Pro-agility unpacked: Variability, comparability and diagnostic value

Change of direction (COD) is an important component of athlete performance and measuring and comparing athletes is an integral aspect of strength and conditioning practice. This article aimed to determine pro-agility shuttle utility, by quantifying variability and normative values for different sports, skill-levels and positions. Limitations of the pro-agility shuttle are identified, as are future research directions. A total of 67 studies were included for review. Pro-agility shuttle reliability was reported in 10 studies across 6 sports; however, comprehensive reliability statistics were absent in most papers. Additionally, only reliability of total-time from stopwatch and timing lights were reported. Data of 32,891 subjects in 12 sports (American football, basketball, cricket, general athletes, hockey, lacrosse, recreational athletes, resistance-trained athletes, rugby, soccer, swimming, and tennis) were extracted and aggregated, establishing sport, skill-level (elite, sub-elite, and novice) and positional normative values, where practical. Elite athletes showed the fastest performance times, whereas sub-elite and novice athletes showed similar spreads in performance, suggesting similar athletic capabilities. In conclusion, the pro-agility shuttle currently has limited diagnostic value and the variability of smaller performance sub-components within pro-agility shuttle should be examined. Furthermore, the value of other technologies such as smart phone, inertial sensor or radar should be investigated.

Pandemic-Induced Reductions on Swim Training Volume and Performance in Collegiate Swimmers

The COVID-19 pandemic caused significant training disruptions during the 2020–2021 season, due to lockdowns, quarantines, and strict adherence to the pandemic protocols. The main purpose of this study was to determine how the pandemic training restrictions affected training volume and performance in one collegiate swim team. Cumulative training volume data across a 28-week season were compared between a pandemic (2020–2021) versus non-pandemic (2019–2020) season. The swimmers were categorized into three groups (sprinters, mid-distance, and long-distance) based on their training group. The performance times of 25 swimmers who competed in the regional championships, during both the non-pandemic and pandemic year, were compared via one-way ANOVA. Twenty-six male and 22 female swimmers commenced the 2020–2021 (pandemic) season, with 23% of the swimmers voluntarily opting out. Three COVID-19 cases were confirmed (2%) by the medical staff, with no long-term effects. Significant reductions in the average swim volume were verified in sprinters (32,867 ± 10,135 vs. 14,800 ± 7995 yards; p < 0.001), mid-distance (26,457 ± 10,692 vs. 17,054 ± 9.923 yards; p < 0.001), and long-distance (37,600 ± 14,430 vs. 22,254 ± 14,418 yards; p < 0.001) swimmers (non-pandemic vs. pandemic season, respectively). In the regional performance analyses, the sprinters swam faster (n = 8; −0.5 ± 0.6 s), while the mid-distance (n = 10; 0.17 ± 2.1 s) and long-distance (n = 7; 6.0 ± 4.9 s) swimmers swam slower (F = 11.76; p = 0.0003; r2 = 0.52). Thus, the pandemic caused significant reductions in swim training volume, with sprinters performing better and long-distance swimmers performing worse at the regional championships.

Gender Effect on the Relationship between Talent Identification Tests and Later World Triathlon Series Performance

Background: We examined the explanatory power of the Spanish triathlon talent identification (TID) tests for later World Triathlon Series (WTS)-level racing performance as a function of gender. Methods: Youth TID (100 m and 1000 m swimming and 400 m and 1000 m running) test performance times for when they were 14–19 years old, and WTS performance data up to the end of 2017, were obtained for 29 female and 24 male “successful” Spanish triathletes. The relationships between the athletes’ test performances and their later best WTS ranking positions and performance times were modeled using multiple linear regression. Results: The swimming and running TID test data had greater explanatory power for best WTS ranking in the females and for best WTS position in the males (R2a = 0.34 and 0.37, respectively, p ≤ 0.009). The swimming TID times were better related to later race performance than were the running TID times. The predictive power of the TID tests for WTS performance was, however, low, irrespective of exercise mode and athlete gender. Conclusions: These results confirm that triathlon TID tests should not be based solely on swimming and running performance. Moreover, the predictive value of the individual tests within the Spanish TID battery is gender specific.

Pandemic-induced Reductions on Swim Training Volume and Performance in Collegiate Swimmers

The COVID-19 pandemic caused significant training disruptions during the 2020-21 season due to lockdowns, quarantines, and strict adherence to pandemic protocols. The main purpose of this study was to determine how pandemic training restrictions affected training volume and performance in one collegiate swim team. Cumulative training volume data, across a 28-week season, were compared between a pandemic (2020-2021) versus non-pandemic (2019-2020) season. Swimmers were categorized into three groups (Sprinters, Mid-Distance, and Long-Distance) based on training group. Performance times in 25 swimmers who competed in Regional Championships, during both the non-pandemic and pandemic year, were compared via 1-way ANOVA. 26 male and 22 female swimmers commenced the 2020-21 (pandemic) season, with 23% of swimmers voluntarily opting out. Three COVID-19 cases were confirmed (2%) by the medical staff with no long-term effects. Significant reductions in average swim volume were verified in Sprinters (32,867&plusmn;10,135 vs.14,800&plusmn;7,995yards;p&lt;0.001), Mid-Distance (26,457&plusmn;10,692 vs.17,054&plusmn;9.923yards;p&lt;0.001), and Long-Distance (37,600&plusmn;14,430 vs.22,254&plusmn;14,418yards;p&lt;0.001) swimmers (non-pandemic vs. pandemic season, respectively). In the Regional performance analyses, the Sprinters swam faster (n=8;-0.5&plusmn;0.6secs), while Mid-Distance (n=10;0.17&plusmn;2.1secs) and Long-Distance (n=7;6.0&plusmn;4.9secs) swimmers swam slower (F=11.76;p=0.0003;r2=0.52). Thus, the pandemic caused significant reductions in swim training volume, with Sprinters performing better and Long-Distance swimmers performing worse at Regional Championships.

Evidence for a Behaviourally Measurable Perseverance Trait in Humans

The aim was to create and study a possible behavioural measure for trait(s) in humans that reflect the ability and motivation to continue an unpleasant behaviour, i.e., behavioural perseverance or persistence (BP). We utilised six different tasks with 54 subjects to measure the possible BP trait(s): cold pressor task, hand grip endurance task, impossible anagram task, impossible verbal reasoning task, thread and needle task, and boring video task. The task performances formed two BP factors. Together, the two-factor solution is responsible for the common variance constituting 37.3% of the total variance in the performances i.e., performance times. Excluding the impossible anagram task, the performance in any given task was better explained by performances in the other tasks (i.e., “trait”, η2 range = 0.131–0.253) than by the rank order variable (“depletion”, i.e., getting tired from the previous tasks, η2 range = 0–0.096).

The Importance of Biological Maturation and Years of Practice in Kayaking Performance

Previous canoe sprint studies evaluated the best paddlers of their categories. This investigation aimed to identify the importance of biological maturation and athletes’ experience in kayaking performance and observe possible differences regarding anthropometry, years of practice, and performance. Eighty under 14 years of age (U14) and fifty under 16 years of age (U16) kayakers aged 13.40 ± 0.54 and 15.25 ± 0.61 years were evaluated. Kayakers were assessed for anthropometry (body mass (kg); stretch stature (cm); and sitting height (cm)), performance (time at 3000 m for U14 and 5000 m for U16 kayakers), and somatic maturation (predicted adult height (PAH) and maturity offset). In the U14 kayakers, years of practice, sitting height, and maturity offset showed significant differences (p < 0.05) between the Top10 and Middle, and Middle and Bottom10 performance times. Significantly higher (p < 0.05) sitting heights were identified between the Top10 and Middle U16 kayakers. Significant differences (p < 0.05) were observed for maturity offset and PAH% between the Top10 and Middle groups compared to the Bottom10 group. In conclusion, this research shows differences in the maturity status of young U14 and U16 kayakers, identifying that the more biologically mature individuals, with more years of specific practice, achieved better performances.

536 Use of A Cost-Efficient Colonoscopy Simulation Model to Improve Endoscopy Skills During the COVID Pandemic

Introduction The use of endoscopic simulators as a learning aid in surgical training has been well established. This has been emphasised during the challenging times of COVID-19. However, their utility for training is countered by the high cost of the equipment, with the most basic simulators costing upwards of £50,000. Method A simple polypectomy simulator model was created using a drain-pipe and surgical gloves. n = 9 junior doctors were timed in their ability to remove the 3 polyps from the simulator. The exercise was repeated over 6 sessions over the course of 3 weeks. Means were compared using ANOVA. Results There was a mean relative reduction of 75% in overall time taken to complete the task(p &lt; 0.0001). This improvement was seen for both surgical trainees(p = 0.005) and FY1 novices(p &lt; 0.0001) and junior doctors reported feeling more confident with basic Colonoscopic skills. Conclusions We have demonstrated an improvement in performance times across both surgical trainees and novices. In today’s era of COVID-19, when direct training opportunities may become more scarce, simple alternatives may become vital in ensuring progression of basic surgical skills such as endoscopy. This cheap polypectomy simulator can be easily re-created across surgical units and can be used as an adjunct to traditional endoscopic training

Data Processing at Scale

<p>The data generation and collection of data have gone through a series of improvements over the past several years. Now, we observe that both aspects of data (generation and collection) have evolved, it creates another dimension – how to process the data at scale, and how to manage it.</p><p> </p><p>Relational DBMS has been a widely accepted idea behind processing and managing data, but it has its own pros and cons, the constraints on data to prevent integrity violation is seen as a trade-off between performance and management. With the advent in the storage, compute and network technology, we have reliably transited the state of relational database management. It’s not yet done. Handling exceptions have been very poor with a single point of failure with traditional DB architecture. However, with distributed systems, it only multiplies the failure points. Failure is expected, and hence the solution for availability is designed around these expected failures. Distributed computing adds functionalities such as performance, availability, and reliability.</p><p>But, that’s not all. We are living in an era, where we communicate very now and then, through different devices. Not only this, we generate, collect, manage data which are of variant types (mostly unstructured, multi-dimensional, carries lots of noise and bias, etc.). NoSQL DBMS, Apache Spark, and Hadoop come to rescue.</p><p> </p><p>One such area that exemplifies the use of big data is the transportation industry, which can encompass shipping, airline data, trucking, and the context we refer to cabs. NYC taxi data is available in an open-dataset that stores, among other things, geospatial data collected from individual taxis as they navigate the streets of New York City. Processing of geospatial data at this scale is very time-consuming and resource-intensive, as anyone who has used ArcGIS on a large dataset can attest. Distributed and parallel data processing presents an opportunity for faster processing of this type of data. The Apache Spark framework is ideal for this task as it is highly efficient with fast performance times. Additionally, it has libraries and APIs built in that allow it to process SQL queries, which many users are likely to be familiar with given its ubiquity.</p><p> </p><p>In the following report, we demonstrate our approaches to perform hot spot analysis on the NYC Taxi data. Hot-zone analysis performs range-join on the rectangle and point, to identify the boundaries from where most pickups happen. Hot-cell analysis uses statistical parameters to identify the zones by also considering time as an additional dimension.</p>

Data Processing at Scale

<p>The data generation and collection of data have gone through a series of improvements over the past several years. Now, we observe that both aspects of data (generation and collection) have evolved, it creates another dimension – how to process the data at scale, and how to manage it.</p><p> </p><p>Relational DBMS has been a widely accepted idea behind processing and managing data, but it has its own pros and cons, the constraints on data to prevent integrity violation is seen as a trade-off between performance and management. With the advent in the storage, compute and network technology, we have reliably transited the state of relational database management. It’s not yet done. Handling exceptions have been very poor with a single point of failure with traditional DB architecture. However, with distributed systems, it only multiplies the failure points. Failure is expected, and hence the solution for availability is designed around these expected failures. Distributed computing adds functionalities such as performance, availability, and reliability.</p><p>But, that’s not all. We are living in an era, where we communicate very now and then, through different devices. Not only this, we generate, collect, manage data which are of variant types (mostly unstructured, multi-dimensional, carries lots of noise and bias, etc.). NoSQL DBMS, Apache Spark, and Hadoop come to rescue.</p><p> </p><p>One such area that exemplifies the use of big data is the transportation industry, which can encompass shipping, airline data, trucking, and the context we refer to cabs. NYC taxi data is available in an open-dataset that stores, among other things, geospatial data collected from individual taxis as they navigate the streets of New York City. Processing of geospatial data at this scale is very time-consuming and resource-intensive, as anyone who has used ArcGIS on a large dataset can attest. Distributed and parallel data processing presents an opportunity for faster processing of this type of data. The Apache Spark framework is ideal for this task as it is highly efficient with fast performance times. Additionally, it has libraries and APIs built in that allow it to process SQL queries, which many users are likely to be familiar with given its ubiquity.</p><p> </p><p>In the following report, we demonstrate our approaches to perform hot spot analysis on the NYC Taxi data. Hot-zone analysis performs range-join on the rectangle and point, to identify the boundaries from where most pickups happen. Hot-cell analysis uses statistical parameters to identify the zones by also considering time as an additional dimension.</p>

Evaluating the influence of music at different sound pressure levels on medical students’ performance of standardized laparoscopic box training exercises

Background The influence of music on the performance of surgical procedures such as laparoscopy is controversial and methodologically difficult to quantify. Here, outcome measurements using laparoscopic box training tools under standardized conditions might offer a feasible approach. To date, the effect of music exposure at different sound pressure levels (SPL) on outcome has not been evaluated systematically for laparoscopic novices. Methods Between May 2017 and October 2018, n = 87 students (49 males, 38 females) from Heidelberg University Medical School performed three different laparoscopy exercises using the “Luebecker Toolbox” that were repeated twice under standardized conditions. Time was recorded for each run. All students were randomly assigned to four groups exposed to the same music compilation but at different SPLs (50–80 dB), an acoustically shielded (earplug) group, or a control group (no intervention). Results Best absolute performance was shown under exposure to 70 dB in all three exercises (a, b, c) with mean performance time of 121, 142, and 115 s (p < 0.05 for a and c). For the control group mean performance times were 157, 144, and 150 s, respectively. In the earplug group, no significant difference in performance was found compared to the control group (p > 0.05) except for exercise (a) (p = 0.011). Conclusion Music exposure seems to have beneficial effects on training performance. In comparison to the control group, significantly better results were reached at 70 dB SPL, while exposure to lower (50 or 60 dB) or higher (80 dB) SPL as well as under acoustic shielding did not influence performance.