Physical Performance, Body Composition, and Oral Health in Community-Residing Older Adults: A Cross-Sectional Study.

Physical activity is essential for healthy aging. This study aimed to identify an association between physical performance, body fat percentage (%BF), and the perception of oral health-related quality of life (OHRQoL) in independent older adults. METHOD: A group of active older adults was selected from a government-sponsored reunion center in Mexico City. OHRQoL was assessed using the General Oral Health Index (GOHAI), and nutritional status was assessed using the Mini Nutritional Assessment (MNA) tool. A short physical performance battery (SPPB) was applied, and, for body composition, DXA (dual X-ray absorptiometry) was conducted. Data were analyzed using logistic regression models, and marginal probabilities were obtained. RESULTS: This study involved 366 participants; their mean age was 73.9 (±6.2) years, and 24.9% had type 2 diabetes mellitus (T2DM). OHRQoL information revealed that pain or discomfort in the oral cavity was perceived by 63.9% of the older adults during the previous three months. The SPPB score was low in 159 (43.44%) participants. The logistic regression model revealed that age (OR = 1.13, p < 0.001), T2DM (OR = 2.10, p = 0.009), the risk of malnutrition/malnutrition (OR = 1.76, p = 0.047), high %BF (OR = 1.09, <0.001), and poor OHRQoL (OR = 1.96, p = 0.009) were associated with deteriorated physical performance. CONCLUSION: OHRQoL self-perception, excess body fat, and nutritional status impacted physical performance. Aging well requires a comprehensive approach.

Estimation of body fat in children with intellectual disability: development and cross-validation of a simple anthropometric method

Abstract Objective Population-level monitoring of body composition requires accurate, biologically-relevant, yet feasible methods for estimating percent body fat (%BF). The aim of this study was to develop and cross-validate an equation for %BF from Body Mass Index (BMI), age, and sex among children with intellectual disability (ID). This study further aimed to examine the performance of an existing BMI-based equation (Deurenberg equation) for %BF in children with ID. Method Participants were 107 children (63 boys; aged 6-15 years) with ID randomly allocated to development (n= 81) and cross-validation (n= 26) samples. Dual-Energy X-Ray Absorptiometry provided the criterion %BF. Results The model including BMI, age, and sex (0 = male; 1 = female) had a significant goodness-of-fit in determining %BF (p< 0.001; R2= 0.69; SEE =5.68%). The equation was: %BF = - 15.416 + (1.394 × BMI) + (4.538 × age) - (0.262 × age2) + (5.489 × sex). The equation was cross-validated in the separate sample based on (i) strong correlation (r = 0.82; p< 0.001) and non-significant differences between actual and predicted %BF (28.6 ± 9.6% and 30.1 ± 7.1%, respectively); (ii) mean absolute error (MAE) = 4.4%; and (iii) reasonable %BF estimations in Bland-Altman plot (mean: 1.48%; 95% CI: 12.5, -9.6). The Deurenberg equation had a large %BF underestimation (mean: -7.1%; 95% CI: 5.3, -19.5), significant difference between actual and estimated %BF (28.6 ± 9.7% and 21.5 ± 7.0%, respectively; p< 0.001), and MAE = 8.1%. Conclusions The developed equation with BMI, sex, and age provides valid %BF estimates for facilitating population-level body fat screening among children with ID.

Estimation of body fat in children with intellectual disability: development and cross-validation of a simple anthropometric method.

OBJECTIVE: Population-level monitoring of body composition requires accurate, biologically-relevant, yet feasible methods for estimating percent body fat (%BF). The aim of this study was to develop and cross-validate an equation for %BF from Body Mass Index (BMI), age, and sex among children with intellectual disability (ID). This study further aimed to examine the performance of an existing BMI-based equation (Deurenberg equation) for %BF in children with ID. METHOD: Participants were 107 children (63 boys; aged 6-15 years) with ID randomly allocated to development (n = 81) and cross-validation (n = 26) samples. Dual-Energy X-Ray Absorptiometry provided the criterion %BF. RESULTS: The model including BMI, age, and sex (0 = male; 1 = female) had a significant goodness-of-fit in determining %BF (p < 0.001; R2 = 0.69; SEE =5.68%). The equation was: %BF = - 15.416 + (1.394 × BMI) + (4.538 × age) - (0.262 × age2) + (5.489 × sex). The equation was cross-validated in the separate sample based on (i) strong correlation (r = 0.82; p < 0.001) and non-significant differences between actual and predicted %BF (28.6 ± 9.6% and 30.1 ± 7.1%, respectively); (ii) mean absolute error (MAE) = 4.4%; and (iii) reasonable %BF estimations in Bland-Altman plot (mean: 1.48%; 95% CI: 12.5, -9.6). The Deurenberg equation had a large %BF underestimation (mean: -7.1%; 95% CI: 5.3, -19.5), significant difference between actual and estimated %BF (28.6 ± 9.7% and 21.5 ± 7.0%, respectively; p < 0.001), and MAE = 8.1%. CONCLUSIONS: The developed equation with BMI, sex, and age provides valid %BF estimates for facilitating population-level body fat screening among children with ID.

Adiposity in Adolescents: The Interplay of Sleep Duration and Sleep Variability.

OBJECTIVE: To assess whether adiposity measures differed according to joint categories of sleep duration and sleep variability in a sample of Mexican adolescents. STUDY DESIGN: A sample of 528 Mexico City adolescents aged 9-17 years wore wrist actigraphs for 6-7 days. Average sleep duration was categorized as age-specific sufficient or insufficient. Sleep variability, the standard deviation of sleep duration, was split at the median into stable versus variable. Adiposity measures-body mass index (BMI)-for-age Z score (BMIz), triceps skinfolds, waist circumference, and percent body fat-were collected by trained assistants. We regressed adiposity measures on combined sleep duration and variability categories. Log binomial models were used to estimate prevalence ratios and 95% CI for obesity (>2 BMIz) by joint categories of sleep duration and variability, adjusting for sex, age, and maternal education. RESULTS: Approximately 40% of the adolescents had insufficient sleep and 13% were obese. Relative to sufficient-stable sleepers, adolescents with insufficient-stable sleep had higher adiposity across all 4 measures (eg, adjusted difference in BMIz was 0.68; 95% CI, 0.35-1.00) and higher obesity prevalence (prevalence ratio, 2.54; 95% CI, 1.36-4.75). Insufficient-variable sleepers had slightly higher BMIz than sufficient-stable sleepers (adjusted difference, 0.30; 95% CI, 0.00-0.59). CONCLUSIONS: Adolescents with consistently insufficient sleep could be at greater risk for obesity. The finding that insufficient-variable sleepers had only slightly higher adiposity suggests that opportunities for "catch-up" sleep may be protective.

Efficacy of WBV as a modality for inducing changes in body composition, aerobic fitness, and muscular strength: a pilot study.

The purpose of this pilot study was to evaluate the effectiveness of whole body vibration (WBV) training as a modality for inducing changes in body composition, cardiovascular condition, and muscular strength in sedentary postmenopausal women. WBV training was compared with other training regimens, ie, aerobic training and circuit resistance training, commonly used to promote weight loss, cardiovascular conditioning, and muscular strength. Postmenopausal women (aged 48-60 years) were randomly assigned to WBV training, circuit resistance training, or aerobic training. Participants trained three times per week for 8 weeks. The training regimens were progressive in nature, with increases in training intensity and duration occurring throughout the 8-week period. Body composition was assessed using dual-energy X-ray absorptiometry analyses. A modified Bruce treadmill protocol was used to assess aerobic capacity (VO2peak) and time to peak exhaustion. Upper and lower body strengths were determined by one repetition maximum (1-RM) chest and leg presses, respectively. Variables were analyzed using separate 3 (exercise mode) × 2 (time) repeated-measures analysis of variance with effect sizes due to the small sample size. No significant main effects or interactions were seen for any body composition variable; however, moderate to large effect sizes (η (2)=0.243 and η (2)=0.257) were detected regarding interactions for percent body fat and lean body mass favoring aerobic training and circuit resistance training. For VO2peak, no significant main effects or interactions were detected (time, η (2)=0.150; P=0.11; time × group, η (2)=0.139; P=0.30); but a significant time effect was observed for time to peak exhaustion (η (2)=0.307; P=0.017). A significant interaction for upper body strength (η (2)=0.464; P=0.007), and main effect for time in lower body strength (η (2)=0.663; P=0.0001) was detected. Post hoc analysis indicated a significant increase in upper body strength for circuit resistance training (P=0.023) and a decrease for WBV training (P=0.015). Our results indicate that WBV may not be an effective alternative to traditional training with regard to body composition or aerobic capacity, but could have a positive impact on lower body strength.

Percent body fat is a better predictor of cardiovascular risk factors than body mass index

The objective of the present study was to evaluate the predictive values of percent body fat (PBF) and body mass index (BMI) for cardiovascular risk factors, especially when PBF and BMI are conflicting. BMI was calculated by the standard formula and PBF was determined by bioelectrical impedance analysis. A total of 3859 ambulatory adult Han Chinese subjects (2173 males and 1686 females, age range: 18-85 years) without a history of cardiovascular diseases were recruited from February to September 2009. Based on BMI and PBF, they were classified into group 1 (normal BMI and PBF, N = 1961), group 2 (normal BMI, but abnormal PBF, N = 381), group 3 (abnormal BMI, but normal PBF, N = 681), and group 4 (abnormal BMI and PBF, N = 836). When age, gender, lifestyle, and family history of obesity were adjusted, PBF, but not BMI, was correlated with blood glucose and lipid levels. The odds ratio (OR) and 95% confidence interval (CI) for cardiovascular risk factors in groups 2 and 4 were 1.88 (1.45-2.45) and 2.06 (1.26-3.35) times those in group 1, respectively, but remained unchanged in group 3 (OR = 1.32, 95%CI = 0.92-1.89). Logistic regression models also demonstrated that PBF, rather than BMI, was independently associated with cardiovascular risk factors. In conclusion, PBF, and not BMI, is independently associated with cardiovascular risk factors, indicating that PBF is a better predictor.