ABSTRACT

Digitally delivered behavioral interventions for chronic pain have been encouraging with effects similar to face-to-face treatment. Although many chronic pain patients benefit from behavioral treatment, a substantial proportion do not improve. To contribute to more knowledge about factors that predict treatment effects in digitally delivered behavioral interventions for chronic pain, the present study analyzed pooled data (N = 130) from three different studies on digitally delivered Acceptance and Commitment Therapy (ACT) for chronic pain. Longitudinal linear mixed-effects models for repeated measures were used to identify variables with significant influence on the rate of improvement in the main treatment outcome pain interference from pre- to post-treatment. The variables were sorted into six domains (demographics, pain variables, psychological flexibility, baseline severity, comorbid symptoms and early adherence) and analysed in a stepwise manner. The study found that shorter pain duration and higher degree of insomnia symptoms at baseline predicted larger treatment effects. The original trials from which data was pooled are registered at clinicaltrials.gov (registration number: NCT03105908 and NCT03344926).

摘要

针对慢性疼痛的数字化行为干预措施令人鼓舞，其效果与面对面治疗类似。尽管许多慢性疼痛患者受益于行为治疗，但很大一部分患者并未得到改善。为了有助于更多地了解预测慢性疼痛数字化行为干预治疗效果的因素，本研究分析了汇总数据（N = 130）来自三项针对慢性疼痛数字化接受与承诺疗法（ACT）的不同研究。使用重复测量的纵向线性混合效应模型来识别对治疗前至治疗后主要治疗结果疼痛干扰改善率有显着影响的变量。这些变量被分为六个领域（人口统计、疼痛变量、心理灵活性、基线严重程度、共病症状和早期依从性）并逐步分析。研究发现，基线时疼痛持续时间越短、失眠症状程度越高，治疗效果越好。汇集数据的原始试验已在 ClinicalTrials.gov 上注册（注册号：NCT03105908 和 NCT03344926）。