Abstract
Background
Patients with chronic kidney disease (CKD) may experience better health outcomes when they engage in physical activity (PA). The aim of the study was to assess the physical activity level of chronic kidney disease (CKD) patients and its potentials risk factor for health.
Methods
A cross-sectional study was carried out at Mymensingh Medical College Hospital, Mymensingh, Bangladesh from October 2023 to January 2024. A total of 253 CKD patients aged 18 years and older at moderate to advanced stages were enrolled in the study. The global physical activity questionnaire (GPAQ) was used to measure the physical activity for health of CKD patients. Physical component summary (PCS) and mental component summary (MCS) scores were measured by Short-Form Health Survey (SF-12) questionnaire. Socio-demographic and medical records were also collected. Both logistic regression and descriptive statistics were used for data.
Results
Of 253 participants (62.8% male, mean age 60.1 years), 41.1% did not meet PA recommendations. Median PA durations were 28.57 min/day for moderate PA (MPA), 8.57 min/day for transport, and 11.43 min/day for recreation. Poor physical function (PCS ≤ 41.04) was observed in 85.8% of participants, and 51.0% had depressive disorders (MCS ≤ 45.6). Logistic regression identified younger age [adjusted OR (AOR) 3.29], moderate stage of CKD (AOR 2.39), good physical function (AOR 3.01), absence of depression (AOR 4.87), and family history of CKD (AOR 2.65) were significant predictors of meeting PA recommendations (p < 0.05).
Conclusions
A substantial proportion of Bangladeshi CKD patients do not meet PA recommendations, with younger age, moderate CKD, better physical function, absence of depression, and family history of CKD predicting higher PA engagement. Targeted interventions addressing these factors, particularly early in CKD progression, are needed to promote PA and improve health outcomes in this population.
Clinical trial number
Not applicable.
Keywords: Chronic kidney disease, Physical activity, Severity of CKD, Global physical activity questionnaire, Moderate-intensity physical activity (MVPA), Moderate physical activity (MPA)
Introduction
Chronic kidney disease (CKD) is a progressive condition marked by declining renal function, typically defined as an estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m² or evidence of persistent kidney damage lasting over three months, regardless of cause [1]. CKD affects approximately 10% of the global population [2], yet prevalence rates are significantly higher in certain regions, including Bangladesh, where an estimated 22.48% of adults are affected [3]. Hospital-based studies in Bangladesh report that 16–18% of individuals in urban and underserved communities live with CKD, with 11% in advanced stages (≥ stage 3) [4]. CKD contributes to substantial morbidity, mortality, and reduced quality of life and has become the ninth leading cause of death worldwide [5].
Physical inactivity is highly prevalent among CKD patients, further accelerating functional decline, increasing hospitalization, and elevating risks for cardiovascular disease and mortality [6]. As renal function worsens, physical activity (PA) levels decline, contributing to frailty, muscle wasting, and diminished quality of life [7–11]. Evidence suggests that PA confers multiple benefits for CKD patients, including improved cardiovascular health, functional capacity, and mental well-being, and may even slow eGFR decline [6, 12–18]. Sedentary behavior is particularly concerning for CKD patients, who are already at an elevated risk for cardiovascular complications due to metabolic and inflammatory changes associated with kidney dysfunction [7]. Moreover, reduced physical activity limits functional capacity and negatively affects the overall quality of life [11]. Furthermore, physical activity has been linked to better quality of life and reduced CKD-related complications [16]. More recent studies suggest that increased PA may slow the decline of eGFR in patients with CKD stages 3 to 4 [7, 17] and reduce the overall burden of CKD [6]. A systematic review further found that higher PA levels were associated with improved survival rates in CKD patients [18]. Thus, promoting PA among CKD patients may help counteract the negative effects of sedentary behavior, such as cardiovascular complications, muscle wasting, and inflammation [17]. Accordingly, the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines recommend at least 150 min of moderate-intensity PA per week for CKD patients [19].
Despite these benefits, CKD patients in low- and middle-income countries like Bangladesh engage in significantly less PA than the general population, with many failing to meet the World Health Organization’s (WHO) recommendation of 600 MET-minutes per week [20]. Contributing factors include physical limitations, mental health issues such as depression and anxiety, demographic variables like age and gender, occupational demands, CKD severity, and socioeconomic barriers [21–22]. However, research on PA engagement in CKD patients in Bangladesh remains scarce, limiting context-specific interventions. Importantly, assessing PA in CKD patients requires reliable and valid tools. A recent consensus highlights the need for both self-reported instruments and objective measures (e.g., accelerometry) in clinical practice to accurately capture PA patterns in this population [22–26]. Nevertheless, self-report tools remain widely used in low-resource settings due to cost-effectiveness and feasibility. Understanding PA determinants in relation to these assessment tools is crucial for developing effective strategies in resource-limited contexts like Bangladesh. Given these gaps, this study was conducted to investigate physical activity engagement among Bangladeshi adults with moderate to advanced CKD. The primary aim was to assess the level of physical activity using a validated self-report tool. The secondary aim was to identify demographic, clinical, and socioeconomic factors associated with meeting recommended physical activity levels. Findings from this study are intended to inform culturally appropriate interventions to promote physical activity and improve health outcomes in this vulnerable population.
Methods
Study design and subject
A cross-sectional study was carried out to the outpatients at the Department of Nephrology, Mymensingh Medical College Hospital, Mymensingh, Bangladesh from October 2023 to January 2024. Patients with moderate to advanced CKD were defined as those with an estimated glomerular filtration rate (eGFR) ≤ 59 ml/min/1.73 kg/m² (i.e., stages 3–5 CKD). The inclusion criteria for this study were CKD patients aged 18 years and over who had a confirmed diagnosis of CKD by a medical doctor who attended the selected hospitals for follow up. The exclusion criteria were the CKD undergoing dialysis or kidney transplants.
Moderate to advanced stages CKD classification
We estimated GFR using the formulae of Chronic Kidney Disease Epidemiology.
Collaboration (CKD-EPI) [27]. CKD stages were classified according to the level of eGFR expressed as mL/min/1.73 m2: Stage-3 (eGFR of ≥ 30–59,); Stage-4 (eGFR of ≥ 15–29) and Stage 5 (eGFR of < 15). Further the CKD patients were categorized into 2 groups moderate (stage 3) and advanced stage (stage 4&5).
Data collection tools
Data was collected using a structured questionnaire capturing socio-demographic characteristics, clinical history, and physical activity, supplemented by medical record extraction and anthropometric measurements. The socio-demographic questionnaire included questions on age, sex, education, income, and other relevant factors. Clinical data, including history of diabetes mellitus, hypertension, cardiovascular disease, and duration of chronic kidney disease, were obtained from patients’ medical records using standardized procedures. Biochemical data, specifically serum creatinine levels, were extracted from the most recent laboratory reports, typically within one month of data collection. Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ), a validated tool developed by the World Health Organization to capture physical activity across work, transport, and recreational domains. Body Mass Index (BMI) was calculated as weight (kg) divided by height squared (m²) and categorized per World Health Organization standards: underweight (< 18.5 kg/m²), normal weight (18.5–24.99 kg/m²), overweight (25.0–29.99 kg/m²), and obese (≥ 30.0 kg/m²).
Short-Form health survey (SF-12)
The KDQoL-36 version has the 24-item kidney disease specific questionnaire as well as the 12-item Short-Form Health Survey (SF-12 version 1) [28]. SF-12 is a shorter version of the more established SF-36 tool for measuring health outcomes [29]. The Short Form (SF)-12 measures physical component summary (PCS) and mental component summary (MCS) functioning (1–12), which have items about general health, activity limits, ability to accomplish desired tasks, depression and anxiety, energy level, and social activities. In this study, the PCS-12 and MCS-12 scores, represented by six items each. The SF-12 summary scores (PCS-12 and MCS-12) also range from 0 to 100 and were calculated using an Excel scoring tool created by the KDQoL work group [28]. This program is available free for download online (http://www.rand.org/health/surveys_tools/kdqol.html). Scores range from 0 to 100, with higher scores indicating better physical and mental health functioning. A score of 41.04 or less on the PCS-12 has been recommended as a cut-off to predict low physical function [30]; while a score of 45.6 or less on the MCS-12 was recommended for depressive disorders was [31].
Measurement of physical activity
Physical activity levels were assessed using the Global Physical Activity Questionnaire (GPAQ), developed by the World Health Organization for surveillance in diverse populations [32]. The GPAQ comprises 16 questions (P1–P16) that collect data on sedentary behavior and physical activity across three domains: work, travel to and from places, and recreational activities. These questions enable the calculation of weekly Metabolic Equivalent of Task (MET) minutes, a standardized measure of energy expenditure. METs represent the ratio of a person’s working metabolic rate to their resting metabolic rate, where one MET equals the energy cost of sitting quietly, approximately 1 kcal/kg/hour. Using GPAQ data, MET minutes per week were calculated for all participants to quantify their physical activity levels, providing a robust basis for analyzing engagement patterns in this study. For the calculation of a person’s overall energy expenditure using GPAQ data, the following MET values were used.
Metabolic equivalent (MET) values assigned to different domains and intensities of physical activity, based on the Global Physical Activity Questionnaire (GPAQ) protocol [33].
| Domain | Activity intensity | MET value |
|---|---|---|
| Work | Moderate | 4.0 |
| Work | Vigorous | 8.0 |
| Transport | Cycling/Walking | 4.0 |
| Recreation | Moderate | 4.0 |
| Recreation | Vigorous | 8.0 |
Total physical activity MET-minutes/week is the sum of the total MET minutes of activity computed for each setting. The WHO recommends a cutoff value of physical activity for health as an equivalent combination of moderate- and vigorous-intensity physical activity reaching at least 600 MET-minutes/week.
Statistical method
The sample size was calculated by the formula n = z2pq/d2. The prevalence of physical activity among CKD patients was (6–34%) depending on the progression of disease [34]. We assumed that the prevalence of global physical activity level of CKD patients is 20% [34]. With a precision of 5% and a confidence interval of 95%, the minimum sample size required was 245. But a total of 253 subjects were included in the study at moderate to advanced stages of CKD. Data was analyzed statistically using SPSS 21.0, developed by SPSS Inc. in Chicago, USA. While categorical variables were given as frequency and percentage.
Depending on analysis, different variables were divided into two or three groups (age, employment status, education level and BMI). Several variables were dichotomized for the purpose of analysis, including income (0 = lower income < 20,000 BDT, 1 = higher income ≥ 20,000 BDT), marital status (1 = married, 0 = other [unmarried, widowed, separated]), and duration of CKD. Presence of diabetes, hypertension, and cardiovascular disease were coded as dichotomous variables (yes = 1, no = 0). Physical activity for health was classified as not meeting recommendations (0) if total physical activity MET-minutes per week was < 600, and as meeting recommendations (1) if ≥ 600, in accordance with WHO guidelines.
Statistical analyses were performed using SPSS version 21.0 (SPSS Inc., Chicago, IL, USA). Categorical variables were summarized as frequencies and percentages, and associations between categorical variables were assessed using the chi-square test. Continuous variables were expressed as median and interquartile range (IQR) due to non-normal distributions, and differences between groups were analyzed using the Mann–Whitney U test. To identify factors associated with meeting physical activity recommendations, variables with p-values < 0.10 in bivariate analyses were entered into a multivariable logistic regression model. Model fit was evaluated using the Hosmer–Lemeshow goodness-of-fit test, and adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. A p-value < 0.05 was considered statistically significant.
Results
Socio-demographic characteristics of the moderate to advanced stage of CKD patients, and association of differences factors
Table 1 depicts the distribution of CKD patients in moderate to advanced stages of CKD, and their relationship with different sociodemographic variables. Out of 253 CKD patients, 62.8% were male, and significantly higher number of females were in advanced stage of CKD. The CKD patients were categorized into two groups, 58.1% in the moderate stage (CKD stage 3) and 41.9% in the advance stage (CKD stage 4&5). About one-third (60.1%) of the CKD patients were in the age group 60 years and higher, but the aged groups did not find any significant differences among CKD severity (Table 1). Almost half (45.0%) of the study participants completed secondary to higher education, and only 8.7% were in job service (Table 1). According to WHO BMI cutoff point, 8.7% study subjects were underweighted (BMI: <18.5), 61.3% were in normal weight (BMI: 18.5–24.99), 26.1% were in overweight (BMI: 25–29.99) and 4.0% were in obese (BMI: ≥ 30.0) (Table 1). About one-fifth (19.8%) of the patients suffered from CKD for more than 2 years. The most usual comorbidity of CKD was hypertension accounting for 87.0%, followed by diabetes mellitus in 54.2% and CVD in 30.8% (Table 1). Gender and presence or absence of diabetes were found to significantly differ among the stages of CKD. Patients’ employment status, aged, family income, comorbidity of hypertension or CVD, duration of CKD, and nutritional status (BMI) were not found significantly associated by ƛ2 square test among the groups of CKD patients (Table 1).
Table 1.
Socio-demographic and clinical characteristics of study participants by CKD stage (N = 253)
| Variables | Total N (%) | Moderate Stage (Stage 3a &b), N (%) | Advanced stage (Stage 4&5), N (%) | p-value |
|---|---|---|---|---|
| Gender | ||||
| Male | 159 (62.8) | 103 (64.8) | 56 (35.2) | 0.006 |
| Female | 94 (37.2) | 44 (46.8) | 50 (53.2) | |
| Both | 253 (100) | 147 (58.1) | 106 (41.9) | |
| Age (Years) | ||||
| < 60 | 101 (39.9) | 62 (61.4) | 39 (38.6) | 0.436 |
| ≥ 60 | 152 (60.1) | 85 (55.9) | 67 (44.1) | |
| Marital Status | ||||
| Married | 181 (71.5) | 105 (58.0) | 76 (42.0) | 1.00 |
| Others (unmarried, separated, Widowed) | 72 (28.5) | 42 (58.3) | 30 (41.7) | |
| Occupation | ||||
| Job Service | 22 (8.7) | 13 (59.1) | 9 (40.9) | 0.264 |
| Housewife | 53 (20.9) | 24 (45.3) | 29 (54.7) | |
| Farmer/Day Laborer | 44 (17.4) | 25 (56.8) | 19 (43.2) | |
| Retired | 40 (15.8) | 26 (65.0) | 14 (35.0) | |
| Others (Student, unemployed) | 94 (37.2) | 59 (62.8) | 35 (37.2) | |
| Educational status | ||||
| Primary and below | 139 (54.9) | 75 (54.0) | 64 (46.0) | 0.160 |
| Secondary to Higher | 114 (45.1) | 72 (63.2) | 42 (36.8) | |
| Income | ||||
| Lower income (< 20000 TK) | 104 (41.1) | 61 (58.7) | 43 (41.3) | 0.898 |
| Higher income (≥ 20000 Tk) | 149 (58.9) | 86 (57.7) | 63 (42.3) | |
| BMI (WHO cut off value, kg/m2) | ||||
| Underweight (BMI < 18.5) | 22 (8.7) | 12 (54.5) | 10 (45.5) | 0.437 |
| Normal (BMI 18.5-24.99) | 155 (61.3) | 85 (54.8) | 70 (45.2) | |
| Overweight (BMI 25-29.99) | 66 (26.1) | 43 (65.2) | 23 (34.8) | |
| Obese (BMI ≥ 30.0) | 10 (4.0) | 7 (70.0) | 3 (30.0) | |
| Duration of CKD (months) | ||||
| Short (< 24) | 204 (80.6) | 122 (59.0) | 82 (40.2) | 0.333 |
| Long (≥ 24) | 49 (19.4) | 25 (51.0) | 24 (49.0) | |
| Family History of CKD | ||||
| Yes | 42 (16.6) | 27 (64.3) | 15 (35.7) | 0.398 |
| No | 211 (83.4) | 120 (56.9) | 91 (43.1) | |
| Diabetics | ||||
| Yes | 137 (54.2) | 68 (49.6) | 69 (50.4) | 0.003 |
| No | 116 (45.8) | 79(68.1) | 37 (31.9) | |
| Hypertension | ||||
| Yes | 220 (87.0) | 125 (56.8) | 95 (43.2) | 0.346 |
| No | 33 (13.0) | 22 (66.7) | 11 (33.3) | |
| CVD | ||||
| Yes | 78 (30.8) | 46 (59.0) | 32 (41.0) | 0.891 |
| No | 175 (69.2) | 101 (57.7) | 74 (42.3) |
BMI, Body Mass Index; CKD, Chronic Kidney Disease; CVD, Cardiovascular Disease; TK, Bangladeshi Taka. P-values were calculated using the Chi-square test
Factors associated with physical and mental health of the CKD patients
Table 2 illustrates the associations between various factors and physical (PCS-12) and mental health (MCS-12) in CKD patients. The mean PCS score was 33.86 ± 6.74, and for MCS score was 44.35 ± 8.48 (Table 2). The PCS score was significantly higher in male than female, and in moderate stage of CKD compared to advanced stage of CKD patients (Table-2). Whereas the MCS score was significantly differs in age groups, income groups, severity of CKD, family history of CKD, occupation, marital status, education level and comorbidities (diabetics, hypertension and CVD) (Table 2). According to PCS and MCS cut off point, 85.8% CKD patients were in poor physical function and about half (51.0%) of the subjects have depression disorder.
Table 2.
Associations between participant characteristics and (a) global physical activity level and (b) SF-12 physical and mental health scores
| Variables | (a) Global Physical Activity Level | (b) SF-12 health survey | |||||
|---|---|---|---|---|---|---|---|
| Total Physical Activity MET minutes | |||||||
| < 600/week N (%) |
≥ 600/week N (%) |
p value | PCS | p value | MCS | p value | |
| Gender | |||||||
| Male | 52 (32.7) | 107 (67.3) | 0.001 | 34.77 ± 6.76 | 0.005 | 44.70 ± 8.44 | 0.390 |
| Female | 52 (55.3) | 42 (44.7) | 32.31 ± 6.46 | 43.75 ± 8.56 | |||
| Both | 104 (41.1) | 149 (58.9) | 33.86 ± 6.74 | 44.35 ± 8.48 | |||
| Age (Years) | |||||||
| < 60 | 25 (24.8) | 76 (75.2) | < 0.001 | 34.17 ± 7.04 | 0.554 | 46.55 ± 6.97 | 0.001 |
| ≥ 60 | 79 (52.0) | 73 (48.0) | 33.65 ± 6.55 | 42.88 ± 9.08 | |||
| Marital Status | |||||||
| Married | 63 (34.8) | 118 (65.2) | 0.002 | 34.08 ± 6.60 | 0.395 | 45.65 ± 7.87 | < 0.001 |
| Others (unmarried, separated, Widowed) | 41 (56.9) | 31 (43.1) | 33.28 ± 7.11 | 41.06 ± 9.10 | |||
| Occupation | |||||||
| Job Service | 2 (9.1) | 20 (90.9) | < 0.001 | 33.73 ± 6.34 | 0.475 | 49.16 ± 6.78 | 0.002 |
| Housewife | 28 (52.8) | 25 (47.2) | 32.74 ± 6.75 | 43.91 ± 7.73 | |||
| Farmer/Day Laborer | 9 (20.5) | 35 (79.5) | 35.29 ± 6.26 | 44.81 ± 8.87 | |||
| Retired | 18 (45.0) | 22 (55.0) | 33.60 ± 7.21 | 46.73 ± 9.02 | |||
| Others (Student, unemployed) | 47 (50.0) | 47 (50.0) | 33.96 ± 6.85 | 42.24 ± 8.25 | |||
| Educational status | |||||||
| Primary and below | 65 (46.8) | 74 (53.2) | 0.054 | 34.04 ± 6.19 | 0.642 | 42.80 ± 7.94 | 0.001 |
| Secondary to Higher | 39 (34.2) | 75 (65.8) | 33.64 ± 7.38 | 46.23 ± 8.77 | |||
| Income | |||||||
| Lower income (< 20000 TK) | 56 (53.8) | 48 (46.2) | 0.001 | 33.62 ± 6.57 | 0.641 | 42.47 ± 8.93 | 0.003 |
| Higher income (≥ 20000 Tk) | 48 (32.2) | 101 (67.8) | 34.02 ± 6.87 | 45.66 ± 7.92 | |||
| BMI (WHO cut off value) | |||||||
| Underweight (BMI < 18.5) | 11 (50.0) | 11 (50.0) | 0.481 | 34.41 ± 6.38 | 0.820 | 43.57 ± 9.00 | 0.303 |
| Normal (BMI 18.5-24.99) | 61 (39.4) | 94 (60.6) | 33.98 ± 6.62 | 43.73 ± 8.13 | |||
| Overweight (BMI 25-29.99) | 26 (39.4) | 40 (60.6) | 33.24 ± 7.08 | 45.59 ± 9.03 | |||
| Obese (BMI ≥ 30.0) | 6 (60.0) | 4 (40.0) | 34.79 ± 7.78 | 47.33 ± 8.73 | |||
| Severity of CKD | |||||||
| Moderate (Stage 3) | 48 (32.7) | 99 (67.3) | 0.002 | 35.17 ± 7.09 | < 0.001 | 45.36 ± 8.23 | 0.025 |
| Advanced (stage 4 &5) | 56 (52.8) | 50 (47.2) | 32.03 ± 5.78 | 42.94 ± 8.65 | |||
| Duration of CKD | |||||||
| Low (< 24 months) | 77 (37.7) | 127 (62.3) | 0.035 | 33.94 ± 6.82 | 0.703 | 44.62 ± 8.22 | 0.287 |
| Long (≥ 24 months | 27 (55.1) | 22 (44.9) | 33.53 ± 6.47 | 43.18 ± 9.48 | |||
| Family History of CKD | |||||||
| Yes | 11 (26.2) | 31 (73.8) | 0.039 | 33.35 ± 7.53 | 0.595 | 46.77 ± 8.12 | 0.042 |
| No | 93 (44.1) | 118 (55.9) | 33.96 ± 6.59 | 43. 86 ± 8.49 | |||
| Diabetics | |||||||
| Yes | 59 (43.1) | 78(56.9) | 0.523 | 33.36 ± 6.59 | 0.204 | 43.12 ± 8.43 | 0.013 |
| No | 45 (38.8) | 71 (61.2) | 34.44 ± 6.90 | 45.79 ± 8.35 | |||
| Hypertension | |||||||
| Yes | 92 (41.8) | 128 (58.2) | 0.576 | 33.76 ± 6.62 | 0.561 | 43.76 ± 8.45 | 0.004 |
| No | 12 (36.4) | 21 (63.6) | 34.50 ± 7.59 | 48.27 ± 7.74 | |||
| CVD | |||||||
| Yes | 39 (50.0) | 39 (50.0) | 0.072 | 32.80 ± 6.72 | 0.097 | 42.16 ± 8.81 | 0.006 |
| No | 65 (37.1) | 110 (62.9) | 34.33 ± 6.72 | 45.32 ± 8.17 | |||
| PCS-12 cutoff point (≤ 41.04) | |||||||
| Good physical function | 8 (22.2) | 28 (77.8) | 0.017 | 36 (14.2) | |||
| Low physical function | 96 (44.2) | 121 (55.8) | 217 (85.8) | ||||
| MCS-12 cut off point (≤ 45.6) | |||||||
| No depression disorder | 25 (20.2) | 99 (79.8) | < 0.001 | 124 (49.0) | |||
| Depression disorder present | 79 (61.2) | 50 (38.8) | 129 (51.0) | ||||
MET, Metabolic Equivalent of Task; PCS, Physical Component Summary; MCS, Mental Component Summary; BMI, Body Mass Index; CKD, Chronic Kidney Disease; CVD, Cardiovascular Disease; TK, Bangladeshi Taka. P-values were calculated using the Chi-square test for categorical variables and independent-samples t-test or Mann–Whitney U test for continuous variables as appropriate
Factors associated with physical activity of the CKD patients
Table 2 highlights significant factors influencing physical activity levels (≥ 600 MET-minutes/week) and health outcomes among CKD patients, emphasizing the interplay of demographic, socioeconomic, and clinical or health variables specifically physical and mental health. Although various factors such as income, severity of CKD, duration of CKD, age of the patients, occupation etc. were found significant in ƛ2 square analysis, but some of those factors were no more significant in multivariate regression analysis.
Table 3 presents a multivariate regression analysis identifying key predictors for achieving ≥ 600 MET-minutes/week of physical activity. After adjustment through multivariate logistic regression analysis, patients aged group, occupations like farming/day labor, severity of CKD, family history of CKD, PCS-12 and MCS-12 were found to be significant (p < 0.05) predictors of meeting physical activity for health (Table 3). When compared to advanced aged group (≥ 60year), the odds of meeting physical activity were about 3 times higher among lower aged group (< 60 years) (AOR = 3.29, p = 0.002) (Table 3). CKD patients working as farmer/day laborer were active in physical activity (AOR = 4.14, p = 0.016) followed by service workers (AOR = 3.24, p = 0.199) as compared to others CKD patients (Student, unemployed or small business). Severity of CKD is another important predictor for physical activity. CKD patients at advanced stage were more vulnerable for meeting physical activity for health as compared to moderate stage of CKD (AOR = 2.39, p = 0.011) (Table 3). Mental health remained a critical factor, as individuals without depressive symptoms were significantly more likely to engage in physical activity (AOR = 4.87, p < 0.001). The family history of CKD was also a significant predictor (AOR = 2.65, p = 0.046). Additionally, good physical function was positively associated with higher physical activity levels (AOR = 3.01, p = 0.033), reinforcing the importance of maintaining functional capacity. In contrast, factors such as family income (p = 0.055) marital status (p = 0.774), educational level (p = 0.481), and duration of CKD (p = 0.861) were not significant predictors after adjustment. These findings underscore the interplay of clinical factors (CKD stage, mental health, physical function), socioeconomic status (family history), and occupational demands in determining physical activity levels, emphasizing the need for targeted interventions to promote and sustain physical activity in CKD patients.
Table 3.
Multivariate regression analysis for determining the potential predictor factors for meeting physical activity for health
| Variables | Meeting Physical activity for health | |
|---|---|---|
| AOR (95% CI) | p-value | |
| Gender | ||
| Male | 1.55 (0.49–4.87) | 0.453 |
| Female | 1.0 | |
| Age (Years) | ||
| < 60 | 3.29 (1.53–7.12) | 0.002 |
| ≥ 60 | 1.0 | |
| Marital Status | ||
| Married | 1.13 (0.48–2.65) | 0.774 |
| Signal and others | 1.0 | |
| Occupation | 0.084 | |
| Job Service | 3.24 (0.54–19.45) | 0.199 |
| Housewife | 1.02 (0.29–3.59) | 0.973 |
| Farmer/Day Laborer | 4.14 (1.29–13.21) | 0.016 |
| Retired | 0.75 (0.27–2.07) | 0.582 |
| Others (Student, unemployed) | 1.0 | |
| Educational status | ||
| Primary and below | 1.36 (0.57–3.25) | 0.481 |
| Secondary to Higher | 1.0 | |
| Income | ||
| Higher income (≥ 20000 Tk) | 2.17 (0.98–4.81) | 0.055 |
| Lower income (< 20000 TK) | 1.0 | |
| Duration of CKD | 0.99 (0.91–1.08) | 0.861 |
| Family History of CKD | ||
| Yes | 2.65 (1.02–6.89) | 0.046 |
| No | 1.0 | |
| Severity of CKD | ||
| Moderate (Stage 3) | 2.39 (1.22–4.70) | 0.011 |
| Advanced (stage 4 &5) | 1.0 | |
| CVD | ||
| Yes | 1.50 (0.73–3.09) | 0.271 |
| No | 1.0 | |
| PCS-12 cutoff point (≤ 41.04) | ||
| Good physical function | 3.01 (1.09–8.27) | 0.033 |
| Low physical function | 1.0 | |
| MCS-12 cut off point (≤ 45.6) | ||
| No depression disorder | 4.87 (2.47–9.59) | < 0.001 |
| Depression disorder present | 1.0 | |
| Constant | 0.05 | < 0.001 |
PCS, Physical Component Summary; MCS, Mental Component Summary; BMI, Body Mass Index; CKD, Chronic Kidney Disease; CVD, Cardiovascular Disease; TK, Bangladeshi Taka
Physical activity pattern of the CKD patients
Table 4 presents the distribution of physical activity patterns among CKD patients, showing that most engage in moderate-intensity physical activity (MPA and MVPA) with limited involvement in work related activities. The median value of moderate-intensity physical activity (MVPA) per day of the CKD patient was 28.57 min, and the moderate physical activity (MPA) was 28.57 min. Whereas the median value of vigorous physical activity (VPA) was zero (0), that indicates the CKD patients mostly involved in moderate activity (Table 4). Gender differences were significant, with males more active than females in MPA and PA for work and transport (p < 0.05). Patients with moderate-stage CKD (Stage 3) demonstrated significantly higher physical activity levels in all domains (MPA, MVPA, work, transport, and recreation) compared to those with advanced CKD stages. Age was another critical factor, as older patients (≥ 60 years) exhibited lower activity levels across various domains, particularly in MPA, PA for transport and recreation (p < 0.05). These findings underline the effect of CKD severity and age on physical activity participation, supporting the need for tailored interventions to address the specific barriers faced by older and more severely affected CKD patients.
Table 4.
Distribution of physical activity patterns of the participant
| Variable | MVPA min/day | MPA min/day | VPA min/day | SB min/day | PA work min/day | PA travel min/day | PA recreation min/day |
|---|---|---|---|---|---|---|---|
| Total | 28.57 (48.71–15.0) | 28.57 (45.71–15.0) | 0 (0–0) | 180 (240 − 120) | 0 (8.57–0) | 8.57 (20.0–5.0) | 11.43 (20.0–5.71) |
| Men | 35.0 (50.0–16.43) | 34.28 (48.57–16.43) | 0 (0–0) | 180 (240–120) | 5.71 (10.71–0) | 12.86 (20.0–7.14) | 11.43 (20.0–5.71) |
| Women | 20.0 (38.04–14.28) | 20.0 (38.03–14.28) | 0 (0–0) | 180 (240 − 120) | 0 (4.64–0) | 5.71 (11.78–2.85) | 11.43 (20.0–5.35) |
| Mann Whitney U test | < 0.001 | 0.001 | 0.017 | 0.852 | < 0.001 | < 0.001 | 0.787 |
| Moderate stage CKD | 35.71 (52.85–18.57) | 35.71 (51.43–18.57) | 0 (0–0) | 180 (240 − 120) | 5.71 (10.71–0) | 10.71 (22.85–5.71) | 12.86 (21.43–5.71) |
| Advanced stage CKD | 20.71 (35.89–14.28) | 20.71 (35.89–14.28) | 0 (0–0) | 180 (240 − 120) | 0 (5.71–0) | 8.57 (14.28–4.28) | 8.57 (17.14–4.29) |
| Mann Whitney U test | < 0.001 | < 0.001 | 0.001 | 0.182 | < 0.001 | 0.001 | 0.018 |
| Aged < 60 year | 35.71 (51.43–20.71) | 35.71(50.35–20.71) | 0(0–0) | 180(240 − 120) | 5.71 (8.57 -0) | 12.86 (21.43–6.78) | 17.14 (25.0- 8.57) |
| Aged ≥ 60 year | 20.71 (41.43–12.86) | 20.71 (41.83–12.86) | 0(0–0) | 180 (240 − 120) | 0 (8.57 -0) | 8.57 (17.14–4.28) | 9.28 (17.14–4.28) |
| Mann Whitney U test | < 0.001 | < 0.001 | 0.013 | 0.135 | 0.146 | 0.003 | < 0.001 |
MVPA, Moderate to vigorous physical activity; MPA, Moderate intensity physical activity; VPA, Vigorous physical activity; SB, Sedentary behaviors; PA, Physical activity
Discussion
Chronic kidney disease (CKD) represents a significant global health burden, particularly in low- and middle-income countries (LMICs) such as Bangladesh, where prevalence continues to rise [3]. The progression of CKD is often accompanied by a cascade of negative outcomes, including impaired physical function, reduced quality of life, and increased comorbidities like cardiovascular disease [7, 35]. Although physical activity (PA) is widely recognized for its therapeutic benefits in managing CKD, particularly in improving cardiovascular health and physical function, its uptake remains suboptimal among CKD patients, especially in resource-limited settings. This study aimed to identify the key factors influencing physical activity levels in CKD patients in Bangladesh, addressing a gap in the literature concerning the determinants of PA in LMICs.
Our findings demonstrate that several factors significantly influence PA engagement among CKD patients, with younger age, physically demanding occupations, moderate CKD stage, better physical function, and mental health emerging as the strongest predictors. Conversely, older age, advanced CKD stages, and depressive symptoms were identified as barriers to PA participation. These results not only align with findings from other studies but also provide new insights into the context-specific challenges faced by CKD patients in Bangladesh.
Age was one of the important predictors of PA engagement. Younger CKD patients were significantly more likely to meet PA guidelines compared to their older counterparts. This observation is consistent with global research, which shows that older adults with CKD experience higher levels of frailty and functional impairment, which can severely limit their ability to engage in PA [36]. The physiological declines associated with aging, compounded by the progression of CKD, exacerbate the barriers to PA in older adults. This finding highlights the importance of age-specific interventions, particularly for older CKD patients. Instead of focusing only on structured exercise, tailored physical activity programs for this group should emphasize balance, strength, and flexibility to improve function and prevent frailty.
Physically demanding occupations such as farming and day labor emerged as strong determinants of PA participation. Patients in these occupations were significantly more likely to engage in PA. This aligns with research demonstrating that manual laborers often surpass PA guidelines due to occupational demands [26]. However, evidence highlights that occupational PA differs from the health benefits of leisure-time PA. Prolonged occupational activity can increase oxidative stress and the risk of cardiovascular complications, which may be harmful for CKD patients [37]– [38]. In contrast, leisure-time PA—including structured activities—is associated with reduced inflammation, improved vascular function, and preserved muscle mass [14]– [15]. Despite its proven benefits, leisure-time PA remains scarce among CKD patients here and globally [34], highlighting an urgent need in Bangladesh for programs promoting safe leisure-time physical activity with controlled intensity and rest periods.
CKD severity also significantly influences PA, with patients in moderate CKD (Stage 3) more likely to meet PA guidelines compared to those in advanced CKD stages (Stages 4 and 5). This is consistent with existing literature, which shows that as CKD progresses, patients experience greater functional decline, fatigue, and mobility limitations, all of which significantly hinder physical activity participation [21]. The findings emphasize the need for early interventions for patients in Stage 3 CKD, when physical function can still be preserved or improved through appropriate physical activity programs. Family history of CKD predicts PA engagement, a novel finding in this context. Patients with family history were more active, possibly due to heightened risk perception or familial encouragement fostering proactive behaviors. Evidence from other chronic diseases supports this association, suggesting that familial exposure often prompts greater health engagement and lifestyle changes [39], such as increased screenings, greater motivation to adopt healthier lifestyles and more family support dynamics which significantly impact health outcomes in chronic disease management. In Bangladesh, where family structures are tight-knit, this could reflect shared health knowledge or support systems promoting PA. This suggests that involving family members in interventions may enhance long-term adherence to PA recommendations and improve health outcomes.
Mental health emerged as one of the strongest predictors of PA participation in our study, with CKD patients without depression being significantly more likely to meet PA guidelines. Depression, affecting 20–30% of CKD patients globally [22], is a significant barrier, by reducing motivation and perpetuating a cycle of inactivity and worsening health. In Bangladesh, where psychological support is scarce and stigma around mental illness persists, this challenge is amplified. Studies show better MCS-12 scores enhance exercise motivation [21], and PA’s anti-inflammatory and mood-boosting effects [17] suggest it could mitigate both CKD-related inflammation and depression. However, milder mental health improvements alone may not overcome physical or economic barriers. Integrating mental health screening and promoting group-based physical activity could help break this cycle in Bangladesh’s resource-limited context. Future research should test such approaches for scalability in LMICs.
Finally, maintaining or improving physical function strongly influences PA engagement in CKD patients in Bangladesh, with those showing good function (PCS-12 > 41.04) are over three times more likely to meet guidelines. This reflects a bidirectional relationship where higher activity levels preserve functional capacity, which in turn enables continued PA participation a dynamic well-documented in CKD [21]. In Bangladesh, scarce rehabilitation resources exacerbate vulnerability as CKD advances. CKD-related declines in muscle mass, strength, and endurance driven by uremic toxins, inflammation, and malnutrition [21] limit patients’ ability to engage in even moderate-intensity activities. Our data align with studies showing that CKD patients with preserved physical function exhibit better cardiovascular health and reduced frailty [15] implying that, physical activity interventions could help interrupt this downward spiral. Early intervention in Stage 3, when physical function is still relatively intact, could maximize benefits. Physical activity programs focusing on resistance exercises, flexibility, and low-impact endurance activities could help preserve muscle mass and independence [14]. Given the lack of formal rehabilitation infrastructure in Bangladesh, community-based or home-based functional training programs should be explored as cost-effective solutions.
Limitations and future research directions
This study offers valuable insights into the determinants of physical activity among CKD patients in Bangladesh, yet several limitations must be acknowledged. The reliance on the Global Physical Activity Questionnaire (GPAQ) for self-reported data introduces the potential for recall bias, which may compromise the accuracy of physical activity measurements. Additionally, the cross-sectional design precludes establishing causal relationships between variables and physical activity levels, limiting the ability to track changes over time. The recruitment of participants exclusively from a single hospital’s outpatient department may introduce selection bias, restricting the generalizability of findings to the broader Bangladeshi CKD population or to diverse cultural and healthcare contexts. Furthermore, unmeasured variables, such as psychological health, family support, or disease-specific factors like renal anemia, uremic toxins, and nutritional deficiencies, could contribute to residual confounding, influencing the results. The exclusion of dialysis patients also narrows the scope, leaving gaps in understanding physical activity determinants across the full CKD spectrum.
To address these limitations, future research should adopt longitudinal designs to explore causal relationships and monitor physical activity trends over time. Incorporating objective measures, such as accelerometers or pedometers, alongside self-reported tools would enhance data accuracy. Expanding sampling to include multiple hospitals and community settings would improve the generalizability of findings. Additionally, accounting for a broader range of psychosocial, cultural, and disease-specific variables would minimize residual confounding. Including dialysis patients and other CKD subgroups in subgroup analyses would provide a more comprehensive understanding of physical activity determinants, ensuring findings are relevant across the CKD continuum.
Implications for clinical practice and public health
The findings underscore the need for multifaceted interventions tailored to the unique barriers and facilitators of physical activity among CKD patients. Interventions should prioritize patients in moderate CKD (Stage 3), where physical function remains relatively intact, to preserve capacity and slow disease progression through structured exercise programs. These programs should emphasize resistance training, flexibility, and low-impact endurance activities to maintain muscle mass and functional independence. Given the high prevalence of depression (20–30%) among CKD patients, integrating mental health screening, such as with the MCS-12 tool, and psychological support into treatment regimens is critical to enhance motivation and adherence to physical activity. Older CKD patients, who face heightened frailty and functional decline, require age-specific programs focusing on balance, strength, and flexibility to sustain mobility.
Socioeconomic disparities also demand attention, as lower-income patients face limited access to exercise facilities, safe spaces, and structured programs. Policies promoting community-based physical activity initiatives or subsidized healthcare services that include exercise promotion could bridge this gap, ensuring equitable access to resources. In Bangladesh’s resource-constrained setting, workplace interventions hold particular promise for patients in physically demanding occupations, such as farmers and laborers. These programs should optimize the health benefits of occupational activity while mitigating risks like oxidative stress or cardiovascular strain through controlled intensity and recovery periods. Additionally, the influence of family history suggests that leveraging Bangladesh’s tight-knit family structures could foster health engagement. Encouraging family involvement in physical activity interventions may enhance adherence by tapping into shared health knowledge and support systems. By combining clinical and public health strategies, these approaches can improve physical activity engagement, enhance health outcomes, and alleviate the CKD burden in low-resource contexts.
Conclusion
This study sheds light on the complex determinants of physical activity among CKD patients in Bangladesh, identifying younger age, moderate CKD stage, physically demanding occupations, absence of depression, good physical function, and family history as key predictors. Younger patients demonstrate greater capacity to meet physical activity guidelines, highlighting the need for age-tailored interventions to support older adults facing frailty. Those in moderate CKD exhibit higher activity levels, underscoring the value of early intervention to preserve function and delay progression. The strong link between mental health and activity highlights the potential role of psychological support, while socioeconomic factors and physical function further shaping participation, pointing to the need for accessible resources and functional training. Early interventions in Stage 3, mental health support, community-based programs, and family engagement offer practical pathways to boost physical activity, improve quality of life, and reduce the CKD burden in resource-limited settings like Bangladesh. Yet causal effects and applicability beyond Bangladesh remain uncertain due to contextual differences. Longitudinal trials are needed to test these strategies and validate their relevance, offering pathways to enhance physical activity and potentially improve quality of life for CKD patients in resource-limited settings. A holistic approach, blending bench-to-bedside strategies, is essential to optimize patient outcomes and effectively manage chronic kidney disease.
Acknowledgements
We thank all the study teams and patients who participated in our study.
Abbreviations
- AOR
Adjusted odds ratio
- BMI
Body mass index
- CKD
Chronic kidney disease
- CVD
Cardiovascular disease
- eGFR
Estimated glomerular filtration rate
- ESKD
End stage kidney disease
- GPAQ
Global physical activity questionnaire
- MCS
Mental component summary
- MET
Metabolic Equivalent of Task
- MPA
Moderate physical activity
- MVPA
Moderate-to-vigorous physical activity
- PA
Physical activity
- PCS
Physical Component Summary
- RRT
Renal replacement therapy
Author contributions
TT and KMRK Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. AAL, BNSM and TR acquired, analyzed and interpreted the data. TT, TR and KMRK critically revised the manuscript for important intellectual content.All authors have reviewed and approved the final version of the manuscript. All authors attest to the accuracy and completeness of the data and confirm that the study was conducted in accordance with the approved protocol.
Funding
This study did not receive any external funding.
Data availability
The data will be available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was performed according to the guidelines suggested by the Declaration of Helsinki, and the study protocol was reviewed and approved by Ethical Review Committee of the Faculty of Biological Sciences, University of Dhaka, Dhaka, Bangladesh (Ref. No.239/Biol. Scs., Date: August 30, 2023). After explaining the purpose of the survey to the participants, those who were willing to take part in the study gave their written informed consent, before the interview.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data will be available from the corresponding author upon reasonable request.