Abstract
Objectives
Physical activity is recommended for disability prevention in the older adult population; however, the level of physical activity required for older adults with chronic kidney disease (CKD) remains unknown. This study aimed to examine the associations between daily physical activity and disability incidence in older adults with and without CKD to determine relevant daily physical activity levels.
Design
Prospective observational study.
Setting and Participants
3,786 community-dwelling older adults aged ≥65 years.
Measurements
Mean daily times spent in light- (LPA) and moderate-to-vigorous physical activity (MVPA) were measured using triaxial accelerometers. CKD was defined by a creatinine estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Disability incidence was identified as long-term care insurance certification during a 60-month follow-up period. Associations between physical activity and disability incidence were examined using Cox proportional hazard models stratified by the CKD status. Non-linear and linear associations were tested using the restricted cubic spline.
Results
A total of 1,054 individuals were identified to have CKD. Disability incidence was higher in the CKD group than in the non-CKD group. The adjusted cox proportional hazard models indicated that a 10-minute increase in MVPA time was associated with lower disability incidence in the non-CKD group (hazard ratio [HR], 0.838; 95% confidence interval [CI]: 0.764–0.918) and the CKD group (HR, 0.859; 95% CI: 0.766–0.960). Linear associations were observed in MVPA for the non-CKD and CKD groups.
Conclusion
Increasing MVPA was associated with lower disability incidence in older adults with and without CKD. These findings can help devise disability prevention strategies for older CKD patients.
Key words: Chronic kidney disease, physical activity, disability
Introduction
Chronic kidney disease (CKD) is a major health concern and a leading cause of decreased healthy life expectancy (1). The condition leads to adverse outcomes such as cardiovascular disease, hospitalization, poor quality of life (QOL), and mortality (2, 3, 4). The prevalence of CKD increases with age (5). In Japan, more than 25–32% of older adults are estimated to have moderate (stage 3) or worse CKD (6). Currently there is no universally established critical treatment for CKD, and the management of CKD-related complications and sequalae requires significant effort.
CKD puts older adults at risk of disability (7, 8). Several symptoms and complications of CKD related to disability have been observed in older patients with CKD, including: anemia, fatigue, skeletal muscle wasting due to chronic inflammation, and skeletal muscle dysfunction (9, 10). Hiraki et al. reported upper and lower extremity function and gait speed declined with the progression of CKD (11). Physical inactivity and malnutrition, as critical risk factors for disability, are also associated with CKD in older adults (12, 13). CKD progression leads to disability in older adults through progressive malnutrition, physical dysfunction, and physical inactivity — a process known as the frail cycle (14). Interventions to prevent disability via a progression of frailty is a significant concern among older adults with CKD.
Promoting physical activity is a key component in disability prevention in older individuals. Previous studies have reported that daily physical activity is effective for preserving muscle mass and preventing disability (15, 16). Recently, favorable effects of physical activity in CKD patients have been shown. CKD patients with increased levels of physical activity scored higher on physical function assessments in a cross-sectional study (17). Exercise interventions, including aerobic and resistance training, and lifestyle interventions are known to promote physical activity improve physical function without adversely affecting kidney function in CKD patients, compared with only usual care for CKD (18, 19). In addition, physical activity is expected to have a positive effect on CKD-related complications, such as cardiovascular risk factors and chronic inflammation (20). Therefore, promoting daily physical activity may have a positive preventive effect against functional disability in older adults with CKD.
Physical activity, especially of moderate-to-vigorous intensity, is an effective intervention for disability prevention in community-dwelling older adults (16). The American College of Sports Medicine (ACSM) recommends moderate-to-vigorous intensity physical activity (MVPA) for older adults and light-to moderate intensity exercise for deconditioned individuals (21). Light-intensity physical activity (LPA) is associated with a reduced level of frailty in older adults with chronic disease (22). In addition to MVPA, LPA may similarly help prevent disability incidence because CKD patients are likely to have low physical function and reduced cardiorespiratory function (11, 23). However, the association between daily physical activity, including relevant intensity, and disability incidence for CKD has not been sufficiently examined. It is necessary to identify these associations and relevant intensity levels of daily physical activity for older adults with CKD. Accordingly, the present study aimed to examine the association of physical activity and disability incidence in community-dwelling older adults, with and without CKD to determine the respective relationships and the relevant physical activity levels in these individuals.
Methods
Participants
This prospective observational study included community-dwelling older adults living in Nagoya and Takahama city, Japan, who were enrolled in a sub-cohort of the National Centre for Geriatrics and Gerontology-Study of Geriatric Syndromes (NCGG-SGS) (24). Baseline surveys were performed between September 2013 to December 2013, and September 2015 to June 2016 at a community center in Nagoya and Takahama city, respectively. This study included 8,457 individuals aged ≥ 65 years who did not require long-term care insurance (LTCI) care in Japan at the time of the baseline survey. Following the baseline survey, participants were asked to wear an accelerometer to measure their physical activity. The study excluded individuals who refused to wear an accelerometer or those who had insufficient physical activity data for analysis (n = 4,099). In addition, individuals with end-stage kidney disease (creatinine estimated glomerular filtration rate [eGFR] <15 mL/min/1.73 m2) (n = 16), dementia (n = 13), Parkinson's disease (n = 18), depression (n = 134), stroke (n = 242), Mini-Mental State Examination (MMSE) scores of ≤19 points (n = 33), decline in basic activities of daily living (ADL) (n = 3), and missing data on confounding variables (n = 113) were excluded as they were considered to have a higher risk of disability at baseline. The remaining sample for analysis included 3,786 participants (mean age: 74.1 ± 4.8 years, women: 57.2%).
This study was approved by the appropriate institutional review board (Approval Number: 1440-2) and meets the guidelines of the responsible government agency. Based on the opt-out approach, we disclosed study information and excluded participants' data when the participants declined to participate directly or via a proxy.
Chronic Kidney Disease definition
CKD was diagnosed based on creatinine-based eGFR <60 mL/min/1.73 m2 (25). Blood samples were collected from each participant between 10.00 and 18.00 hours in a non-fasting state. The collected samples were then stored at room temperature for 30 min to clot, at which time the samples were centrifuged for 15 min. Serum was then collected and stored at 10°C until analysis. Analyses were conducted in a single laboratory (Special Reference Laboratories, Tokyo, Japan). Creatinine-based eGFR values were calculated using the new Japanese coefficient-modified Modification of Diet in Renal Disease study equation: eGFR (mL/min/1.73 m2) = 194 × (serum creatinine)−1.094 × (age)−0.287 (× 0.739 for women) (26).
Physical activity measurement
This study measured the amount of time engaged in physical activity using triaxial accelerometers (GT40-20 and HW-100: Kao Corporation, Tokyo, Japan) set at a 4-second epoch length. The intensity level of an activity was calculated according to the same algorithm implemented in the Kenz Lifecorder (Suzuken Corporation, Limited: Aichi, Japan) (26), on a scale of 0.5 (minimal intensity of movement) to 9 (maximal intensity of movement). The accelerometer non-wearing time was defined as >35 consecutive minutes of 0 acceleration count.
In the present study, intensity levels 1–3 and 4–9 were defined as LPA (≥1.6 to <3 metabolic equivalents of task [METs]) and MVPA (≥3 METs). We calculated the daily time taken to perform LPAs and MVPAs by adding up all epochs in a day according to each intensity. Upon conclusion of the baseline survey, we asked participants to wear an accelerometer on the waist throughout the day, apart from bathing time and sleeping time in Takahama city. We configured the display of the accelerometer as no display, but it could not be set as blind in Takahama city. The accelerometer data was considered valid if the participants wore the device >10 hours a day for at least 7 of the initial 14 days, as recommended in a previous study (28). We then calculated the mean daily time spent engaged in LPA and MVPA.
Disability incidence
Disability incidence was identified as the time point following the baseline survey when the participants received LTCI disability certification and were determined to require care. The mean follow-up period was 60 months. Every Japanese adult aged ≥65 years is eligible for institutional and community-based services from the LTCI according to the ADL disability. Older adults who required nursing care services from the LTCI were examined to determine their current physical and mental status based on self-report information and information provided by their families. The assessment was conducted in two steps. First, the individual's physical function, basic ADL, cognitive function, behavioral and psychological symptoms, and social adaptation ability were assessed by trained local government officials, and the results were entered into a computer to determine the temporary care requirement level based on the estimated time required for care. Subsequently, this assessed care requirement level was confirmed by the Nursing Care Needs Certification Board, which included medical doctors, nurses, and other experts, based on the initial assessment and attending medical doctor's opinion (29).
Confounding variables
Data on age, sex, self-reported educational history, and smoking and alcohol drinking status was collected. Further, trained nurses collected a history of comorbidity (heart disease, hypertension, hyperlipidemia, diabetes, osteoporosis, and pulmonary disease) and the number of prescribed medications. The MMSE scores, Geriatric Depression Scale 15 (GDS-15) scores, body mass index (BMI), grip strength, and usual walking speed were assessed by trained staff. Grip strength was measured using a Smedley-type handheld dynamometer (Grip-D, Takei Ltd., Niigata, Japan). Walking speed was measured in seconds over a 2.4-m distance. Participants were instructed to transverse this distance at their usual walking speed. Participants walked for an additional 2-m before and after the measurement to ensure a consistent speed during measurement.
Statistical analysis
Differences in baseline sociodemographic and clinical characteristics between the non-CKD and CKD group were evaluated using Student's t-test for continuous variables and chi-square test for categorical variables. During follow-up, the cumulative incidence of disability (according to the CKD status) was calculated with Kaplan-Meier curves, and intergroup differences were estimated by the log-rank test.
Hazard ratios (HRs) and 95% confidence intervals (CIs) of disability incidence during the follow-up period were calculated according to the baseline CKD status and level of mean daily time spent in MVPA and LPA, as continuous variables. These values were calculated for all participants as well as the CKD. Non-CKD groups using Cox proportional hazard (CPH) models were adjusted for confounding variables related to disability incidence and CKD status. These included: age, sex, years of education, number of medications, comorbidity, MMSE scores, GDS-15 scores (0–4 or ≥5 points), usual walking speed (<1.0 m/sec or ≥1.0 m/sec) (30), grip strength (<28 kg or ≥28 kg for men, and <18 kg or ≥18 kg for women) (31), BMI, smoking habits, and alcohol consumption. The P values for interaction between CKD status and each level of physical activity were calculated with the multivariate CPH model. The mean daily time spent in each physical activity parameter was computed as the number of 10 minutes, because 10 minutes of behavioral change was considered feasible and acceptable (32). We added the restricted cubic spline with three knots in the adjusted models to test possible non-linear and linear associations between time engaged in physical activity and disability incidence. Knots were placed at 5th, 50th, and 95th percentiles according to a former study (33). The linearity of the association was examined using the Wald test.
Statistical significance was defined as P <0.05. All analyses were performed in R (version 4.1.0; R Core Team, Vienna Australia) including ‘Survival’ and ‘rms’ package.
Results
Table 1 presents the characteristics of all study participants (n = 3,786) and each group defined by the CKD status. CKD was diagnosed in 1,054 (mean age: 74.1 ± 5.0 years, women: 57.2%) individuals. Mean eGFR values in the CKD group were 52.2 mL/min/1.73 m2. 904, 138, and 12 out of 1,054 participants were classified as mildly to moderately (45≤, 60> mL/min/1.73 m2), moderately to severely (30≤, 45> mL/min/1.73 m2), and severely decreased kidney function (15≤, 30> mL/min/1.73 m2), respectively. Age, sex, MMSE scores, prevalence of comorbidities (heart disease, hypertension, and hyperlipidemia), number of medications, BMI, grip strength, walking speed, and smoking status were significantly different between these groups. Daily time spent engaged in LPA and MVPA were both higher in the non-CKD group.
Table 1.
Characteristics of the participants and comparison between participants with and without CKD
| Overall participants (n = 3,786) | non-CKD group (n = 2,732) | CKD group (n = 1,054) | p-value | |
|---|---|---|---|---|
| Age, years | 74.1 (5.0) | 73.3 (4.8) | 76.1 (4.8) | <0.001 |
| Sex, women, n (%) | 2,166 (57.2%) | 1,622 (59.4%) | 544 (51.6%) | <0.001 |
| Education, years | 11.7 (2.5) | 11.6 (2.5) | 11.8 (2.7) | 0.229 |
| MMSE scores, points | 26.8 (2.4) | 26.9 (2.4) | 26.6 (2.4) | <0.001 |
| GDS score, points | 2.6 (2.5) | 2.6 (2.5) | 2.7 (2.6) | 0.232 |
| Comorbidity | ||||
| Heart disease, n (%) | 630 (16.6%) | 390 (14.3%) | 240 (22.8%) | <0.001 |
| Hypertension, n (%) | 1,790 (47.3%) | 1,195 (43.7%) | 595 (56.5%) | <0.001 |
| Diabetes, n (%) | 467 (12.3%) | 322 (11.8%) | 145 (13.8%) | 0.110 |
| Hyperlipidemia, n (%) | 1,426 (37.7%) | 1,000 (36.6%) | 426 (40.4%) | 0.033 |
| Osteoporosis, n (%) | 489 (12.9%) | 346 (12.7%) | 143 (13.6%) | 0.491 |
| Respiratory disease, n (%) | 529 (14.0%) | 372 (13.6%) | 157 (14.9%) | 0.334 |
| Number of medications, n | 3.0 (2.5) | 2.8 (2.4) | 3.6 (2.8) | <0.001 |
| BMI, kg/m2 | 23.0 (3.1) | 22.9 (3.1) | 23.4 (3.0) | <0.001 |
| Grip strength, kg | 27.0 (7.5) | 26.8 (7.4) | 27.4 (8.0) | 0.036 |
| Walking speed, m/sec | 1.11 (0.21) | 1.12 (0.20) | 1.10 (0.21) | 0.004 |
| Current smoker, n (%) | 245 (6.5%) | 192 (7.0%) | 53 (5.0%) | 0.030 |
| Current alcohol drinker, n (%) | 1,432 (37.8%) | 1,055 (38.6%) | 377 (35.8%) | 0.114 |
| Accelerometer wearing time, min/day | 846.2 (115.5) | 847.6 (115.0) | 842.5 (116.9) | 0.227 |
| LPA, min/day | 44.1 (17.5) | 45.4 (18.0) | 40.7 (15.8) | <0.001 |
| MVPA, min/day | 27.6 (18.3) | 28.6 (18.6) | 25.1 (17.0) | <0.001 |
| eGFR, mL/min/1.73 m2 | 68.2 (14.0) | 74.3 (10.8) | 52.2 (6.9) | <0.001 |
Data are presented as mean (SD) or n (%). Student's t-test for continuous variables and chi-square test for categorical variable were performed. MMSE: Mini-Mental State Examination, GDS: Geriatric Depression Scale, BMI: Body Mass Index, LPA: Light Physical Activity, MVPA: Moderate-to-Vigorous Physical Activity, eGFR: Estimated Glomerular Filtration Rate.
Disability incidence was 24.5, 19.6, and 37.5/1000 person-years for all participants, non-CKD group and CKD group, respectively. Kaplan-Meier curves according to the CKD status are shown in Figure 1. Survival analyses performed using the Kaplan-Meier log-rank tests showed that the probability of disability incidence was significantly higher in the CKD group (P < 0.01). Participants in the CKD group had a significantly increased risk of disability incidence compared with those in the non-CKD group (HR, 1.309; 95% CI, 1.081–1.585; P = 0.006) in the adjusted CPH model.
Figure 1.
Estimates of survival rate in relation to disability incidence according to the CKD status of the groups
Table 2 shows the results and forest plots of crude and adjusted CPH models used to examine the association of time engaged in physical activity and disability incidence in all participants, as well as each CKD status group. MVPA (HR, 0.834; 95% CI, 0.772–0.901; P < 0.001) was significantly associated with decreased disability incidence in the overall participants, conversely LPA showed no evidence (HR, 0.949; 95% CI, 0.887–1.016; P = 0.135) in the adjusted model. MVPA was associated with decreased disability incidence in the non-CKD group (HR, 0.838; 95% CI, 0.764–0.918; P < 0.001), while LPA showed no evidence (HR, 0.950; 95% CI, 0.879–1.029; P = 0.214). Similarly, MVPA was associated with decreased disability incidence in the CKD group (HR, 0.859; 95% CI, 0.766-0.960; P = 0.009), while LPA showed no evidence (HR, 0.929; 95% CI, 0.835–1.033; P = 0.172). The P values for interaction were 0.400 for LPA and 0.729 for MVPA.
Table 2.
Estimated relations between neighborhood walkability (walk scores) and sarcopenia scores
| Model | Hazard ratio (95% CI) | P value | |
|---|---|---|---|
| All Patients - Crude model | |||
| LPA, 10min/day | 0.747 (0.700 - 0-797) | <0.001 | |
| MVPA, 10min/day | 0.708(0.657 - 0.762) | <0.001 | |
| All participants - Adjusted model | |||
| LPA, 10min/day | 0.949 (0.887 - 1.016) | 0.135 | |
| MVPA, 10min/day | 0.834(0.772 - 0.901) | <0.001 | |
| non-CKD group - Crude model | |||
| LPA, 10min/day | 0.776 (0.716 - 0.842) | <0.001 | |
| MVPA, 10min/day | 0.692(0.628 - 0.762) | <0.001 | |
| non-CKD group - Adjusted model | |||
| LPA, 10min/day | 0.950(0.879 - 1.029) | 0.214 | |
| MVPA, 10min/day | 0.838 (0.764 - 0.918) | <0.001 | |
| CKD group - Crude model | |||
| LPA, 10min/day | 0.725 (0.651 - 0.809) | <0.001 | |
| MVPA, 10min/day | 0.746(0.665 - 0.837) | <0.001 | |
| CKD group - Adjusted model | |||
| LPA, 10min/day | 0.929(0.835 - 1.033) | 0.172 | |
| MVPA, 10min/day | 0.859 (0.766 - 0.960) | 0.009 | |
CKD: Chronic Kidney Disease, LPA: Light Physical Activity, MVPA: Moderate-to-Vigorous Physical Activity; Adjusted for age, sex, education, Geriatric Depression Scale, Mini-Mental State Examination, Body Mass Index, number of medications, heart disease, hypertension, hyperlipidemia, diabetes, osteoporosis, respiratory disease, grip strength, walking speed, smoking status and alcohol status; Statistical significance was defined by a p-value <0.05.
Figure 2 shows the restricted cubic splines between the time engaged in physical activity and adjusted HR of disability incidence for all participants, as well as each CKD status group. Linear and dose-response associations were observed between MVPA and disability incidence in all adjusted models (P for linear < 0.001; P for non-linear = 0.549 in all participants, P for linear < 0.001; P for non-linear = 0.445 in the non-CKD group, and P for linear = 0.037; P for non-linear = 0.787 in the CKD group). No significant associations were observed between LPA and disability incidence (P for linear = 0.101; P for non-linear = 0.073 in all participants, P for linear = 0.267; P for non-linear = 0.124 in the non-CKD group, and P for linear = 0.346; P for non-linear = 0.305 in the CKD group).
Figure 2.
Dose-response associations between light-intensity physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) and disability incidence for all participants and each CKD status group, fitted using restricted cubic splines (A and B; all participants, C and D; non-CKD group, E and F; CKD group)
Knots were placed at 5th, 50th, and 95th percentiles of each intensity of physical activity time. Black line shows hazard ratios and shaded area shows 95% confidence intervals.
Discussion
In the present longitudinal study of community-dwelling older Japanese people, we identified 1,054 of 3,784 (27.9%) individuals with CKD. Of these, 904 (85.7%) individuals in the CKD group were classified as having mildly to moderately decreased kidney function, thus the severity of CKD was relatively mild. These individuals had a significantly higher risk of disability than those without CKD. The amount of physical activity, both LPA and MVPA, was lower in the CKD group. In the multiple CPH model, a higher MVPA level was associated with lower disability incidence in all participants. In both the non-CKD and CKD groups, MVPA was associated with decreased disability incidence, whereas LPA showed no evidence. There were linear, dose-response associations between time engaged in MVPA and disability incidence in both the non-CKD and CKD groups. These results indicate that engaging in higher amounts of MVPA related to disability prevention, even in community-dwelling older adults with non-severe CKD.
All adjusted analyses showed protective and dose-response associations between MVPA and disability incidence, regardless of the CKD status. Previous studies report that MVPA is correlated with a lower risk of disability incidence, frailty, sarcopenia, and mortality for older adults (34, 35, 36, 37). In particular, MVPA decreases in older adults with disability compared with LPA (38, 39). In addition, moderate or above intensity exercise (e.g., MVPA) intervention improves cardiorespiratory fitness (CRF), muscular function, and QOL for CKD patients (40). Participants in the present study lived independently in the community and it can be inferred that they may have more preserved CRF compared to disabled people (41). Although CKD patients reportedly have decreased CRF (23, 42), most individuals in the CKD group did not have severely decreased kidney function. Thus, higher intensity of physical activity might be related to both the non-CKD and CKD groups because of preserved CRF. The difference in MVPA for the non-CKD and CKD group was small and both groups had enough MVPA amount for physical activity guideline of world health organization (43). It indicated that the CKD group had a similar physical activity level compared with those in the non-CKD group. Therefore, MVPA might associate with disability incidence in the CKD group, like general older adults.
LPA has favorable effects (44), such as: on improving depressive symptoms, cognitive function, and cardiovascular disease and mortality. The ACSM recommends lower intensity physical activity for deconditioned older adults due to low CRF among this group (21). We hypothesized that the lower intensity of physical activity, i.e., LPA might be associated with the CKD group, but significant associations were not observed. Although LPA has favorable effects on health outcomes, its intensity is inferior to the same amount of MVPA (45, 46). CRF in the CKD group might not have decreased compared with the non-CKD group in the present study because participants in the CKD group did not have severely decreased kidney function. In addition, individuals in the CKD group were older and tended to have more comorbidities, however physical functions were not decreased compared with those in the non-CKD group. Thus, LPA did not have sufficient intensity to decrease disability incidence for individuals in the CKD group, who had preserved CRF and physical function. However, LPA was related to frailty and disability incidence in older adults with chronic disease, knee arthritis, and sarcopenia (22, 47). Further studies including CKD participants with severely decreased kidney function and lower physical function should be conducted to identify the clear association between LPA and disability incidence. Restricted cubic splines of disability incidence in LPA showed slight increased HR at high LPA. We considered the reason for the spline's shape is interchangeability between LPA and MVPA in total physical activity time. Participants with a lower risk of disability incidence might have a high amount of total physical activity time with greater composition of MVPA time, because MVPA has superior protective effect for disability incidence compared with LPA. Therefore, participants with high total physical activity time including the greater composition of LPA, i.e., less composition of MVPA might have relatively higher disability incidence, compared with the greater composition of MVPA.
A previous systematic review indicated that moderate or above intensity aerobic exercise increases not only CRF but also lower limb function in middle aged CKD patients (48). Another systematic review has shown that higher amounts of physical activity are associated with a lower risk of poor future clinical outcomes such as mortality, impaired physical function, and decline in the kidney function in patients with CKD (49). The findings of the present study are consistent with these benefits, further supporting the promotion of physical activity in CKD patients.
The major strength of the present study was the large sample size used and the implementation of personal accelerometers to objectively measure physical activity. However, the present study also had some limitations. First, the participants in this study had relatively good health condition overall, all were able to independently participate in the baseline survey and use the accelerometer. A previous study indicated that non-participation in optional accelerometry tracking was associated with several cardiometabolic risk factors compared with voluntary participation (50). Thus, self-selection bias attributed to participation in the baseline survey and electing to use personal accelerometers may have resulted in an underestimation of the disability incidence. Second, we could not analyze with additional eGFR classification such as moderately to severely (30≤, 45> mL/min/1.73 m2), and severely decreased kidney function (15≤, 30> mL/min/1.73 m2), due to small sample size of each class. Third, the accelerometers used in this study could not measure certain types of physical activity, such as the use of stationary bikes or other forms of stationary machine exercise. To compensate for this, physical activity data in the present study were underestimated in individuals who underwent these exercises regularly. Fourth, longitudinal change of physical activity data was not collected. Several patterns of physical activity change in older adults including stable, increasing, or decreasing were reported (51). These fluctuations might affect disability incidence of the present study. Finally, the accuracy of creatinine eGFR assessments is lower than that of the creatinine-cystatin C combined eGFR equation (52). We have defined CKD with an eGFR cut-off value of 60 mL/ kg/1.73 m2, but the accuracy of our CKD classification may be reduced by the use of the creatinine eGFR measurement. Further study is needed to adjust these discrepancies and ensure accurate classification of CKD.
In conclusion, moderate or greater intensity of daily physical activity showed dose-response associations with a lower disability incidence in community-dwelling older adults with and without CKD. These findings indicate the need to promote physical activity to prevent disability in older adults with non-severe CKD.
Acknowledgment
We thank the healthcare staff for their valued assistance with the study assessments. We would like to thank Editage (https://www.editage.jp) for English language editing. We would like to thank the JSPS KAKENHI Grant-in-Aid for Young Scientists (grant number: 20K18975) for their support.
Author Contributions
IC was involved in study concept and design, data interpretation, and the manuscript preparation. SL, SB, KM and KH involved in the acquisition of subjects and data. OK, KT, MM, YY and NT critically revised the report, commented on the drafts of manuscript. HS approved the final report.
Sponsors role
Kao corporation provided the accelerometers. Other funders played no role in the design, methods, subject recruitment, data collections, analysis and preparation or paper.
Ethical standards
Ethical standards for epidemiological study were adhered to according to guidelines from the Ministry of Health, Labour and Welfare, Japan.
Conflicts of Interest
The work in Takahama city was supported by a Research Funding for Longevity Sciences (27–22) from the NCGG, Grant-in-Aid for Scientific Research (A) (26242059), joint research with Kao Corporation, and expenses for R&D were commissioned by Takahama city. This work in Nagoya city was supported by the Strategic Basic Research Programs Redesigning Communities for Aged Society (RISTEX) of the Japan Science and Technology Agency (JST), Health and Labor Sciences Research Grants, and joint research with Kao Corporation.
References
- 1.GBD 2019 DiseasesInjuries Collaborators Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1204–1222. doi: 10.1016/S0140-6736(20)30925-9. 10.1016/S0140-6736(20)30925-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Gansevoort RT, Correa-Rotter R, Hemmelgarn BR, et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet. 2013;382:339–352. doi: 10.1016/S0140-6736(13)60595-4. 10.1016/S0140-6736(13)60595-4 PubMed PMID: 23727170. [DOI] [PubMed] [Google Scholar]
- 3.Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296–1305. doi: 10.1056/NEJMoa041031. 10.1056/NEJMoa041031 PubMed PMID: 15385656. [DOI] [PubMed] [Google Scholar]
- 4.Perlman RL, Finkelstein FO, Liu L, et al. Quality of life in chronic kidney disease (CKD): A cross-sectional analysis in the Renal Research Institute-CKD study. Am J Kidney Dis. 2005;45:658–666. doi: 10.1053/j.ajkd.2004.12.021. 10.1053/j.ajkd.2004.12.021 PubMed PMID: 15806468. [DOI] [PubMed] [Google Scholar]
- 5.Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson DS, Hobbs FD. Global prevalence of chronic kidney disease—A systematic review and meta-analysis. PLoS One. 2016;11:e0158765. doi: 10.1371/journal.pone.0158765. 10.1371/journal.pone.0158765 PubMed PMID: 27383068, PMCID 4934905. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Imai E, Horio M, Watanabe T, et al. Prevalence of chronic kidney disease in the Japanese general population. Clin Exp Nephrol. 2009;13:621–630. doi: 10.1007/s10157-009-0199-x. 10.1007/s10157-009-0199-x PubMed PMID: 19513802. [DOI] [PubMed] [Google Scholar]
- 7.Yamada M, Arai H, Nishiguchi S, et al. Chronic kidney disease (CKD) is an independent risk factor for long-term care insurance (LTCI) need certification among older Japanese adults: A two-year prospective cohort study. Arch Gerontol Geriatr. 2013;57:328–332. doi: 10.1016/j.archger.2013.03.009. 10.1016/j.archger.2013.03.009 PubMed PMID: 23566448. [DOI] [PubMed] [Google Scholar]
- 8.Smyth A, Glynn LG, Murphy AW, et al. Mild chronic kidney disease and functional impairment in community-dwelling older adults. Age Ageing. 2013;42(4):488–494. doi: 10.1093/ageing/aft007. 10.1093/ageing/aft007 PubMed PMID: 23438445. [DOI] [PubMed] [Google Scholar]
- 9.Wang XH, Mitch WE. Mechanisms of muscle wasting in chronic kidney disease. Nat Rev Nephrol. 2014;10:504–516. doi: 10.1038/nrneph.2014.112. 10.1038/nrneph.2014.112 PubMed PMID: 24981816, PMCID 4269363. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sabatino A, Cuppari L, Stenvinkel P, Lindholm B, Avesani CM. Sarcopenia in chronic kidney disease: “What have we learned so far? J Nephrol. 2021;34:1347–1372. doi: 10.1007/s40620-020-00840-y. 10.1007/s40620-020-00840-y PubMed PMID: 32876940. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Hiraki K, Yasuda T, Hotta C, et al. Decreased physical function in pre-dialysis patients with chronic kidney disease. Clin Exp Nephrol. 2013;17:225–231. doi: 10.1007/s10157-012-0681-8. 10.1007/s10157-012-0681-8 PubMed PMID: 22911116. [DOI] [PubMed] [Google Scholar]
- 12.Lee J, Walker ME, Gabriel KP, Vasan RS, Xanthakis V. Associations of accelerometer-measured physical activity and sedentary time with chronic kidney disease: The Framingham Heart Study. PLoS One. 2020;15:e0234825. doi: 10.1371/journal.pone.0234825. 10.1371/journal.pone.0234825 PubMed PMID: 32542048, PMCID 7295223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Zha Y, Qian Q. Protein nutrition and malnutrition in CKD and ESRD. Nutrients. 2017;9:208. doi: 10.3390/nu9030208. 10.3390/nu9030208 PMCID 5372871. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.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–M156. doi: 10.1093/gerona/56.3.m146. 10.1093/gerona/56.3.M146 PubMed PMID: 11253156. [DOI] [PubMed] [Google Scholar]
- 15.Mijnarends DM, Koster A, Schols JMGA, et al. Physical activity and incidence of sarcopenia: The population-based AGES-Reykjavik Study. Age Ageing. 2016;45:614–620. doi: 10.1093/ageing/afw090. 10.1093/ageing/afw090 PubMed PMID: 27189729, PMCID 5027639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Pahor M, Guralnik JM, Ambrosius WT, et al. Effect of structured physical activity on prevention of major mobility disability in older adults: The LIFE study randomized clinical trial. JAMA. 2014;311:2387–2396. doi: 10.1001/jama.2014.5616. 10.1001/jama.2014.5616 PubMed PMID: 24866862, PMCID 4266388. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Hotta C, Hiraki K, Izawa KP, Sakurada T, Shibagaki Y. Physical activity in patients with pre-dialysis chronic kidney disease is related to physical function. Clin Exp Nephrol. 2020;24:1189–1190. doi: 10.1007/s10157-020-01939-z. 10.1007/s10157-020-01939-z PubMed PMID: 32748308. [DOI] [PubMed] [Google Scholar]
- 18.Hamada M, Yasuda Y, Kato S, et al. The effectiveness and safety of modest exercise in Japanese patients with chronic kidney disease: A single-armed interventional study. Clin Exp Nephrol. 2016;20:204–211. doi: 10.1007/s10157-015-1147-6. 10.1007/s10157-015-1147-6 PubMed PMID: 26209188. [DOI] [PubMed] [Google Scholar]
- 19.Howden EJ, Coombes JS, Strand H, Douglas B, Campbell KL, Isbel NM. Exercise training in CKD: Efficacy, adherence, and safety. Am J Kidney Dis. 2015;65:583–591. doi: 10.1053/j.ajkd.2014.09.017. 10.1053/j.ajkd.2014.09.017 PubMed PMID: 25458662. [DOI] [PubMed] [Google Scholar]
- 20.Zelle DM, Klaassen G, van Adrichem E, et al. Physical inactivity: a risk factor and target for intervention in renal care. Nat Rev Nephrol. 2017;13(3):152–168. doi: 10.1038/nrneph.2016.187. 10.1038/nrneph.2016.187 PubMed PMID: 28138130. [DOI] [PubMed] [Google Scholar]
- 21.Garber CE, Blissmer B, Deschenes MR, et al. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: Guidance for prescribing exercise. Med Sci Sports Exerc. 2011;43:1334–1359. doi: 10.1249/MSS.0b013e318213fefb. 10.1249/MSS.0b013e318213fefb PubMed PMID: 21694556. [DOI] [PubMed] [Google Scholar]
- 22.Mañas A, Del P-C, Guadalupe-Grau A, et al. Reallocating accelerometer-assessed sedentary time to light or moderate- to vigorous-intensity physical activity reduces frailty levels in older adults: An isotemporal substitution approach in the TSHA study. J Am Med Dir Assoc. 2018;19:185. doi: 10.1016/j.jamda.2017.11.003. 10.1016/j.jamda.2017.11.003 PubMed PMID: 29269096. [DOI] [PubMed] [Google Scholar]
- 23.Howden EJ, Weston K, Leano R, et al. Cardiorespiratory fitness and cardiovascular burden in chronic kidney disease. J Sci Med Sport. 2015;18:492–497. doi: 10.1016/j.jsams.2014.07.005. 10.1016/j.jsams.2014.07.005 PubMed PMID: 25127529. [DOI] [PubMed] [Google Scholar]
- 24.Shimada H, Tsutsumimoto K, Lee S, et al. Driving continuity in cognitively impaired older drivers. Geriatr Gerontol Int. 2016;16:508–514. doi: 10.1111/ggi.12504. 10.1111/ggi.12504 PubMed PMID: 25953032. [DOI] [PubMed] [Google Scholar]
- 25.Stevens PE, Levin A. Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158:825–830. doi: 10.7326/0003-4819-158-11-201306040-00007. 10.7326/0003-4819-158-11-201306040-00007 PubMed PMID: 23732715. [DOI] [PubMed] [Google Scholar]
- 26.Matsuo S, Imai E, Horio M, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis. 2009;53:982–992. doi: 10.1053/j.ajkd.2008.12.034. 10.1053/j.ajkd.2008.12.034 PubMed PMID: 19339088. [DOI] [PubMed] [Google Scholar]
- 28.Gorman E, Hanson HM, Yang PH, Khan KM, Liu-Ambrose T, Ashe MC. Accelerometry analysis of physical activity and sedentary behavior in older adults: A systematic review and data analysis. Eur Rev Aging Phys Act. 2014;11:35–49. doi: 10.1007/s11556-013-0132-x. 10.1007/s11556-013-0132-x PubMed PMID: 24765212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Yamada M, Arai H. Long-term care system in Japan. Ann Geriatr Med Res. 2020;24:174–180. doi: 10.4235/agmr.20.0037. 10.4235/agmr.20.0037 PubMed PMID: 32829572, PMCID 7533196. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Tsutsumimoto K, Doi T, Shimada H, et al. Combined effect of slow gait speed and depressive symptoms on incident disability in older adults. J Am Med Dir Assoc. 2016;17:123–127. doi: 10.1016/j.jamda.2015.08.012. 10.1016/j.jamda.2015.08.012 PubMed PMID: 26412017. [DOI] [PubMed] [Google Scholar]
- 31.Satake S, Arai H. The revised Japanese version of the Cardiovascular Health Study criteria (revised J-CHS criteria) Geriatr Gerontol Int. 2020;20:992–993. doi: 10.1111/ggi.14005. 10.1111/ggi.14005 PubMed PMID: 33003255. [DOI] [PubMed] [Google Scholar]
- 32.Ministry of Health LaW, Japan. Active Guide. Japanese Official Physical Activity Guidelines for Health Promotion. Accessed 2 June. 2021;2013 https://www.mhlw.go.jp/stf/houdou/2r9852000002xple.html. [Google Scholar]
- 33.Desquilbet L, Mariotti F. Dose-response analyses using restricted cubic spline functions in public health research. Stat Med. 2010;29:1037–1057. doi: 10.1002/sim.3841. PubMed PMID: 20087875. [DOI] [PubMed] [Google Scholar]
- 34.Saint-Maurice PF, Troiano RP, Matthews CE, Kraus WE. Moderate-to-vigorous physical activity and all-cause mortality: Do bouts matter? J Am Heart Assoc. 2018;7 doi: 10.1161/JAHA.117.007678. 10.1161/JAHA.117.007678 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Shah RC, Buchman AS, Leurgans S, Boyle PA, Bennett DA. Association of total daily physical activity with disability in community-dwelling older persons: A prospective cohort study. BMC Geriatr. 2012;12:63. doi: 10.1186/1471-2318-12-63. 10.1186/1471-2318-12-63 PubMed PMID: 23072476, PMCID 3492176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Makino K, Lee S, Lee S, et al. Daily physical activity and functional disability incidence in community-dwelling older adults with chronic pain: A prospective cohort study. Pain Med. 2019;20:1702–1710. doi: 10.1093/pm/pny263. 10.1093/pm/pny263 PubMed PMID: 30561732. [DOI] [PubMed] [Google Scholar]
- 37.Cooper AJM, Lamb MJE, Sharp SJ, Simmons RK, Griffin SJ. Bidirectional association between physical activity and muscular strength in older adults: Results from the UK Biobank study. Int J Epidemiol. 2017;46:141–148. doi: 10.1093/ije/dyw054. PubMed PMID: 27209633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Chen T, Honda T, Chen S, Narazaki K, Kumagai S. Dose-response association between accelerometer-assessed physical activity and incidence of functional disability in older Japanese adults: A 6-year prospective study. J Gerontol A Biol Sci Med Sci. 2020;75:1763–1770. doi: 10.1093/gerona/glaa046. 10.1093/gerona/glaa046 PubMed PMID: 32134454, PMCID 7494030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Loprinzi PD, Sheffield J, Tyo BM, Fittipaldi-Wert J. Accelerometer-determined physical activity, mobility disability, and health. Disabil Health J. 2014;7:419–425. doi: 10.1016/j.dhjo.2014.05.005. 10.1016/j.dhjo.2014.05.005 PubMed PMID: 25224982. [DOI] [PubMed] [Google Scholar]
- 40.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: 10.1053/j.ajkd.2014.03.020. 10.1053/j.ajkd.2014.03.020 PubMed PMID: 24913219. [DOI] [PubMed] [Google Scholar]
- 41.de Oliveira Brito LV, Maranhao Neto GA, Moraes H, Emerick RF, Deslandes AC. Relationship between level of independence in activities of daily living and estimated cardiovascular capacity in elderly women. Arch Gerontol Geriatr. 2014;59:367–371. doi: 10.1016/j.archger.2014.05.010. 10.1016/j.archger.2014.05.010 PubMed PMID: 24948514. [DOI] [PubMed] [Google Scholar]
- 42.Kirkman DL, Muth BJ, Stock JM, Townsend RR, Edwards DG. Cardiopulmonary exercise testing reveals subclinical abnormalities in chronic kidney disease. Eur J Prev Cardiol. 2018;25:1717–1724. doi: 10.1177/2047487318777777. 10.1177/2047487318777777 PubMed PMID: 29808762. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–1462. doi: 10.1136/bjsports-2020-102955. 10.1136/bjsports-2020-102955 PubMed PMID: 33239350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Amagasa S, Machida M, Fukushima N, et al. Is objectively measured light-intensity physical activity associated with health outcomes after adjustment for moderate-to-vigorous physical activity in adults? A systematic review. Int J Behav Nutr Phys Act. 2018;15:65. doi: 10.1186/s12966-018-0695-z. 10.1186/s12966-018-0695-z PubMed PMID: 29986718, PMCID 6038338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Qiu S, Cai X, Jia L, et al. Does objectively-measured light-intensity physical activity reduce the risk of cardiovascular mortality? A meta-analysis. Eur Heart J Qual Care Clin Outcomes. 2021;7:496–504. doi: 10.1093/ehjqcco/qcaa051. 10.1093/ehjqcco/qcaa051 PubMed PMID: 32514573. [DOI] [PubMed] [Google Scholar]
- 46.Grgic J, Dumuid D, Bengoechea EG, Shrestha N, Bauman A, Olds T, Pedisic Z. Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: A systematic scoping review of isotemporal substitution studies. Int J Behav Nutr Phys Act. 2018;15:69. doi: 10.1186/s12966-018-0691-3. 10.1186/s12966-018-0691-3 PubMed PMID: 30001713, PMCID 6043964. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Dunlop DD, Song J, Semanik PA, et al. Relation of physical activity time to incident disability in community dwelling adults with or at risk of knee arthritis: Prospective cohort study. BMJ. 2014;348:g2472. doi: 10.1136/bmj.g2472. 10.1136/bmj.g2472 PubMed PMID: 24782514, PMCID 4004786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Nicolodi GV, Della Méa Plentz R, Righi NC, Stein C. Effects of aerobic exercise on patients with pre-dialysis chronic kidney disease: a systematic review of randomized controlled trials. Disabil Rehabil 2021:1–10. Doi: 10.1080/09638288.2021.1900929. [DOI] [PubMed]
- 49.MacKinnon HJ, Wilkinson TJ, Clarke AL, et al. The association of physical function and physical activity with all-cause mortality and adverse clinical outcomes in nondialysis chronic kidney disease: A systematic review. Ther Adv Chronic Dis. 2018;9:209–226. doi: 10.1177/2040622318785575. 10.1177/2040622318785575 PubMed PMID: 30364521, PMCID 6196637. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Weymar F, Braatz J, Guertler D, et al. Characteristics associated with non-participation in 7-day accelerometry. Prev Med Rep. 2015;2:413–U8. doi: 10.1016/j.pmedr.2015.05.003. 10.1016/j.pmedr.2015.05.003 PubMed PMID: 26844098, PMCID 4721365. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Jefferis BJ, Sartini C, Ash S, et al. Trajectories of Objectively Measured Physical Activity in Free-Living Older Men. Med Sci Sport Exerc. 2015;47(2):343–349. doi: 10.1249/MSS.0000000000000410. 10.1249/MSS.0000000000000410 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367:20–29. doi: 10.1056/NEJMoa1114248. 10.1056/NEJMoa1114248 PubMed PMID: 22762315, PMCID 4398023. [DOI] [PMC free article] [PubMed] [Google Scholar]
Uncited references
- 27.Kumahara H, Schutz Y, Ayabe M, et al. The use of uniaxial accelerometry for the assessment of physical-activity-related energy expenditure: A validation study against whole-body indirect calorimetry. Br J Nutr. 2004;91:235–243. doi: 10.1079/BJN20031033. 10.1079/BJN20031033 PubMed PMID: 14756909. [DOI] [PubMed] [Google Scholar]