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Introduction. This personal study provides several aspects of the importance of body composition assessment in rehabilitation process in order to manage fat mass (FM), fat-free mas imbalances (FFM), pre-sarcopenia status, sarcopenia and risks association and to improve global functionality. Health outcomes and risk estimations regarding fat mass and skeletal muscle mass (SMM) plays a major role and should be integrated into the rehabilitation process routine in order to avoid functional impairment and physical disability by applying specific kinetic programs. Material and method. A number of 14 subjects classified as outpatients who have received physical therapy at home- kinesiotherapy for post-fracture / dislocation status of the lower limbs in accordance with the medical recommendations and legislation in force. At the end of the rehabilitation phase, the body composition was measured using bio impedance in order to adjust the next step of the active rehabilitation. The measurements were obtained with a completely bioelectrical impedance analyzer (BIA). Single frequency BIA (SF-BIA) was used. For each subject major body compartments determined as FFM (including bone mineral tissue, total body water-TBW and visceral protein), SMM and FM were measured as a tissue-system by means of linear empirical equations stored in the system memory together with personal physical data. IBM SPSS software version 25 was used for statistical analysis. Results and discussions. Four age groups determined as follows: 21.43% for 18-39 years, 50-69 years, >70 years each and 35.71% for 40-49 years, based on the rate of muscle loss, because its integrity is essential for rehabilitation program. From the 14 subjects there are 57.14 % men and 42.86% women, from urban environment 78.57% and rural 21.43%. Mean Age is 48.79 years ± 18.792 Std. Deviation. Fat mass from BIA recorded 21.43% cases low and normal each, and high/very high 57.14% of total cases. Consequently, of BMI (body mass index) association, 57.14% are at normal weight, 35.71% overweight and with obesity and 7.14% underweight. One Sample Chi-Square test applied to BMI Type Associate with FM reveals the statistical significance, < .05(.014). Fat-free mass index (FFMI), fat mass index (FMI), skeletal mass index (SMI) were computed by adjusted with height square. FMI somatotype components results are 64.3% adipose cases, 21.4% intermediate and 14.3% lean. One Sample Chi-Square test applied to FMI Types reveals the statistical significance < .05(.046). Regression equation of standard BMI and FMI with scatter plots for 77.8% of cases was computed in the present study. FFMI somatotype components recorded 57.1% intermediate cases, 21.4% slender and solid each. Regression equation of standard BMI and FFMI with scatter plots for 57.4% of cases was computed. Three patients exceeded 15 seconds at the chair stand test so probable sarcopenia was identified. From BIA were extracted the value for the skeletal mass and SMI was calculated by height adjusted: 13 (92.86%) cases have normal values and one (7.14%) case have optimal value. Regression equation of standard BMI and SMI with scatter plots for 66.4% of cases was computed. Pearson correlation (CI =99%) denotes strong statistical relationship between BMI and FMI (r=0.882), FFMI (r=0.815), Age (r=0.659), Water (r=-0.693). FMI also correlates strongly with Age (r= 0.707), Water (r=-0.925) and Proteins values (r=-0.819). FFMI also correlates strongly with SMI (r=0.984). Water correlates with Protein (r=0.848, CI = 99%). Beta regression analysis strongly correlates SMI prediction with FFMI (ß=0.731), Water (ß=0.138) and Protein (ß=-0.370) for p |
