Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
Add more filters










Database
Language
Publication year range
1.
J Nutr ; 146(9): 1660-9, 2016 09.
Article in English | MEDLINE | ID: mdl-27466602

ABSTRACT

BACKGROUND: To our knowledge the efficacy of soy-dairy protein blend (PB) supplementation with resistance exercise training (RET) has not been evaluated in a longitudinal study. OBJECTIVE: Our aim was to determine the effect of PB supplementation during RET on muscle adaptation. METHODS: In this double-blind randomized clinical trial, healthy young men [18-30 y; BMI (in kg/m(2)): 25 ± 0.5] participated in supervised whole-body RET at 60-80% 1-repetition maximum (1-RM) for 3 d/wk for 12 wk with random assignment to daily receive 22 g PB (n = 23), whey protein (WP) isolate (n = 22), or an isocaloric maltodextrin (carbohydrate) placebo [(MDP) n = 23]. Serum testosterone, muscle strength, thigh muscle thickness (MT), myofiber cross-sectional area (mCSA), and lean body mass (LBM) were assessed before and after 6 and 12 wk of RET. RESULTS: All treatments increased LBM (P < 0.001). ANCOVA did not identify an overall treatment effect at 12 wk (P = 0.11). There tended to be a greater change in LBM from baseline to 12 wk in the PB group than in the MDP group (0.92 kg; 95% CI: -0.12, 1.95 kg; P = 0.09); however, changes in the WP and MDP groups did not differ. Pooling data from combined PB and WP treatments showed a trend for greater change in LBM from baseline to 12 wk compared with MDP treatment (0.69 kg; 95% CI: -0.08, 1.46 kg; P = 0.08). Muscle strength, mCSA, and MT increased (P < 0.05) similarly for all treatments and were not different (P > 0.10) between treatments. Testosterone was not altered. CONCLUSIONS: PB supplementation during 3 mo of RET tended to slightly enhance gains in whole-body and arm LBM, but not leg muscle mass, compared with RET without protein supplementation. Although protein supplementation minimally enhanced gains in LBM of healthy young men, there was no enhancement of gains in strength. This trial was registered at clinicaltrials.gov as NCT01749189.


Subject(s)
Dietary Supplements , Exercise , Muscle, Skeletal/drug effects , Resistance Training , Whey Proteins/administration & dosage , Adaptation, Physiological , Adolescent , Adult , Body Composition , Body Mass Index , Body Weight , Double-Blind Method , Humans , Male , Muscle Strength/drug effects , Testosterone/blood , Young Adult
3.
J Am Diet Assoc ; 110(1): 91-4, 2010 Jan.
Article in English | MEDLINE | ID: mdl-20102832

ABSTRACT

This research tested whether children could categorize foods more accurately and speedily when presented with child-generated rather than professionally generated food categories, and whether a graphically appealing browse procedure similar to the Apple iTunes (Cupertino, CA) "cover flow" graphical user interface accomplished this better than the more common tree-view structure. In Fall 2008, 104 multiethnic children ages 8 to 13 were recruited at the Baylor College of Medicine (Houston, TX) and randomly assigned to two browse procedures: cover flow (collages of foods in a category) or tree view (food categories in a list). Within each browse condition children categorized the same randomly ordered 26 diverse foods to both child and professionally organized categories (with method randomly sequenced per child). Acceptance of categorization was determined by registered dietitians. Speed of categorization was recorded by the computer. Differences between methods were determined by repeated measures analysis of variance. Younger children (8 to 9 years old) tended to have lower acceptance and longer speeds of categorization. The quickest categorization was obtained with child categories in a tree structure. Computerized dietary reporting by children can use child-generated food categories and tree structures to organize foods for browsing in a hierarchically organized structure to enhance speed of categorization, but not accuracy. A computerized recall may not be appropriate for children 9 years of age or younger.


Subject(s)
Computers, Handheld , Food/classification , Nutrition Assessment , Psychology, Child , Adolescent , Age Distribution , Analysis of Variance , Child , Female , Humans , Male , Mental Recall , Reproducibility of Results , Time Factors
SELECTION OF CITATIONS
SEARCH DETAIL
...