Journal of Physical Activity and Health, 2014, 11, 1393 -1400 http://dx.doi.org/10.1123/jpah.2012-0149 © 2014 Human Kinetics, Inc.
Official Journal of ISPAH www.JPAH-Journal.com ORIGINAL RESEARCH
Exercise, Energy Expenditure, and Body Composition in People With Spinal Cord Injury Ricardo A. Tanhoffer, Aldre I. P. Tanhoffer, Jacqueline Raymond, Andrew P. Hills, and Glen M Davis Background: The objective of this study was to verify the long-term effects of exercise on energy expenditure and body composition in individuals with spinal cord injury (SCI), as very little information is available on this population under free-living conditions. Methods: Free-living energy expenditure and body composition using doubly labeled water (DLW) was measured in 13 individuals with SCI, subdivided in 2 groups: (1) sedentary (SED; N = 7) and (2) regularly engaged in any exercise program, for at least 150 min·wk-1 (EXE; N = 6). Results: The total daily energy expenditure (TDEE) was significantly higher in the EXE group (33 ± 4.5 kcal·kg-1·day-1) if compared with SED group (27 ± 4.3 kcal·kg-1·day-1). The percentage of body fat was significantly higher in SED group than in EXE group (38 ± 6% and 28 ± 9%). Conclusion: Our findings revealed that, despite the severity of SCI, the actual ACSM’s guidelines for weight management for healthy adults exercise could significantly increase TDEE and BMR and improve body composition in individuals who regularly perform exercise. However, the EXE group still showed a high percentage of body fat, suggesting that a more specific approach might be considered (ie, increased intensity or volume, or combining with a diet program). Keywords: wheelchair users, physical activity, doubly labeled water For the general population, even small imbalances between energy intake and energy expenditure (EE) over time can lead to overweight or obesity. Increasing as few as 100 kcal·day-1, may cause weight gain, and on the other hand, reducing this “energy gap” could help to prevent or even reduce body adiposity.1 Obviously, maintaining the energy balance between energy intake and EE is critical for weight management. Energy balance is profoundly affected in individuals with spinal cord injury (SCI), as their daily EE has been demonstrated to be lower than the able-bodied population.2 Among other factors, this is attributed to a reduced fat-free mass (FFM), manly due to muscle paralysis in their affected muscles, leading to a severe proteolysis,2–6 and to the low levels of physical activity, owing to reduced ability to perform exercises and also architectural barriers7 found in this population. As a consequence of their lower daily EE, obesity is a common secondary complication after SCI, which in turn, body adiposity is highly associated with the development of cardiovascular disease.8 Conversely, exercise has been demonstrated to play in important role for weight management or reduction, as it may increase the total daily energy expenditure (TDEE), by affecting 2 of its components: basal metabolic rate (BMR) and physical activity-associated energy expenditure (PAEE). A simple bout of exercise will acutely R Tanhoffer ([email protected]), A Tanhoffer, Raymond, and Davis are with the Clinical Exercise and Rehabilitation Unit, Faculty of Health Sciences, University of Sydney, Australia. R Tanhoffer, Raymond, and Davis are also with the Exercise Health & Performance Research Group, Faculty of Health Sciences, University of Sydney, Australia. A Tanhoffer is also with the Clinical & Rehabilitation Sciences Research Group, Faculty of Health Sciences, University of Sydney, Australia. Hills is with the Mater Mother’s Hospital, Mater Medical Research Institute and Griffith Health Institute, Griffith University, Australia.
impact the daily PAEE, which over time, if this augmented level of physical activity is maintained, may increase the amount of fatfree mass, reflecting in higher energy requirements during BMR. In fact, exercise has been strongly associated with mortality risk, demonstrating that the higher the EE due to exercise, the lower the risk of all causes of death in older adults.9 In fact, research on body composition and exercise for individuals with SCI have been recently developed, and these authors are demonstrating the importance of specific programs aiming weight management for this population.4,10,11 Although the effects of exercise on some risk factors for cardiovascular disease are well documented in individuals with SCI such as glucose intolerance and dyslipidaemia,12,13 the impact of long-term exercise on EE and body composition are poorly investigated in wheelchair user. Therefore, as physical activity can be used to improve energy balance and reduce common pathologies associated to obesity, and a specific exercise program for weight management is undefined for individual with SCI, in this study we aimed to verify the impact of the long-term exercise, commonly performed during free-living conditions, on EE and body composition, in people with SCI. We hypothesize that the higher the level of physical activity, the more improved the body composition of our volunteers.
Methods Participants Thirteen people with SCI were recruited for this study. Inclusion criteria were (a) aged between 18 to 65 years, (b) at least 1 year postinjury, and (c) use of a manual wheelchair as the main form of locomotion. Participants were assigned in 2 different groups according to their level of engagement in exercise. If participants performed regular and structured exercise at least 3 times per week 1393
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(minimum of 150 min of exercise weekly), for a minimum of 6 months of continuous training, they were assigned to the exercise group (EXE). If individuals performed structured exercise equal or less than 60 min weekly, they were assigned to sedentary group (SED). Informed consent was gained from all the participants. Approval for this study was granted by the University of Sydney Human Research Ethics Committee.
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Protocol Participants attended the Clinical Exercise & Rehabilitation Unit on 4 occasions (Figure 1). Anthropometric and demographics assessments and basal metabolic rate were conducted on their first visit. All participants were instructed to not perform any vigorous exercise, and refrain from smoking and alcohol at least 48 hours before testing. An arm-crank maximal test was performed on the second visit to assess their fitness level, at least 48 hours after the first visit. On the third visit, participants collected the baseline urine sample and drank the doubly labeled water (DLW) dose. On the fourth and last visit, all urine samples and the physical activity self-report sheet were delivered by the participants.
Anthropometrics and Demographics The following anthropometric measurements were made: mass, stature (measured in supine) and waist circumference (WC). Body
mass was taken with participants wearing light clothing, to the nearest 0.1 kg using a digital scale (K Tron, Arizona, USA). The weight of their wheelchair was subtracted from the total weight of the participant in his wheelchair. Stature was measured using a metal measuring tape (Gulick Tape Measure), in a supine position. WC was measured in triplicate, with participants in the supine position. WC measurements were made at the midway between the last rib and the iliac crest. Landmarks were identified and marked before the first measurement. Body mass index (kg·m2) was calculated from mass and stature. The following demographic information was recorded: age, gender, neurological level and severity of lesion, and time since injury.
Basal Metabolic Rate BMR was measured following a 12-h fast. Participants were instructed to lie supine and remain awake. Following 30-min in this rested position, BMR was measured over the subsequent 30 minutes. For BMR, an open-circuit spirometry metabolic measurement system (Medical Graphics Corporation, St Paul, Minnesota, USA) with a mouthpiece was used to perform breathby-breath gas analysis. The Weir equation was used to calculate BMR.14 Simultaneously, HR was measured continuously via 3-lead ECG (CR55 Portascope, Cardiac Recorders Ltd, London) and interfaced with data from the metabolic measurement cart.
Figure 1 — Protocol used. Abbreviations: BMR, basal metabolic rate; VO2peak, peak oxygen uptake; RMR, resting metabolic rate; DLW, doubly labeled water; PA, physical activity.
Exercise and Energy Expenditure After SCI 1395
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Total Daily Energy Expenditure TDEE was measured by DLW. All DLW samples were prepared and provided by Queensland University of Technology. Participants presented after a fast of at least 6-h to maximize absorption of the tracer elements. A baseline urine specimen was collected and the doubly labeled water (10% enriched 18O and 99% 2H) was administered orally, with the dose based on body mass (1.35g of DLW × body mass in kg). A second urine sample was collected 6-h after the priming DLW dose. Then, 1 specimen of urine was collected daily, over the next 2 weeks at approximately the same time of day as the first basal urine specimen. The time collection should be recorded in a sheet provided by researchers during the administration of the DLW dose. The participants collected the samples themselves and stored them refrigerated at home in sterile plastic containers delivered by the researchers on the first day. The urine samples were collected on the final day by the researchers. Samples were analyzed for 2H2O and H218O by isotope ratio mass spectrometry at Queensland University of Technology. Total energy expenditure (kcal) over the 2-week period was divided by 14 to estimate mean TDEE.15
Physical Activity-Associated Energy Expenditure (PAEE) The DLW technique quantifies only TDEE, therefore, PAEE was estimated as (TDEE × 0.90) – BMR, where 0.90 refers to the thermic effect of meals constant fraction of 10%.16 Therefore, removing the energy expenditure during food metabolism and during basal conditions from the TDEE, the remaining energy refers to the energy expended in any free-living physical activity performed by an individual per day.
Body Composition Isotope dilution (DLW) was also used for body composition analysis, and this technique has been using to validate other methods, such as bioelectrical impedance analysis (BIA).17 Using an isotope ratio mass spectrometry at Queensland University of Technology, 2H and 18O enrichment of the samples are measured to calculate the total body water (TBW) content. Although this technique rely on assumption of no abnormal fluid shifts are presented and this issue can be presented in SCI (ie, lower extremity edema), this method has been demonstrated be valid to assess body fat in this population.18 Derivation of fat-free mass (FFM) and fat mass (FM) from TBW is calculated as follow: First, TBW was determined from the deuterium oxide dilution space of deuterium exchange with nonaqueous compartment of the body, using the following equation:
F1 N1 = F2 N 2 where N1 is the pool size of the dose in moles and N2 is the pool size of the distribution space in moles. F1 is enrichment of the dose and F2 is the enrichment of the distribution space. The 2 compartment model divides the body weight into fat mass (FM) and fat-free mass (FFM) (Weight [kg] = FM [kg] + FFM [kg]). Body weight is measured along with TBW, from which FFM is estimated. FFM is derived from TBW using a hydration coefficient, that is, the fraction of FFM comprised of water (FFM = TBW ÷ hydration coefficient). Cellular hydration (including FFM) in all animals is controlled within strict limits, and the commonly used hydration coefficient is of 0.732. Once FFM has been estimated, FM
and per cent body fat are calculated using the equations (FM [kg] = weight [kg] – FFM [kg]) and (% body fat = 100 Å ~FM/weight).15
Self-Reported Physical Activity A self report diary was completed by participants during the observation period (14 days). Information about physical activity such as type, duration and self-perceived intensity of activity (very light, light, moderate, intense and very intense), as well as date and time of the day were recorded over the 2 weeks DLW protocol. All participants who were engaged in exercise programs performed their physical activities under professional supervision, at gyms or sport associations. To compute the time spent in exercise weekly, we just considered those activities self-reported/assessed in moderate intensity or higher. To measure the exercise intensity and to compare with the 14-day self-report sheet later, the same researcher went to the local where they usually perform exercise and randomly followed some of the participant’s exercise session/bout (one session each week for every participant). In case of individuals with paraplegia, they were asked to wear a heart rate monitor (Polar RS800CX, Polar Electro, Finland). Heart rate was measured at rest and during exercise. In addition, individuals with paraplegia and tetraplegia were asked to indicate the RPE (from 6 to 20), at rest and at the end of exercise, only for the EXE group. After, we compared the self-rated exercise intensity against the measured heart rate reserve during their exercise bout.
Statistical Analysis Data are presented as mean ± SD. All analysis were performed using SPSS 19.0 and results were statistically significant at P < .05. Two-way ANOVA was performed to determine whether there were differences between variables, with adjustments with the Bonferroni test, as a pots-hoc test. Pearson’s correlation coefficient (R) and was used to determine the association between variables.
Results Background characteristics of all participants are presented in Table 1 and Table 2 portrays the main difference between groups. All participants were male. Sixty-two percent of participants had complete spinal cord lesions compared with 38% incomplete. Level of lesion was distributed in paraplegia (69%) and tetraplegia (31%). There was no significant difference on average age (39 ± 12 and 40 ± 15 years, for SED and EXE groups, respectively) and stature (1.77 ± 0.15 and 1.75 ± 0.13 m, for SED and EXE, respectively). The EXE group performed, on average, 221 ± 72 min·wk-1 of moderate exercise (or higher intensity) per week against an average of 28 ± 31 min·wk-1 of exercise (P < .001). Exercises performed by participants were functional electrical stimulation leg cycling, arm cranking, hand-cycling, weight lifting, circuit training, wheelchair quad-rugby, wheelchair tennis, and swimming. Groups did not show significant difference in body mass (85 ± 16 kg and 72 ± 12 kg, for SED and EXE, respectively) and BMI (27 ± 2 kg·m2 and 24 ± 4 kg·m2, for SED and EXE, respectively). However, percentage of fat-free mass (%FFM) was significantly higher in EXE group (73 ± 8%) than in SED cohort (61 ± 5%). Percentage of body fat (%FM) was significantly lower in the EXE (28 ± 9%) than in the SED group (38 ± 6%). Waist circumference (WC) was significantly higher in the SED (102 ± 8 cm) group than in EXE group (81 ± 11 cm).
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Table 1 Information of the Participants Participant
Gender
Group
Type of activity
Time (min/wk)
Injury level
TSI
3
Male
SED
Weight-lifting
60
C7-A
11.5
5
Male
SED
None
0
T12-A
35
7
Male
SED
None
0
T11-A
7
8
Male
SED
Arm-cranking
45
C6-A
11
10
Male
SED
None
0
T10-C
11
11
Male
SED
Arm-cranking
60
T6-A
4
12
Male
SED
None
0
C5-C
4
Mean ± SD
–
–
28 ± 31
–
12 ± 10
Participant
Gender
Group
Type of activity
Time (min/wk)
Injury level
TSI
1
Male
EXE
FES + Circuit training
270
T5-A
16
2
Male
EXE
Swimming + Weight-Lifting
180
T8-A
16
4
Male
EXE
Hand-Cycling
150
T12-A
5
6
Male
EXE
Wheelchair Tennis
180
T8-C
5
6
Male
EXE
Wheelchair Rugby + Hand-Cycling
350
T4-B
4
13
Male
EXE
Wheelchair Rugby + Hand-Cycling
200
C6-C
9
Mean ± SD
–
–
221 ± 72
–
9±6
Abbreviation: SED, sedentary; EXE, exercise; TSI, time since injury in years; Data are presented as Mean ± SD (standard deviation).
Table 2 Anthropometrics and Demographics of the Groups ALL (N = 13)
SED (N = 7)
EXE (N = 6)
Age (years)
40 ± 13
39 ± 12
40 ± 15
TSI (years)
10 ± 9
11 ± 11
9±6
Body mass (kg)
79 ± 15
85 ± 16
72 ± 12
1.76 ± 0.14
1.78 ± 0.15
1.75 ± 0.13
BMI (kg/m2)
25 ± 3
27 ± 2
24 ± 4
FM (%)
33 ± 9
38 ± 6
28 ± 9*
Stature (m)
FFM (%)
67 ± 9
61 ± 5
73 ± 8*
WC (cm)
93 ± 14
102 ± 8
81 ± 11*
123 ± 115
24 ± 30
221 ± 72*
18 ± 9
15 ± 4
21 ± 3*
Exercise (min/wk) VO2peak (ml/kg/min)
Note. Data are presented as Mean ± SD. Abbreviations: TSI, time since injury; VO2peak, peak oxygen uptake; BMI, body mass index; FM, fat mass; FFM, fat-free mass; WC, waist circumference. * P < .05 in relation to SED.
The TDEE, BMR (Figure 2) and VO2peak were significantly higher in the EXE group (33 ± 4.5 kcal·kg-1·day-1; 21 ± 1.7 kcal·kg1·day-1; 21 ± 3 ml·kg-1·min-1) if compared with SED group (27 ± 4.3 kcal·kg-1·day-1; 16 ± 1.7 kcal·kg-1·day-1; 15 ± 4 ml·kg-1·min-1). Although slightly higher in the EXE cohort, PAEE was not significantly different between groups (8 ± 4.1 kcal·kg-1·day-1 and 9 ± 4.3 kcal·kg-1·day-1, for SED and EXE groups, respectively) (Figure 2). Pearson’s correlation revealed that % FM was significantly correlated with TDEE (R = –0.68; P = .008), BMR (R = –0.60; P = .037), PAEE (R = –0.50; P = .035) VO2peak (R = –0.60; P = .023), WC (R = .73; P = .011), time of exercise performed weekly (R = –0.70; P = .005) and with % FFM (R = –0.98; P < .001), but not with other variables such as body mass, age, TSI and BMI. In
our study, correlation between level of injury and % FM was not detectable, as the number of individuals with tetraplegia was small (31%) within the entire cohort.
Discussion Our group is the first to measure TDEE in individuals with SCI, using the DLW technique. This has allowed us to accurately report energy expenditure during their very normal daily routine, without the interference of laboratory tests or devices they have to carry attached in their body to assess energy expenditure or its components. In fact, the DLW technique is a noninvasive approach for measuring free-living energy expenditure over a period of up to 14
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Exercise and Energy Expenditure After SCI 1397
Figure 2 — Energy expenditure in the different groups. Data are presented as Mean ± SD. Abbreviations: TDEE, total daily energy expenditure; BMR, basal metabolic rate; PAEE, physical activity–associated energy expenditure; SED, “sedentary” group; EXE, “exercise” group. * Statistically significant difference between groups (SED against EXE) (P < .05).
Figure 3 — Comparison of time of exercise performed weekly (left y axis) and peak oxygen uptake (right y axis). Data are presented as Mean ± SD. Abbreviations: VO2peak, peak oxygen uptake; SED, “sedentary” group; EXE, “exercise” group. * Statistically significant difference between groups (SED against EXE) (P < .05).
days, and has been used across a variety of populations including premature infants,19 children and adolescents,20 obese adults,21 hospitalized patients,22 pregnant and lactating women,23 athletes,24 and elderly cohorts.25 DLW is currently accepted as the ‘gold standard’ technique for free-living energy expenditure assessment.26,27 However, the technique can only be used to indicate the average daily EE and cannot provide detailed information about physical activity variation within a specific time frame. As for PAEE, we demonstrated that our EXE cohort had to perform approximately 200 min·wk-1 of moderate exercise (221 ± 72 min·wk-1 for EXE group against 28 ± 31 min·wk-1 for SED cohort; P < .001) (Figure 3) to spend only extras 1.2 kcal·kg-1·day-1 in comparison with the sedentary individuals (9.3 ± 4.3 kcal·kg-1·day-1 and 8.1 ± 4.0 kcal·kg-1·day-1, for EXE and SED groups, respectively; P > .05) (Figure 2). The SD was in the exercise duration for the SED group was greater than the mean can be explained by the fact that there was participants who did not performed physical activities at all (0 min of exercise per week) and there was a participant who performed some weekly exercise, but below the minimum required to be assigned in the EXE group (60 min·wk-1). Exercises performed by participants were functional electrical stimulation leg cycling, arm-cranking, hand-cycling, weight lifting, circuit training, wheelchair quad-rugby, wheelchair tennis, and swimming. The PAEE of our cohort is quite modest if we consider the volume and intensity of exercise performed by the EXE group. Doing simple mathematics to illustrate our finding at the clinical levels, the difference between groups was only 1.2 kcal·kg-1·day-1, and taking an 80 kg-person as an example, the gap between doing exercise or being sedentary would be of 100 kcal·day-1. Appling a hypothetical situation (ie, that our population performed 100% of their moderate activities exclusively at 4 METs and considering again an 80 kg-person as example) and based on the information available in the literature for the able-bodied population (ie, 1 MET = 1 kcal·kg-1·h-1), the estimated PAEE during the same 200 min·wk-1 for an 80 kg ablebodied individual would be ~150 kcal·d-1, or 33% above of the 100 kcal·d-1 demonstrated in our study. This low “effect” of exercise on energy expenditure may be explained due to the reduced skeletal muscle mass in addition to an alteration in the contractile properties
found in this population as a result of paralysis.28 Therefore, we can speculate that the acute impact of exercise on energy expenditure (in terms of daily PAEE) is lower in SCI than in the general population. Our findings are corroborated by the study conducted by Collins and colleagues,29 where the energy expenditure of 27 different physical activities were analyzed in 170 people with SCI and compared with able-bodied individuals. The authors reported energy expenditure data for a comprehensive number of physical activities and based on their findings, they proposed a MET value of 2.7 mL·kg-1·min-1 for individuals with SCI rather than the traditional 3.5 mL·kg-1·min-1 for able-bodied population.29 Therefore, both studies suggest that SCI individuals must perform more exercise (in terms of volume or intensity) to achieve the same levels of energy expenditure compared with able-bodied individuals. After adjusting for differences in body size by expressing EE per kg body mass, BMR ranged from 16 ± 1.7 kcal·kg-1·day-1 for the SED group to 21 ± 1.7 kcal·kg-1·day-1 for the EXE cohort (P < .001) (Figure 2). This difference can be explained by the level of injury and therefore, an increased lean tissue, corroborating the data presented by Bauman at all.2 Indeed, skeletal muscle is an important organ for energy metabolism, as studies have shown that lean body mass is the major determinant of energy expenditure, with minor influences of fat mass, age and gender30 and FFM explains up to 80% of BMR.31 Although BMR is quantitatively the main component of TDEE, and therefore, indicating that the higher BMR found in the EXE group may had direct influence in TDEE, as it was 13% higher in this group compared with the SED individuals (33 ± 4.5 kcal·kg-1·day-1 and 27 ± 4.3 kcal·kg-1·day-1, for EXE and SED, respectively; P < .05), the difference in in lean mass is better explained by a function of the level of the injury and not of the PA (Figure 2). As BMR accounts for the largest proportion of TDEE, even small changes in this component of TDEE might induce longterm benefits for weight management.32 In fact, in our study, all energy expenditure components (ie, TDEE, BMR, and PAEE) were significantly correlated with % FM, and individuals who performed exercise had 26% less body fat than the sedentary group. However, despite the fact of the important improvements in TDEE, BMR and %FM of the EXE group compared with the SED
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cohort, the values found in the former group are quite modest in comparison with the able-bodied individuals. Indeed, several studies have demonstrated that all those physiological components are lower in people with SCI under community-dwelling conditions (or higher, in the case of % FM)33–35 when compared to the ablebodied population. In addition, even with the EXE group meeting the minimum AHA and ACSM’s exercise recommendation for health and weight management, these individuals still demonstrated high levels of body adiposity if compared with the able-bodied population. In fact, based on standard clinical definitions for percent of body fat (normal < 25% and obese ≥ 25%, for men),33 both groups in our study are considered obese (38 ± 6% and 28 ± 9% for SED and EXE, respectively), and this is an interesting data of this study, as it suggests that SCI individuals need more attention concerning exercise prescription aiming health/weight management. VO2peak, which is an important indicator of cardiovascular fitness36 and is inversely correlated to obesity and all causes of mortality, including coronary heart disease (CHD),37 was significantly improved due to the long term of regular exercise, as it was 29% higher in the EXE group than in those sedentary individual, indicating the positive impact of regular physical activity (Figure 3). However, our individuals have demonstrated quite low values (15 ± 4 ml·kg-1·min-1 = SED; and 21 ± 3 ml·kg-1·min-1 = EXE) if compared with fit healthy young adults (57 ± 8 ml·kg-1·min-1) or even in comparison with ambulatory individuals with low level of fitness (38 ± 3 ml·kg-1·min-1),38 although our data were consistent with other findings in SCI.39,40 Many factors may contribute to such a low level of cardiovascular fitness in SCI, including a less efficiency in performing arm exercise compared with the legs, impairment of the sympathetic nervous system and poor venous return as a result of the lower limbs paralysis.41 However, despite the fact of the lower values for VO2peak reported in our study for SCI compared with the ambulatory individuals, it does not necessarily means the former group is not taking the healthy benefits of exercise, as the lower oxygen uptake might be attributed mostly to the physical/physiological impairment rather than reduced levels of exercise. In other words, they are not doing more than 200 minutes of exercise weekly in vain. For example, TDEE, BMR and body composition were significantly improved in the EXE group compared with the SED, and Sui et al has demonstrated in a cohort of 2603 elderly people that the level of cardiovascular fitness was a significant predictor of mortality in older adults, independently of overall or abdominal obesity.37 That means that those individuals who performed regular exercise, even with higher levels of body fat, have greater longevity. However, our study is inconclusive on the relationship between levels of exercise and adiposity with risk for CHD, as we did not assess blood markers (eg, lipid profiles, insulinaemia, inflammatory markers), and therefore, we cannot effectively “measure” the impact of exercise upon risk factors for CHD. Indeed, CHD is one of the main causes of mortality among SCI individuals,42 and despite the fact we did not assess blood markers for CHD, we also measured WC and BMI, which have been used as surrogates for obesity and CHD in the able-bodied population,43 and although not entirely conclusive, preliminary evidences in SCI population indicate a significant association between WC with obesity and CHD.44,45 In our study, WC was significantly associated with body fatness (R = .73; P = .011), however, based on standard clinical definitions for WC (normal < 102 cm and abdominal obesity ≥ 102, for men) and body adiposity (normal < 25% and obese ≥ 25%, for men),33 only in the SED group (WC = 102 ± 8 cm, and % FM = 38 ± 6%) WC could predict obesity. Therefore, despite the positive
correlation, our ‘raw data’ (Table 2) suggest that WC cannot accurately predict % of FM, and this corroborates to other studies.46,47 Conversely, in the EXE group, body fatness was not predicted by WC, as it was 81 ± 11 cm (considered normal) and FM was 28 ± 9% (considered obese). In addition, if we analyze the entire cohort as a single group (N = 13), again, WC could not predict body adiposity (WC = 93 ± 14 cm, and % FM = 33 ± 9%). BMI, which is another important marker of obesity in the able-bodied population but not for SCI,48 in our study was neither correlated with % FM or body mass nor a good predictor for obesity, as BMI indicated “normal” values for both groups, while % FM demonstrated excess of body adiposity. Therefore, our findings corroborate with other studies49–51 indicating that WC is a better indicator of obesity in SCI than BMI, however, our results suggest that WC still requires further attention and cannot be used as a %FM predictor.
Limitations The sample size of this study was limited. Recruitment of individuals with SCI is a well known challenge for clinical researchers. Yet the results of this study are still highly relevant, since they have revealed future directions for understanding TDEE and PAEE in the context of body adiposity accumulation for SCI wheelchair users. A second limitation was to the experimental design. In the current study, for technical and practical reasons, we did not assess energy intake in our subjects, and participants’ excessive body fat could be solely attributed to a high caloric diet. However, as the same participants in this study also participated as volunteers in other research projects (and therefore they were weighted in the same scale, by the same researcher and under the same criteria, but for a different study), we verified that all subjects were weight stable during the last 4 to 6 months (78.64 ± 15.07 and 78.86 ± 15.16 for the entire cohort; N = 13). In addition, no difference was found for either group analyzed separately. Finally, although we used the term “long term effects of exercise,” this study used a cross-sectional design. “Long term” refers to the time spent by the individuals engaged in their regular physical activities (at least 6 months).
Conclusion In conclusion, long-term exercise may have significantly improved energy expenditure in the EXE group, probably due to an increase in FFM and therefore, inducing higher BMR and TDEE. In addition, this increment in energy expenditure due to regular exercise, might have positively impacted in body composition, in terms of lower % FM and WC, suggesting that the AHA and ACMS’ exercise recommendation weight management for healthy adults were effective for this population. We also demonstrated that TDEE, BMR, PAEE, VO2peak, WC, % FFM, and time of exercise performed per week but not body mass, TSI, and BMI were significantly correlated with % FM. These may be attributed, at least in part, to the low impact of exercise on PAEE, indicating that this population might need a more specific approach for tailoring exercise programs, with special attention to exercise components such as type, volume, duration and intensity, aiming weight management and its health benefits. Acknowledgments The authors are grateful to Dr Patricia Ruell and Dr Connie Wishart for the technical support. Mr Ricardo A. Tanhoffer is a doctoral candidate and has been awarded with the University of Sydney International Research Scholarship (USIRS). There are no personal or financial relationships with
Exercise and Energy Expenditure After SCI 1399 other people or organizations that could represent potential conflicts of interest relating to the authors or their respective institutions. The results of the current study do not constitute endorsement by ACSM.
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References 1. Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: where do we go from here? Science. 2003;299(5608):853–855. PubMed doi:10.1126/science.1079857 2. Bauman WA, Spungen AM, Wang J, Pierson RN. The relationship between energy expenditure and lean tissue in monozygotic twins discordant for spinal cord injury. J Rehabil Res Dev. 2004;41(1):1–8. PubMed doi:10.1682/JRRD.2004.01.0001 3. Castro MJ, Apple DF, Staron RS, Campos GER, Dudley GA. Influence of complete spinal cord injury on skeletal muscle within 6 mo of injury. J Appl Physiol. 1999;86(1):350–358. PubMed 4. Spungen AM, Adkins RH, Stewart CA, et al. Factors influencing body composition in persons with spinal cord injury: a cross-sectional study. J Appl Physiol. 2003;95(6):2398–2407. PubMed 5. Gorgey AS, Dudley GA. Skeletal muscle atrophy and increased intramuscular fat after incomplete spinal cord injury. Spinal Cord. 2007;45(4):304–309. PubMed 6. Maruyama Y, Mizuguchi M, Yaginuma T, et al. Serum leptin, abdominal obesity and the metabolic syndrome in individuals with chronic spinal cord injury. Spinal Cord. 2008;46(7):494–499. PubMed doi:10.1038/sj.sc.3102171 7. Buchholz AC, McGillivray CF, Pencharz PB. Physical activity levels are low in free-living adults with chronic paraplegia. Obes Res. 2003;11(4):563–570. PubMed doi:10.1038/oby.2003.79 8. Gater DR. Obesity after spinal cord injury. Phys Med Rehabil Clin N Am. 2007;18(2):333–351 vii. PubMed doi:10.1016/j.pmr.2007.03.004 9. Manini TM, Everhart JE, Patel KV, et al. Daily activity energy expenditure and mortality among older adults. JAMA. 2006;296(2):171–179. PubMed doi:10.1001/jama.296.2.171 10. Gorgey AS, Mather KJ, Cupp HR, Gater DR. Effects of resistance training on adiposity and metabolism after spinal cord injury. Med Sci Sports Exerc. 2012;44(1):165–174. PubMed doi:10.1249/ MSS.0b013e31822672aa 11. Gorgey AS, Shepherd C. Skeletal muscle hypertrophy and decreased intramuscular fat after unilateral resistance training in spinal cord injury: case report. J Spinal Cord Med. 2010;33(1):90–95. PubMed 12. Nash MS, Jacobs PL, Mendez AJ, Goldberg RB. Circuit resistance training improves the atherogenic lipid profiles of persons with chronic paraplegia. J Spinal Cord Med. 2001;24(1):2–9. PubMed 13. de Groot PCE, Hjeltnes N, Heijboer AC, Stal W, Birkeland K. Effect of training intensity on physical capacity, lipid profile and insulin sensitivity in early rehabilitation of spinal cord injured individuals. Spinal Cord. 2003;41(12):673–679. PubMed doi:10.1038/sj.sc.3101534 14. Weir JBdV. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949;109:1–9. PubMed 15. Bluck LD, Forsum E, Hills A, Kurpad A, Mokhtar N, Preston T, Ramirez-Zea M, Scoeller D, Slater C, Valencia Juillerat M, Wells J, Westerterp K. Assessment of body composition and total energy expenditure in humans using stable isotope technique. Vol 3. Vienna: International Atomic Energy Agency; 2009. 16. Ekelund U, Yngve A, Brage S, Westerterp K, Sjostrom M. Body movement and physical activity energy expenditure in children and adolescents: how to adjust for differences in body size and age. Am J Clin Nutr. 2004;79(5):851–856. PubMed 17. Aleman-Mateo H, Rush E, Esparza-Romero J, et al. Prediction of fat-free mass by bioelectrical impedance analysis in older adults from developing countries: a cross-validation study using the deuterium
dilution method. J Nutr Health Aging. 2010;14(6):418–426. PubMed doi:10.1007/s12603-010-0031-z 18. Spungen AM, Bauman WA, Wang J, Pierson RN. Measuremen of body-fat in individuals with tetraplegia—a comparison of 8 clinical methods. Paraplegia. 1995;33(7):402–408. PubMed doi:10.1038/ sc.1995.90 19. Brunton JA, Saigal S, Winthrop AL, Atkinson SA. Body composition by doubly labelled water (DLW) and dual energy x-ray absorptiometry (DXA) in tiny premature infants (PI). FASEB J. 1996;10(3):1328. 20. Torun B. Energy requirements of children and adolescents. Public Health Nutr. 2005;8(7A):968–993. PubMed doi:10.1079/ PHN2005791 21. Ekelund U, Sarnblad S, Brage S, Ryberg J, Wareham NJ, Aman J. Does physical activity equally predict gain in fat mass among obese and nonobese young adults? Int J Obes. 2007;31(1):65–71. PubMed doi:10.1038/sj.ijo.0803361 22. Rai T, D’Souza G, Elia A, Kurpad AV. Measurement of 24 h energy expenditure in male tuberculosis patients. Indian J Med Res. 2006;124(6):665–676. PubMed 23. Butte NF, King JC. Energy requirements during pregnancy and lactation. Public Health Nutr. 2005;8(7A):1010–1027. PubMed 24. Ekelund U, Yngve A, Westerterp K, Sjostrom M. Energy expenditure assessed by heart rate and doubly labeled water in young athletes. Med Sci Sports Exerc. 2002;34(8):1360–1366. PubMed doi:10.1097/00005768-200208000-00019 25. Rothenberg EM. Resting, activity and total energy expenditure at age 91-96 compared to age 73. J Nutr Health Aging. 2002;6(3):177–178. 26. Westerterp KR, Bouten CVC. Physical activity assessment: comparison between movement registration and doubly labeled water method. Z. Ernahrung. 1997;36(4):263–267. PubMed doi:10.1007/ BF01617795 27. Westerterp KR, Plasqui G. Physical activity and human energy expenditure. Curr Opin Clin Nutr Metab Care. 2004;7(6):607–613. PubMed doi:10.1097/00075197-200411000-00004 28. Nash MS, Gater DG. Exercise to reduce obesity in SCI. Top Spinal Cord Inj Rehabil. 2007;12(4):76–93. doi:10.1310/sci1204-76 29. Collins EG, Gater D, Kiratli J, Butler J, Hanson K, Langbein WE. Energy cost of physical activities in persons with spinal cord injury. Med Sci Sports Exerc. 2010;42(4):691–700. PubMed doi:10.1249/ MSS.0b013e3181bb902f 30. Tappy L, Binnert C, Schneiter P, Schneiter P. Energy expenditure, physical activity and body-weight control. Proc Nutr Soc. 2003;62(3):663–666. PubMed doi:10.1079/PNS2003280 31. Sparti A, DeLany JP, delaBretonne JA, Sander GE, Bray GA. Relationship between resting metabolic rate and the composition of the fat-free mass. Metabolism. 1997;46(10):1225–1230. PubMed doi:10.1016/ S0026-0495(97)90222-5 32. Kirk EP, Donnelly JE, Smith BK, et al. Minimal resistance training improves daily energy expenditure and fat oxidation. Med Sci Sports Exerc. 2009;41(5):1122–1129. PubMed doi:10.1249/ MSS.0b013e318193c64e 33. Bray GA. Fat distribution and body weight. Obes Res. 1993;1(3):203– 205. PubMed doi:10.1002/j.1550-8528.1993.tb00613.x 34. Monroe MB, Tataranni PA, Pratley R, Manore MM, Skinner JS, Ravussin E. Lower daily energy expenditure as measured by a respiratory chamber in subjects with spinal cord injury compared with control subjects. Am J Clin Nutr. 1998;68(6):1223–1227. PubMed 35. Buchholz AC, McGillivray CF, Pencharz PB. Differences in resting metabolic rate between paraplegic and able-bodied subjects are explained by differences in body composition. Am J Clin Nutr. 2003;77(2):371–378. PubMed
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1400 Tanhoffer et al 36. Haisma JA, Bussmann JB, Stain HJ, et al. Changes in physical capacity during and after inpatient rehabilitation in subjects with a spinal cord injury. Arch Phys Med Rehabil. 2006;87(6):741–748. PubMed doi:10.1016/j.apmr.2006.02.032 37. Sui XM, LaMonte MJ, Laditka JN, et al. Cardiorespiratory fitness and adiposity as mortality predictors in older adults. JAMA. 2007;298(21):2507–2516. PubMed doi:10.1001/jama.298.21.2507 38. Smith TJ, Pikosky MA, Grediagin A, et al. Aerobic fitness does not modulate protein metabolism in response to increased exercise: a controlled trial. Nutrition & Metabolism. 2009;6:28. PubMed 39. Widman LM, Abresch RT, Styne DM, McDonald CM. Aerobic fitness and upper extremity strength in patients aged 11 to 21 years with spinal cord dysfunction as compared to ideal weight and overweight controls. J Spinal Cord Med. 2007;30:S88–S96. PubMed 40. Zoeller RF, Riechman SE, Dabayebeh IM, Goss FL, Robertson RJ, Jacobs PL. Relation between muscular strength and cardiorespiratory fitness in people with thoracic-level paraplegia. Arch Phys Med Rehabil. 2005;86(7):1441–1446. PubMed doi:10.1016/j.apmr.2004.11.032 41. Phillips WT, Kiratli BJ, Sarkarati M, et al. Effect of spinal cord injury on the heart and cardiovascular fitness. Curr Probl Cardiol. 1998;23(11):641–716. PubMed doi:10.1016/S0146-2806(98)80003-0 42. Garshick E, Kelley A, Cohen S, et al. A prospective assessment of mortality in chronic spinal cord injury. Spinal Cord. 2005;43(7):408–416. PubMed doi:10.1038/sj.sc.3101729 43. Dalton M, Cameron AJ, Zimmet PZ, et al. Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intern Med. 2003;254(6):555–563. PubMed doi:10.1111/j.13652796.2003.01229.x
44. Demirel S, Demirel G, Tukek T, Erk O, Yilmaz H. Risk factors for coronary heart disease in patients with spinal cord injury in Turkey. Spinal Cord. 2001;39(3):134–138. PubMed doi:10.1038/sj.sc.3101135 45. Maki KC, Briones ER, Langbein WE, et al. Associations between serum-lipids and indicators of adiposity in men with spinal-cord injury. Paraplegia. 1995;33(2):102–109. PubMed doi:10.1038/sc.1995.24 46. Gorgey AS, Gater DR. A preliminary report on the effects of the level of spinal cord injury on the association between central adiposity and metabolic profile. PM R. 2011;3(5):440–446. PubMed doi:10.1016/j. pmrj.2011.01.011 47. Maruyama Y, Mizuguchi M, Yaginuma T, et al. Serum leptin, abdominal obesity and the metabolic syndrome in individuals with chronic spinal cord injury. Spinal Cord. 2008;46(7):494–499. PubMed doi:10.1038/sj.sc.3102171 48. Clasey JL, Gater DG. Body composition assessment in adults with spinal cord injury. Top Spinal Cord Inj Rehabil. 2007;12(4):8–19. doi:10.1310/sci1204-8 49. Buchholz AC, McGillivray CF, Pencharz PB. The use of bioelectric impedance analysis to measure fluid compartments in subjects with chronic paraplegia. Arch Phys Med Rehabil. 2003;84(6):854–861. PubMed doi:10.1016/S0003-9993(02)04950-X 50. Finnie AK, Buchholz AC, Ginis KAM, Grp SSR. Current coronary heart disease risk assessment tools may underestimate risk in community-dwelling persons with chronic spinal cord injury. Spinal Cord. 2008;46(9):608–615. PubMed doi:10.1038/sc.2008.21 51. Laughton GE, Buchholz AC, Martin Ginis KA, Goy RE. Lowering body mass index cutoffs better identifies obese persons with spinal cord injury. Spinal Cord. 2009;47(10):757–762. PubMed doi:10.1038/ sc.2009.33
Official Journal of ISPAH www.JPAH-Journal.com ORIGINAL RESEARCH
Exercise, Energy Expenditure, and Body Composition in People With Spinal Cord Injury Ricardo A. Tanhoffer, Aldre I. P. Tanhoffer, Jacqueline Raymond, Andrew P. Hills, and Glen M Davis Background: The objective of this study was to verify the long-term effects of exercise on energy expenditure and body composition in individuals with spinal cord injury (SCI), as very little information is available on this population under free-living conditions. Methods: Free-living energy expenditure and body composition using doubly labeled water (DLW) was measured in 13 individuals with SCI, subdivided in 2 groups: (1) sedentary (SED; N = 7) and (2) regularly engaged in any exercise program, for at least 150 min·wk-1 (EXE; N = 6). Results: The total daily energy expenditure (TDEE) was significantly higher in the EXE group (33 ± 4.5 kcal·kg-1·day-1) if compared with SED group (27 ± 4.3 kcal·kg-1·day-1). The percentage of body fat was significantly higher in SED group than in EXE group (38 ± 6% and 28 ± 9%). Conclusion: Our findings revealed that, despite the severity of SCI, the actual ACSM’s guidelines for weight management for healthy adults exercise could significantly increase TDEE and BMR and improve body composition in individuals who regularly perform exercise. However, the EXE group still showed a high percentage of body fat, suggesting that a more specific approach might be considered (ie, increased intensity or volume, or combining with a diet program). Keywords: wheelchair users, physical activity, doubly labeled water For the general population, even small imbalances between energy intake and energy expenditure (EE) over time can lead to overweight or obesity. Increasing as few as 100 kcal·day-1, may cause weight gain, and on the other hand, reducing this “energy gap” could help to prevent or even reduce body adiposity.1 Obviously, maintaining the energy balance between energy intake and EE is critical for weight management. Energy balance is profoundly affected in individuals with spinal cord injury (SCI), as their daily EE has been demonstrated to be lower than the able-bodied population.2 Among other factors, this is attributed to a reduced fat-free mass (FFM), manly due to muscle paralysis in their affected muscles, leading to a severe proteolysis,2–6 and to the low levels of physical activity, owing to reduced ability to perform exercises and also architectural barriers7 found in this population. As a consequence of their lower daily EE, obesity is a common secondary complication after SCI, which in turn, body adiposity is highly associated with the development of cardiovascular disease.8 Conversely, exercise has been demonstrated to play in important role for weight management or reduction, as it may increase the total daily energy expenditure (TDEE), by affecting 2 of its components: basal metabolic rate (BMR) and physical activity-associated energy expenditure (PAEE). A simple bout of exercise will acutely R Tanhoffer ([email protected]), A Tanhoffer, Raymond, and Davis are with the Clinical Exercise and Rehabilitation Unit, Faculty of Health Sciences, University of Sydney, Australia. R Tanhoffer, Raymond, and Davis are also with the Exercise Health & Performance Research Group, Faculty of Health Sciences, University of Sydney, Australia. A Tanhoffer is also with the Clinical & Rehabilitation Sciences Research Group, Faculty of Health Sciences, University of Sydney, Australia. Hills is with the Mater Mother’s Hospital, Mater Medical Research Institute and Griffith Health Institute, Griffith University, Australia.
impact the daily PAEE, which over time, if this augmented level of physical activity is maintained, may increase the amount of fatfree mass, reflecting in higher energy requirements during BMR. In fact, exercise has been strongly associated with mortality risk, demonstrating that the higher the EE due to exercise, the lower the risk of all causes of death in older adults.9 In fact, research on body composition and exercise for individuals with SCI have been recently developed, and these authors are demonstrating the importance of specific programs aiming weight management for this population.4,10,11 Although the effects of exercise on some risk factors for cardiovascular disease are well documented in individuals with SCI such as glucose intolerance and dyslipidaemia,12,13 the impact of long-term exercise on EE and body composition are poorly investigated in wheelchair user. Therefore, as physical activity can be used to improve energy balance and reduce common pathologies associated to obesity, and a specific exercise program for weight management is undefined for individual with SCI, in this study we aimed to verify the impact of the long-term exercise, commonly performed during free-living conditions, on EE and body composition, in people with SCI. We hypothesize that the higher the level of physical activity, the more improved the body composition of our volunteers.
Methods Participants Thirteen people with SCI were recruited for this study. Inclusion criteria were (a) aged between 18 to 65 years, (b) at least 1 year postinjury, and (c) use of a manual wheelchair as the main form of locomotion. Participants were assigned in 2 different groups according to their level of engagement in exercise. If participants performed regular and structured exercise at least 3 times per week 1393
1394 Tanhoffer et al
(minimum of 150 min of exercise weekly), for a minimum of 6 months of continuous training, they were assigned to the exercise group (EXE). If individuals performed structured exercise equal or less than 60 min weekly, they were assigned to sedentary group (SED). Informed consent was gained from all the participants. Approval for this study was granted by the University of Sydney Human Research Ethics Committee.
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Protocol Participants attended the Clinical Exercise & Rehabilitation Unit on 4 occasions (Figure 1). Anthropometric and demographics assessments and basal metabolic rate were conducted on their first visit. All participants were instructed to not perform any vigorous exercise, and refrain from smoking and alcohol at least 48 hours before testing. An arm-crank maximal test was performed on the second visit to assess their fitness level, at least 48 hours after the first visit. On the third visit, participants collected the baseline urine sample and drank the doubly labeled water (DLW) dose. On the fourth and last visit, all urine samples and the physical activity self-report sheet were delivered by the participants.
Anthropometrics and Demographics The following anthropometric measurements were made: mass, stature (measured in supine) and waist circumference (WC). Body
mass was taken with participants wearing light clothing, to the nearest 0.1 kg using a digital scale (K Tron, Arizona, USA). The weight of their wheelchair was subtracted from the total weight of the participant in his wheelchair. Stature was measured using a metal measuring tape (Gulick Tape Measure), in a supine position. WC was measured in triplicate, with participants in the supine position. WC measurements were made at the midway between the last rib and the iliac crest. Landmarks were identified and marked before the first measurement. Body mass index (kg·m2) was calculated from mass and stature. The following demographic information was recorded: age, gender, neurological level and severity of lesion, and time since injury.
Basal Metabolic Rate BMR was measured following a 12-h fast. Participants were instructed to lie supine and remain awake. Following 30-min in this rested position, BMR was measured over the subsequent 30 minutes. For BMR, an open-circuit spirometry metabolic measurement system (Medical Graphics Corporation, St Paul, Minnesota, USA) with a mouthpiece was used to perform breathby-breath gas analysis. The Weir equation was used to calculate BMR.14 Simultaneously, HR was measured continuously via 3-lead ECG (CR55 Portascope, Cardiac Recorders Ltd, London) and interfaced with data from the metabolic measurement cart.
Figure 1 — Protocol used. Abbreviations: BMR, basal metabolic rate; VO2peak, peak oxygen uptake; RMR, resting metabolic rate; DLW, doubly labeled water; PA, physical activity.
Exercise and Energy Expenditure After SCI 1395
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Total Daily Energy Expenditure TDEE was measured by DLW. All DLW samples were prepared and provided by Queensland University of Technology. Participants presented after a fast of at least 6-h to maximize absorption of the tracer elements. A baseline urine specimen was collected and the doubly labeled water (10% enriched 18O and 99% 2H) was administered orally, with the dose based on body mass (1.35g of DLW × body mass in kg). A second urine sample was collected 6-h after the priming DLW dose. Then, 1 specimen of urine was collected daily, over the next 2 weeks at approximately the same time of day as the first basal urine specimen. The time collection should be recorded in a sheet provided by researchers during the administration of the DLW dose. The participants collected the samples themselves and stored them refrigerated at home in sterile plastic containers delivered by the researchers on the first day. The urine samples were collected on the final day by the researchers. Samples were analyzed for 2H2O and H218O by isotope ratio mass spectrometry at Queensland University of Technology. Total energy expenditure (kcal) over the 2-week period was divided by 14 to estimate mean TDEE.15
Physical Activity-Associated Energy Expenditure (PAEE) The DLW technique quantifies only TDEE, therefore, PAEE was estimated as (TDEE × 0.90) – BMR, where 0.90 refers to the thermic effect of meals constant fraction of 10%.16 Therefore, removing the energy expenditure during food metabolism and during basal conditions from the TDEE, the remaining energy refers to the energy expended in any free-living physical activity performed by an individual per day.
Body Composition Isotope dilution (DLW) was also used for body composition analysis, and this technique has been using to validate other methods, such as bioelectrical impedance analysis (BIA).17 Using an isotope ratio mass spectrometry at Queensland University of Technology, 2H and 18O enrichment of the samples are measured to calculate the total body water (TBW) content. Although this technique rely on assumption of no abnormal fluid shifts are presented and this issue can be presented in SCI (ie, lower extremity edema), this method has been demonstrated be valid to assess body fat in this population.18 Derivation of fat-free mass (FFM) and fat mass (FM) from TBW is calculated as follow: First, TBW was determined from the deuterium oxide dilution space of deuterium exchange with nonaqueous compartment of the body, using the following equation:
F1 N1 = F2 N 2 where N1 is the pool size of the dose in moles and N2 is the pool size of the distribution space in moles. F1 is enrichment of the dose and F2 is the enrichment of the distribution space. The 2 compartment model divides the body weight into fat mass (FM) and fat-free mass (FFM) (Weight [kg] = FM [kg] + FFM [kg]). Body weight is measured along with TBW, from which FFM is estimated. FFM is derived from TBW using a hydration coefficient, that is, the fraction of FFM comprised of water (FFM = TBW ÷ hydration coefficient). Cellular hydration (including FFM) in all animals is controlled within strict limits, and the commonly used hydration coefficient is of 0.732. Once FFM has been estimated, FM
and per cent body fat are calculated using the equations (FM [kg] = weight [kg] – FFM [kg]) and (% body fat = 100 Å ~FM/weight).15
Self-Reported Physical Activity A self report diary was completed by participants during the observation period (14 days). Information about physical activity such as type, duration and self-perceived intensity of activity (very light, light, moderate, intense and very intense), as well as date and time of the day were recorded over the 2 weeks DLW protocol. All participants who were engaged in exercise programs performed their physical activities under professional supervision, at gyms or sport associations. To compute the time spent in exercise weekly, we just considered those activities self-reported/assessed in moderate intensity or higher. To measure the exercise intensity and to compare with the 14-day self-report sheet later, the same researcher went to the local where they usually perform exercise and randomly followed some of the participant’s exercise session/bout (one session each week for every participant). In case of individuals with paraplegia, they were asked to wear a heart rate monitor (Polar RS800CX, Polar Electro, Finland). Heart rate was measured at rest and during exercise. In addition, individuals with paraplegia and tetraplegia were asked to indicate the RPE (from 6 to 20), at rest and at the end of exercise, only for the EXE group. After, we compared the self-rated exercise intensity against the measured heart rate reserve during their exercise bout.
Statistical Analysis Data are presented as mean ± SD. All analysis were performed using SPSS 19.0 and results were statistically significant at P < .05. Two-way ANOVA was performed to determine whether there were differences between variables, with adjustments with the Bonferroni test, as a pots-hoc test. Pearson’s correlation coefficient (R) and was used to determine the association between variables.
Results Background characteristics of all participants are presented in Table 1 and Table 2 portrays the main difference between groups. All participants were male. Sixty-two percent of participants had complete spinal cord lesions compared with 38% incomplete. Level of lesion was distributed in paraplegia (69%) and tetraplegia (31%). There was no significant difference on average age (39 ± 12 and 40 ± 15 years, for SED and EXE groups, respectively) and stature (1.77 ± 0.15 and 1.75 ± 0.13 m, for SED and EXE, respectively). The EXE group performed, on average, 221 ± 72 min·wk-1 of moderate exercise (or higher intensity) per week against an average of 28 ± 31 min·wk-1 of exercise (P < .001). Exercises performed by participants were functional electrical stimulation leg cycling, arm cranking, hand-cycling, weight lifting, circuit training, wheelchair quad-rugby, wheelchair tennis, and swimming. Groups did not show significant difference in body mass (85 ± 16 kg and 72 ± 12 kg, for SED and EXE, respectively) and BMI (27 ± 2 kg·m2 and 24 ± 4 kg·m2, for SED and EXE, respectively). However, percentage of fat-free mass (%FFM) was significantly higher in EXE group (73 ± 8%) than in SED cohort (61 ± 5%). Percentage of body fat (%FM) was significantly lower in the EXE (28 ± 9%) than in the SED group (38 ± 6%). Waist circumference (WC) was significantly higher in the SED (102 ± 8 cm) group than in EXE group (81 ± 11 cm).
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Table 1 Information of the Participants Participant
Gender
Group
Type of activity
Time (min/wk)
Injury level
TSI
3
Male
SED
Weight-lifting
60
C7-A
11.5
5
Male
SED
None
0
T12-A
35
7
Male
SED
None
0
T11-A
7
8
Male
SED
Arm-cranking
45
C6-A
11
10
Male
SED
None
0
T10-C
11
11
Male
SED
Arm-cranking
60
T6-A
4
12
Male
SED
None
0
C5-C
4
Mean ± SD
–
–
28 ± 31
–
12 ± 10
Participant
Gender
Group
Type of activity
Time (min/wk)
Injury level
TSI
1
Male
EXE
FES + Circuit training
270
T5-A
16
2
Male
EXE
Swimming + Weight-Lifting
180
T8-A
16
4
Male
EXE
Hand-Cycling
150
T12-A
5
6
Male
EXE
Wheelchair Tennis
180
T8-C
5
6
Male
EXE
Wheelchair Rugby + Hand-Cycling
350
T4-B
4
13
Male
EXE
Wheelchair Rugby + Hand-Cycling
200
C6-C
9
Mean ± SD
–
–
221 ± 72
–
9±6
Abbreviation: SED, sedentary; EXE, exercise; TSI, time since injury in years; Data are presented as Mean ± SD (standard deviation).
Table 2 Anthropometrics and Demographics of the Groups ALL (N = 13)
SED (N = 7)
EXE (N = 6)
Age (years)
40 ± 13
39 ± 12
40 ± 15
TSI (years)
10 ± 9
11 ± 11
9±6
Body mass (kg)
79 ± 15
85 ± 16
72 ± 12
1.76 ± 0.14
1.78 ± 0.15
1.75 ± 0.13
BMI (kg/m2)
25 ± 3
27 ± 2
24 ± 4
FM (%)
33 ± 9
38 ± 6
28 ± 9*
Stature (m)
FFM (%)
67 ± 9
61 ± 5
73 ± 8*
WC (cm)
93 ± 14
102 ± 8
81 ± 11*
123 ± 115
24 ± 30
221 ± 72*
18 ± 9
15 ± 4
21 ± 3*
Exercise (min/wk) VO2peak (ml/kg/min)
Note. Data are presented as Mean ± SD. Abbreviations: TSI, time since injury; VO2peak, peak oxygen uptake; BMI, body mass index; FM, fat mass; FFM, fat-free mass; WC, waist circumference. * P < .05 in relation to SED.
The TDEE, BMR (Figure 2) and VO2peak were significantly higher in the EXE group (33 ± 4.5 kcal·kg-1·day-1; 21 ± 1.7 kcal·kg1·day-1; 21 ± 3 ml·kg-1·min-1) if compared with SED group (27 ± 4.3 kcal·kg-1·day-1; 16 ± 1.7 kcal·kg-1·day-1; 15 ± 4 ml·kg-1·min-1). Although slightly higher in the EXE cohort, PAEE was not significantly different between groups (8 ± 4.1 kcal·kg-1·day-1 and 9 ± 4.3 kcal·kg-1·day-1, for SED and EXE groups, respectively) (Figure 2). Pearson’s correlation revealed that % FM was significantly correlated with TDEE (R = –0.68; P = .008), BMR (R = –0.60; P = .037), PAEE (R = –0.50; P = .035) VO2peak (R = –0.60; P = .023), WC (R = .73; P = .011), time of exercise performed weekly (R = –0.70; P = .005) and with % FFM (R = –0.98; P < .001), but not with other variables such as body mass, age, TSI and BMI. In
our study, correlation between level of injury and % FM was not detectable, as the number of individuals with tetraplegia was small (31%) within the entire cohort.
Discussion Our group is the first to measure TDEE in individuals with SCI, using the DLW technique. This has allowed us to accurately report energy expenditure during their very normal daily routine, without the interference of laboratory tests or devices they have to carry attached in their body to assess energy expenditure or its components. In fact, the DLW technique is a noninvasive approach for measuring free-living energy expenditure over a period of up to 14
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Exercise and Energy Expenditure After SCI 1397
Figure 2 — Energy expenditure in the different groups. Data are presented as Mean ± SD. Abbreviations: TDEE, total daily energy expenditure; BMR, basal metabolic rate; PAEE, physical activity–associated energy expenditure; SED, “sedentary” group; EXE, “exercise” group. * Statistically significant difference between groups (SED against EXE) (P < .05).
Figure 3 — Comparison of time of exercise performed weekly (left y axis) and peak oxygen uptake (right y axis). Data are presented as Mean ± SD. Abbreviations: VO2peak, peak oxygen uptake; SED, “sedentary” group; EXE, “exercise” group. * Statistically significant difference between groups (SED against EXE) (P < .05).
days, and has been used across a variety of populations including premature infants,19 children and adolescents,20 obese adults,21 hospitalized patients,22 pregnant and lactating women,23 athletes,24 and elderly cohorts.25 DLW is currently accepted as the ‘gold standard’ technique for free-living energy expenditure assessment.26,27 However, the technique can only be used to indicate the average daily EE and cannot provide detailed information about physical activity variation within a specific time frame. As for PAEE, we demonstrated that our EXE cohort had to perform approximately 200 min·wk-1 of moderate exercise (221 ± 72 min·wk-1 for EXE group against 28 ± 31 min·wk-1 for SED cohort; P < .001) (Figure 3) to spend only extras 1.2 kcal·kg-1·day-1 in comparison with the sedentary individuals (9.3 ± 4.3 kcal·kg-1·day-1 and 8.1 ± 4.0 kcal·kg-1·day-1, for EXE and SED groups, respectively; P > .05) (Figure 2). The SD was in the exercise duration for the SED group was greater than the mean can be explained by the fact that there was participants who did not performed physical activities at all (0 min of exercise per week) and there was a participant who performed some weekly exercise, but below the minimum required to be assigned in the EXE group (60 min·wk-1). Exercises performed by participants were functional electrical stimulation leg cycling, arm-cranking, hand-cycling, weight lifting, circuit training, wheelchair quad-rugby, wheelchair tennis, and swimming. The PAEE of our cohort is quite modest if we consider the volume and intensity of exercise performed by the EXE group. Doing simple mathematics to illustrate our finding at the clinical levels, the difference between groups was only 1.2 kcal·kg-1·day-1, and taking an 80 kg-person as an example, the gap between doing exercise or being sedentary would be of 100 kcal·day-1. Appling a hypothetical situation (ie, that our population performed 100% of their moderate activities exclusively at 4 METs and considering again an 80 kg-person as example) and based on the information available in the literature for the able-bodied population (ie, 1 MET = 1 kcal·kg-1·h-1), the estimated PAEE during the same 200 min·wk-1 for an 80 kg ablebodied individual would be ~150 kcal·d-1, or 33% above of the 100 kcal·d-1 demonstrated in our study. This low “effect” of exercise on energy expenditure may be explained due to the reduced skeletal muscle mass in addition to an alteration in the contractile properties
found in this population as a result of paralysis.28 Therefore, we can speculate that the acute impact of exercise on energy expenditure (in terms of daily PAEE) is lower in SCI than in the general population. Our findings are corroborated by the study conducted by Collins and colleagues,29 where the energy expenditure of 27 different physical activities were analyzed in 170 people with SCI and compared with able-bodied individuals. The authors reported energy expenditure data for a comprehensive number of physical activities and based on their findings, they proposed a MET value of 2.7 mL·kg-1·min-1 for individuals with SCI rather than the traditional 3.5 mL·kg-1·min-1 for able-bodied population.29 Therefore, both studies suggest that SCI individuals must perform more exercise (in terms of volume or intensity) to achieve the same levels of energy expenditure compared with able-bodied individuals. After adjusting for differences in body size by expressing EE per kg body mass, BMR ranged from 16 ± 1.7 kcal·kg-1·day-1 for the SED group to 21 ± 1.7 kcal·kg-1·day-1 for the EXE cohort (P < .001) (Figure 2). This difference can be explained by the level of injury and therefore, an increased lean tissue, corroborating the data presented by Bauman at all.2 Indeed, skeletal muscle is an important organ for energy metabolism, as studies have shown that lean body mass is the major determinant of energy expenditure, with minor influences of fat mass, age and gender30 and FFM explains up to 80% of BMR.31 Although BMR is quantitatively the main component of TDEE, and therefore, indicating that the higher BMR found in the EXE group may had direct influence in TDEE, as it was 13% higher in this group compared with the SED individuals (33 ± 4.5 kcal·kg-1·day-1 and 27 ± 4.3 kcal·kg-1·day-1, for EXE and SED, respectively; P < .05), the difference in in lean mass is better explained by a function of the level of the injury and not of the PA (Figure 2). As BMR accounts for the largest proportion of TDEE, even small changes in this component of TDEE might induce longterm benefits for weight management.32 In fact, in our study, all energy expenditure components (ie, TDEE, BMR, and PAEE) were significantly correlated with % FM, and individuals who performed exercise had 26% less body fat than the sedentary group. However, despite the fact of the important improvements in TDEE, BMR and %FM of the EXE group compared with the SED
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1398 Tanhoffer et al
cohort, the values found in the former group are quite modest in comparison with the able-bodied individuals. Indeed, several studies have demonstrated that all those physiological components are lower in people with SCI under community-dwelling conditions (or higher, in the case of % FM)33–35 when compared to the ablebodied population. In addition, even with the EXE group meeting the minimum AHA and ACSM’s exercise recommendation for health and weight management, these individuals still demonstrated high levels of body adiposity if compared with the able-bodied population. In fact, based on standard clinical definitions for percent of body fat (normal < 25% and obese ≥ 25%, for men),33 both groups in our study are considered obese (38 ± 6% and 28 ± 9% for SED and EXE, respectively), and this is an interesting data of this study, as it suggests that SCI individuals need more attention concerning exercise prescription aiming health/weight management. VO2peak, which is an important indicator of cardiovascular fitness36 and is inversely correlated to obesity and all causes of mortality, including coronary heart disease (CHD),37 was significantly improved due to the long term of regular exercise, as it was 29% higher in the EXE group than in those sedentary individual, indicating the positive impact of regular physical activity (Figure 3). However, our individuals have demonstrated quite low values (15 ± 4 ml·kg-1·min-1 = SED; and 21 ± 3 ml·kg-1·min-1 = EXE) if compared with fit healthy young adults (57 ± 8 ml·kg-1·min-1) or even in comparison with ambulatory individuals with low level of fitness (38 ± 3 ml·kg-1·min-1),38 although our data were consistent with other findings in SCI.39,40 Many factors may contribute to such a low level of cardiovascular fitness in SCI, including a less efficiency in performing arm exercise compared with the legs, impairment of the sympathetic nervous system and poor venous return as a result of the lower limbs paralysis.41 However, despite the fact of the lower values for VO2peak reported in our study for SCI compared with the ambulatory individuals, it does not necessarily means the former group is not taking the healthy benefits of exercise, as the lower oxygen uptake might be attributed mostly to the physical/physiological impairment rather than reduced levels of exercise. In other words, they are not doing more than 200 minutes of exercise weekly in vain. For example, TDEE, BMR and body composition were significantly improved in the EXE group compared with the SED, and Sui et al has demonstrated in a cohort of 2603 elderly people that the level of cardiovascular fitness was a significant predictor of mortality in older adults, independently of overall or abdominal obesity.37 That means that those individuals who performed regular exercise, even with higher levels of body fat, have greater longevity. However, our study is inconclusive on the relationship between levels of exercise and adiposity with risk for CHD, as we did not assess blood markers (eg, lipid profiles, insulinaemia, inflammatory markers), and therefore, we cannot effectively “measure” the impact of exercise upon risk factors for CHD. Indeed, CHD is one of the main causes of mortality among SCI individuals,42 and despite the fact we did not assess blood markers for CHD, we also measured WC and BMI, which have been used as surrogates for obesity and CHD in the able-bodied population,43 and although not entirely conclusive, preliminary evidences in SCI population indicate a significant association between WC with obesity and CHD.44,45 In our study, WC was significantly associated with body fatness (R = .73; P = .011), however, based on standard clinical definitions for WC (normal < 102 cm and abdominal obesity ≥ 102, for men) and body adiposity (normal < 25% and obese ≥ 25%, for men),33 only in the SED group (WC = 102 ± 8 cm, and % FM = 38 ± 6%) WC could predict obesity. Therefore, despite the positive
correlation, our ‘raw data’ (Table 2) suggest that WC cannot accurately predict % of FM, and this corroborates to other studies.46,47 Conversely, in the EXE group, body fatness was not predicted by WC, as it was 81 ± 11 cm (considered normal) and FM was 28 ± 9% (considered obese). In addition, if we analyze the entire cohort as a single group (N = 13), again, WC could not predict body adiposity (WC = 93 ± 14 cm, and % FM = 33 ± 9%). BMI, which is another important marker of obesity in the able-bodied population but not for SCI,48 in our study was neither correlated with % FM or body mass nor a good predictor for obesity, as BMI indicated “normal” values for both groups, while % FM demonstrated excess of body adiposity. Therefore, our findings corroborate with other studies49–51 indicating that WC is a better indicator of obesity in SCI than BMI, however, our results suggest that WC still requires further attention and cannot be used as a %FM predictor.
Limitations The sample size of this study was limited. Recruitment of individuals with SCI is a well known challenge for clinical researchers. Yet the results of this study are still highly relevant, since they have revealed future directions for understanding TDEE and PAEE in the context of body adiposity accumulation for SCI wheelchair users. A second limitation was to the experimental design. In the current study, for technical and practical reasons, we did not assess energy intake in our subjects, and participants’ excessive body fat could be solely attributed to a high caloric diet. However, as the same participants in this study also participated as volunteers in other research projects (and therefore they were weighted in the same scale, by the same researcher and under the same criteria, but for a different study), we verified that all subjects were weight stable during the last 4 to 6 months (78.64 ± 15.07 and 78.86 ± 15.16 for the entire cohort; N = 13). In addition, no difference was found for either group analyzed separately. Finally, although we used the term “long term effects of exercise,” this study used a cross-sectional design. “Long term” refers to the time spent by the individuals engaged in their regular physical activities (at least 6 months).
Conclusion In conclusion, long-term exercise may have significantly improved energy expenditure in the EXE group, probably due to an increase in FFM and therefore, inducing higher BMR and TDEE. In addition, this increment in energy expenditure due to regular exercise, might have positively impacted in body composition, in terms of lower % FM and WC, suggesting that the AHA and ACMS’ exercise recommendation weight management for healthy adults were effective for this population. We also demonstrated that TDEE, BMR, PAEE, VO2peak, WC, % FFM, and time of exercise performed per week but not body mass, TSI, and BMI were significantly correlated with % FM. These may be attributed, at least in part, to the low impact of exercise on PAEE, indicating that this population might need a more specific approach for tailoring exercise programs, with special attention to exercise components such as type, volume, duration and intensity, aiming weight management and its health benefits. Acknowledgments The authors are grateful to Dr Patricia Ruell and Dr Connie Wishart for the technical support. Mr Ricardo A. Tanhoffer is a doctoral candidate and has been awarded with the University of Sydney International Research Scholarship (USIRS). There are no personal or financial relationships with
Exercise and Energy Expenditure After SCI 1399 other people or organizations that could represent potential conflicts of interest relating to the authors or their respective institutions. The results of the current study do not constitute endorsement by ACSM.
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