Cuenca-Martínez Ferran, López-Bueno Laura, Suso-Martí Luis, Varangot-Reille Clovis, Calatayud Joaquín, Herranz-Gómez Aida, Romero-Palau Mario, Casaña José
Exercise Intervention for Health Research Group (EXINH-RG), Department of Physiotherapy, University of Valencia, 46010 Valencia, Spain.
Department of Psychology, University of Valencia, 46010 Valencia, Spain.
J Clin Med. 2022 Mar 24;11(7):1806. doi: 10.3390/jcm11071806.
The main aim of this systematic review and meta-analysis (MA) was to assess the effectiveness of online behavior modification techniques (e-BMT) in the management of chronic musculoskeletal pain.
We conducted a search of Medline (PubMed), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, APA PsychInfo, and Psychological and Behavioral Collections, from inception to the 30 August 2021. The main outcome measures were pain intensity, pain interference, kinesiophobia, pain catastrophizing and self-efficacy. The statistical analysis was conducted using RStudio software. To compare the outcomes reported by the studies, we calculated the standardized mean difference (SMD) over time and the corresponding 95% confidence interval (CI) for the continuous variables.
Regarding pain intensity (vs. usual care/waiting list), we found a statistically significant trivial effect size in favor of e-BMT (n = 5337; SMD = -0.17; 95% CI -0.26, -0.09). With regard to pain intensity (vs. in-person BMT) we found a statistically significant small effect size in favor of in-person BMT (n = 486; SMD = 0.21; 95%CI 0.15, 0.27). With respect to pain interference (vs. usual care/waiting list) a statistically significant small effect size of e-BMT was found (n = 1642; SMD = -0.24; 95%CI -0.44, -0.05). Finally, the same results were found in kinesiophobia, catastrophizing, and self-efficacy (vs. usual care/waiting list) where we found a statistically significant small effect size in favor of e-BMT.
e-BMT seems to be an effective option for the management of patients with musculoskeletal conditions although it does not appear superior to in-person BMT in terms of improving pain intensity.
本系统评价和荟萃分析(MA)的主要目的是评估在线行为修正技术(e-BMT)在慢性肌肉骨骼疼痛管理中的有效性。
我们对Medline(PubMed)、护理及相关健康文献累积索引(CINAHL)、科学网、美国心理学会心理学文摘数据库(APA PsychInfo)以及心理与行为文集进行了检索,检索时间跨度从各数据库建库至2021年8月30日。主要结局指标包括疼痛强度、疼痛干扰、运动恐惧、疼痛灾难化和自我效能感。使用RStudio软件进行统计分析。为比较各研究报告的结果,我们计算了随时间变化的标准化均值差(SMD)以及连续变量对应的95%置信区间(CI)。
关于疼痛强度(与常规护理/等待列表相比),我们发现支持e-BMT的效应量在统计学上具有显著的微小差异(n = 5337;SMD = -0.17;95%CI -0.26,-0.09)。关于疼痛强度(与面对面BMT相比),我们发现支持面对面BMT的效应量在统计学上具有显著的小差异(n = 486;SMD = 0.21;95%CI 0.15,0.27)。关于疼痛干扰(与常规护理/等待列表相比),发现e-BMT的效应量在统计学上具有显著的小差异(n = 1642;SMD = -0.24;95%CI -0.44,-0.05)。最后,在运动恐惧、灾难化和自我效能感(与常规护理/等待列表相比)方面也发现了相同的结果,即支持e-BMT的效应量在统计学上具有显著的小差异。
e-BMT似乎是管理肌肉骨骼疾病患者的一种有效选择,尽管在改善疼痛强度方面似乎并不优于面对面BMT。