OBJECTIVE: The purpose of this study was to determine the effects of seat height, wheelchair mass and grip on mobility performance among wheelchair basketball players and to investigate whether these effects differ between classification levels. METHODS: Elite wheelchair basketball players with a low (n= 11, class 1 or 1.5) or high (n= 10, class 4 or 4.5) classification performed a field-based wheelchair mobility performance (WMP) test. Athletes performed the test six times in their own wheelchair, of which five times with different configurations, a higher or lower seat height, with additional distally or centrally located extra mass, and with gloves. The effects of these configurations on performance times and the interaction with classification were determined. RESULTS: Total performance time on the WMP test was significantly reduced when using a 7.5% lower seat height. Additional mass (7.5%) and glove use did not lead to changes in performance time. Effects were the same for the two classification levels. CONCLUSIONS: The methodology can be used in a wheelchair fitting process to search for the optimal individual configuration to enhance mobility performance. Out of all adjustments possible, this study focused on seat height, mass and grip only. Further research can focus on these possible adjustments to optimize mobility performance in wheelchair basketball. DOI: 10.3233/TAD-190251 LinkedIn: https://www.linkedin.com/in/annemarie-de-witte-9582b154/ https://www.linkedin.com/in/rienkvdslikke/ https://www.linkedin.com/in/moniqueberger/
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Introduction: To determine if athletes with coordination impairment (CI) can continue playing wheelchair rugby (WR), while an evidence-based classification system, including impairment tests for CI is not yet available. This is a defensible practise if they show similar activity limitations as athletes with other eligible impairment types (OI) within the same sports class. Methods: Standardised activities were measured in 58 elite WR athletes; 14 with CI and 44 with OI. Wheelchair activities consisted of 20-meter sprint, 12-meter sprint with full stop, intermittent sprint (3-meter sprint, stop, 3-meter sprint, stop, 6-meter sprint with full stop), sprint-curve-slalom-curve, turn on the spot 180°, turn on the spot 90°, stop, turn 90°in the same direction, X-test (short circuit with sharp turns) without the ball. Ball activities consisted of maximal throwing distance, precision throwing short (25% of maximum throw) and long (75% of maximal throw) distance and X-test with the ball (pick-up the ball and dribble whilst pushing). Descriptive statistics were used and Spearman’s Rank correlation was assessed for athletes with CI and OI for each outcome measure. Differences between athletes with CI and OI were assessed using a Mann-Whitney U test. Results: Most activities showed a high correlation with the athlete class in both athletes with CI and athletes with OI. Furthermore, outcome measures of athletes with CI overlapped with athletes with OI in the same sports class for all activities. There was a trend for worse performance in athletes with CI in turn on the spot 90°, stop, turn 90°in the same direction, the short distance one handed precision throw (P 0.11)and in the X-test with the ball (P 0.10). Discussion: Despite the current lack of evidence based impairment tests for CI, it is a defensible practise to not exclude athletes with CI from WR with the current classification system. The trends for differences in performance that were found can support athletes and coaches in optimising performance of athletes with CI.
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The carbon dioxide emissions of aviation play an important role in many studies and databases. But unfortunately, a detailed and reliable overview of emission factors, and algorithms to calculate these based on factors like seating class, airline type, and aircraft type, did not exist for the Dutch aviation sector. This study calculated such emissions for a sample of over 5000 international flights in 2019 from the 5 Dutch main airports. The data about the flights were gathered from FlightRadar and enriched with seating capacities specific to the airline performing ten flights. in this way, emissions could be assigned to each of the four seating classes (economy, economy-plus, business and first). By aggregating the data to airline types and distance of the flight, algorithms were developed that help researchers and policy-makers to calculate the emissions. Societal IssueThe carbon footprint of Dutch aviation is about 10% of the total footprint. To prevent the world to exceed 1.5 degrees C and enter 'dangerous climate change', emissions need to decline to zero before 2050. This study helps assess and understand current aviation emissions from Dutch airports.Benefit to societyThe results were an update of emissions factors as used by the funding organisation, MilieuCentraal, and the official emission factors list (https://www.co2emissiefactoren.nl/lijst-emissiefactoren/).
