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Phys Act Nutr > Volume 27(4); 2023 > Article
Choi, Ahn, and Jung: Estimation of peak oxygen consumption in individuals with spinal cord injury patients using multiple linear regression analysis: a preliminary study

Abstract

[Purpose]

This study aims to develop a regression model to estimate peak oxygen consumption (VO2peak) in individuals with spinal cord injury (SCI) by employing different variables.

[Methods]

In this study, 34 participants were divided into two groups: 19 with cervical injury (CI) and 15 with thoracic injury (TI). Key measurements included VO2peak and related factors such as age, height, weight, body mass index (BMI), fat-free mass, body fat percentage, limb and trunk circumferences, spinal cord independence (SCIM III), Korean activities of daily living (K-ADL), and respiratory functions (forced vital capacity (FVC), peak expiratory flow (PEF), and maximum voluntary ventilation (MVV)). Statistical analyses were conducted using forward selection regression to examine the relationships between these variables.

[Results]

Height, calf circumference, SCIM III score, and PEF were key variables in all patients with SCI (TSCI). For patients with CI, the key variables were height, calf circumference, and MVV, whereas for patients with TI, the key variable was calf circumference. The average explanatory powers of the VO2peak regression model for TSCI were 70.3% (R2) and 66.2% (adjusted R2), with an average standard error of estimate (SEE) of 2.94 ml/kg/min. The average explanatory power for patients with CI was 71.7% (R2) and 66.1% (adjusted R2), with an average SEE of 1.88 ml/kg/min. The average explanatory power for patients with TI was 55.9% (R2) and 52.5% (adjusted R2), with an average SEE of 3.41 ml/kg/min. There was no significant difference between the VO2peak measured and predicted VO2peak for each type of injury.

[Conclusion]

The regression model for estimating VO2peak in SCI patients in this preliminary study is as follows: TSCI=39.684-0.144×(Height)-0.513×(Calf)+0.136×(SCIM III)+1.187×(PEF), CI=38.842-0 .158×(Height) - 0.371×(Calf)+0.093×(MVV), TI=42.325-0.813×(Calf).

INTRODUCTION

Individuals with spinal cord injuries (SCI) experience a decline in volitional motor control and sensation due to various causes of spinal nerve damage, leading to a decrease in the ability to perform daily tasks and an overall reduction in activity levels [1]. In particular, cord injuries result in limitations in respiratory function and vascular circulation, which can exacerbate physical isolation and further impact mental health, leading to stress and depression. Therefore, efforts to improve the quality of life of these individuals are essential [2].
Physical activity is recommended as the minimum method for living a healthy life and is effective for all individuals. However, the level of physical activity in individuals with spinal cord injuries often does not meet minimum requirements. The insufficient maintenance of cardiovascular health is critical for this population. Reduced activity levels further decrease daily life activities, and the lack of participation in regular activity programs exacerbates this situation. Once this vicious cycle begins, it can lead to a decrease in functional task ability, diminished independence, and reduced cardiovascular health, thus increasing the risk of cardiovascular diseases [1,3,4].
This issue extends beyond merely reducing activity levels. In individuals with spinal cord injuries, extreme inactivity leads to an increased incidence of secondary complications such as diabetes mellitus, hypertension, and atherogenic lipid profiles [5,6]. It also exacerbates problems such as muscle weakness and atrophy, skeletal changes, muscle tension, spasticity, and issues with the circulatory and respiratory systems. The increased morbidity rate of secondary complications, early onset, and the associated increase in early mortality rates are well documented [7,8].
For individuals with SCI, regular physical activity is known to improve fitness and psychological stability and is a means to engage in a normal social life. Participation in sports activities enhances health-related fitness components, such as cardiopulmonary capacity and muscle strength in individuals with spinal cord injuries [9]. Moreover, improvements in sensory and motor functions through such activities can aid in daily life skills and prevent secondary diseases, as indicated by various studies [10,11]. Therefore, active physical activity and exercise participation have a significantly positive impact on lifestyle diseases and quality of life in individuals with spinal cord injuries, making it essential to engage in regular physical activity to maintain fitness. An accurate and effective assessment of cardiopulmonary function is necessary to maintain physical activity and fitness. Currently, VO2peak is used to evaluate cardiopulmonary function; however, this method is complicated and expensive because of the use of gas analyzers, and peak exercise tests are limited in their applicability to the older adult individuals and individuals with disability.
VO2peak is an important indicator for predicting cardiovascular mortality; however, accurate measurement of VO2 in individuals with SCI complex gas analysis, peak graded exercise tests (GXT), expensive equipment, lengthy protocols, and skilled examiners. Measuring VO2peak values in patients with autonomic dysfunction and upper limb contractures is particularly challenging [12,13]. To address these issues, previous studies have developed various exercise prediction models, including non-exercise and maximal-exertion exercise test models, to measure VO2peak [14,15]. The non-exercise prediction equation model, which indirectly estimates the VO2peak, is a simple, fast, and convenient method that does not require complex experimental equipment or maximal effort.
Although various studies have proposed predictive models for estimating VO2peak in age-specific healthy populations, research on predicting VO2peak in individuals with SCI remains limited [16,17]. Furthermore, while some studies have developed estimation formulas through GXT using heart rate and perceived exertion, it is crucial to estimate regression models that include not only direct oxygen consumption measurements but also factors such as body composition, circumference, and questionnaires. This study aims to develop a regression formula that can estimate VO2peak in individuals with SCI using several readily measurable variables that are applicable in real-world settings. Therefore, the objective of this study is to propose a regression model that estimates the VO2peak in individuals with SCI using a range of variables, including arm ergometer measurements, body composition, circumferences, survey data, and lung function. This model is designed to facilitate the effective development of exercise programs for promoting the health of individuals with SCI. It is intended to be readily applicable in real-world settings.

METHODS

Participants

All subjects were evaluated for the type and degree of SCI and disability by specialists in rehabilitation medicine, orthopedics, neurosurgery, neurology, and internal medicine (rheumatology division) at medical institutions equipped with diagnostic tools, including X-ray facilities. This study targeted individuals with SCI with the following selection criteria: 1) SCI of a non-progressive nature, whether due to trauma or other causes; 2) a confirmed injury level at or below the T2; 3) a minimum of one year since discharge from rehabilitation; 4) age ranging from 18 to 65 years; 5) regular use of a push-rim wheelchair, with the capability of propelling themselves over a distance of at least 100 meters unassisted; 6) utilization of wheelchairs equipped with quick-release axles. Participants were excluded from the study based on the following criteria: 1) those currently under palliative care; 2) SCI resulting from congenital anomalies, progressive neurological diseases, or Guillain-Barré syndrome; 3) presence of upper limb pain severe enough to restrict wheelchair propulsion; 4) past history of shoulder, elbow, or wrist fractures or dislocations that continue to cause discomfort; 5) a background of heart or lung conditions potentially aggravated by intense exercise.
The sample size was calculated using G*Power 3.1.9, as described previously [18]. Applying an effect size of 0.63, a power of 80%, and a significance level of 0.05, the sample size was calculated to be 20 individuals. In our study, the sample size was 32, with a power of 95%. Considering the dropout rate for reasons such as withdrawal from the experiment, insincerity, or pain complaints, 35 participants were initially selected. Eventually, 34 individuals participated in the study, with one withdrawing from the study due to personal reasons. The study cohort comprised 19 individuals with cervical spine injuries and 14 individuals with thoracic spine injuries. All the participants were fully informed about the study and voluntarily agreed to participate. All study procedures were approved by the Institutional Review Board of Dongseo University (2021-019-HR-02) and conducted in accordance with the Declaration of Helsinki.

Experimental design

Participants were asked to visit the laboratory twice. They were instructed to avoid strenuous exercise and physical activity within 24 hours before the visit and to ensure at least 6 hours of sleep before visiting the laboratory. In the initial session, participants underwent a body composition assessment using the Inbody BWA device (Biospace Ltd., Seoul, Korea) and a peak cardiopulmonary exercise evaluation using the Quark CPET system (Cosmed, Rome, Italy). Independence assessments and a Korean activities of daily living (K-ADL) scale, body circumference measurements, and pulmonary function tests (Quark CPET, Cosmed, Rome, Italy) were performed on the second visit, at least 72 hours later.

Body composition

Height was measured in the lying position from the sole to the top of the head using a tape measure. Weight was measured using a wheelchair scale (WCS-200; CAS, Seoul, Korea) and the weight of the participants was obtained by subtracting the weight of the wheelchair from the total weight when seated in the wheelchair. Body composition analysis was performed using the InBody BWA (Biospace Ltd., Seoul, Korea), a bioelectrical impedance analysis device specifically designed for individuals with disabilities. The analysis measured body mass index (BMI), free fat mass (FFM), body fat (BF), percentage of BF.

Circumferences

A tape measure was used to measure the circumference of individuals with spinal cord injuries. The circumferences of the left and right biceps, forearm, wrist, waist, hip, thigh, calf, and ankle muscles were measured.

Peak oxygen consumption

The graded exercise test (GXT) was based on a study by van der Scheer et al. [19]. The participants were advised to avoid intense physical activity 24 h before the GXT and to have a light meal two to three hours before the test while avoiding caffeine, alcohol, and smoking. Incremental exercise was measured using the Quark CPET System (Cosmed, Rome, Italy), with calibration of the testing equipment performed one hour before the experiment for accuracy. Upon arrival at the laboratory, the participants were thoroughly briefed about the test procedures and precautions, followed by stretching as guided by the examiner, and VO2peak was measured using an arm ergometer. To ensure accurate gas analysis, masks fitting the face size were used to prevent air leakage, and resting values for the heart rate and respiratory variables were collected while the participants were sitting quietly before starting the measurement. Pedaling on the arm ergometer started at 60 rpm with no resistance and was increased by either 5 or 10 W/min (5 W/min for quadriplegic patients and 10 W/min for paraplegic patients) until the participant reached voluntary fatigue (i.e., was unable to maintain a speed of 30 rpm). The measurement was terminated if any of the early cessation criteria outlined in the American College of Sports Medicine guidelines were met, including a 2 mm descent in the ST segment, increased neurological symptoms (such as blurred vision and dizziness), persistent ventricular tachycardia, or chest discomfort. The GXT was concluded under the following criteria: Termination occurred when despite maximum effort, a plateau in heart rate (HR) was observed. Additionally, the test was stopped when the highest Respiratory Exchange Ratio (RER) reached ≥ 1.1. The test was also terminated when the participant voluntarily expressed a desire to stop [20].

Questionnaire

The Spinal Cord Independence Measure III (SCIM III) assesses independence and is composed of three main categories: self-care, respiration, sphincter management, and mobility, and is divided into 19 sub-categories. Points were allocated as 20, 40, and 40 for each category, respectively, with a total score of 100. The Korean Activities of Daily Living (K-ADL) scale was used to measure the individual’s ability to perform daily activities. It comprises seven parameters, including ‘changing clothes,’ ‘washing face,’ ‘brushing teeth,’ ‘washing hair,’ ‘taking a bath or shower,’ ‘eating prepared meals,’ ‘getting up from bed and going out of the room,’ ‘using the toilet,’ and ‘managing bowel and bladder control.’ The total score ranges from a minimum of 7 to a maximum of 21, with higher scores indicating greater dependence on others for daily activities.

Pulmonary function

Pulmonary function was measured using Quark CPET (Cosmed, Rome, Italy) to assess forced vital capacity (FVC), peak expiratory flow (PEF), and maximal voluntary ventilation (MVV). Pulmonary volume measurements were conducted with the participants standing upright, with their noses clipped, and breathing through a mouthpiece. After normal breathing for two to three breaths, the participants took a deep breath and exhaled forcefully and rapidly without interruption, continuing the exhalation effort for five to six seconds, with a minimum duration of at least three seconds. This test was repeated at least three times to determine the maximal values of FVC and PEF. MVV, which measures the total capacity of respiratory effort, was assessed by instructing the participant to breathe as deeply and rapidly as possible through a spirometer for a predetermined period, typically lasting 15 to 30 seconds.

Statistical Analysis

In the multiple regression model, the following key assumptions were met: (1) linearity; (2) homoscedasticity; (3) independence of errors; (4) normality; and (5) independence of independent variables [21]. The mean and standard deviation were calculated for all measured parameters. The Shapiro-Wilk test was used to check the normal distribution of all outcome variables. Regression coefficients (β values) were examined to validate independent variables for linear regression analysis. Regression analysis using the forward selection method was conducted to predict VO2peak using age; height; weight; BMI; FFM; BF; percentage of BF; circumference of upper arm, forearm, wrist, waist, belly, thigh, calf, and ankle; SCIM III; K-ADL; FVC; PEF; and MVV. To compare the observed and estimated VO2peak, a paired t-test was utilized. The research rigorously followed the fundamental principles of regression analysis, ensuring linearity, independence, homoscedasticity, normal distribution, absence of autocorrelation, and outlier exclusion. The bias was determined by the discrepancy between the actual and predicted VO2peak values. For statistical evaluations, SPSS version 25.0 (IBM Corporation, Armonk, NY, USA) was used; the threshold for statistical significance was set at a p-value of 0.05.

RESULTS

Evaluation of the relationship between dependent variables and measured VO2peak

Outliers were screened using a criterion where standard residual absolute values exceeded 3; no outliers were found in the VO2peak prediction model. Furthermore, a forward selection method was employed for estimating the regression model. Correlations between the observed VO2peak and the dependent variables are detailed in Table 2.

Assessing the validity of regression models and independent variables

The validity of each regression model was evaluated using an F-test, while the significance of the regression coefficients of independent variables was determined through a t-test. Following an exploratory data analysis, integrated regression models that were statistically significant were formulated using chosen independent variables. These models were applicable to all patients with traumatic spinal cord injury (TSCI) and specifically to those with cervical (CI) and thoracic injuries (TI). For patients with SCI, the variables were height, calf circumference, independence index, and PEF; for patients with CI, the variables were height, calf circumference, and MVV; and for patients with TI, the variable was calf circumference. These results were statistically significant, as shown in Table 3.

Evaluation of the effectiveness of regression models and equations

To assess the performance of the regression models, we calculated the coefficient of determination (R2), the adjusted R2, and the standard error of estimate (SEE). VO2peak regression models for all patients with SCI and specifically for patients with cervical or thoracic spinal cord injury were developed using their respective independent variables. The average explanatory power of the VO2peak regression model for all patients with SCI was 70.3% (R2) and 66.2% (adjusted R2), with an average SEE of 2.94 ml/kg/min. For patients with cervical spinal cord injury, the average explanatory powers were 71.7% (R2) and 66.1% (adjusted R2), with an average SEE of 1.88 ml/kg/min. For patients with thoracic spinal cord injury patients, the average explanatory powers were 55.9% (R2) and 52.5% (adjusted R2), with an average SEE of 3.41 ml/kg/min (Table 4).

Difference between measured VO2peak and predicted VO2peak

In this study, no significant difference was found between the VO2peak measured using a metabolic gas analyzer and the VO2peak predicted by the equation for all patients with SCI and for each type of injury. The average bias between the measured VO2peak and the predicted VO2peak was as follows: TSCI (total spinal cord injury) = 0.00 ml/kg/min, CI (cervical injury) = −0.05 ml/kg/min, TI (thoracic Injury) = −0.01 kcal, as shown in Table 5. The measured VO2peak and predicted VO2peak showed similar average values, and the correlation coefficients indicated a significant correlation (TSCI: R = 0.839, p = 0.000; CI: R = 0.847, p = 0.000; TI: R = 0.748, p = 0.001), as depicted in Figure 1.

DISCUSSION

Individuals with SCI show differences in the stimulation of neurons in the sympathetic nervous system according to the level of injury, resulting in varying levels of physical function. Particularly, those with higher levels of injury face limitations when performing long-duration high-intensity exercises [3,22]. In individuals with SCI who exhibit various forms of motor disorders and sensory loss in the trunk and lower limbs, damage to the autonomic nervous system leads to cardiovascular decline characterized by heart rate limitations and reduced oxygen consumption, increasing sedentary lifestyles. This eventually leads to a decrease in lean body mass and aerobic capacity, causing complications, such as osteoporosis and renal failure, which can ultimately lead to death [6,9]. Therefore, in individuals with spinal cord injuries, the recovery of physical function is necessary for daily living, and the assessment of VO2peak as an evaluation of cardiopulmonary function is considered an important factor.
To assess cardiopulmonary function in individuals with SCI, gas load tests were conducted, similar to those performed on able-bodied individuals. These tests involved the direct measurement of metabolic gases or their indirect estimation through three-minute or six-minute treadmill tests [15,23,24]. However, the use of gas analyzers and equipment like arm ergometers and treadmills can be burdensome for individuals with SCI and may not be easily applicable in field settings. Therefore, developing a regression model to estimate VO2peak in individuals with SCI is important. This study aims to develop an equation to estimate the VO2peak in individuals with spinal cord injuries.
In this study, an equation to estimate VO2peak measured using a gas analyzer was derived using variables such as body composition, anthropometric measurements, physical activity-related survey responses, and pulmonary function in individuals with SCI. The accuracy of the estimation equation in multiple linear regression analysis was indicated by R2, adjusted R2, and SEE. Upon examining the correlation between VO2peak and various dependent variables in individuals with SCI, significant correlations were found between maximal oxygen consumption and height, calf circumference, independent indices, PEF, and MVV. Other variables, such as hip and thigh circumference, also showed significant correlations but were excluded as final variables after analysis using a stepwise method.
VO2peak regression models for all patients with SCI and specifically for patients with CI or TI patients were developed using their respective independent variables. The average explanatory power of the VO2peak regression model for all patients with SCI was 70.3% (R2) and 66.2% (adjusted R2), with an average SEE of 2.94 ml/kg/min. For patients with CI, the average explanatory powers were 71.7% (R2) and 66.1% (adjusted R2), with an average SEE of 1.88 ml/kg/min. For patients with TI, the average explanatory powers were 55.9% (R2) and 52.5% (adjusted R2), with an average SEE of 3.41 ml/kg/min.
Most studies have been conducted on individuals without disabilities, and it has been reported that equations estimating VO2peak, analyzed using variables such as weight, girth, body fat percentage, and BMI, show high explanatory power [25]. Wier et al. [26] reported that an equation for estimating VO2peak in the general adult population, using variables such as waist circumference, body fat percentage, and BMI, showed R values of approximately 0.80-0.82, R2 values of 0.62 and 0.55, and an SEE of approximately 4.72-4.90. Additionally, Li et al. [27] found that an equation estimated using weight, BMI, and body fat percentage in young adults had an explanatory power of 62.6% and SEE of 4.39. A recent study by Lee et al. [15] estimated the VO2peak in male patients with SCI using age and height. The results demonstrated a correlation with R = 0.771, R2 = 0.595, and SEE = 3.187, which aligns with the levels observed in our study. However, the study conducted by Lee et al. focussed on individuals with thoracic and lumbar spine injuries, suggesting that the physical function and fitness levels influenced by body composition may have affected their results. In contrast, our study focused on individuals with CI and TI, indicating that pulmonary function may be more crucial than body composition.
Although variables such as oxygen intake, heart rate, and body composition are commonly used to estimate energy expenditure, they may not be appropriate for individuals with SCI because of abnormal heart rate responses caused by autonomic nervous system damage [14]. This necessitates the exploration of new predictive factors. In particular, a reduction in sympathetic nervous system stimulation neurons can cause disorders in catecholamine secretion of the adrenal medulla during exercise, leading to decreased levels of blood catecholamines, heart rate, cardiac output, and ventilation, ultimately limiting cardiopulmonary function [28,29]. Therefore, an equation that includes pulmonary function can be considered as a factor for predicting VO2peak in individuals with SCI. The significant correlation found in this study between VO2peak and MVV and PEF occurred because respiratory capacity is highly correlated with VO2peak in individuals with SCI, irrespective of the muscle function used to push a wheelchair [30,31]. In individuals with severe injuries, muscles in the shoulder, arm, and wrist are primarily used to propel a wheelchair. However, as the frequency of wheelchair use increases, individuals are developing techniques to conserve muscle strength. Therefore, pulmonary function-related variables may be more useful than muscle-related variables in individuals with severe injuries.
Previous research by Kim et al. [32] suggested that easily measurable independent variables and large sample sizes yield higher regression coefficients. Although the current study had a somewhat smaller sample size, it was still able to achieve explanatory power similar to that of previous studies. Future studies with larger sample sizes, as suggested in previous studies, are expected to yield higher regression coefficients. Additionally, previous studies have proposed predictive equation models based on deep learning regression using machine learning and artificial neural networks with easily measurable independent variables. Lee et al. [33] compared the performance of deep learning using artificial neural networks and multiple regression analysis models in predicting the fitness levels of older adult individuals and found that the deep learning model showed superior performance. Although the multiple regression analysis model also provided high explanatory results, it had the limitation of not being able to predict non-linear characteristics. Recently, neural network deep-learning and machine-learning techniques capable of representing nonlinear characteristics have been proposed to improve the estimation accuracy of predictive models [34,35].
Therefore, in this study, it was anticipated that the equation for estimating VO2peak in individuals with SCI, using variables such as height, calf circumference, independence index, and pulmonary function can be utilized without temporal or spatial limitations. However, this study had several limitations. Surveys on physical activity are subjective and may pose challenges to objective assessment. The focus of this study on participants residing in Busan and Gyeongnam limits the generalizability to all individuals with spinal cord injuries. The small sample size precluded the development of sex-specific regression models and validity tests. Further research is required to overcome these limitations. This study was developed to create a simple equation for estimating the cardiopulmonary function of individuals with SCI, as measured by VO2peak. The developed estimation equation could be used in the future to conveniently assess the fitness levels of individuals with SCI, helping determine the intensity of various exercise programs or individualized exercise prescriptions.
In conclusion, this study showed that VO2peak in individuals with SCI can be explained by variables related to height, calf circumference, independence index, and lung function. The multiple regression equation can be useful for setting various exercise program intensities or individualized exercise prescriptions for individuals with SCI. This equation, which requires simple parameter measurements, offers clinical advantages in terms of time and space for real-world applications. Future research will need to explore not only multiple regression equations but also predictive models using machine learning and deep learning.

Acknowledgments

This work was supported by the Dongseo University Research Fund of 2023 (DSU-20230001).

Figure 1.
Scatter plot between measured and predicted VO2peak. TSCI=Total spinal cord injury; CI=Cervical spinal cord injury; TI=Thoracic spinal cord injury; VO2=oxygen consumption.
pan-2023-0034f1.jpg
Table 1.
Characteristics of subjects.
Variables TSCI (n=34) CI (n=19) TI (n=15)
Age (yrs) 44.7 ± 8.2 45.6 ± 9.0 43.7 ± 7.2
Duration of illness (month) 189.2 ± 122.0 198.9 ± 120.4 176.8 ± 127.1
Height (cm) 172.0 ± 8.8 173.7 ± 9.1 169.9 ± 8.1
Weight (kg) 73.1 ± 13.9 72.5 ± 13.5 73.8 ± 14.8
Body mass index (kg/㎡) 45.0 ± 7.7 45.2 ± 7.8 44.7 ± 7.9
Fat-free mass (kg) 27.9 ± 9.5 27.3 ± 9.1 28.6 ± 10.4
Fat mas (kg) 37.5 ± 7.8 37.1 ± 8.0 38.0 ± 7.8
Percent body fat (%) 24.7 ± 4.8 24.1 ± 4.6 25.6 ± 5.0
VO2peak (ml/kg/min) 13.7 ± 5.1 11.0 ± 3.2 17.1 ± 4.9
Left upper arm (cm) 33.2 ± 5.0 31.9 ± 5.7 34.9 ± 3.6
Right upper arm (cm) 33.0 ± 4.7 31.7 ± 5.4 34.6 ± 3.0
Left forearm (cm) 26.1 ± 5.1 25.7 ± 3.2 26.6 ± 7.0
Right forearm (cm) 26.4 ± 5.4 25.8 ± 3.5 27.3 ± 7.1
Left wrist (cm) 17.0 ± 1.2 16.7 ± 1.3 17.3 ± 1.1
Right wrist (cm) 17.1 ± 1.3 16.9 ± 1.4 17.4 ± 1.2
Waist (cm) 94.8 ± 13.0 94.1 ± 14.0 95.7 ± 12.1
Belly (cm) 99.7 ± 14.3 99.3 ± 14.8 100.1 ± 14.2
Left thigh (cm) 38.7 ± 4.4 38.3 ± 3.9 39.1 ± 5.2
Right thigh (cm) 38.5 ± 4.8 37.9 ± 3.9 39.2 ± 5.8
Left calf (cm) 30.9 ± 3.8 30.6 ± 3.4 31.1 ± 4.5
Right calf (cm) 30.7 ± 4.5 30.6 ± 3.8 30.9 ± 5.3
Left ankle (cm) 21.4 ± 1.8 21.3 ± 1.4 21.6 ± 2.3
Right ankle (cm) 21.6 ± 1.9 21.5 ± 1.4 21.8 ± 2.4
SCIM Ⅲ (point) 45.8 ± 12.3 43.3 ± 14.0 49.1 ± 9.2
K-ADL (point) 16.3 ± 3.6 14.8 ± 4.0 18.3 ± 1.8
FVC (L) 3.7 ± 1.0 3.4 ± 1.0 4.1 ± 0.9
PEF (L/second) 7.0 ± 2.3 6.2 ± 2.1 8.1 ± 2.0
MVV (L/min) 131.1 ± 32.5 117.9 ± 28.8 147.9 ± 29.8

Values are expressed as mean ± SD. TSCI=Total spinal cord injury; CI=Cervical spinal cord injury; TI=Thoracic spinal cord injury; VO2=oxygen consumption; SCIM III=Spinal Cord Independence Measure III; K-ADL=Korean activities of daily living; FVC=Forced vital capacity; PEF=peak expiratory flow; MVV=Maximal voluntary ventilation.

Table 2.
Correlation between dependent variables and measured VO2peak for estimating regression model.
Variables TSCI (n=34) CI (n=19) TI (n=15)
Age (yrs) Correlation (p-value) -.162 (.359) .081 (.742) -.333 (.225)
Height (cm) Correlation (p-value) -.360 (.037)* -.267 (.269) -.335 (.222)
Weight (kg) Correlation (p-value) -.314 (.071) -.196 (.422) -.628 (.012)*
Body mass index (kg/㎡) Correlation (p-value) -.143 (.421) -.053 (.830) -.509 (.053)
Fat-free mass (kg) Correlation (p-value) -.259 (.139) .037 (.881) -.603 (.017)*
Fat mas (kg) Correlation (p-value) -.273 (.118) -.323 (.177) -.456 (.088)
Percent body fat (%) Correlation (p-value) -.143 (.421) -.308 (.199) -.165 (.556)
Upper arm (cm) Correlation (p-value) .135 (.448) .031 (.899) -.253 (.363)
Forearm (cm) Correlation (p-value) .081 (.647) .128 (.601) -.034 (.904)
Wrist (cm) Correlation (p-value) .219 (.214) .089 (.717) .157 (.577)
Waist (cm) Correlation (p-value) -.245 (.162) -.212 (.383) -.534 (.040)*
Belly (cm) Correlation (p-value) -.359 (.037)* -.270 (.264) -.678 (.005)*
Thigh (cm) Correlation (p-value) -.373 (.030)* -.318 (.185) -.700 (.004)*
Calf (cm) Correlation (p-value) -.423 (.013)* -.314 (.190) -.748 (.001)*
Ankle (cm) Correlation (p-value) -.373 (.030)* -.213 (.380) -.695 (.004)*
SCIM III (point) Correlation (p-value) .451 (.007)* .511 (.025)* .329 (.232)
K-ADL (point) Correlation (p-value) .550 (.001)* .522 (.022)* .265 (.339)
FVC (L) Correlation (p-value) .219 (.213) .093 (.705) -.052 (.855)
PEF (L/second) Correlation (p-value) .560 (.001)* .508 (.026)* .368 (.177)
MVV (L/min) Correlation (p-value) .581 (.000)* .570 (.011)* .321 (.243)

Values are expressed as mean ± SD. TSCI=Total spinal cord injury; CI=Cervical spinal cord injury; TI=Thoracic spinal cord injury; VO2=oxygen consumption; SCIM III=Spinal Cord Independence Measure III; K-ADL=Korean activities of daily living; FVC=Forced vital capacity; PEF=peak expiratory flow; MVV=Maximal voluntary ventilation.

Table 3.
Significance level of the regression coefficient of the independent variable for each estimated regression model.
Model F-value p-value Unstandardized coefficients Standardized coefficients t-value p-value
TSCI (constant) 17.183 .000 39.684 3.488 .002*
Height -.144 -.249 -2.053 .049*
Calf -.513 -.401 -3.514 .001*
SCIM Ⅲ .136 .330 2.797 .009*
PEF 1.187 .527 4.750 .000*
CI (constant) 12.680 .000 38.842 4.423 .000*
Height -.158 -.445 -2.991 .009*
Calf -.371 -.408 -2.822 .013*
MVV .093 .825 5.553 .000*
TI (constant) 16.487 .001 42.325 6.744 .000*
Calf -.813 -.748 -4.060 .001*

* Statistically significant, p < 0.05.

TSCI=Total spinal cord injury; CI=Cervical spinal cord injury; TI=Thoracic spinal cord injury; SCIM III=Spinal Cord Independence Measure III; PEF=peak expiratory flow; MVV=Maximal voluntary ventilation.

Table 4.
Estimated regression equations predicting VO2peak of spinal cord injury adults.
Model R R2 Adjusted R2 p-value SEE
TSCI VO2peak (ml/kg/min) = 39.684 - 0.144 × (Height) - 0.513 × (Calf) + 0.136 × (SCIM Ⅲ) + 1.187 × (PEF) .839 .703 .662 .000 2.94
CI VO2peak (ml/kg/min) = 38.842 - 0.158 × (Height) - 0.371 × (Calf) + 0.093 × (MVV) .847 .717 .661 .000 1.88
TI VO2peak (ml/kg/min) = 42.325 - 0.813 × (Calf) .748 .559 .525 .001 3.41

* Statistically significant, p < 0.05.

TSCI=Total spinal cord injury; CI=Cervical spinal cord injury; TI=Thoracic spinal cord injury; VO2=oxygen consumption; SCIM III=Spinal Cord Independence Measure III; PEF=peak expiratory flow; MVV=Maximal voluntary ventilation; SEE=standard error of estimate.

Table 5.
Measured and predicted VO2peak of spinal cord injury adults.
Model Mean S.D. Bias t-value p-value
Predicted TSCI VO2peak (ml/kg/min) 13.66 5.06 0.00 0.001 .999
Measured TSCI VO2peak (ml/kg/min) 13.66 4.25
Predicted CI VO2peak (ml/kg/min) 10.95 3.23 -0.05 -0.119 .907
Measured CI VO2peak (ml/kg/min) 11.00 2.74
Predicted TI VO2peak (ml/kg/min) 17.09 4.94 -0.01 -0.017 .987
Measured TI VO2peak (ml/kg/min) 17.11 3.69

TSCI=Total spinal cord injury; CI=Cervical spinal cord injury; TI=Thoracic spinal cord injury; VO2=oxygen consumption; Bias=measured VO2peak - predicted VO2peak.

REFERENCES

1. Nas K, Yazmalar L, Şah V, Aydın A, Öneş K. Rehabilitation of spinal cord injuries. World J Orthop 2015;6:8-16.
crossref pmid pmc
2. Phillips AA, Krassioukov AV. Cardiovascular dysfunction following spinal cord injury. Neurological Aspects of Spinal Cord Injury 2017;Neurological Aspects of Spinal Cord Injury-61.
crossref
3. Perrier MJ, Stork MJ, Ginis KAM, SHAPE-SCI Research Group. Type, intensity and duration of daily physical activities performed by adults with spinal cord injury. Spinal Cord 2017;55:64-70.
crossref pmid pdf
4. Duan R, Qu M, Yuan Y, Lin M, Liu T, Huang W, Gao J, Zhang M, Yu X. Clinical benefit of rehabilitation training in spinal cord injury: a systematic review and meta-analysis. Spine (Phila Pa 1976) 2021;46:E398-410.
pmid
5. Grigorean VT, Sandu AM, Popescu M, Iacobini MA, Stoian R, Neascu C, Strambu V, Popa F. Cardiac dysfunctions following spinal cord injury. J Med Life 2009;2:133-45.
pmid pmc
6. Gater Jr DR, Farkas GJ, Tiozzo E. Pathophysiology of neurogenic obesity after spinal cord injury. Top Spinal Cord Inj Rehabil 2021;27:1-10.
crossref pdf
7. Fernhall B, Heffernan K, Jae SY, Hedrick B. Health implications of physical activity in individuals with spinal cord injury: a literature review. J Health Hum Serv Adm 2008;30:468-502.
pmid
8. Lee KH, Hong SM, Shin YA. Physical fitness and exercise strategy according to general characteristics of spinal cord injured person. Exerc Sci 2019;28:110-21.
crossref pdf
9. Evans N, Wingo B, Sasso E, Hicks A, Gorgey AS, Harness E. Exercise recommendations and considerations for persons with spinal cord injury. Arch Phys Med Rehabil 2015;96:1749-50.
crossref pmid
10. Williams TL, Smith B, Papathomas A. The barriers, benefits and facilitators of leisure time physical activity among people with spinal cord injury: a meta-synthesis of qualitative findings. Health Psychol Rev 2014;8:404-25.
crossref pmid
11. van der Scheer JW, Ginis KAM, Ditor DS, Goosey-Tolfrey VL, Hicks AL, West CR, Wolfe DL. Effects of exercise on fitness and health of adults with spinal cord injury. Neurology 2017;89:736-45.
crossref pmid
12. Henke AM, Billington ZJ, Gater Jr DR. Autonomic dysfunction and management after spinal cord injury: a narrative review. J Pers Med 2022;12:1110.
crossref pmid pmc
13. Wecht JM, Harel NY, Guest J, Kirshblum SC, Forrest GF, Bloom O, Ovechkin AV, Harkema S. Cardiovascular autonomic dysfunction in spinal cord injury: epidemiology, diagnosis, and management. Semin Neurol 2020;40:550-9.
crossref pmid
14. de Zepetnek JOT, Au JS, Hol AT, Eng JJ, MacDonald MJ. Predicting peak oxygen uptake from submaximal exercise after spinal cord injury. Appl Physiol Nutr Metab 2016;41:775-81.
crossref pmid pmc
15. Lee BS, Bae JH, Choi YJ, Lee JA. Predicting maximum oxygen uptake from non-exercise and submaximal exercise tests in paraplegic men with spinal cord injury. Healthcare (Basel) 2023;11:763.
crossref pmid pmc
16. Buttar KK, Saboo N, Kacker S. A review: maximal oxygen uptake (VO2max) and its estimation methods. Int J Phys Educ Sports Hhealth 2019;6:24-32.

17. Ashfaq A, Cronin N, Müller P. Recent advances in machine learning for maximal oxygen uptake (VO2max) prediction: a review. Inform Med Unlocked 2022;28:100863.

18. Bass A, Brosseau R, Décary S, Gauthier C, Gagnon DH. Comparison of the 6-min propulsion and arm crank ergometer tests to assess aerobic fitness in manual wheelchair users with a spinal cord injury. Am J Phys Med Rehabil 2020;99:1099-108.
crossref pmid
19. van der Scheer JW, Hutchinson MJ, Paulson T, Ginis KAM, Goosey-Tolfrey VL. Reliability and validity of subjective measures of aerobic intensity in adults with spinal cord injury: a systematic review. PM R 2018;10:194-207.
crossref pmid pdf
20. Oviedo GR, Alamo JM, Niño-Mendez O, Travier N, Ventura JL, Soto-Montañez CA, Vidal J, Medina J, Javierre C. Physiological responses in males with and without spinal cord injury to recumbent synchronous versus seated asynchronous arm crank stress tests. Retos 2021;39:565-71.

21. Kumari K, Yadav S. Linear regression analysis study. J Pract Cardiovasc Sci 2018;4:33-6.
crossref
22. Goosey-Tolfrey V, Lenton J, Goddard J, Oldfield V, Tolfrey K, Eston R. Regulating intensity using perceived exertion in spinal cord-injured participants. Med Sci Sports Exerc 2010;42:608-13.
crossref pmid
23. Eerden S, Dekker R, Hettinga FJ. Maximal and submaximal aerobic tests for wheelchair-dependent persons with spinal cord injury: a systematic review to summarize and identify useful applications for clinical rehabilitation. Disabil Rehabil 2018;40:497-521.
crossref pmid
24. Hong SH, Yang HI, Kim DI, Gonzales TI, Brage S, Jeon JY. Validation of submaximal step tests and the 6-min walk test for predicting maximal oxygen consumption in young and healthy participants. Int J Environ Res Public Health 2019;16:4858.
crossref pmid pmc
25. George JD, Paul SL, Hyde A, Bradshaw DI, Vehrs PR, Hager RL, Yanowitz FG. Prediction of maximum oxygen uptake using both exercise and non-exercise data. Meas Phys Educ Exerc Sci 2009;13:1-12.
crossref
26. Wier LT, Jackson AS, Ayers GW, Arenare B. Nonexercise models for estimating VO 2 max with waist girth, percent fat, or BMI. Med Sci Sports Exerc 2006;38:555-61.
crossref pmid
27. Li F, Tu YT, Yeh HC, Ho CA, Yang CP, Kuo YC, Ho CS. Feasibility of predicting maximal oxygen uptake by using the efficiency factor in healthy men. Sci Rep 2023;13:16760.
crossref pmid pmc pdf
28. Partida E, Mironets E, Hou S, Tom VJ. Cardiovascular dysfunction following spinal cord injury. Neural Regen Res 2016;11:189-94.
crossref pmid pmc
29. Rosales-Antequera C, Viscor G, Araneda OF. Inflammation and oxidative stress as common mechanisms of pulmonary, autonomic and musculoskeletal dysfunction after spinal cord injury. Biology (Basel) 2022;11:550.
crossref pmid pmc
30. Gee CM, Williams AM, Sheel AW, Eves ND, West CR. Respiratory muscle training in athletes with cervical spinal cord injury: effects on cardiopulmonary function and exercise capacity. J Physiol 2019;597:3673-85.
crossref pmid pdf
31. Gervasi SF, Orvieto S, Sollazzo F, Bianco M, Cuccaro F, Zeppilli P, Palmieri V. Peak oxygen uptake in sportsmen with spinal cord injury: importance of body composition. Eur J Phys Rehabil Med 2022;58:199-205.
crossref pmid
32. Lee SH, Ju HS, Lee SH, Kim SW, Park HY, Kang SW, Song YE, Lim K, Jung H. Estimation of health-related physical fitness (HRPF) levels of the general public using artificial neural network with the national fitness award (NFA) datasets. Int J Environ Res Public Health 2021;18:10391.
crossref pmid pmc
33. Kim SW, Park HY, Jung H, Lee J, Lim K. Estimation of health-related physical fitness using multiple linear regression in Korean adults: national fitness award 2015-2019. Front Physiol 2021;12:668055.
crossref pmid pmc
34. Lee SH, Lee SH, Kim SW, Park HY, Lim K, Jung H. Estimation of functional fitness of Korean older adults using machine learning techniques: the national fitness award 2015-2019. Int J Environ Res Public Health 2022;19:9754.
pmid pmc
35. Kim SW, Park HY, Jung H, Lim K. Development of functional fitness prediction equation in Korean older adults: the national fitness award 2015-2019. Front Physiol 2022;13:896093.
crossref pmid pmc


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