This file was dowloaded from the institutional repository Brage NIH -brage.bibsys.no/nih Federolf, P., Roos, L., Nigg, B. (2013) AbstractPostural control research describes ankle-, hip-, or multi-joint strategies as mechanisms to control upright posture. The objectives of this study were, first, development of an analysis technique facilitating a direct comparison of the structure of such multi-segment postural movement patterns between subjects; second, comparison of the complexity of postural movements between three stances of different difficulty level; and third, investigation of between-subject differences in the structure of postural movements and of factors that may contribute to these differences.Twenty-nine subjects completed 100-second trials in bipedal (BP), tandem (TA) and one-leg stance (OL). Their postural movements were recorded using 28 reflective markers distributed over all body segments. These marker coordinates were interpreted as 84-dimensional posture vectors, normalized, concatenated from all subjects, and submitted to a principal component analysis (PCA) to extract principal movement components (PM). The PMs were characterized by determining their relative contribution to the subject's entire postural movements and the smoothness of their time series.Four, eight, and nine PM were needed to represent 90% of the total variance in BP, TA, and OL, respectively, suggesting that increased task difficulty is associated with increased complexity of the movement structure. Different subjects utilized different combinations of PMs to control their posture. In several PMs, the relative contribution of a PM to a subject's overall postural movements correlated with the smoothness of the PM's time series, suggesting that utilization of specific postural PMs may depend on the subject's ability to control the PM's temporal evolution.3

From 1980 to 2000, physical fitness decreased and body mass index (BMI) increased in the population of many industrialized countries. Little is known about these trends after the year 2000. This study aimed to investigate physical fitness performance, physical activity (PA) behavior, and BMI of young, male Swiss adults between 2006 and 2015. For this purpose, results from the Swiss Armed Forces mandatory recruitment were used. A total of 306 746 male conscripts provided complete fitness test data, mean ± SD (range from 5th to 95th percentile): 20 ± 1 (18‐21) years, 178 ± 7 (168‐189) cm; 74 ± 13 (58‐97) kg, predicted maximal oxygen consumption of 49.9 ± 4.6 (41.8‐56.9) mL/kg/min (Conconi test), 125 ± 58 (43‐232) seconds in trunk muscle strength test (prone bridge), 2.31 ± 0.24 (1.90‐2.66) m in standing long jump, 6.46 ± 0.73 (5.30‐7.70) m in seated shot put (2 kg medical‐ball shot) and 45.6 ± 12.2 (29.9‐66.7) seconds in one‐leg standing test (sum of both legs; eyes closed after 10 seconds and head tilted back after 20 seconds). In the investigated population, 73.8% fulfilled basic PA recommendations, 46.2% were classified as regularly vigorously active. Performances in aerobic endurance and muscle power did not show secular changes over time. However, core stability performance and PA behavior increased, while balance ability decreased over this 10‐year period. Average BMI increased by 2.0% between 2006 and 2010 and did not change thereafter. Male Swiss adults are at least as physically fit as they were a decade ago. The secular trends of decreasing physical performances and increasing BMI have stopped, and self‐reported sport participation and leisure time PA have been increased in the observed population over the last 10 years.

BackgroundThe aim of this study was to assess the accuracy of three different sport watches in estimating energy expenditure during aerobic and anaerobic running.MethodsTwenty trained subjects ran at different intensities while wearing three commercial sport watches (Suunto Ambit2, Garmin Forerunner920XT, and Polar V800). Indirect calorimetry was used as the criterion measure for assessing energy expenditure. Different formulas were applied to compute energy expenditure from the gas exchange values for aerobic and anaerobic running.ResultsThe accuracy of the energy expenditure estimations was intensity-dependent for all tested watches. During aerobic running (4–11 km/h), mean absolute percentage error values of −25.16% to +38.09% were observed, with the Polar V800 performing most accurately (stage 1: −12.20%, stage 2: −3.61%, and stage 3: −4.29%). The Garmin Forerunner920XT significantly underestimated energy expenditure during the slowest stage (stage 1: −25.16%), whereas, the Suunto Ambit2 significantly overestimated energy expenditure during the two slowest stages (stage 1: 38.09%, stage 2: 36.29%). During anaerobic running (14–17 km/h), all three watches significantly underestimated energy expenditure by −21.62% to −49.30%. Therefore, the error in estimating energy expenditure systematically increased as the anaerobic running speed increased.ConclusionsTo estimate energy expenditure during aerobic running, the Polar V800 is recommended. By contrast, the other two watches either significantly overestimated or underestimated energy expenditure during most running intensities. The energy expenditure estimations generated during anaerobic exercises revealed large measurement errors in all tested sport watches. Therefore, the algorithms for estimating energy expenditure during intense activities must be improved before they can be used to monitor energy expenditure during high-intensity physical activities.