Differential Characteristics of Fatigue–Pain–Sleep Disturbance–Depression Symptom Cluster and Influencing Factors of Patients With Advanced Cancer During Treatment

医学 萧条(经济学) 睡眠障碍 生活质量(医疗保健) 物理疗法 潜在类模型 逻辑回归 人口 横断面研究 失眠症 内科学 精神科 统计 护理部 数学 环境卫生 病理 经济 宏观经济学
作者
Yanxin Ye,Kai Zeng,Qing Lan,Jie Luo,Suting Liu,Jingxia Miao,Jingwen Liang,Yi Yu,Ming‐Hui Zhao,Lili Zhang
出处
期刊:Cancer Nursing [Ovid Technologies (Wolters Kluwer)]
标识
DOI:10.1097/ncc.0000000000001316
摘要

Patients with advanced cancer may experience symptom clusters during treatment (eg, fatigue, pain, sleep disturbance, depression). Understanding the characteristics and factors associated with symptom cluster classes among this patient population is essential for effective symptom management.The aims of this study were to identify symptom cluster (fatigue-pain-sleep disturbance-depression) classes and explore influencing factors in patients with advanced cancer during the treatment.A cross-sectional survey was conducted in an oncology department of a tertiary hospital in China from September 2020 to March 2021. Cancer patients (stage III/IV) 18 years or older completed the questionnaires on pain, fatigue, sleep disturbance, depression, physical activity, and exercise self-efficacy. Latent class analysis and multinomial logistic regression were used.Three hundred sixty-five patients who were male (65.2%) and younger than 60 years (59.5%) completed questionnaires. Three symptom cluster classes were identified: class 1 ("low symptom burden" class), class 2 ("fatigue-insomnia" class), and class 3 ("high symptom burden" class), with a percentage of 54.5%, 38.6%, and 6.8%, respectively. The quality-of-life score, introversion/extroversion, economic burden, Karnofsky Performance Status, albumin level, and exercise self-efficacy were significantly different among the 3 classes (P < .05).Patients with advanced cancer were classified into 3 distinct classes, with class 1 having the best function. Results from this study reveal that Karnofsky Performance Status, albumin level, and exercise self-efficacy were significant factors for the latent classes of symptom cluster.Exercise self-efficacy is important for personalized interventions and improving symptom management efficiency.
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