Abstract
Background
Understanding how frailty affects health-related quality of life (HRQOL) in those with chronic kidney disease (CKD) could assist in the development of management strategies to improve outcomes for this vulnerable patient group. This study aimed to evaluate the relationship between frailty and HRQOL in patients with CKD Stages 4 and 5 (G4–5) and those established on haemodialysis (G5D).
Methods
Ninety participants with chronic kidney disease (CKD G4–5D) were recruited between December 2016 and December 2017. Frailty was assessed using the Frailty Phenotype, which included assessments of unintentional weight loss, weakness (handgrip strength), slowness (walking speed), physical activity and self-perceived exhaustion. HRQOL was assessed using the RAND 36-Item Health Survey Version 1.0 (SF-36).
Results
Nineteen (21%) patients were categorized as frail. Frailty, when adjusted for age, gender, dialysis dependence and comorbidity, had a significant effect on five of the eight SF-36 domains: physical functioning, role limitations due to emotional problems, energy/fatigue, social functioning and pain. Regression modelling best explained the variation in the physical functioning domain (adj. R2 = 0.27, P < 0.001), with frailty leading to a 26-point lower score. Exhaustion was the only Frailty Phenotype component that had a significant effect on scores across all SF-36 domains.
Conclusions
Frailty is independently associated with worse HRQOL in patients with CKD G4–5D, with self-perceived exhaustion being the most significant Frailty Phenotype component contributing to HRQOL. Efforts should be made to identify frail patients with CKD so that management strategies can be offered that aim to improve morbidity, mortality and patient-reported outcomes, including HRQOL and fatigue.
Keywords: chronic kidney disease, end-stage kidney disease, frailty, geriatric nephrology, haemodialysis, quality of life
INTRODUCTION
Frailty is the result of a sustained deterioration in multiple physiological processes that leads to a state of increased vulnerability associated with disability, hospitalizations and an increased mortality risk [1]. The prevalence of frailty is markedly higher in those with chronic kidney disease (CKD) than in the general older population [2, 3]. The trajectory from robustness to frailty is associated with progressive renal impairment, with significant muscle wasting, a major contributor to physical frailty in CKD patients, occurring prior to the commencement of dialysis [4–6]. Importantly, frailty is an independent risk factor for falls, hospitalization and death in those with CKD [2, 4, 7–14].
Irrespective of frailty status, patients with CKD have a considerable symptom burden, high health care utilization and poor health-related quality of life (HRQOL) [15–18]. Although frailty is linked with worse HRQOL in the general older population, the relationship between frailty and HRQOL is less certain in those with CKD [19]. The Frailty Phenotype is an operationalized definition of the construct of frailty and has been well studied in CKD cohorts [1, 2]. It is a composite measure that involves five distinct components, including assessments of unintentional weight loss, weakness, slowness, physical activity and exhaustion. The relative significance of these individual components on HRQOL in patients with CKD is not known. Understanding how frailty and its components affect HRQOL in those with CKD could assist in the development of targeted management strategies to improve outcomes for this vulnerable patient group.
The purpose of this study was to (i) evaluate the relationship between frailty, categorized by the Frailty Phenotype, and HRQOL and (ii) assess the relative significance of individual components of the Frailty Phenotype on HRQOL in patients with CKD Stages 4 and 5 (G4–5) and those established on haemodialysis (G5D).
MATERIALS AND METHODS
Study design and participant selection
This was a secondary analysis of data from the Frailty Assessment in Chronic Kidney Disease study that evaluated the diagnostic accuracy of frailty screening methods in a cohort of patients with advanced CKD [20]. Participants were recruited from nephrology outpatient clinics and two haemodialysis units at Lancashire Teaching Hospitals National Health Service (NHS) Foundation Trust between December 2016 and December 2017. Patients ≥18 years of age with CKD G4–5D were eligible for participation in the study. Exclusion criteria included patients who had a lower limb amputation, metastatic carcinoma, unstable angina or who had been diagnosed in the preceding 3 months with a myocardial infarction, transient ischaemic attack or stroke. Written informed consent was obtained for all participants. Ethical approval was obtained from the NHS Health Research Authority (IRAS ID 216379) and the study was conducted in accordance with the Declaration of Helsinki.
Data collection
Baseline demographic and clinical characteristics data were collected from medical records and during the participant interview and assessment. These data included age, height, weight, comorbidities, medication history, smoking history, blood pressure, falls history and laboratory variables.
A Charlson Comorbidity Index (CCI) score was calculated for all participants [21]. The CCI is a commonly used assessment of comorbidity that is predictive of outcomes in CKD populations [22–24].
A Karnofsky Performance Status Scale assessment, providing a measure of perceived performance that has been well-studied in CKD cohorts, was performed on all participants by a clinician [25, 26].
The Mini Mental State Examination (MMSE), a widely used screening tool for cognitive impairment, was performed on all participants [27, 28]. A cut-off ≤27 has a higher sensitivity for identifying cognitive impairment in symptomatic populations than the conventional cut-off of <24 [27].
All participants completed the Seniors in the Community: Risk Evaluation for Eating and Nutrition Index (SCREEN I), which is a validated nutritional risk screening tool for community-dwelling older adults [29, 30]. A score ≤50 has been suggested to identify individuals at nutritional risk [30].
Frailty was assessed using the Frailty Phenotype, which included assessments of unintentional weight loss, weakness (hand grip strength), slowness (walking speed), physical activity and self-perceived exhaustion. Frailty was diagnosed if three or more Frailty Phenotype components were present [1]:
The unintentional weight loss component was defined as a loss of ≥10 pounds or ≥5% body weight over the preceding 12 months [1].
Hand grip strength (Takei 5101 GRIP-D dynamometer, Takei Scientific Instruments Co., Niigata, Japan) was assessed in the seated position with the elbow positioned at 90 degrees, supported by the arm of a chair and the dynamometer supported by the assessor [31]. Both arms were examined, with the highest score from three efforts from each side being used for analysis. The body mass index and gender-stratified hand grip strength cut-offs proposed by the original Frailty Phenotype were used to describe weakness [1].
Walking speed was assessed by asking participants to walk 15 ft (4.57 m) at their normal walking pace on two occasions. Participants were advised to use their walking aid, if they normally used one. Infrared timing gates (Brower Timing System 2012, Brower Timing Systems, Draper, UT, USA) were used to record walking time. The fastest of two trials was used for analysis. Participants physically unable to complete the assessment were assigned the slowest time from within the cohort. The height- and gender-stratified walking speed cut-offs suggested by the original Frailty Phenotype were used to describe slowness [1].
Physical activity was assessed using a modified version of the Minnesota Leisure Time Questionnaire [32]. Low physical activity was defined as <383 kcal/week for men and <270 kcal/week for women [1].
Participants were read two statements from the Center for Epidemiological Studies Depression Scale to assess self-perceived exhaustion: (i) I felt that everything I did was an effort and (ii) I could not get going [33]. Participants were then asked, ‘How often did you feel this way?’ and were provided the following scale: 0 = rarely or none of the time, 1 = some of the time, 2 = moderate amount of the time, 3 = most of the time. Self-perceived exhaustion was described if an answer ≥2 was given for either statement [1].
HRQOL was assessed using the RAND 36-Item Health Survey Version 1.0 (SF-36), which is validated in general and CKD populations [34–38]. The SF-36 consists of 36 questions and assesses 8 domains of HRQOL: physical functioning, role limitations due to physical problems, role limitations due to emotional problems, emotional well-being, social functioning, energy/fatigue, pain and general health perceptions [34]. The answers to designated questions are transformed to create scores for HRQOL domains. The domain scores range from 0 to 100, with lower scores indicating worse HRQOL [34, 35]. The SF-36 also asks ‘Compared to one year ago, how would you rate your health in general now?’ Participants answer on a 1–5 scale, with 1 being ‘much better now than one year ago’ and 5 being ‘much worse now than one year ago’.
Statistical analysis
As a secondary analysis, no prospective sample size calculation was performed for the outcomes reported. Descriptive statistics were used to summarize demographic and clinical characteristics data. Pearson’s (for continuous data) or Spearman’s correlation (for ordinal data) was used to assess the correlation between SF-36 domain scores and Frailty Phenotype score, age and CCI score. Multiple linear regression was used to assess the magnitude of the association between frailty and SF-36 domain scores, adjusting for age, gender, dialysis-dependence and CCI scores, as well as the magnitude of the association between Frailty Phenotype components and SF-36 domain scores. A P-value <0.05 was considered statistically significant. All statistical analyses were performed on SPSS statistical software (version 24; IBM, Armonk, NY, USA).
RESULTS
Ninety participants completed the Frailty Phenotype assessment. Table 1 demonstrates the demographics and clinical characteristics of the overall cohort and of non-frail and frail participants. Nineteen (21%) participants were categorized as frail. Figure 1 illustrates the prevalence of Frailty Phenotype components.
Table 1.
Participant baseline demographic and clinical characteristics data
| Characteristics | Overall (n = 90) | Non-frail (n = 71) | Frail (n = 19) |
|---|---|---|---|
| Age (years) | 69 ± 13 | 68 ± 13 | 73 ± 11 |
| Female, n (%) | 45 (50) | 30 (42) | 15 (79) |
| BMI (kg/m2) | 29 ± 6 | 29 ± 6 | 28 ± 6 |
| CKD Stage | |||
| CKD G4-5, n (%) | 60 (67) | 51 (72) | 9 (47) |
| CKD G5D, n (%) | 30 (33) | 20 (28) | 10 (53) |
| CCI, median (IQR) | 3 (2) | 3 (2) | 4 (4) |
| Diabetes mellitus, n (%) | 24 (27) | 16 (23) | 8 (42) |
| Karnofsky score, median (IQR) | 70 (30) | 80 (20) | 60 (20) |
| Medications | 9 ± 4 | 8 ± 3 | 11 ± 5 |
| Current or ex-smoker, n (%) | 49 (54) | 40 (56) | 9 (47) |
| MMSE score ≤27a, n (%) | 18 (20) | 13 (19) | 5 (29) |
| Fall within last 6 months, n (%) | 16 (18) | 11 (15) | 5 (26) |
| SCREEN I score ≤50, n (%) | 70 (78) | 53 (75) | 17 (89) |
| Blood pressure (mmHg) | |||
| Systolic | 148 ± 20 | 148 ± 19 | 149 ± 25 |
| Diastolic | 72 ± 14 | 74 ± 14 | 67 ± 15 |
| Laboratory variables | |||
| Haemoglobin (g/L) | 116.3 ± 13.3 | 117.6 ± 12.7 | 111.4 ± 14.6 |
| White cell count (×109/L) | 7.7 ± 2.5 | 7.6 ± 2.5 | 8.0 ± 2.6 |
| CRPb (mg/L), median (IQR) | 5.3 (10.0) | 5.0 (10.7) | 5.5 (8.4) |
| Albumin (g/L) | 40.9 ± 3.3 | 41.3 ± 3.3 | 39.6 ± 3.3 |
| Total protein (g/L) | 67.4 ± 5.6 | 67.7 ± 5.3 | 66.2 ± 6.6 |
Data presented as mean ± standard deviation unless otherwise specified.
MMSE data were available for 87 participants.
CRP data were available for 64 participants.
BMI, body mass index; CRP, C-reactive protein; IQR, interquartile range.
FIGURE 1.
Prevalence of Frailty Phenotype components. Due to some participants having more than 1 Frailty Phenotype component, the sum of all the Frailty Phenotype components exceeds 100%.
Participant characteristics and HRQOL
Mean SF-36 scores divided by frailty status, Frailty Phenotype components, age <65 or ≥65 years, gender and dialysis-dependence are shown in Table 2. Frail participants had significantly lower mean SF-36 scores in the following domains: physical functioning, role limitations due to physical health, energy/fatigue, social functioning and pain. Participants categorized as weak or slow also had significantly lower scores in these SF-36 domains. In addition to these domains, participants with low physical activity had significantly lower scores in the role limitations due to emotional problems domain. Those categorized as suffering from exhaustion had significantly lower scores across all SF-36 domains, whereas there was no significant difference in the mean SF-36 domain scores for participants who reached the unintentional weight loss threshold. Only participants categorized as weak had significantly higher (worse) median scores for the question regarding health change.
Table 2.
SF-36 scores divided by frailty status, Frailty Phenotype component, age (< or ≥65 years), gender and dialysis dependence
| Physical functioning | Role limitations due to physical health | Role limitations due to emotional problems | Energy/ fatigue | Emotional well-being | Social functioning | Pain | General health | Change in health | |
|---|---|---|---|---|---|---|---|---|---|
| Frail status | |||||||||
| Non-frail | 58.1 ± 29.5 | 45.5 ± 43.9 | 63.4 ± 40.7 | 47.6 ± 22.5 | 74.8 ± 19.5 | 74.3 ± 29.6 | 68.7 ± 26.8 | 28.9 ± 18.6 | 3.0 (1.0) |
| Frail | 22.9 ± 21.7* | 21.1 ± 28.0** | 45.6 ± 50.0 | 27.1 ± 17.1* | 67.9 ± 24.8 | 52.0 ± 31.5** | 39.2 ± 27.1* | 27.6 ± 19.0 | 4.0 (2.0) |
| Weight loss | |||||||||
| Non-frail | 52.4 ± 31.4 | 41.4 ± 43.0 | 59.8 ± 42.5 | 44.2 ± 23.4 | 74.1 ± 20.7 | 69.6 ± 31.5 | 63.3 ± 29.0 | 30.0 ± 18.3 | 3.0 (1.0) |
| Frail | 30.7 ± 25.7 | 28.6 ± 30.4 | 57.1 ± 53.5 | 32.9 ± 14.4 | 64.1 ± 21.1 | 69.6 ± 29.6 | 52.5 ± 33.7 | 35.7 ± 20.9 | 3.0 (2.0) |
| Weakness | |||||||||
| Non-frail | 63.8 ± 28.1 | 49.5 ± 43.1 | 64.5 ± 43.1 | 48.7 ± 24.5 | 73.7 ± 21.9 | 76.3 ± 31.7 | 69.4 ± 28.1 | 27.0 ± 18.4 | 3.0 (1.0) |
| Frail | 36.3 ± 28.6* | 30.4 ± 39.2*** | 54.3 ± 43.0 | 37.3 ± 19.9*** | 73.0 ± 19.8 | 62.2 ± 29.3*** | 54.9 ± 29.1*** | 30.3 ± 18.8 | 4.0 (1.0**) |
| Slowness | |||||||||
| Non-frail | 58.1 ± 29.3 | 45.5 ± 43.5 | 61.0 ± 42.2 | 48.0 ± 22.1 | 75.2 ± 19.3 | 73.4 ± 30.0 | 70.1 ± 25.7 | 29.5 ± 18.3 | 3.0 (2.0) |
| Frail | 22.9 ± 22.3* | 21.1 ± 30.3** | 54.3 ± 47.4 | 25.5 ± 17.2* | 66.7 ± 25.0 | 55.3 ± 32.6*** | 33.9 ± 24.2* | 25.3 ± 19.5 | 4.0 (1.0) |
| Physical activity | |||||||||
| Non-frail | 59.2 ± 29.3 | 47.4 ± 44.1 | 69.0 ± 38.4 | 51.5 ± 21.2 | 76.0 ± 18.7 | 78.0 ± 28.0 | 68.1 ± 26.8 | 30.8 ± 31.3 | 3.0 (1.3) |
| Frail | 35.3 ± 29.6* | 27.6 ± 35.5*** | 42.7 ± 46.6** | 28.4 ± 18.5* | 68.6 ± 23.6 | 54.3 ± 31.4* | 52.3 ± 31.3** | 24.7 ± 15.9 | 4.0 (1.8) |
| Exhaustion | |||||||||
| Non-frail | 65.7 ± 30.0 | 54.5 ± 42.4 | 76.9 ± 33.4 | 56.4 ± 17.0 | 79.8 ± 15.7 | 81.7 ± 26.3 | 78.1 ± 19.7 | 32.1 ± 18.7 | 3.0 (1.0) |
| Frail | 30.2 ± 25.0* | 21.1 ± 33.7* | 36.0 ± 44.1* | 25.3 ± 17.3* | 64.5 ± 23.7* | 53.0 ± 30.1* | 41.1 ± 26.8* | 23.8 ± 17.4*** | 3.5 (2.0) |
| Age | |||||||||
| <65 years | 49.8 ± 30.9 | 35.2 ± 42.3 | 43.2 ± 46.1 | 33.1 ± 24.8 | 67.4 ± 25.1 | 57.4 ± 36.1 | 56.9 ± 32.4 | 21.3 ± 14.7 | 3.0 (2.0) |
| ≥65 years | 51.1 ± 31.9 | 42.6 ± 42.2 | 66.7 ± 40.2*** | 47.6 ± 20.9*** | 75.9 ± 18.3 | 74.8 ± 27.6 | 64.9 ± 27.8 | 31.7 ± 19.2** | 3.0 (1.0) |
| Gender | |||||||||
| Male | 60.3 ± 29.7 | 45.0 ± 42.5 | 57.8 ± 43.5 | 48.6 ± 21.7 | 74.8 ± 21.0 | 74.4 ± 27.8 | 66.3 ± 27.5 | 31.4 ± 20.2 | 3.0 (1.5) |
| Female | 41.1 ± 30.4** | 35.7 ± 41.8 | 61.5 ± 43.2 | 38.0 ± 23.2*** | 72.0 ± 21.0 | 64.7 ± 34.0 | 58.7 ± 30.9 | 25.8 ± 16.4 | 3.0 (1.5) |
| CKD Stage | |||||||||
| CKD G4-5 | 58.5 ± 29.9 | 44.3 ± 42.5 | 62.8 ± 42.6 | 44.9 ± 22.7 | 74.4 ± 20.8 | 72.7 ± 31.2 | 64.8 ± 28.3 | 29.5 ± 19.7 | 3.0 (1.0) |
| CKD G5D | 35.1 ± 28.8* | 32.8 ± 41.1 | 53.3 ± 44.3 | 40.0 ± 23.5 | 71.3 ± 21.1 | 63.3 ± 31.0 | 58.0 ± 31.3 | 26.9 ± 16.1 | 2.0 (1.0) |
Data presented as mean ± SD or median (IQR)
P < 0.001;
P < 0.01;
P < 0.05
SD, standard deviation; IQR, interquartile range.
Participants <65 years of age had significantly lower SF-36 scores in the following domains: role limitations due to emotional problems, energy/fatigue and general health. Female participants had significantly lower scores in the physical functioning and energy/fatigue SF-36 domains. Participants receiving dialysis had significantly lower scores in only the physical functioning SF-36 domain.
Correlation between HRQOL and frailty, age and comorbidity
Table 3 demonstrates the correlation between SF-36 domains and Frailty Phenotype score, age and CCI score. There was a significant negative correlation between all domain scores of the SF-36, except the general health domain, and Frailty Phenotype score. The correlation coefficients indicated a strong association between the physical functioning and energy/fatigue domain scores and the Frailty Phenotype score. There was a moderate association between the social functioning and pain domain scores and the Frailty Phenotype score. There was a significant positive correlation, though the coefficients revealed a weak association, between age and the following domain scores: role limitations due to emotional problems, emotional well-being, social functioning and general health. There was a significant negative correlation between CCI score and the SF-36 pain domain score; again, this coefficient suggested only a weak association.
Table 3.
Correlation between SF-36 domains and Frailty Phenotype score, age and CCI score
| Physical functioning | Role limitations due to physical health | Role limitations due to emotional problems | Energy/fatigue | Emotional well-being | Social functioning | Pain | General health | |
|---|---|---|---|---|---|---|---|---|
| Frailty Phenotype score, ρ | −0.65* (−0.77 to −0.50) | −0.38* (−0.55 to −0.20) | −0.35** (−0.54 to −0.15) | −0.65* (−0.76 to −0.51) | −0.27** (−0.47 to −0.06) | −0.52* (−0.68 to −0.33) | −0.53* (−0.68 to −0.36) | −0.11 (−0.31–0.11) |
| Age, r | −0.05 (−0.24–0.15) | 0.01 (−0.20–0.22) | 0.29** (0.08–0.48) | 0.17 (−0.06–0.39) | 0.23*** (0.01–0.42) | 0.25*** (0.02–0.46) | 0.08 (−0.14–0.30) | 0.22*** (0.03–0.41) |
| CCI score, ρ | −0.14 (−0.34–0.08) | −0.18 (−0.38–0.02) | 0.06 (−0.16–0.27) | −0.09 (−0.30–0.13) | 0.07 (−0.14–0.28) | −0.09 (−0.31–0.13) | −0.24*** (−0.43 to −0.04) | −0.01 (−0.23–0.21) |
Data presented as correlation coefficient (95% confidence interval).
P < 0.001;
P < 0.01;
P < 0.05.
Influence of frailty on HRQOL
Regression analyses assessing the magnitude of the association between frailty and SF-36 domains are presented in Table 4. Frailty, when adjusted for age, gender, dialysis-dependence and CCI score, had a significant effect on the following SF-36 domains: physical functioning, role limitations due to emotional problems, energy/fatigue, social functioning and pain. Regression modelling best explained the variation in the physical functioning domain score, with frailty leading to a 26-point lower score.
Table 4.
Regression analyses assessing the influence of frailty, age, gender, dialysis-dependence and comorbidity on SF-36 domains
| SF-36 domain | Unstandardized β coefficient (95% CI) | Standardized β coefficient | P-value |
|---|---|---|---|
| Physical functioning (adj. R2 = 0.27, P < 0.001) | |||
| Frail | −25.75 (−41.19 to −10.32) | −0.34 | 0.001 |
| Age | −0.05 (−0.51–0.41) | −0.02 | 0.82 |
| Female | −10.01 (−22.06–2.04) | −0.16 | 0.1 |
| Dialysis | −17.49 (−30.29 to −4.69) | −0.26 | 0.01 |
| CCI | −1.71 (−5.95–2.53) | −0.08 | 0.42 |
| Role limitations due to physical health (adj. R2 = 0.04, P = 0.13) | – | – | – |
| Role limitations due to emotional problems (adj. R2 = 0.10, P = 0.02) | |||
| Frail | −28.74 (−52.24 to −5.23) | −0.27 | 0.02 |
| Age | 1.05 (0.35–1.75) | 0.32 | 0.004 |
| Female | 9.59 (−8.75–27.94) | 0.11 | 0.3 |
| Dialysis | 0.22 (−19.26–19.71) | 0.002 | 0.98 |
| CCI | 2.46 (−3.99–8.92) | 0.08 | 0.45 |
| Energy/fatigue (adj. R2 = 0.16, P = 0.001) | |||
| Frail | −20.28 (−32.33 to −8.22) | −0.36 | 0.001 |
| Age | 0.46 (0.10–0.82) | 0.26 | 0.01 |
| Female | −6.90 (−16.31–2.51) | −0.15 | 0.15 |
| Dialysis | 2.35 (−7.64–12.34) | 0.05 | 0.64 |
| CCI | −0.97 (−4.28–2.34) | −0.06 | 0.56 |
| Emotional well-being (adj. R2 = 0.05, P = 0.09) | – | – | – |
| Social functioning (adj. R2 = 0.14, P = 0.004) | |||
| Frail | −23.41 (−40.07 to −6.74) | −0.31 | 0.01 |
| Age | 0.75 (0.25–1.24) | 0.31 | 0.003 |
| Female | −5.31 (−18.32–7.70) | −0.09 | 0.42 |
| Dialysis | −0.36 (−14.17–13.46) | −0.01 | 0.96 |
| CCI | −1.21 (−5.79–3.37) | −0.05 | 0.6 |
| Pain (adj. R2 = 0.18, P = 0.001) | |||
| Frail | −28.08 (−43.33 to −12.83) | −0.39 | <0.001 |
| Age | 0.40 (−0.06–0.85) | 0.18 | 0.09 |
| Female | −2.02 (−13.93–9.88) | −0.04 | 0.74 |
| Dialysis | −0.16 (−12.81–12.48) | −0.003 | 0.98 |
| CCI | −3.82 (−8.01–0.37) | −0.18 | 0.07 |
| General health (adj. R2 = 0.02, P = 0.23) | – | – | – |
Adj. R2, adjusted R2.
Regression analyses assessing the magnitude of the association between Frailty Phenotype components and SF-36 domains are displayed in Table 5. Self-perceived exhaustion was the only Frailty Phenotype component that had a significant effect on scores across all SF-36 domains. Unintentional weight loss did not have a significant effect on any of the SF-36 domain scores. Low physical activity had significant effects on physical functioning, role limitations due to emotional problems, energy/fatigue and social functioning domains. Weakness had a significant effect on the physical functioning and general health domains, whereas slowness had a significant effect on only the pain domain.
Table 5.
Regression analyses assessing the influence of Frailty Phenotype components on SF-36 domains
| SF-36 domain | Unstandardized β coefficient (95% CI) | Standardized β coefficient | P-value |
|---|---|---|---|
| Physical functioning (adj. R2 = 0.40, P < 0.001) | |||
| Weight loss frail | −3.55 (−24.00–16.90) | −0.03 | 0.73 |
| Weakness frail | −11.89 (−23.41 to −0.37) | −0.19 | 0.04 |
| Slowness frail | −12.63 (−28.04–2.79) | −0.17 | 0.11 |
| Physical activity frail | −11.76 (−23.11 to −0.40) | −0.18 | 0.04 |
| Exhaustion frail | −22.85 (−34.91 to −10.79) | −0.36 | <0.001 |
| Role limitations due to physical health (adj. R2 = 0.13, P = 0.01) | |||
| Weight loss frail | 0.17 (−32.93–33.28) | 0.001 | 0.99 |
| Weakness frail | −4.82 (−23.47–13.83) | −0.06 | 0.61 |
| Slowness frail | −4.07 (−29.03–20.89) | −0.04 | 0.75 |
| Physical activity frail | −10.38 (−28.76–8.01) | −0.12 | 0.27 |
| Exhaustion frail | −27.44 (−46.97 to −7.91) | −0.32 | 0.01 |
| Role limitations due to emotional problems (adj. R2 = 0.26, P < 0.001) | |||
| Weight loss frail | −0.26 (−31.53–31.01) | −0.002 | 0.99 |
| Weakness frail | 5.26 (−12.35–22.87) | 0.06 | 0.55 |
| Slowness frail | 22.88 (−0.69–46.45) | 0.22 | 0.06 |
| Physical activity frail | −21.00 (−38.36 to −3.64) | −0.23 | 0.02 |
| Exhaustion frail | −46.12 (−64.56 to −27.67) | −0.53 | <0.001 |
| Energy/fatigue (adj. R2 = 0.54, P < 0.001) | |||
| Weight loss frail | −0.07 (−13.19–13.06) | −0.001 | 0.99 |
| Weakness frail | 3.96 (−3.43–11.36) | 0.09 | 0.29 |
| Slowness frail | −4.34 (−14.23–5.55) | −0.08 | 0.39 |
| Physical activity frail | −15.56 (−22.85 to −8.27) | −0.33 | <0.001 |
| Exhaustion frail | −27.30 (−35.04 to −19.56) | −0.59 | <0.001 |
| Emotional well-being (adj. R2 = 0.12, P = 0.01) | |||
| Weight loss frail | −6.29 (−22.68–10.10) | −0.08 | 0.45 |
| Weakness frail | 7.21 (−2.03–16.44) | 0.17 | 0.12 |
| Slowness frail | 0.07 (−12.29–12.43) | 0.001 | 0.99 |
| Physical activity frail | −4.51 (−13.61–4.59) | −0.10 | 0.33 |
| Exhaustion frail | −16.56 (−26.23 to −6.90) | −0.40 | 0.001 |
| Social functioning (Adj. R2 = 0.24, P < 0.001) | |||
| Weight loss frail | 10.02 (−12.89–32.93) | 0.09 | 0.39 |
| Weakness frail | −1.35 (−14.26–11.56) | −0.02 | 0.84 |
| Slowness frail | −0.87 (−18.14–16.40) | −0.01 | 0.92 |
| Physical activity frail | −16.89 (−29.61 to −4.16) | −0.26 | 0.01 |
| Exhaustion frail | −24.62 (−38.14 to −11.11) | −0.39 | <0.001 |
| Pain (adj. R2 = 0.44, P < 0.001) | |||
| Weight loss frail | 10.71 (−7.69–29.10) | 0.10 | 0.25 |
| Weakness frail | 4.64 (−5.73–15.00) | 0.08 | 0.38 |
| Slowness frail | −23.57 (−37.44 to −9.71) | −0.33 | 0.001 |
| Physical activity frail | −3.12 (−13.33–7.10) | −0.05 | 0.55 |
| Exhaustion frail | −30.41 (−41.27 to −19.56) | −0.52 | <0.001 |
| General health (adj. R2 = 0.08, P = 0.04) | |||
| Weight loss frail | 11.17 (−3.82–26.16) | 0.16 | 0.14 |
| Weakness frail | 8.67 (0.23–17.12) | 0.24 | 0.04 |
| Slowness frail | −3.73 (−15.03–7.57) | −0.08 | 0.51 |
| Physical activity frail | −4.62 (−12.94–3.70) | −0.12 | 0.27 |
| Exhaustion frail | −10.36 (−19.20 to −1.52) | −0.28 | 0.02 |
Adj. R2, adjusted R2.
DISCUSSION
To our knowledge, this is the first study that explores the relationship between frailty, as categorized by the original Frailty Phenotype, and HRQOL in those with CKD G4–5 and CKD G5D. Furthermore, it is the first study that assesses the relative significance of individual Frailty Phenotype components on HRQOL in this distinct patient group. Studies by Mansur et al. [13] and Lee et al. [14] have demonstrated that frailty is associated with worse HRQOL in those with CKD. However, both studies used a modified version of the Frailty Phenotype to categorize frailty, replacing objective measures of grip strength and walking speed with a self-report assessment of physical function. Such an approach has been shown to substantially overestimate the prevalence of frailty [39]. Furthermore, the self-report assessment used was the physical functioning domain of the SF-36, which was also used within the assessment of HRQOL [13, 14, 35] Additionally, the ‘vitality domain’ of the SF-36 was used to determine the exhaustion component of their modified Frailty Phenotype [13, 14, 35]. Therefore it is difficult to interpret the findings, given the overlap of the frailty and HRQOL assessments. Iyasere et al. [40] demonstrated that frailty was associated with worse HRQOL, symptom burden and depression scores in those with dialysis-dependent CKD. Their study used the Clinical Frailty Scale that relies upon a health care professional’s assessment of frailty based upon descriptors of levels of frailty [41]. Although not as well studied as the Frailty Phenotype in CKD populations, the Clinical Frailty Scale has been shown to be an accurate screening tool for frailty (categorized by the Frailty Phenotype) [20] and predictive of mortality in patients with CKD [24, 42].
Our study confirms that frailty is significantly associated with worse HRQOL in patients with CKD G4–5D. Frailty Phenotype scores correlated with seven of the eight domains of the SF-36. Frail participants had significantly lower mean scores across five of the eight domains, specifically physical functioning, role limitations due to physical health, energy/fatigue, social functioning and pain domains. When adjusted for age, gender, dialysis-dependence and CCI, frailty was independently associated with at least a 20-point lower score in physical functioning, role limitations due to emotional problems, energy/fatigue, social functioning and pain SF-36 domains. Notably, when adjusting for frailty, the burden of comorbidity had no effect on SF-36 scores. Older age was not associated with worse HRQOL; in fact, older age was associated with a modest improvement in several SF-36 domains. This relationship has been reported previously and is perhaps due to changes in emotional regulation with age [43–45]. Female participants had worse HRQOL, specifically in physical functioning and energy/fatigue domains, a finding that has been reported elsewhere in the literature [43, 46, 47]. However, there was no significant effect noted within the regression model that included frailty. Frailty was an independent predictor of poor HRQOL in this advanced CKD cohort, highlighting the importance of the construct of frailty, over and above more traditional predictors of HRQOL [43], and emphasizing the importance of frailty screening in advanced CKD populations.
Participants categorized as exhausted, regardless of whether they were classified as frail overall, had lower mean scores across all SF-36 domains. Depending on the SF-36 domain, the exhaustion Frailty Phenotype component was associated with 10- to 46-point lower scores. Studies within the general older population have also found that this domain has the greatest effect on HRQOL [47, 48]. Exhaustion, also known as fatigue, is a commonly reported and especially problematic symptom in patients with advanced CKD, particularly for those receiving dialysis [49–51]. Fatigue is not only associated with worse HRQOL, but also survival in advanced CKD, with the Impact of Fatigue on Outcomes in the Hemodialysis (HEMO) Study demonstrating that an increase of 10 points in ‘vitality score’ was associated with a 10% increase in mean survival [52, 53]. Accordingly, addressing the causes of fatigue may be associated with improved HRQOL and survival in non-frail and frail patients alike. This is a challenging undertaking, as fatigue is a complex multidimensional and multifactorial issue [50]. Appropriate management of renal anaemia, adequate nutrition and prompt management of concurrent medical problems is essential [50]. However, there is also an association between fatigue and psychological distress; therefore, therapies that address mood and anxiety issues may also be associated with an improvement in fatigue symptoms [50, 54]. Sleep disorders are common in those with advanced CKD [55]. Cognitive behavioural therapy leads to improved sleep quality and reduced fatigue, thus it may be a useful therapy for frail patients with CKD [56]. Furthermore, low physical activity levels are associated with increased levels of fatigue [57]. Exercise improves fatigue in the general population and has been shown to improve HRQOL and fatigue in those with advanced CKD [58–60]. Evidence suggests that exercise training can improve physical function and HRQOL in frail older adults [61–63]. However, studies have not targeted patients with CKD who are pre-frail or frail, a group of patients who are typically poorly represented in interventional studies [64, 65]. Further evidence is needed on the feasibility of a rehabilitation programme for frail patients with advanced CKD. Ultimately, management strategies likely need to be multimodal and multidisciplinary, including nutritional, psychological and rehabilitation components [50, 66]. Additional evaluation of the relationship between fatigue and HRQOL in frail advanced CKD populations is needed, particularly to assess the relative contributions of physical capacity and psychological well-being.
There are acknowledged limitations of this study. First, the cross-sectional study design does not allow for conclusions to be made on causation. Longitudinal studies are required to assess for a causal relationship between frailty and HRQOL. Second, further investigation within more culturally diverse populations is needed given that participants within this study were recruited from a single-centre with a predominantly White British population. Finally, this is a secondary analysis of a study that was powered to assess the diagnostic accuracy of frailty screening methods in advanced CKD; therefore, the results presented in this analysis should be interpreted judiciously.
Conclusions
Frailty is independently associated with worse HRQOL in patients with CKD G4–5D. Exhaustion, or fatigue, is the most significant Frailty Phenotype component contributing to worse HRQOL in those with advanced CKD. Efforts should be made to identify frail patients with CKD so that management strategies can be offered that aim to improve morbidity, mortality and patient-reported outcomes, including HRQOL and fatigue. Additional study is needed to determine the most significant contributors to fatigue in frail patients with advanced CKD so treatment can be tailored for this vulnerable group of patients.
ACKNOWLEDGEMENTS
Dr Miland Joshi provided advice on statistical analyses. Alastair Petrie and Atinuke Afolabi assisted in study assessments.
CONFLICT OF INTEREST STATMENT
A.C.N. receives non-financial support from the National Institute for Health Research (NIHR) Lancashire Clinical Research Facility. Unrelated to this body of work, A.P.D. has received lecture fees from speaking at the invitation of MSD and received travel support from Pharmacosmos. The views expressed are those of the authors and not necessarily those of the NHS, NIHR or the Department of Health.
The results presented in this article have not been published previously in whole or part, except in abstract form. Preliminary data were published in the American Society of Nephrology Kidney Week 2017 Conference Abstract Supplement. This study is a secondary analysis of the Frailty Assessment in CKD study [20]. Demographic and clinical characteristics data are the same in both articles, as data were collected from the same cohort.
REFERENCES
- 1. Fried LP, Tangen CM, Walston J. et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001; 56: M146–156 [DOI] [PubMed] [Google Scholar]
- 2. Chowdhury R, Peel NM, Krosch M. et al. Frailty and chronic kidney disease: a systematic review. Arch Gerontol Geriatr 2016; 68: 135–142 [DOI] [PubMed] [Google Scholar]
- 3. Collard RM, Boter H, Schoevers RA. et al. Prevalence of frailty in community-dwelling older persons: a systematic review. J Am Geriatr Soc 2012; 60: 1487–1492 [DOI] [PubMed] [Google Scholar]
- 4. Roshanravan B, Khatri M, Robinson-Cohen C. et al. A prospective study of frailty in nephrology-referred patients with CKD. Am J Kidney Dis 2012; 60: 912–921 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Ballew SH, Chen Y, Daya NR. et al. Frailty, kidney function, and polypharmacy: The Atherosclerosis Risk in Communities (ARIC) study. Am J Kidney Dis 2017; 69: 228–236 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. John SG, Sigrist MK, Taal MW. et al. Natural history of skeletal muscle mass changes in chronic kidney disease stage 4 and 5 patients: an observational study. PloS One 2013; 8: e65372. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Johansen KL, Chertow GM, Jin C. et al. Significance of frailty among dialysis patients. J Am Soc Nephrol 2007; 18: 2960–2967 [DOI] [PubMed] [Google Scholar]
- 8. Bao Y, Dalrymple L, Chertow GM. et al. Frailty, dialysis initiation, and mortality in end-stage renal disease. Arch Intern Med 2012; 172: 1071–1077 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. McAdams-DeMarco MA, Law A, Salter ML. et al. Frailty as a novel predictor of mortality and hospitalization in individuals of all ages undergoing hemodialysis. J Am Geriatr Soc 2013; 61: 896–901 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. McAdams-DeMarco MA, Suresh S, Law A. et al. Frailty and falls among adult patients undergoing chronic hemodialysis: a prospective cohort study. BMC Nephrol 2013; 14: 224 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. McAdams-DeMarco MA, Law A, King E. et al. Frailty and mortality in kidney transplant recipients. Am J Transplant 2015; 15: 149–154 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. McAdams-DeMarco MA, Tan J, Salter ML. et al. Frailty and cognitive function in incident hemodialysis patients. Clin J Am Soc Nephrol 2015; 10: 2181–2189 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Mansur HN, Colugnati FA, Grincenkov FR. et al. Frailty and quality of life: a cross-sectional study of Brazilian patients with pre-dialysis chronic kidney disease. Health Qual Life Outcomes 2014; 12: 27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Lee SJ, Son H, Shin SK: Influence of frailty on health-related quality of life in pre-dialysis patients with chronic kidney disease in Korea: a cross-sectional study. Health Qual Life Outcomes 2015; 13: 70 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Brown SA, Tyrer FC, Clarke AL. et al. Symptom burden in patients with chronic kidney disease not requiring renal replacement therapy. Clin Kidney J 2017; 10: 788–796 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Khan SS, Kazmi WH, Abichandani R. et al. Health care utilization among patients with chronic kidney disease. Kidney Int 2002; 62: 229–236 [DOI] [PubMed] [Google Scholar]
- 17. McClellan WM, Abramson J, Newsome B. et al. Physical and psychological burden of chronic kidney disease among older adults. Am J Nephrol 2010; 31: 309–317 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. van de Luijtgaarden MWM, Caskey FJ, Wanner C. et al. Uraemic symptom burden and clinical condition in women and men of ≥65 years of age with advanced chronic kidney disease: results from the EQUAL study. Nephrol Dial Transplant 2018; 10.1093/ndt/gfy155 [DOI] [PubMed] [Google Scholar]
- 19. Kojima G, Iliffe S, Jivraj S. et al. Association between frailty and quality of life among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health 2016; 70: 716–721 [DOI] [PubMed] [Google Scholar]
- 20. Nixon AC, Bampouras TM, Pendleton N. et al. Diagnostic accuracy of frailty screening methods in advanced chronic kidney disease. Nephron 2019; 141: 147–155 [DOI] [PubMed] [Google Scholar]
- 21. Charlson ME, Pompei P, Ales KL. et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40: 373–383 [DOI] [PubMed] [Google Scholar]
- 22. Beddhu S, Bruns FJ, Saul M. et al. A simple comorbidity scale predicts clinical outcomes and costs in dialysis patients. Am J Med 2000; 108: 609–613 [DOI] [PubMed] [Google Scholar]
- 23. Fried L, Bernardini J, Piraino B.. Charlson comorbidity index as a predictor of outcomes in incident peritoneal dialysis patients. Am J Kidney Dis 2001; 37: 337–342 [DOI] [PubMed] [Google Scholar]
- 24. Pugh J, Aggett J, Goodland A. et al. Frailty and comorbidity are independent predictors of outcome in patients referred for pre-dialysis education. Clin Kidney J 2016; 9: 324–329 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Mor V, Laliberte L, Morris JN. et al. The Karnofsky Performance Status Scale: an examination of its reliability and validity in a research setting. Cancer 1984; 53: 2002–2007 [DOI] [PubMed] [Google Scholar]
- 26. van Loon IN, Wouters TR, Boereboom FT. et al. The relevance of geriatric impairments in patients starting dialysis: a systematic review. Clin J Am Soc Nephrol 2016; 11: 1245–1259 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Kukull WA, Larson EB, Teri L. et al. The Mini-Mental State Examination score and the clinical diagnosis of dementia. J Clin Epidemiol 1994; 47: 1061–1067 [DOI] [PubMed] [Google Scholar]
- 28. Tombaugh TN, McIntyre NJ. The mini-mental state examination: a comprehensive review. J Am Geriatr Soc 1992; 40: 922–935 [DOI] [PubMed] [Google Scholar]
- 29. Keller HH. The SCREEN I (Seniors in the Community: Risk Evaluation for Eating and Nutrition) index adequately represents nutritional risk. J Clin Epidemiol 2006; 59: 836–841 [DOI] [PubMed] [Google Scholar]
- 30. Keller HH, McKenzie JD, Goy RE.. Construct validation and test-retest reliability of the seniors in the community: risk evaluation for eating and nutrition questionnaire. J Gerontol A Biol Sci Med Sci 2001; 56: M552–558 [DOI] [PubMed] [Google Scholar]
- 31. Roberts HC, Denison HJ, Martin HJ. et al. A review of the measurement of grip strength in clinical and epidemiological studies: towards a standardised approach. Age Ageing 2011; 40: 423–429 [DOI] [PubMed] [Google Scholar]
- 32. Taylor HL, Jacobs DR Jr, , Schucker B. et al. A questionnaire for the assessment of leisure time physical activities. J Chronic Dis 1978; 31: 741–755 [DOI] [PubMed] [Google Scholar]
- 33. Orme JG, Reis J, Herz EJ.. Factorial and discriminant validity of the Center for Epidemiological Studies Depression (CES-D) scale. J Clin Psychol 1986; 42: 28–33 [DOI] [PubMed] [Google Scholar]
- 34.RAND Health Care. RAND Medical Outcomes Study. 36-Item Short Form Survey Instrument (SF-36). https://www.rand.org/health-care/surveys_tools/mos/36-item-short-form/survey-instrument.html
- 35. Ware JE Jr, Sherbourne CD.. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992; 30: 473–483 [PubMed] [Google Scholar]
- 36. Liem YS, Bosch JL, Arends LR. et al. Quality of life assessed with the Medical Outcomes Study Short Form 36-Item Health Survey of patients on renal replacement therapy: a systematic review and meta-analysis. Value Health 2007; 10: 390–397 [DOI] [PubMed] [Google Scholar]
- 37. Pagels AA, Soderkvist BK, Medin C. et al. Health-related quality of life in different stages of chronic kidney disease and at initiation of dialysis treatment. Health Qual Life Outcomes 2012; 10: 71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Lowrie EG, Curtin RB, LePain N. et al. Medical outcomes study short form-36: a consistent and powerful predictor of morbidity and mortality in dialysis patients. Am J Kidney Dis 2003; 41: 1286–1292 [DOI] [PubMed] [Google Scholar]
- 39. Painter P, Kuskowski M.. A closer look at frailty in ESRD: getting the measure right. Hemodial Int 2013; 17: 41–49 [DOI] [PubMed] [Google Scholar]
- 40. Iyasere OU, Brown EA, Johansson L. et al. Quality of life and physical function in older patients on dialysis: a comparison of assisted peritoneal dialysis with hemodialysis. Clin J Am Soc Nephrol 2016; 11: 423–430 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Rockwood K, Song X, MacKnight C. et al. A global clinical measure of fitness and frailty in elderly people. CMAJ 2005; 173: 489–495 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Alfaadhel TA, Soroka SD, Kiberd BA. et al. Frailty and mortality in dialysis: evaluation of a clinical frailty scale. Clin J Am Soc Nephrol 2015; 10: 832–840 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Mujais SK, Story K, Brouillette J. et al. Health-related quality of life in CKD patients: correlates and evolution over time. Clin J Am Soc Nephrol 2009; 4: 1293–1301 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Trief PM, Wade MJ, Pine D. et al. A comparison of health-related quality of life of elderly and younger insulin-treated adults with diabetes. Age Ageing 2003; 32: 613–618 [DOI] [PubMed] [Google Scholar]
- 45. Isaacowitz DM, Livingstone KM, Castro VL. Aging and emotions: experience, regulation, and perception. Curr Opin Psychol 2017; 17: 79–83 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Alcaniz M, Sole-Auro A. Feeling good in old age: factors explaining health-related quality of life. Health Qual Life Outcomes 2018; 16: 48 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Mulasso A, Roppolo M, Rabaglietti E. The role of individual characteristics and physical frailty on health related quality of life (HRQOL): a cross sectional study of Italian community-dwelling older adults. Arch Gerontol Geriatr 2014; 59: 542–548 [DOI] [PubMed] [Google Scholar]
- 48. Lin CC, Li CI, Chang CK. et al. Reduced health-related quality of life in elders with frailty: a cross-sectional study of community-dwelling elders in Taiwan. PLoS One 2011; 6: e21841. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Urquhart-Secord R, Craig JC, Hemmelgarn B. et al. Patient and caregiver priorities for outcomes in hemodialysis: an international nominal group technique study. Am J Kidney Dis 2016; 68: 444–454 [DOI] [PubMed] [Google Scholar]
- 50. Artom M, Moss-Morris R, Caskey F. et al. Fatigue in advanced kidney disease. Kidney Int 2014; 86: 497–505 [DOI] [PubMed] [Google Scholar]
- 51. Jhamb M, Liang K, Yabes J. et al. Prevalence and correlates of fatigue in chronic kidney disease and end-stage renal disease: are sleep disorders a key to understanding fatigue? Am J Nephrol 2013; 38: 489–495 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Jhamb M, Argyropoulos C, Steel JL. et al. Correlates and outcomes of fatigue among incident dialysis patients. Clin J Am Soc Nephrol 2009; 4: 1779–1786 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53. Jhamb M, Pike F, Ramer S. et al. Impact of fatigue on outcomes in the Hemodialysis (HEMO) Study. Am J Nephrol 2011; 33: 515–523 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54. Jhamb M, Weisbord SD, Steel JL. et al. Fatigue in patients receiving maintenance dialysis: a review of definitions, measures, and contributing factors. Am J Kidney Dis 2008; 52: 353–365 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55. Merlino G, Piani A, Dolso P. et al. Sleep disorders in patients with end-stage renal disease undergoing dialysis therapy. Nephrol Dial Transplant 2006; 21: 184–190 [DOI] [PubMed] [Google Scholar]
- 56. Chen H-Y, Cheng IC, Pan Y-J. et al. Cognitive-behavioral therapy for sleep disturbance decreases inflammatory cytokines and oxidative stress in hemodialysis patients. Kidney Int 2011; 80: 415–422 [DOI] [PubMed] [Google Scholar]
- 57. Gordon PL, Doyle JW, Johansen KL.. Postdialysis fatigue is associated with sedentary behavior. Clin Nephrol 2011; 75: 426–433 [PubMed] [Google Scholar]
- 58. Puetz TW. Physical activity and feelings of energy and fatigue: epidemiological evidence. Sports Med 2006; 36: 767–780 [DOI] [PubMed] [Google Scholar]
- 59. Heiwe S, Jacobson SH.. Exercise training in adults with CKD: a systematic review and meta-analysis. Am J Kidney Dis 2014; 64: 383–393 [DOI] [PubMed] [Google Scholar]
- 60. Wilkinson TJ, Watson EL, Gould DW. et al. Twelve weeks of supervised exercise improves self-reported symptom burden and fatigue in chronic kidney disease: a secondary analysis of the ‘ExTra CKD’ trial. Clin Kidney J 2019; 12: 113–121 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61. de Labra C, Guimaraes-Pinheiro C, Maseda A. et al. Effects of physical exercise interventions in frail older adults: a systematic review of randomized controlled trials. BMC Geriatr 2015; 15: 154 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62. Theou O, Stathokostas L, Roland KP. et al. The effectiveness of exercise interventions for the management of frailty: a systematic review. J Aging Res 2011; 2011: 569194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63. Clegg AP, Barber SE, Young JB. et al. Do home-based exercise interventions improve outcomes for frail older people? Findings from a systematic review. Rev Clin Gerontol 2012; 22: 68–78 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64. Ferrucci L, Guralnik JM, Studenski S. et al. Designing randomized, controlled trials aimed at preventing or delaying functional decline and disability in frail, older persons: a consensus report. J Am Geriatr Soc 2004; 52: 625–634 [DOI] [PubMed] [Google Scholar]
- 65. McMurdo ME, Roberts H, Parker S. et al. Improving recruitment of older people to research through good practice. Age Ageing 2011; 40: 659–665 [DOI] [PubMed] [Google Scholar]
- 66. Turner G, Clegg A. et al. Best practice guidelines for the management of frailty: a British Geriatrics Society, Age UK and Royal College of General Practitioners report. Age Ageing 2014; 43: 744–747 [DOI] [PubMed] [Google Scholar]