焦慮、憂鬱和自我效能應對策略與生活品質的關係: 台灣肺癌患者研究的橫斷面研究

背景：生活品質(QoL) 是腫瘤內科疾病管理和治療的必要結果，它備受焦慮、症狀負擔、認知和應對策略等因素影響。人們普遍認為焦慮和憂鬱會對肺癌 (LC) 患者的生活品質產生負面影響。然而，它在現實上並沒有得到很好的證實。了解肺癌患者的焦慮、憂鬱程度和自我效能感與生活品質之間的相關性，對於未來開展相關預防措施和臨床實踐具有重大意義。目的：本研究的目的是為了探討台灣肺癌患者的焦慮、憂鬱和自我效能應對策略與生活品質之間的關係。方法：本研究採用Moos 和 Schaefe 的理論。它探討了焦慮、憂鬱和自我效能應對策略與社會人口、醫學特徵和生活品質的關係。在納入和排除研究對象標準後，我們使用自訂的問卷來獲得研究參與者的人口統計特徵、醫療數據、吸煙狀況和對 covid-19 的擔憂。除此之外，我們也使用了幾份經過驗證的問卷，例如醫院焦慮和憂鬱量表 (HADS)來評估焦慮憂鬱程度、12 項健康調查簡表(SF-12)、癌症治療功能評估-肺癌分量表 (FATC-LCS) 來觀察參與者的生活品質，及癌症行為量表-簡表（CBI-Brief）來探討參與者的自我效能應對策略。結果：本研究成功招募了181名參與者。在本研究中使用的量表都擁有良好的可靠性：醫院焦慮和憂鬱量表（HADS）(Cronbach’s α=0.89)、癌症行為量表-簡表（CBI）(Cronbach’s α=0.88)、12項健康調查簡表（SF-12）(Cronbach’s α=0.88) 和癌症治療功能評估-肺癌分量表（FACT-LCS）(Cronbach‘s α=0.70)。研究結果顯示，焦慮與身理生活品質（QoL-PCS）有顯著的低度相關（r=-0.156，p<0.01），與癌症治療功能評估（FACT-LCS）有中度相關（-0.331，p<0.01），與心理生活品質（QoL-MCS）有高度相關（-0.706， p<0.01)。至於憂鬱，所有生活品質量表都顯示了中度的相關性。除此之外, 本研究也把癌症行為量表的子領域在生活品質進行了測試，發現癌症行為量表裡的子領域一（ D1）與身理生活品質（QoL-PCS）和癌症治療功能評估（FACT-LCS）的著低度相關，與心理生活品質（QoL-MCS）的中度相關。癌症行為量表裡的子領域二（D2）與所有生活品質卻只有低度相關，而癌症行為量表裡的子領域三（D3）與所有生活品質有著中度相關。至於癌症行為量表裡的子領域四（D4），僅在心裡生活品質（QoL-MCS ）上有中度相關。簡單線性回歸和多元線性回歸顯示，48.6% 的身理生活品質（QoL-PCS）評分可由癌症治療功能評估-肺癌分量表（ FACT-LCS）分數、切除治療和 70 歲以上的年齡來預測。至於心理生活品質（QoL-MCS）評分，41.9% 的變化則受癌症治療功能評估-肺癌分量表（ FACT-LCS）分數、焦慮評分、癌症行為量表裡的子領域一（ D1）和疾病持續時間來影響。關於癌症治療功能評估-肺癌（FACT-LCS），收入、癌症分期和癌症行為量表裡的子領域 4 預測了 16.7% 的變化。邏輯回歸顯示楔形/分段治療類型（勝算比，3.066；p=0.028）和癌症治療功能評估-肺癌分量表（FACT-LCS）分數（勝算比, 1.31; p=0.001) 是促成身理生活品質（QoL-PCS）良好得分的 2 個最重要因素。至於心理生活品質(QoL-MCS)，只有癌症治療功能評估-肺癌分量表( FACT-LCS)的分數（勝算比，1.287，p=0.004）有助於獲得好分數的概率。結論：持續評估患者的狀況有助於控制及改善症狀、增加患者對治療的滿意度和改善生活品質，並可能提高肺癌患者的生存率。在我們的研究中，我們發現焦慮、某些人口因素和自我效能感是生活品質的重要預測因素，我們希望這些發現可疑讓醫療政策上有個適當的改變，並提早提供篩檢好讓患者可以及時得到適當的治療。

關鍵字

焦慮；憂鬱；自我效能；生活品質；台灣肺癌患者

並列摘要

Background: Quality of life (QoL) is a necessary outcome in illness management and treatment in medical oncology and is influence by variables like anxiety, symptom burden, perceptions and coping strategies. Anxiety and depression were widely believed to negatively impair the QoL of lung cancer (LC) patients. However, empirical evidence is not conclusive. Understanding the level of anxiety, depression and self-efficacy in relation to QoL in LC patients is essential for the development of relevant interventions and clinical practices. Objectives: The purpose of this study is to examine the association between anxiety, depression and self-efficacy coping strategy and QoL in Taiwanese LC patients. Methodology: This study employs a framework based on Moos and Schaefe. It investigated the linked between anxiety, depression and self-efficacy coping strategies in relation to socio-demographic, medical characteristic and QoL variables. After applying inclusion and exclusion criteria, study participants were chosen using complete enumeration method. After applying inclusion and exclusion criteria, study participants were chosen using the complete enumeration technique. To investigate participants’ demographic characteristics, medical data, smoking status and covid-19 concern, a researcher-designed questionnaire was use. Perception of anxiety and depression were measured using HADS, QoL using SF-12 and FATC-LCS while CBI-B was selected for self-efficacy coping strategies. Results: In this study, 181 participants were recruited consecutively. In this study, the HADS (Cronbach's a=0.89), the CBI (Cronbach's a=0.88), the SF-12 (Cronbach's a=0.88), and the FACT-LCS (Cronabach's a=0.69) all demonstrated excellent reliability. In summary, the correlation between anxiety and QoL-PCS was found to be weak (r=-0.156, p<0.01), good with FACT-LCS (r=-0.331, p<0.01) and strong with QoL-MCS (r=-0.706, p<0.01). Regarding depression, a good correlation was seen between all QoL measures. Subdomain D1 of self-efficacy has a weak correlation with QoL-PCS and FACT-LCS, but a good correlation with QoL-MCS. Sub-domain D2 showed weak correlation with all the QoL while subdomain D3 displayed good correlation with all the QoL. As for subdomain D4, good correlation was reported only on QoL-MCS. Simple linear regression followed by multiple linear regression demonstrated that 48.6% of the variance in the QoL-PCS score is predicted by the FACT-LCS score, resection treatment, low income and age above 70. As for QoL-MCS score, 41.9% of the variance is determined by the FACT-LCS scores, anxiety scores, subdomain D1 of self-efficacy scores and duration of illness. Income, cancer stage, and CBI subdomain 4 predict 16.7% of the variance for the FACT-LCS. Logistic regression showed that wedge/segmental treatment type (odds ratio, 3.066; p=0.028) and FACT-LCS (odds ratio, 1.31; p=0.001) were the two most influential factors contributing to good score of SF-12 PCS. As for the SF-12 MCS, only FACT-LCS (odds ratio, 1.287, p=0.004) contributes to the likelihood of obtaining a good score. Conclusion: Continuous assessment of PROs is related with a higher likelihood of discussing patient outcomes during consultations, improved symptom control, increased patient satisfaction and QoL and may increase LC patients’ survival rate. In this study, it was shown that anxiety, certain demographic characteristics, and self-efficacy are major predictors of QoL, and we believe that these findings will encourage the implementation of a system that provides mandatory screening of PROs from the outset. In addition, we seek to raise awareness among healthcare professionals and cancer patients, in particular, on their right to receive this form of screening and intervention methods, such as supportive care needs/social support, if necessary.

並列關鍵字

Anxiety ； Depression ； Self-efficacy ； QoL ； LC patients

參考文獻

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