Department of Physiotherapy, Faculty of Health Sciences, Universidad de Malaga, Málaga, Spain.
Institute of Biomedical Research in Málaga, Málaga, Spain.
PLoS One. 2019 Apr 22;14(4):e0215662. doi: 10.1371/journal.pone.0215662. eCollection 2019.
Although breast cancer mortality is decreasing, morbidity following treatment remains a significant issue, as patients face symptoms such as cancer-related fatigue (CRF). The aim of the present study is to develop a classification system that monitors fatigue via integration of an objective clinical assessment with patient self-report. Forty-three women participated in this research. Participants were post-treatment breast cancer survivors who had been surgically treated for their primary tumour with no evidence of neoplastic disease at the time of recruitment. Self-perceived fatigue was assessed with the Spanish version of the Piper Fatigue Scale-Revised (R-PFS). Objective fatigue was assessed by the 30 second Sit-to-Stand (30-STS) test. Confirmatory factor analysis was done with Maximum Likelihood Extraction (MLE). Internal consistency was obtained by Cronbach's α coefficients. Bivariate correlation showed that 30-STS performance was negatively-inversely associated with R-PFS. The MANOVA model explained 54.3% of 30-STS performance variance. Using normalized scores from the MLE, a classification system was developed based on the quartiles. This study integrated objective and subjective measures of fatigue to better allow classification of patient CRF experience. Results allowed development of a classification index to classify CRF severity in breast cancer survivors using the relationship between 30-STS and R-PFS scores. Future research must consider the patient-perceived and clinically measurable components of CRF to better understand this multidimensional issue.
尽管乳腺癌死亡率正在下降,但治疗后的发病率仍然是一个重大问题,因为患者面临着诸如与癌症相关的疲劳(CRF)等症状。本研究的目的是开发一种分类系统,通过将客观的临床评估与患者自我报告相结合来监测疲劳。共有 43 名女性参与了这项研究。参与者为接受过手术治疗的乳腺癌治疗后幸存者,在招募时没有肿瘤疾病的证据。自我感知的疲劳用西班牙语版 Piper 疲劳量表修订版(R-PFS)进行评估。客观疲劳通过 30 秒坐立测试(30-STS)进行评估。使用最大似然提取(MLE)进行验证性因素分析。内部一致性通过 Cronbach's α 系数获得。双变量相关显示,30-STS 表现与 R-PFS 呈负相关。MANOVA 模型解释了 30-STS 表现方差的 54.3%。使用 MLE 的归一化分数,根据四分位数开发了一种分类系统。本研究综合了疲劳的客观和主观测量,以更好地对患者 CRF 体验进行分类。结果允许使用 30-STS 和 R-PFS 分数之间的关系开发一种分类指数,以对乳腺癌幸存者的 CRF 严重程度进行分类。未来的研究必须考虑到患者感知和临床可测量的 CRF 成分,以更好地理解这个多维问题。