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Exploring psychosocial factors associated with frailty incidence among patients undergoing maintenance hemodialysis
Author(s) -
Yuan Huaihong,
Zhang Yingjun,
Xue Guifang,
Yang Yujie,
Yu Shaobin,
Fu Ping
Publication year - 2020
Publication title -
journal of clinical nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 102
eISSN - 1365-2702
pISSN - 0962-1067
DOI - 10.1111/jocn.15225
Subject(s) - psychosocial , medicine , social support , pittsburgh sleep quality index , depression (economics) , quality of life (healthcare) , gerontology , comorbidity , logistic regression , physical therapy , psychology , psychiatry , sleep quality , nursing , macroeconomics , economics , psychotherapist , cognition
Aim and objective We investigated the correlation between the frailty status of maintenance haemodialysis (MHD) patients and psychosocial factors. Background Varying degrees of frailty have been reported in MHD patients, which affect their quality of life. Design We adopted a cross‐sectional design in this study. Methods Clinical data of 187 patients at our centre were collected from December 2017–June 2018 using a cross‐sectional survey. Psychosocial factors were measured using the Pittsburgh Sleep Quality Index (PSQI), Hospital Anxiety and Depression Scale, 10‐item Connor‐Davidson Resilience Scale (CD‐RISC), Chronic Disease Self‐Efficacy Scales and Perceived Social Support Scale. Frailty status was estimated using the fatigue, resistance, ambulation, illnesses and loss of weight (FRAIL) scale. Spearman's correlation and multiple logistic regression analysis were conducted to identify the risk factors for frailty. This study complied with the STROBE checklist. Results Of 187 patients, 11 cases (5.9%) of frailty were identified. Patient's age, comorbidities, parathyroid hormone level, sleep quality and depression were positively correlated with frailty ( p < .05), while psychological resilience and social support were negatively correlated with frailty ( p < .05). Logistic regression analysis revealed four risk factors for frailty among MHD patients, including age ( p = .004), comorbidities ( p = .023), depression ( p = .023) and sleep disorders ( p = .029). Conversely, protective factors included high psychological resilience ( p = .019) and social support ( p = .039). Conclusion Among MHD patients, the risk factors for frailty included age, comorbidity, depression and sleep disturbance, whereas the protective factors included psychological resilience and social support. Relevance to clinical practice Frailty is not only common among older patients, but also among people of all age groups suffering from chronic diseases. Therefore, it is important to consider the health status of MHD patients and adopt targeted nursing strategies to alleviate symptoms of frailty and improve physical condition by the following ways: postpone the progress of comorbidities, improve sleep quality, control the symptoms of depression, foster psychological resilience and facilitate support from social and family.