Effect of Physical Training on Body Composition in Brazilian Military.

The military are selected on the basis of physical standards and are regularly involved in strong physical activities, also related to particular sports training. The aims of the study were to analyze the effect of a 7-month military training program on body composition variables and the suitability of specific 'bioelectrical impedance vector analysis' (spBIVA), compared to DXA, to detect the changes in body composition. A sample of 270 male Brazilian cadets (19.1 ± 1.1 years), composed of a group practicing military physical training routine only (MT = 155) and a group involved in a specific sport training (SMT = 115), were measured by body composition assessments (evaluated by means of DXA and spBIVA) at the beginning and the end of the military routine year. The effect of training on body composition was similar in SMT and MT groups, with an increase in LST. DXA and spBIVA were correlated, with specific resistance (Rsp) and reactance (Xcsp) positively related to fat mass (FM), FM%, LST, and lean soft tissue index (LSTI), and phase angle positively related to LST and LSTI. Body composition variations due to physical training were recognized by spBIVA: the increase in muscle mass was indicated by the phase angle and Xcsp increase, and the stability of FM% was consistent with the unchanged values of Rsp. Military training produced an increase in muscle mass, but no change in FM%, independently of the sample characteristics at baseline and the practice of additional sports. SpBIVA is a suitable technique for the assessment of body composition in military people.

Physical training over 6 months is associated with improved changes in phase angle, body composition, and blood glucose in healthy young males.

OBJECTIVES: The aim of this study was to evaluate the association between phase angle, body composition, and blood glucose changes in healthy young males after 6 months of physical training. METHODS: Volunteers, 98 healthy males (18.8 ± 0.5 years), had 6 months of progressive physical training (5 days a week, 90 minutes a day). Resistance, reactance, and phase angle were obtained by bioelectrical impedance analysis, body composition (fat mass, bone mineral content [BMC], and lean soft tissue [LST]) by dual-energy X-ray absorptiometry, and blood glucose by reflectance photometry. Measurements were made at rest and in a fasted state, both before and after the training period. RESULTS: Phase angle, reactance, BMC, and LST significantly increased (0.6°, 3.8 Ω, 0.1 kg, and 1.9 kg, respectively; P < .01), whereas resistance and blood glucose decreased (-11.2 Ω and -4.1 mg/dL; P < .01). Changes in resistance and reactance explained those changes observed in LST (R2 = .26 and .16, respectively), but phase angle changes were not related to body composition and blood glucose alterations (P < .05). CONCLUSIONS: A 6-month period of physical training was associated with positive changes in phase angle, body composition, and blood glucose in healthy young males, reinforcing the importance of maintaining a physically active lifestyle.

Adaptive thermogenesis and changes in body composition and physical fitness in army cadets.

BACKGROUND: To analyze the association between a 34-week military training on body composition, physical fitness and compensatory changes in resting energy expenditure (REE) recognized as adaptive thermogenesis (AT). We also explored if regional body composition changes were related to AT. METHODS: Twenty-nine male army cadets, aged 17 to 22 years were tested at baseline (T0) and after 34-weeks military training (T1). Physical training was performed 5 days/week during 90 minutes/day. Measurements included body composition by dual-energy x-ray absorptiometry; physical fitness by 3000-m running, pull-up, 50-m freestyle swimming, push-up and sit-up tests; REE measured by indirect calorimetry (REEm) and predicted from fat-free mass (FFM), fat mass (FM) and ethnicity at T0 (REEp). %AT was calculated using values at T1: 100(REEm/REEp-1); and AT (kcal/day) as %AT/100 multiplied by baseline REEm. RESULTS: Physical training was associated with increases of lean soft tissue (LST) (∆1.2±1.3 kg), FM (∆1.4±1.3 kg), FFM (∆1.2±1.3 kg) and physical fitness (P<0.01), but no REE changes (∆59.6±168.9 kcal/day) and AT were observed (P>0.05). Though a large variability was found, AT was partially explained by trunk LST (r2=0.17, P=0.027). Individuals showing a higher AT response demonstrated a higher trunk LST increase (∆0.8±0.7 kg, P<0.05). CONCLUSIONS: The military training increased LST, FM, FFM and physical fitness. Though no mean changes in AT occurred, a large individual variability was observed with some participants increasing REE beyond the expected body composition changes, suggesting a spendthrift phenotype. Changes of trunk LST may play an important role in the AT response observed in these individuals.

Accuracy of Bioelectrical Impedance Analysis in Estimated Longitudinal Fat-Free Mass Changes in Male Army Cadets.

Introduction: Bioelectrical impedance analysis (BIA) is a practical and rapid method for making a longitudinal analysis of changes in body composition. However, most BIA validation studies have been performed in a clinical population and only at one moment, or point in time (cross-sectional study). The aim of this study is to investigate the accuracy of predictive equations based on BIA with regard to the changes in fat-free mass (FFM) in Brazilian male army cadets after 7 mo of military training. The values used were determined using dual-energy X-ray absorptiometry (DXA) as a reference method. Materials and Methods: The study included 310 male Brazilian Army cadets (aged 17-24 yr). FFM was measured using eight general predictive BIA equations, with one equation specifically applied to this population sample, and the values were compared with results obtained using DXA. The student's t-test, adjusted coefficient of determination (R2), standard error of estimation (SEE), Lin's approach, and the Bland-Altman test were used to determine the accuracy of the predictive BIA equations used to estimate FFM in this population and between the two moments (pre- and post-moment). Results: The FFM measured using the nine predictive BIA equations, and determined using DXA at the post-moment, showed a significant increase when compared with the pre-moment (p < 0.05). All nine predictive BIA equations were able to detect FFM changes in the army cadets between the two moments in a very similar way to the reference method (DXA). However, only the one BIA equation specific to this population showed no significant differences in the FFM estimation between DXA at pre- and post-moment of military routine. All predictive BIA equations showed large limits of agreement using the Bland-Altman approach. Conclusion: The eight general predictive BIA equations used in this study were not found to be valid for analyzing the FFM changes in the Brazilian male army cadets, after a period of approximately 7 mo of military training. Although the BIA equation specific to this population is dependent on the amount of FFM, it appears to be a good alternative to DXA for assessing FFM in Brazilian male army cadets.

Validity of Bioelectrical Impedance Analysis to Estimation Fat-Free Mass in the Army Cadets.

BACKGROUND: Bioelectrical Impedance Analysis (BIA) is a fast, practical, non-invasive, and frequently used method for fat-free mass (FFM) estimation. The aims of this study were to validate predictive equations of BIA to FFM estimation in Army cadets and to develop and validate a specific BIA equation for this population. METHODS: A total of 396 males, Brazilian Army cadets, aged 17-24 years were included. The study used eight published predictive BIA equations, a specific equation in FFM estimation, and dual-energy X-ray absorptiometry (DXA) as a reference method. Student's t-test (for paired sample), linear regression analysis, and Bland-Altman method were used to test the validity of the BIA equations. RESULTS: Predictive BIA equations showed significant differences in FFM compared to DXA (p < 0.05) and large limits of agreement by Bland-Altman. Predictive BIA equations explained 68% to 88% of FFM variance. Specific BIA equations showed no significant differences in FFM, compared to DXA values. CONCLUSION: Published BIA predictive equations showed poor accuracy in this sample. The specific BIA equations, developed in this study, demonstrated validity for this sample, although should be used with caution in samples with a large range of FFM.

Minimum Time to Achieve the Steady State and Optimum Abbreviated Period to Estimate the Resting Energy Expenditure by Indirect Calorimetry in Healthy Young Adults.

BACKGROUND: The optimum abbreviated period for measurement by indirect calorimetry (IC) to estimate the resting energy expenditure (REE), including the acclimation period, in healthy individuals has not been established. This study aimed to determine the acclimation time required to achieve the REE steady state during a 30-minute IC measurement and to define the optimum abbreviated measurement period in the steady state to estimate the REE in healthy young adults. METHODS: Thirty-nine volunteers (27 men and 12 women; age, 18-31 years) were recruited. The REE was obtained by IC over 30 minutes. Friedman's test was used to compare the coefficient of variation (CV%) among all 5-minute intervals (REE5). To compare the REE values obtained during the first REE5 interval in the steady state with the REE average values of the subsequent measurements, Student paired t test, linear regression, and Bland-Altman test were used. RESULTS: The CV% of the first REE5 (mean ± standard deviation: 19.9% ± 13.2%) was significantly higher (P < .0001) than that of all other REE5 (second REE5: 7.4% ± 3.8%; third: 7.8% ± 5.2%; fourth: 7.1% ± 3.9%; fifth: 8.0% ± 5.7%; sixth: 8.0% ± 4.5%). No significant difference was found between the second REE5 and the REE average values of the last 20 minutes. The second REE5 explained 90% of the REE average of the last 20 minutes, with the 95% limits of agreement by the Bland-Altman test ranging from -142.92 to 150.44 kcal/d. CONCLUSION: Ten minutes can be used as an abbreviated alternative for IC measurements in healthy young adults, and values of the first 5-minute interval should be discarded.