Abstract
Background:
To investigate the mediating effect of 24-hour movement behaviors on the relationship between chronic kidney disease (CKD) and mortality risk; additionally, to assess the health benefits of reallocating sedentary behavior (SB) with physical activity.
Methods:
This prospective cohort study included 88,795 participants (aged 43–78) from the UK Biobank with valid accelerometer data. A compositional mediation model assessed whether 24-hour movement behaviors mediate the CKD–mortality association. A flexible parametric hazards model was used to estimate life expectancy under various reallocation scenarios of 24-hour movement behaviors.
Results:
During a median follow-up of 8.10 years, 3673 participants met CKD criteria, and 3117 died. CKD increased all-cause mortality risk through greater SB and reduced light-intensity physical activity and moderate to vigorous physical activity (MVPA; hazard ratios = 1.040; 95% confidence interval, 1.030–1.050). Reallocating 20 minutes per day of SB to MVPA increased life expectancy by 1.70 years at age 45 (95% confidence interval, 0.51–4.34).
Conclusions:
Twenty four-hour movement behaviors partially mediate mortality risk in CKD, with MVPA showing the strongest protective effect. Interventions that reduce SB and increase MVPA can improve survival in CKD populations.
Keywords: physical activity, compositional mediation model, isotemporal substitution
Approximately, 9.1% of people globally have chronic kidney disease (CKD),1 imposing a substantial burden on healthcare systems and leading to a heightened risk of all-cause mortality and reduced quality of life.2 Predictions suggest that by 2040, CKD will rank as the fifth leading cause of years of life lost globally.3 This growing burden places a significant strain on both patients and society. To address this, evidence-based interventions are necessary to mitigate the impact of CKD.
Among various evidence-based interventions, exercise has emerged as one of the most effective approaches in improving various health outcomes among CKD patients.4 Nevertheless, these individuals often engage in lower levels of physical activity and spend more time in sedentary behavior (SB) than the general population, leading to poorer physical function and performance.5 Early studies identified physical inactivity as a key risk factor for adverse outcomes in CKD,6 and insufficient physical activity has been observed to significantly increase mortality risk among CKD patients, whereas achieving the recommended levels of physical activity is associated with a 56% reduction in risk,7 a protective effect consistent across all CKD stages.8 Patients with end-stage renal disease (ESRD) face particular challenges: severe limitations in achieving moderate to vigorous physical activity (MVPA) are associated with the highest mortality risk.9
Recent research has reported that physical activity can mitigate the mediating effect of SB on the association between SB and estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes mellitus,10 and both observational and intervention studies indicate that replacing SB with physical activity is associated with improved eGFR.11 This implies a potential mediating effect of 24-hour movement behaviors on kidney disease-related health outcomes. Particularly within CKD patients the mediating role of 24-hour movement behaviors—including SB, light-intensity physical activity (LPA), MVPA, and sleep—in the relationship between CKD and mortality remains insufficiently explored and the potential impact of CKD on mortality risk through variations in movement patterns and activity durations remains unclear. A comprehensive investigation is needed to assess the indirect effects of CKD-induced changes in 24-hour movement behaviors on mortality risk and to identify the potential health benefits of targeted movement behavior interventions.
Additionally, life expectancy estimates offer a more intuitive understanding for both medical professionals and the general public.12 However, much of the current research primarily reports hazard ratios to assess the impact of behavioral modifications, with limited focus on the potential health benefits. This is particularly true when considering the isotemporal substitution of 24-hour movement behaviors, an area with scarce evidence in terms of its effects on health outcomes.
Therefore, we established a compositional mediation model and applied flexible parametric hazards model to explore the role of 24-hour movement behaviors as a mediator in the relationship between CKD and mortality. Additionally, we estimated the potential life years gained by replacing SB with physical activity. This research aims to guide CKD patients in adopting healthier lifestyle and exercise habits, improving their quality of life, and minimizing disease-related damage.
Methods
Study Population
This study used data from the UK Biobank which is a large, long-term prospective cohort study (application # 90162). It includes 502,180 community-dwelling adults. We selected a subset of individuals who participated in accelerometer testing and were followed up until December 2022 for death registration data. We declare that all data can be publicly obtained in the UK Biobank repository. The UK Biobank study was approved by the Northwest Multi-Center Research Ethics Committee in the United Kingdom (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics), and all participants provided written informed consent.
The study included 502,180 participants. We excluded participants who did not wear accelerometers (n = 398,601), and those with inadequate accelerometer data quality, or improper wearing. Given that changes in measured values captured at baseline may lead to an erroneous estimation of the association between risk factors and outcomes, we updated the data for participants who developed the disease before accelerometer measurement, considering their disease onset time as their entry time into the cohort. This was done to prevent patients who developed the disease before accelerometer measurement from being incorrectly identified as nondiseased at baseline, adjusting for regression dilution bias. To avoid outcome bias caused by reverse causality, we excluded data from new cases that occurred before accelerometer measurement within 1 year and deaths that occurred within 1 year after accelerometer measurement. Additionally, we excluded any data with missing values. The analytical sample size was 88,795, including 3673 CKD cases and 85,122 individuals without CKD (non-CKD). The inclusion and exclusion process is illustrated in Figure S1 in Supplementary Materials (available online), and a schematic diagram of covariate handling in this study is provided in Figure S2 and see Tables S1 and S2 in Supplementary Materials (available online).
Assessment of Physical Activity—24-Hour Movement Behaviors
Each study participant was invited to wear an Axivity AX3 wrist-worn triaxial accelerometer for 7 days between February 2013 and December 2015, to continuously record acceleration at a sampling rate of 100 Hz (1 Hz is 1 sample per second) with a dynamic range of ±8 g. Researchers asked participants to continue with normal daily activities during the recording period and wear the device at all times, including while sleeping or bathing. Then, we applied a published machine learning method to classify the accelerometer data.13 This model was trained using annotated data from the CAPTURE-24 study and utilized motion cameras and motion logs for activity labeling. The final classification included 4 categories of 24-hour movement behaviors data: Sleep, SB, LPA, and MVPA. The lrEM algorithm was utilized to impute 24-hour movement behaviors data with zero values.14
Assessment of Chronic Kidney Disease (ICD-10)
CKD is defined as kidney damage or a decrease in the GFR below 60 mL/min/1.73 m2 for 3 or more months.15 This can be identified through International Statistical Classification of Diseases and Related Health Problems 10th revision (ICD-10) codes16 (N03, N06, N08, N11, N12, N13, N14, N15, N16, N18, N19, N20, and N21) in primary care data, hospital inpatient data, death register records, and self-report data. Participants with preaccelerometer CKD diagnosis were included, while new incident cases within 1-year preceding accelerometer assessment were excluded.
Assessment of Covariates
The covariates used included in our analysis are, age, sex, ethnicity (White, non-White), body mass index (BMI) classified according to World Health Organization standards (BMI < 18.5: underweight, 18.5 ≤ BMI < 25: normal weight, 25 ≤ BMI < 30: overweight, and BMI ≥ 30: obesity), smoking status (never smoked, former smoker, current smoker), alcohol intake frequency (never, special occasions only, 1–3 times a month, once or twice a week, 3 or 4 times a week, daily or almost daily), employment status (in paid employment or self-employed, retired, looking after home and/or family, other), Townsend deprivation index, as well as hypertension, diabetes, cancer, and gout.
Statistical Analysis
Descriptive statistics were employed to analyze the characteristics of participants. Categorical variables were represented as frequencies (percentages), while continuous variables were expressed as mean (SD). 24-hour movement behaviors (sleep, LPA, MVPA, and SB) were reported as geometric mean. To investigate the mediating role of 24-hour movement behaviors in the relationship between CKD and health outcomes, and to assess the potential benefits of physical activity interventions, 2 models were established. The first model constructed a compositional mediation model using 24-hour movement behaviors as the mediator. The second model employed a flexible parametric hazards model to evaluate the health benefits of modifying movement behaviors and to identify the highest risk 24-hour movement patterns.
In the first model, we attempted to utilize a compositional mediation model to examine the mediating effect of 24-hour movement behaviors between CKD and all-cause mortality. In this model, 24-hour movement behaviors essentially constituted compositional data. To mitigate numerical collinearity issues, the Isometric Log Ratio transformation was introduced to transform compositional data into Euclidean space. Subsequently, based on CKD exposure, a multiple linear regression model was established to explore the influence of CKD on 24-hour movement behaviors. Then a stratified Cox proportional hazards model was employed to estimate hazard ratios for the association between 24-hour movement behaviors and all-cause mortality, with stratification by cancer history, hypertension status, smoking status, and ethnicity due to violations of the proportional hazards assumption confirmed via Schoenfeld residuals.
In the second model, a flexible parametric Royston-Parmar proportion-hazards model was employed to elucidate the potential differences between MVPA and SB in terms of equivalent substitution and mortality risk.12 Subsequently, life gains were derived by examining differences in expected lifetime before and after time substitution. In this study, we predicted life expectancy based on the substitution of the isochronous intervals between the ages of 45 and 100. Considering that the recommended level of MVPA for CKD patients is more than 150 minutes per week (more than 20 min/d),17 an increase far beyond this basis is not particularly meaningful. Therefore, in this study, we set the reference values for sleep, LPA, MVPA, and SB to 560, 270, 10 (approximately the 25th percentile), and 600, respectively. Furthermore, following Dumuid et al18 we generated exploratory, model-based risk surfaces and summarized relative hazard using percentile bands (25th/50th/75th) to describe low-, median-, and high-risk regions. Full methodological details and numerical outputs are provided exclusively in the Supplementary Materials (available online) as hypothesis-generating results and are not intended as clinical thresholds or actionable recommendations.
Four sensitivity analyses were conducted to examine the stability of our models and results. In the initial sensitivity analysis, the inclusion criteria for the disease cohort were refined by excluding self-reported cases of CKD, thereby enhancing the specificity of the study population. For the second sensitivity analysis, the random forest imputation method from the “mice” package in R was utilized to impute missing values for all covariates, and the results were reevaluated. In the third sensitivity analysis, zero values of MVPA were imputed using an addition of +0.001 instead of the expectation-maximization algorithm, and the data were reanalyzed. In the final sensitivity analysis, both covariates and the history of disease were defined using the information from the initial assessment. In addition, we prespecified exploratory supplementary analyses that incorporated accelerometry-derived sleep efficiency and the Sleep Regularity Index as covariates; owing to substantial missingness (>33% overall; >60% in CKD), results are reported in the Supplementary Materials only.
All tests were 2-tailed, with P < .05 considered significant. Statistical analysis was conducted using R programming (version 4.3.2), while data cleaning was performed using Python 3.12. We estimated the 95% confidence interval (CI) for effect indicators using the bootstrap method with 1000 iterations. The detailed model formulas are provided in the “Supplementary Method” section in Supplementary Materials (available online).
Results
The average duration of accelerometer wear time for the entire cohort was 6.85 days. Figure 1 illustrates the proportion of the geometric mean of the 4 activity components in the non-CKD and CKD groups, demonstrating that CKD patients spend more time in sleep and SB and less time in LPA and MVPA.
Figure 1 —
A: The proportion of geometric mean of 24-hour movement behaviors. B: The original distribution of 24-hour movement behaviors. CKD indicates chronic kidney disease; LPA, light-intensity physical activity; MVPA, moderate to vigorous physical activity; SB, sedentary behavior.
Table 1 summarizes the baseline characteristics of the study participants. A total of 88,795 participants met the study criteria, with the majority being White. The mean age of the overall population during the accelerometer measurement period was 61.7 years (SD: 7.8), with 4.14% of participants having CKD. The prevalence of CKD was significantly associated with smoking status, alcohol consumption, employment status, BMI level, Townsend deprivation index, and other factors.
Table 1.
Baseline Characteristics Between CKD and Without CKD Groups
| Characteristics | All population (N = 88,795) | Non-CKD (n = 85,122) | CKD (n = 3673) |
|---|---|---|---|
|
| |||
| Age, mean (SD) | 61.7 (7.8) | 61.5 (7.8) | 64.8 (7.2) |
| Sex, % | |||
| Female | 50,478 (56.8) | 48,844 (57.4) | 1634 (44.5) |
| Male | 38,317 (43.2) | 36,278 (42.6) | 2039 (55.5) |
| Ethnicity, % | |||
| White | 82,010 (92.4) | 78,603 (92.3) | 3407 (92.8) |
| Non-White | 6785 (7.6) | 6519 (7.7) | 266 (7.2) |
| BMI, % | |||
| Underweight | 529 (0.6) | 517 (0.6) | 12 (0.3) |
| Normal | 34,931 (39.3) | 33,948 (39.9) | 983 (26.8) |
| Overweight | 36,469 (41.1) | 34,908 (41.0) | 1561 (42.5) |
| Obesity | 16,866 (19.0) | 15,749 (18.5) | 1117 (30.34) |
| Smoking status, % | |||
| Never | 51,086 (57.5) | 49,163 (57.8) | 1923 (52.4) |
| Previous | 31,821 (35.8) | 30,351 (35.7) | 1470 (40.0) |
| Current | 5888 (6.6) | 5608 (6.6) | 280 (7.6) |
| Alcohol intake frequency, % | |||
| Never | 4951 (5.6) | 4649 (5.5) | 302 (8.2) |
| Special occasions only | 8512 (9.6) | 8001 (9.4) | 511 (13.9) |
| 1–3 times a month | 9669 (10.9) | 9278 (10.9) | 391 (10.6) |
| 1–2 times a week | 22,348 (25.2) | 21,405 (25.1) | 943 (25.7) |
| 3–4 times a week | 23,329 (26.3) | 22,521 (26.5) | 808 (22.0) |
| Daily or almost daily | 19,986 (22.5) | 19,268 (22.6) | 718 (19.5) |
| Employment status, % | |||
| Employed | 54,269 (61.1) | 52,537 (61.7) | 1732 (47.2) |
| Retired | 28,855 (32.5) | 27,184 (31.9) | 1671 (47.2) |
| Other | 5671 (6.4) | 5401 (6.3) | 270 (7.4) |
| TDI, mean (SD) | −1.7 (2.8) | −1.7 (2.8) | −1.6 (2.8) |
| Geometrical, mean (SD) | |||
| Sleep | 543.8 (73.7) | 543.3 (73.2) | 555.2 (82.6) |
| LPA | 297.2 (98.1) | 298.3 (98.0) | 270.9 (98.0) |
| MVPA | 27.9 (34.7) | 28.3 (34.8) | 24.0 (31.7) |
| SB | 571.1 (108.5) | 570.1 (108.3) | 593.5 (111.3) |
| Disease, % | |||
| With cancer | 12,412 (14.0) | 11,674 (13.7) | 738 (20.1) |
| With diabetes | 3795 (4.3) | 3310 (3.9) | 485 (13.2) |
| With hypertension | 23,250 (26.2) | 21,348 (25.1) | 1902 (51.8) |
| With gout | 2104 (2.4) | 1790 (2.1) | 314 (8.5) |
| Death register, % | 3117 (3.5) | 2830 (3.3) | 287 (7.8) |
Abbreviations: BMI, body mass index; CKD, chronic kidney disease; LPA, light-intensity physical activity; MVPA, moderate to vigorous physical activity; SB, sedentary behavior; TDI, Townsend deprivation index.
As shown in Figure 2 and Table 2, both the mediating effects of 24-hour movement behaviors components and the direct effect of CKD are significant. We observed that CKD increases sedentary time, consequently reducing LPA and MVPA, indirectly increasing the risk of mortality. The total indirect effect on mortality was significant (adjusted hazard ratios = 1.040; 95% CI, 1.030–1.050), with the mediating effect accounting for 13.37%.
Figure 2 —
The impact of CKD on mortality is demonstrated through 24-hour movement behaviors. CKD indicates chronic kidneydisease; LPA, light-intensity physical activity; MVPA, moderate to vigorous physical activity; SB, sedentary behavior.
Table 2.
The Effects of CKD Mediated by 24-Hour Movement Behaviors
| Sleep | SB | LPA | MVPA | Indirect | Direct | Total | |
|---|---|---|---|---|---|---|---|
|
| |||||||
| Adjusted HR | 1.001 | 1.005 | 1.012 | 1.022 | 1.040 | 1.286 | 1.337 |
| Adjusted HR (lower) | 0.999 | 1.002 | 1.007 | 1.015 | 1.030 | 1.128 | 1.172 |
| Adjusted HR (upper) | 1.003 | 1.008 | 1.018 | 1.029 | 1.050 | 1.451 | 1.507 |
| Proportion | 0.32% | 1.59% | 4.13% | 7.34% | 13.37% | 86.63% | 100.00% |
Abbreviations: CKD, chronic kidney disease; HR, hazard ratios; LPA, light-intensity physical activity; MVPA, moderate to vigorous physical activity; SB, sedentary behavior.
According to Figure 3, replacing SB with MVPA at different levels shows positive effects, with younger age and deeper substitution resulting in greater benefits in life expectancy. For instance, during a 20-minute reallocation period (+20 min MVPA, −20 min SB), 45-year-old CKD patients can extend their expected lifespan by 1.70 years (95% CI, 0.51–4.34), while in the non-CKD population the extension is 0.77 years (95% CI, 0.38–2.25).
Figure 3 —
Life expectancy benefits from different MVPA substitutions for SB in the CKD group. Note: Estimates at ages ≥79 years are out-of-sample extrapolations and should be interpreted with caution. CKD indicates chronic kidney disease; MVPA, moderate to vigorous physical activity; SB, sedentary behavior.
See Table S3 in Supplementary Materials (available online) shows a global association between the 24-hour movement behavior and the risk of mortality (P < .001). The analyses identify the combination of MVPA < 0.5 hours per day, LPA < 4.5 hours per day, and SB > 11 hours per day as being associated with the highest mortality risk in the CKD population.
We also performed 4 sensitivity analyses, and the main findings remained robust across different imputation methods and alternative definitions of CKD, showing no substantial changes in effect estimates or statistical significance. Detailed results are presented in Figures S5 to S12 and see Tables S5 to S12 in the Supplementary Materials (available online).
Discussion
In this study, we utilized a compositional mediation model specifically adapted for compositional mediators, enabling the precise estimation of each 24-hour movement behavior’s effect on mortality. We further employed flexible parametric models to quantify life expectancy gains associated with activity reallocations.
Our findings showed that CKD patients had significantly higher SB and sleep duration, accompanied by lower levels of LPA and MVPA compared with non-CKD individuals. The results indicated that CKD increases Sleep and SB while reducing both LPA and MVPA. Previous research has also reported similar results that CKD leads to reduced physical activity, increased SB, and even a lack of physical activity in some end-stage CKD patients.19 These changes in physical activity behavior may be attributed to factors, such as mitochondrial dysfunction, inflammation, oxidative stress, metabolic acidosis, and other uremia-related factors.20 For instance, CKD patients have less oxygen delivery per unit of time to the vessels during physical activity compared with healthy individuals, leading to increased blood lactate production and a higher susceptibility to lactic acidosis, resulting in muscle fatigue and consequently reduced activity, thus perpetuating a vicious cycle.21 These factors lead to changes in skeletal muscle structure and function, promoting muscle protein breakdown metabolism and consumption, thereby impairing strength and physical performance in CKD patients. As the disease progresses, the physical function and exercise capability of CKD patients gradually decline.22
Our findings suggest that 24-hour movement behaviors partially mediate the association between CKD and mortality, accounting for approximately 13.37% of the increased risk (adjusted hazard ratios = 1.040; 95% CI, 1.030–1.050). Previous studies have also investigated the mediating effects of physical activity on SB and eGFR in patients with type 2 diabetes mellitus,10 revealing that physical activity mediates the health status of kidney function. Additionally, multiple studies indicated an association between increased SB and elevated mortality risk in CKD patients, and substituting SB with physical activity effectively reduces mortality risk.23,24 In individuals with impaired kidney function, the all-cause mortality risk associated with SB is 1.64 times higher than that of nonsedentary individuals.25 Potential reasons for the occurrence of health-related events include: (1) Increased SB leads to a continuous decline in skeletal muscle strength26 and is a risk factor for cardiovascular disease.27 Prolonged SB may result in microvascular dysfunction, alterations in lipoprotein metabolism, decreased insulin sensitivity, and obesity, among other adverse health events; (2) Accumulation of adipose tissue due to SB may induce inflammation and oxidative stress,28 whereas physical activity can reduce levels of inflammatory cytokines, lowering the risk of such adverse events; and (3) Engaging in LPA and MVPA can increase muscle strength, slow the decline in eGFR, and provide protective effects against depression, diabetes, and hypertension, thus reducing the risk of multisystem diseases.29
Importantly, beyond the 13.37% mediated via 24-hour movement behaviors, the remaining 86.63% likely reflects nonmediated pathways—notably cardiovascular complications, hemodynamic/volume and blood pressure dysregulation, vascular calcification, anemia/mineral–bone disorder, metabolic–endocrine disturbances, systemic inflammation/oxidative stress, and uremic toxin burden.30–33 This interpretation does not diminish the relevance of movement interventions; it places their likely benefits in proper context. Increasing MVPA and reducing sedentary time are feasible, low-cost and scalable strategies that can complement standard CKD care, but they should be regarded as adjuncts rather than complete risk offsets.
Using a flexible hazards model to predict life expectancy benefits in CKD patients, replacing SB with MVPA for more than 20 minutes at any age yields significant improvements in life expectancy. Evidence indicates that even small changes in physical activity levels can enhance exercise tolerance and cardiovascular responsiveness in CKD patients, improve quality of life, and boost survival rates.29,34 Notably, even for dialysis-stage CKD patients, exercise can significantly reduce mortality rates and is widely both safe and beneficial, as supported by numerous studies.9,35 Therefore, it is imperative to encourage both nondialysis and dialysis CKD patients to engage in physical activity and reduce SB.
This study has several strengths. First, we utilized accelerometer-derived 24-hour movement behavior data, providing objective and accurate measurements that significantly reduce recall bias inherent in self-reported methods. Second, employing machine learning for classifying movement behaviors allowed for population-specific and robust categorization, surpassing traditional threshold-based approaches.36 Additionally, integrating compositional mediation modeling with flexible parametric techniques enabled us to dissect the individual contributions of movement behaviors and assess their nuanced impact on life expectancy, effectively handling the interdependent nature of compositional data.
Several limitations should be considered. First, UK Biobank’s low response rate and participant profile that is healthier, less socioeconomically deprived, and predominantly White imply selection and healthy volunteer biases, limiting external generalizability.37 Second, physical activity was assessed using a single 7-day accelerometer recording, which may not capture long-term within-person variability. However, longitudinal evidence indicates moderate to excellent reliability over 2 to 3 years, with about 90% of participants remaining in the same or an adjacent activity quartile, suggesting that a single 7-day measurement is reasonably stable.38 Third, lifestyle and biochemical data were collected prior to accelerometer measures, potentially missing subsequent minor behavioral changes.39,40 Fourth, part of the follow-up overlapped with the COVID-19 pandemic, which may have introduced additional biases.41 Finally, although we excluded CKD first diagnosed within 1 year prior to accelerometry and deaths within 1 year thereafter, CKD ascertainment and accelerometer assessment were still temporally proximate for a small subset of participants; thus, reverse causation and bidirectional influences cannot be entirely excluded, though their impact is likely reduced. Moreover, despite adjustment for demographic, socioeconomic, lifestyle and comorbidity variables, residual and time-varying confounding may persist and could influence both activity levels and mortality. Our estimates of direct and indirect effects rely on the assumptions of no unmeasured confounding of the exposure–mediator, mediator–outcome, and exposure–outcome relations and that CKD precedes subsequent changes in 24-hour movement behaviors.
Conclusions
This study found that CKD was associated with a redistribution of the 24-hour movement behavior composition—less LPA and MVPA, and more sleep and sedentary time. In compositional mediation analyses, 24-hour movement behaviors partially mediated the CKD–mortality association, MVPA contributed the largest share of the modeled indirect effect, and under isotemporal substitution, reallocating time from SB to MVPA was associated with longer predicted life expectancy.
Supplementary Material
Key Points.
24-hour movement behavior partially mediates the association between chronic kidney disease and increased mortality, with reduced moderate-to-vigorous physical activity playing a pivotal mediating role.
Reallocating sedentary time toward higher intensity physical activity is associated with longer predicted life expectancy for people living with chronic kidney disease.
Acknowledgments
This research has been conducted using the UK Biobank Resource under Application Number 90162. We are grateful to all the participants in UK Biobank, and to every person who contributed to data collection and management.
Funding Source:
The Natural Science Foundation of Guangdong Province, China (grant number: 2025A1515012265); The Science and Technology of Guangzhou, China (grant number: 33124108).
References
- 1.Bikbov B, Purcell CA, Levey AS, et al. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2020;395(10225):709–733. doi: 10.1016/S0140-6736(20)30045-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mills KT, Xu Y, Zhang W, et al. A systematic analysis of worldwide population-based data on the global burden of chronic kidney disease in 2010. Kidney Int. 2015;88(5):950–957. doi: 10.1038/ki.2015.230 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Foreman KJ, Marquez N, Dolgert A, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet. 2018;392(10159):2052–2090. doi: 10.1016/S0140-6736(18)31694-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Beetham KS, Krishnasamy R, Stanton T, et al. Effect of a 3-year lifestyle intervention in patients with chronic kidney disease: a randomized clinical trial. J Am Soc Nephrol. 2022;33(2):431–441. doi: 10.1681/ASN.2021050668 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Barker J, Smith Byrne K, Doherty A, et al. Physical activity of UK adults with chronic disease: cross-sectional analysis of accelerometer-measured physical activity in 96 706 UK Biobank participants. Int J Epidemiol. 2019;10:294. doi: 10.1093/ije/dyy294 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Bowlby W, Zelnick LR, Henry C, et al. Physical activity and metabolic health in chronic kidney disease: a cross-sectional study. BMC Nephrol. 2016;17(1):187. doi: 10.1186/s12882-016-0400-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Beddhu S, Baird BC, Zitterkoph J, Neilson J, Greene T. Physical activity and mortality in chronic kidney disease (NHANES III). Clin J Am Soc Nephrol. 2009;4(12):1901–1906. doi: 10.2215/CJN.01970309 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kim J, Hyun Y, Lee KB, et al. Moderate–vigorous physical activity and clinical outcomes in adults with nondialysis chronic kidney disease. J Clin Med. 2021;10(15):3365. doi: 10.3390/jcm10153365 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Stack AG, Molony DA, Rives T, Tyson J, Murthy BVR. Association of physical activity with mortality in the US dialysis population. Am J Kidney Dis. 2005;45(4):690–701. doi: 10.1053/j.ajkd.2004.12.013 [DOI] [PubMed] [Google Scholar]
- 10.Wang X, Yan N, Pan R, Dang Y, Wang L. Examining the links between physical activity, sitting time, and renal function in T2DM patients. Diab Metab Syndrome Obes. 2023;16:2469–2476. doi: 10.2147/DMSO.S408677 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Hara M, Nishida Y, Tanaka K, et al. Moderate to vigorous physical activity and sedentary behavior are independently associated with renal function: a cross-sectional study. J Epidemiol. 2023;33(6):285–293. doi: 10.2188/jea.JE20210155 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Chudasama YV, Khunti K, Gillies CL, et al. Healthy lifestyle and life expectancy in people with multimorbidity in the UK Biobank: a longitudinal cohort study. PLoS Med. 2020;17(9):e1003332. doi: 10.1371/journal.pmed.1003332 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Walmsley R, Chan S, Smith-Byrne K, et al. Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease. Br J Sports Med. 2022;56(18):1008–1017. doi: 10.1136/bjsports-2021-104050 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Palarea-Albaladejo J, Martín-Fernández JA. zCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemomet Intell Lab Syst. 2015;143:85–96. doi: 10.1016/j.chemolab.2015.02.019 [DOI] [Google Scholar]
- 15.ICD-10 Version:2019. https://icd.who.int/browse10/2019/en#/N18
- 16.Geng T, Li X, Ma H, Heianza Y, Qi L. Adherence to a healthy sleep pattern and risk of chronic kidney disease: the UK Biobank study. Mayo Clin Proceed. 2022;97(1):68–77. doi: 10.1016/j.mayocp.2021.08.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Baker LA, March DS, Wilkinson TJ, et al. Clinical practice guideline exercise and lifestyle in chronic kidney disease. BMC Nephrol. 2022;23(1):75. doi: 10.1186/s12882-021-02618-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Dumuid D, Wake M, Burgner D, et al. Balancing time use for children’s fitness and adiposity: evidence to inform 24-hour guidelines for sleep, sedentary time and physical activity. PLoS One. 2021;16(1):e0245501. doi: 10.1371/journal.pone.0245501 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Avesani CM, Trolonge S, Deleaval P, et al. Physical activity and energy expenditure in haemodialysis patients: an international survey. Nephrol Dial Transplant. 2012;27(6):2430–2434. doi: 10.1093/ndt/gfr692 [DOI] [PubMed] [Google Scholar]
- 20.Wang K, Liu Q, Tang M, et al. Chronic kidney disease-induced muscle atrophy: molecular mechanisms and promising therapies. Biochem Pharmacol. 2023;208:115407. doi: 10.1016/j.bcp.2022.115407 [DOI] [PubMed] [Google Scholar]
- 21.Macdonald JH, Fearn L, Jibani M, Marcora SM. Exertional fatigue in patients with CKD. Am J Kidney Dis. 2012;60(6):930–939. doi: 10.1053/j.ajkd.2012.06.021 [DOI] [PubMed] [Google Scholar]
- 22.Reese PP, Cappola AR, Shults J, et al. Physical performance and frailty in chronic kidney disease. Am J Nephrol. 2013;38(4):307–315. doi: 10.1159/000355568 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Beddhu S, Wei G, Marcus RL, Chonchol M, Greene T. Light-intensity physical activities and mortality in the United States general population and CKD subpopulation. Clin J Am Soc Nephrol. 2015;10(7):1145–1153. doi: 10.2215/CJN.08410814 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Hishii S, Miyatake N, Nishi H, et al. Relationship between sedentary behavior and all-cause mortality in Japanese chronic hemodialysis patients: a prospective cohort study. Acta Med Okayama. 2019;73(5):419–425. doi: 10.18926/AMO/57372 [DOI] [PubMed] [Google Scholar]
- 25.Chuang MH, Wang HW, Huang YT, Ho CH, Jiang MY. Association of sedentary lifestyle with all-cause and cause-specific mortality in adults with reduced kidney function. Kidney360. 2024;5(1):33–43. doi: 10.34067/KID.0000000000000313 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ramsey KA, Rojer AGM, D’andrea L, et al. The association of objectively measured physical activity and sedentary behavior with skeletal muscle strength and muscle power in older adults: a systematic review and meta-analysis. Ageing Res Rev. 2021;67:101266. doi: 10.1016/j.arr.2021.101266 [DOI] [PubMed] [Google Scholar]
- 27.Hamilton MT, Hamilton DG, Zderic TW. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes. 2007;56(11):2655–2667. doi: 10.2337/db07-0882 [DOI] [PubMed] [Google Scholar]
- 28.Friedenreich CM, Ryder-Burbidge C, McNeil J. Physical activity, obesity and sedentary behavior in cancer etiology: epidemiologic evidence and biologic mechanisms. Mol Oncol. 2021;15(3):790–800. doi: 10.1002/1878-0261.12772 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Barcellos FC, Santos IS, Umpierre D, Bohlke M, Hallal PC. Effects of exercise in the whole spectrum of chronic kidney disease: a systematic review. Clin Kidney J. 2015;8(6):753–765. doi: 10.1093/ckj/sfv099 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Jankowski J, Floege J, Fliser D, Böhm M, Marx N. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation. 2021;143(11):1157–1172. doi: 10.1161/CIRCULATIONAHA.120.050686 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Cachofeiro V, Goicochea M, Vinuesa SG, Oubiña P, Lahera V, Luño J. Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease: new strategies to prevent cardiovascular risk in chronic kidney disease. Kidney Int. 2008;74:S4–S9. doi: 10.1038/ki.2008.516 [DOI] [PubMed] [Google Scholar]
- 32.Nakashima A, Kato K, Ohkido I, Yokoo T. Role and treatment of insulin resistance in patients with chronic kidney disease: a review. Nutrients. 2021;13(12):4349. doi: 10.3390/nu13124349 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.El Chamieh C, Liabeuf S, Massy Z. Uremic toxins and cardiovascular risk in chronic kidney disease: what have we learned recently beyond the past findings? Toxins. 2022;14(4):280. doi: 10.3390/toxins14040280 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Zelle DM, Klaassen G, Van Adrichem E, Bakker SJL, Corpeleijn E, Navis G. Physical inactivity: a risk factor and target for intervention in renal care. Nat Rev Nephrol. 2017;13(5):318–318. doi: 10.1038/nrneph.2017.44 [DOI] [PubMed] [Google Scholar]
- 35.Sheng K, Zhang P, Chen L, Cheng J, Wu C, Chen J. Intradialytic exercise in hemodialysis patients: a systematic review and meta-analysis. Am J Nephrol. 2014;40(5):478–490. doi: 10.1159/000368722 [DOI] [PubMed] [Google Scholar]
- 36.Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Sci Rep. 2018;8(1):7961. doi: 10.1038/s41598-018-26174-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Fry A, Littlejohns TJ, Sudlow C, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186(9):1026–1034. doi: 10.1093/aje/kwx246 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Keadle SK, Shiroma EJ, Kamada M, Matthews CE, Harris T, Lee IM. Reproducibility of accelerometer-assessed physical activity and sedentary time. Am J Prev Med. 2017;52(4):541–548. doi: 10.1016/j.amepre.2016.11.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Godfrey A, Lord S, Galna B, Mathers JC, Burn DJ, Rochester L. The association between retirement and age on physical activity in older adults. Age Ageing. 2014;43(3):386–393. doi: 10.1093/ageing/aft168 [DOI] [PubMed] [Google Scholar]
- 40.Schermer EE, Engelfriet PM, Blokstra A, Verschuren WMM, Picavet HSJ. Healthy lifestyle over the life course: population trends and individual changes over 30 years of the Doetinchem cohort study. Front Public Health. 2022;10:966155. doi: 10.3389/fpubh.2022.966155 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Xiang Y, Zhang R, Qiu J, So HC. Association of COVID-19 with risks of hospitalization and mortality from other disorders post-infection: a study of the UK Biobank. Infect Dis. 2022;10:2811. doi: 10.1101/2022.03.23.22272811 [DOI] [Google Scholar]
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