OBJECTIVES: To investigate whether post-treatment fatigue among men treated for prostate cancer varies by treatment, demographics, or pretreatment general and disease-specific health-related quality of life. We also sought to describe the baseline characteristics of men who were fatigued at follow-up to allow for interventions in those at greatest risk. METHODS: We conducted a secondary analysis on data gathered from men with prostate cancer at biopsy and after treatment by examining factors that predicted for post-treatment fatigue. RESULTS: Univariate and multivariate analysis results demonstrated that post-treatment fatigue was associated with baseline fatigue, role limitations due to emotional problems, treatment type, and treatment location. RESULTS: Univariate analysis showed that those who were fatigued at follow-up were more likely to have been treated at a public facility (P = 0.0017), be nonwhite (Latino, African American, or Asian Pacific-Islander; P = 0.0362), be married (P = 0.0413), be not employed at least part-time (P = 0.0327), to have one or more comorbidities (P = 0.0005), and to have scored lower in all domains of the RAND 36-Item Health Survey and UCLA Prostate Cancer Index at baseline (all P < or = 0.05) than those not fatigued at follow-up. Those who declined from baseline energy levels were more likely to have had lower baseline energy scores (P < 0.0001), to have been treated in a public facility (P = 0.0578), and to have had a baseline prostate-specific antigen level of 10 ng/mL or greater (P = 0.059) than those who remained at their baseline energy level. Lower baseline role-emotional scores were associated with both fatigue at follow-up and a decline from baseline at follow-up. CONCLUSIONS: Men with lower pretreatment quality-of-life measures may be at increased risk of fatigue after prostate cancer treatment.
OBJECTIVES: To investigate whether post-treatment fatigue among men treated for prostate cancer varies by treatment, demographics, or pretreatment general and disease-specific health-related quality of life. We also sought to describe the baseline characteristics of men who were fatigued at follow-up to allow for interventions in those at greatest risk. METHODS: We conducted a secondary analysis on data gathered from men with prostate cancer at biopsy and after treatment by examining factors that predicted for post-treatment fatigue. RESULTS: Univariate and multivariate analysis results demonstrated that post-treatment fatigue was associated with baseline fatigue, role limitations due to emotional problems, treatment type, and treatment location. RESULTS: Univariate analysis showed that those who were fatigued at follow-up were more likely to have been treated at a public facility (P = 0.0017), be nonwhite (Latino, African American, or Asian Pacific-Islander; P = 0.0362), be married (P = 0.0413), be not employed at least part-time (P = 0.0327), to have one or more comorbidities (P = 0.0005), and to have scored lower in all domains of the RAND 36-Item Health Survey and UCLA Prostate Cancer Index at baseline (all P < or = 0.05) than those not fatigued at follow-up. Those who declined from baseline energy levels were more likely to have had lower baseline energy scores (P < 0.0001), to have been treated in a public facility (P = 0.0578), and to have had a baseline prostate-specific antigen level of 10 ng/mL or greater (P = 0.059) than those who remained at their baseline energy level. Lower baseline role-emotional scores were associated with both fatigue at follow-up and a decline from baseline at follow-up. CONCLUSIONS:Men with lower pretreatment quality-of-life measures may be at increased risk of fatigue after prostate cancer treatment.
Authors: David H Henry; Hema N Viswanathan; Eric P Elkin; Shana Traina; Shawn Wade; David Cella Journal: Support Care Cancer Date: 2008-01-17 Impact factor: 3.603
Authors: Norbert Köhler; Lutz Gansera; Sigrun Holze; Michael Friedrich; Udo Rebmann; Jens-Uwe Stolzenburg; Michael C Truss; Dirk Fahlenkamp; Hans-Jörg Scholz; Elmar Brähler Journal: Support Care Cancer Date: 2014-05-14 Impact factor: 3.603
Authors: Karol M Pencina; Arthur L Burnett; Thomas W Storer; Wen Guo; Zhuoying Li; Adam S Kibel; Grace Huang; Michelle Blouin; Donna L Berry; Shehzad Basaria; Shalender Bhasin Journal: J Clin Endocrinol Metab Date: 2021-07-13 Impact factor: 5.958