment effect modifier (p ≤ 0.05). These findings may assist in more targeted treatment adaptation and translation as well as the planning of future clinical trials. Perspective This secondary analysis of the MI-NAV trial found that analgesic medication use, age and self-perceived health may modify the effect of two vocational interventions on reducing sickness absence in people with musculoskeletal disorders.
更多
查看译文
关键词
Treatment effect modifiers,vocational interventions,musculoskeletal disorders,chronic pain
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要