Accelerometer reporting of PA in intervention studies has been poor and improved only minimally over time. We provide recommendations to improve accelerometer reporting and include a template to standardise reports.

This study examined whether a carbohydrate + casein hydrolysate (CHO+ProH) beverage improved time-trial performance vs. a CHO beverage delivering approximately 60 g CHO/hr. Markers of muscle disruption and recovery were also assessed. Thirteen male cyclists (VO2peak = 60.8 +/- 1.6 ml . kg-1 . min-1) completed 2 computer-simulated 60-km time trials consisting of 3 laps of a 20-km course concluding with a 5-km climb (approximately 5% grade). Participants consumed 200 ml of CHO (6%) or CHO+ProH beverage (6% + 1.8% protein hydrolysate) every 5 km and 500 ml of beverage immediately postexercise. Beverage treatments were administered using a randomly counterbalanced, double-blind design. Plasma creatine phosphokinase (CK) and muscle-soreness ratings were assessed immediately before and 24 hr after cycling. Mean 60-km times were 134.4 +/- 4.6 and 135.0 +/- 4.0 min for CHO+ProH and CHO beverages, respectively. All time differences between treatments occurred during the final lap, with protein hydrolysate ingestion explaining a significant (p < .05) proportion of between-trials differences over the final 20 km (44.3 +/- 1.6, 45.0 +/- 1.6 min) and final 5 km (16.5 +/- 0.6, 16.9 +/- 0.6 min). Plasma CK levels and muscle-soreness ratings increased significantly after the CHO trial (161 +/- 53, 399 +/- 175 U/L; 15.8 +/- 5.1, 37.6 +/- 5.7 mm) but not the CHO+ProH trial (115 +/- 21, 262 +/- 88 U/L; 20.9 +/- 5.3, 32.2 +/- 7.1 mm). Late-exercise time-trial performance was enhanced with CHO+ProH beverage ingestion compared with a beverage containing CHO provided at maximal exogenous oxidation rates during exercise. CHO+ProH ingestion also prevented increases in plasma CK and muscle soreness after exercise.

BackgroundThe efficacy of chocolate milk (CM) as a recovery beverage following a period of increased training duration (ITD) was studied in intercollegiate soccer players.Methods13 subjects completed one week of normal 'baseline' training followed by four days of ITD. After each day of ITD, subjects received either a high-carbohydrate (504 kcal; CHO: 122 g; 2 g Fat) or isocaloric CM (504 kcal; 84 g CHO; 28 g Pro; 7 g Fat) recovery beverage. Serum creatine kinase (CK), myoglobin (Mb), muscle soreness, fatigue ratings and isometric quadriceps force (MVC) were obtained prior to ITD, and following 2- and 4-days of ITD. Performance tests (T-drill, vertical jump) were performed within training sessions. Treatments were administered in a randomly counterbalanced protocol, and subjects repeated the procedures with the alternate beverage following a two-week washout period.ResultsMean daily training time and HR increased (p < 0.05) between baseline training and ITD, with no differences between treatments. No treatment*time effects were observed for Mb, muscle soreness, fatigue ratings and MVC. However, serum CK was significantly lower (p < 0.05) following four days of ITD with CM (316.9 ± 188.3 U·L-1) compared to CHO (431.6 ± 310.8 U·L-1). No treatment differences were observed for the performance tests.ConclusionsPost-exercise CM provided similar muscle recovery responses to an isocaloric CHO beverage during four-days of ITD. Future studies should investigate if the attenuated CK levels observed with CM have functional significance during more demanding periods of training.

This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities for the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. Impacts of varying number of sensors in terms of activity classification accuracy are also evaluated. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors.

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