Depression, Rather Than Cancer-Related Fatigue or Insomnia, Decreased the Quality of Life of Cancer Patients

TPS9651 Background: Up to 100% of cancer patients report cancer-related fatigue (CRF) during chemotherapy which co-occurs with impaired cardiopulmonary (CPF) and neuromuscular function (NMF), chronically up-regulated inflammatory responses, and low metabolic energy expenditure. CRF interferes with completion of treatment, increases cancer morbidity and mortality, and impairs quality of life (QOL). Exercise is one of the most promising treatments available for CRF, but no large multicenter phase III RCTs have confirmed these findings and little is known about the mechanisms through which exercise may impact CRF. Methods: We are conducting a nationwide multicenter phase III RCT (N=692) through the URCC CCOP Research Base with 23 CCOP affiliates. The primary aim is to determine if exercise will significantly improve CRF, and secondarily, CPF, NMF, inflammation, energy expenditure, and QOL compared to standard care in cancer patients receiving chemotherapy. The exercise intervention is our standardized, individually-tailored, home-based walking and progressive resistance program, “Exercise for Cancer Patients” (EXCAP, 7 days/wk, 6wks). To be eligible, patients must: 1) have a confirmed diagnosis of cancer with no leukemia or metastasis, 2) be chemotherapy naïve and scheduled to start, 3) not be receiving concurrent radiation, 4) have a KPS of >70, 5) be ≥21 years of age, 6) have no contraindications to exercise or functional testing, and 7) not be currently exercising. CRF and all secondary outcomes are assessed at baseline (pre-chemotherapy), 3 weeks (mid-intervention), and 6 weeks (post-intervention). Measures include: 1) CRF-Brief Fatigue Inventory, 2) CPF-6-minute walk test, 3) NMF-handgrip dynamometry, 4) inflammation-serum ELISA levels, 5) energy expenditure-actigraphy, and 6) quality of life-Functional Assessment of Chronic Illness Therapy. The DSMC reviewed the trial in October of 2012 and suggested it continue as planned. 532 participants have been enrolled in 36 months. Funding: NCI U10CA037420, U10CA37402-28, K07CA120025, K07CA132916, 1R25CA102618, ACS MRSG1300101CCE. Clinical trial information: NCT00924651.

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Effects of Exercise on Cancer-related Fatigue and Quality of Life in Prostate Cancer Patients Undergoing Androgen Deprivation Therapy: A Meta-analysis of Randomized Clinical Trials

Chinese Medical Sciences Journal ◽

10.24920/j1001-9242.2007.002 ◽

2017 ◽

Vol 32 (1) ◽

pp. 13-21 ◽

Cited By ~ 6

Author(s):

Bo Yang ◽

Jiansheng Wang ◽

Jiansheng Wang

Keyword(s):

Quality Of Life ◽

Prostate Cancer ◽

Clinical Trials ◽

Cancer Patients ◽

Androgen Deprivation Therapy ◽

Randomized Clinical Trials ◽

Meta Analysis ◽

Androgen Deprivation ◽

Cancer Related Fatigue

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Cancer Related Fatigue and Quality of Life in Patients with Advanced Prostate Cancer Undergoing Chemotherapy

BioMed Research International ◽

10.1155/2016/3989286 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 22

Author(s):

Andreas Charalambous ◽

Christiana Kouta

Keyword(s):

Quality Of Life ◽

Prostate Cancer ◽

Cancer Patients ◽

Advanced Prostate Cancer ◽

Survival Times ◽

Structured Interviews ◽

Cross Sectional ◽

Cancer Related Fatigue ◽

Eortc Qlq

Cancer related fatigue (CRF) is a common and debilitating symptom that can influence quality of life (QoL) in cancer patients. The increase in survival times stresses for a better understanding of how CRF affects patients’ QoL. This was a cross-sectional descriptive study with 148 randomly recruited prostate cancer patients aiming to explore CRF and its impact on QoL. Assessments included the Cancer Fatigue Scale, EORTC QLQ-C30, and EORTC QLQ-PR25. Additionally, 15 in-depth structured interviews were performed. Quantitative data were analyzed with simple and multiple regression analysis and independent samplest-test. Qualitative data were analyzed with the use of thematic content analysis. The 66.9% of the patients experienced CRF with higher levels being recorded for the affective subscale. Statistically significant differences were found between the patients reporting CRF and lower levels of QoL (mean = 49.1) and those that did not report fatigue and had higher levels of QoL (mean = 72.1). The interviews emphasized CRF’s profound impact on the patients’ lives that was reflected on the following themes: “dependency on others,” “loss of power over decision making,” and “daily living disruption.” Cancer related fatigue is a significant problem for patients with advanced prostate cancer and one that affects their QoL in various ways.

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Factors That Related To Cancer Related Fatigue

South East Asia Nursing Research ◽

10.26714/seanr.1.1.2019.20-26 ◽

2019 ◽

Vol 1 (1) ◽

pp. 20

Author(s):

Santoso Tri Nugroho

Keyword(s):

Quality Of Life ◽

Sleep Quality ◽

Cancer Patients ◽

Economic Status ◽

Nursing Intervention ◽

Inclusion Criteria ◽

Cancer Related Fatigue ◽

Nursing Literature ◽

Google Search

About 40% to 100% of cancer patients complaint of fatigue. Cancer Related Fatigue is the most disturbing symptom compared to another symptom, like nausea and vomiting. Persistent cancer-related fatigue can impact on patient quality of life because the patient becoming too tired to involve in the activity. Need study to recognize factors that related to fatigue so that as a nurse we can choose accurate nursing intervention to overcome cancer-related fatigue. Objective: To identify factors related to cancer-related fatigue. Methods: Literature were searched via Google scholar and Google search with keyword: fatigue, cancer, and nursing. Literature were in full text and published by the year 1999-2016. Literature that was in inclusion criteria than be analyzed. Result: From 6 kinds of literature that were analyzed we found out factors that are related to cancer-related fatigue. That is characteristic (age, sex), sociodemography (economic status), stadium cancer, exercise, pain, depression, and sleep quality. But the factor that most related to cancer-related fatigue remains unclear. Conclusion: Age, sex, economic status, stadium, exercise, pain, depression and sleep quality are factors that can be used as predictor fatigue.

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