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ObjectivesThe global COVID-19 pandemic produced large-scale health and economic complications. Older people and those with comorbidities are particularly vulnerable to this virus, with nursing homes and long term care facilities (LTCF) experiencing significant morbidity and mortality associated with COVID-19 outbreaks. The aim of this rapid systematic review was to investigate measures implemented in LTCF to reduce transmission of COVID-19 and their effect on morbidity and mortality of residents, staff and visitors.SettingLong-term care facilities.ParticipantsResidents, staff and visitors of facilities.Primary and secondary outcome measuresDatabases (PubMed, EMBASE, CINAHL, Cochrane Databases and repositories and MedRXiv prepublished database) were systematically searched from inception to 27 July 2020 to identify studies reporting assessment of interventions to reduce transmission of COVID-19 in nursing homes among residents, staff or visitors. Outcome measures include facility characteristics, morbidity data, case fatalities and transmission rates. Due to study quality and heterogeneity, no meta-analysis was conducted.ResultsThe search yielded 1414 articles, with 38 studies included. Reported interventions include mass testing, use of personal protective equipment, symptom screening, visitor restrictions, hand hygiene and droplet/contact precautions, and resident cohorting. Prevalence rates ranged from 1.2% to 85.4% in residents and 0.6% to 62.6% in staff. Mortality rates ranged from 5.3% to 55.3% in residents.ConclusionsNovel evidence in this review details the impact of facility size, availability of staff and practices of operating between multiple facilities, and for-profit status of facilities as factors contributing to the size and number of COVID-19 outbreaks. No causative relationships can be determined; however, this review provides evidence of interventions that reduce transmission of COVID-19 in LTCF.Prospero registration numberCRD42020191569.

This study aimed to 1) identify what starting block outcome kinetics have the greatest relationship to 15 m start time; 2) investigate key mechanistic determinants of the block phase and how these forces are sequenced. One hundred and fifty-two high-level competitive swimmers were included in the study. Linear mixed modelling identified four on-block outcome kinetic variables (work, average power, horizontal take-off velocity (HTOV), and average acceleration) as having a very large relationship (R2 = 0.79–0.83) to 15 m start time, with average power having the most substantial impact. On-block force sequencing started with the rear leg, followed by upper limb grab forces and the front leg. Further exploration of underlying determinants was performed for average power and HTOV of the centre of mass. Multiple linear regression identified grab resultant peak force, rear resultant average force, front horizontal peak force, and resultant peak force as significant predictors of average power (R2 = 0.88). HTOV was predicted using the same variables, apart from the inclusion of rear horizontal peak force instead of rear resultant average force (R2 = 0.73). These findings may influence how strength and conditioning and skill acquisition interventions are designed to improve swim start performance. [ABSTRACT FROM AUTHOR]

Purpose: There is uncertainty as to which knee angle during a squat jump (SJ) produces maximal jump performance. Importantly, understanding this information will aid in determining appropriate ratios for assessment and monitoring of the explosive characteristics of athletes. Method: This study compared SJ performance across different knee angles--90 degrees, 100 degrees, 110 degrees, 120 degrees, 130 degrees, and a self-selected depth--for jump height and other kinetic characteristics. For comparison between SJ and an unconstrained dynamic movement, participants also performed a countermovement jump from a self-selected depth. Thirteen participants (M[subscript age] = 25.4 ± 3.5 years, M[subscript height] = 1.8 ± 0.06 m, M[subscript weight] = 79.8 ± 9.5 kg) were recruited and tested for their SJ performance. Results: In the SJ, maximal jump height (35.4 ± 4.6 cm) was produced using a self-selected knee angle (98.7 ± 11.2 degrees). Differences between 90 degrees,100 degrees, and self-selected knee angles for jump height were trivial (ES ± 90% CL = 90 degrees-100 degrees 0.23 ± 0.12, 90 degrees-SS -0.04 ± 0.12, 100 degrees-SS -0.27 ± 0.20; 0.5-2.4 cm) and not statistically different. Differences between all other knee angles for jump height ranged from 3.8 ± 2.0 cm (mean ± 90% CL) to 16.6 ± 2.2 cm. A similar outcome to jump height was observed for velocity, force relative to body weight, and impulse for the assessed knee angles. Conclusions: For young physically active adult men, the use of a self-selected depth in the SJ results in optimal performance and has only a trivial difference to a constrained knee angle of either 90° or 100°.

Objectives: The purpose of this study was to document the longitudinal strength and power characteristic changes and race performance changes of a skeleton athlete. Method: Longitudinal strength and power changes were assessed with strength and power diagnostic testing over a 9-year period. Trends over 9 years for relative strength were analyzed using a linear model. Push-start time was recorded across multiple tracks. Trends over 9 years for start performance at each track were assessed using a mixed-effects linear model to account for the impact of different tracks. Lower-body strength and power changes were assessed via a 1-repetition-maximum squat and a body-weight countermovement jump. The relationship between strength and power changes was assessed over time. The relationship between strength changes and start performance was determined by assessing the fixed effect of relative strength changes on push-start time. Results: Relative lower-body strength ranged from 1.6 kg per body weight to 1.9 kg per body weight and showed a significant mean improvement of 0.05 kg per body weight per year (R2 =.71, P <.01). A negative correlation (R2 =.79) between relative strength changes and push-start performance across multiple tracks was found. The mixed-effects model indicated that push-start time improved significantly year to year (0.02 s; P <.001; R2 =.74) when controlling for the effect of track. Conclusions: The longitudinal analysis of push-start time and the associations with changes in strength suggest that training this quality can have a positive effect on push-start performance. [ABSTRACT FROM AUTHOR]

Purpose: First, to examine whether heart rate variability (HRV) responses can be modeled effectively via the Banister impulse-response model when the session rating of perceived exertion (sRPE) alone, and in combination with subjective well-being measures, are utilized. Second, to describe seasonal HRV responses and their associations with changes in critical speed (CS) in competitive swimmers. Methods: A total of 10 highly trained swimmers collected daily 1-minute HRV recordings, sRPE training load, and subjective well-being scores via a novel smartphone application for 15 weeks. The impulse-response model was used to describe chronic root mean square of the successive differences (rMSSD) responses to training, with sRPE and subjective well-being measures used as systems inputs. Changes in CS were obtained from a 3-minute all-out test completed in weeks 1 and 14. Results: The level of agreement between predicted and actual HRV data was R2 =.66 (.25) when sRPE alone was used. Model fits improved in the range of 4% to 21% when different subjective well-being measures were combined with sRPE, representing trivial-to-moderate improvements. There were no significant differences in weekly group averages of log-transformed (Ln) rMSSD (P =.34) or HRV coefficient of variation of Ln rMSSD (P =.12); however, small-to-large changes (d = 0.21–1.46) were observed in these parameters throughout the season. Large correlations were observed between seasonal changes in HRV measures and CS (changes in averages of Ln rMSSD: r =.51, P =.13; changes in coefficient of variation of Ln rMSSD: r = −.68, P =.03). Conclusion: The impulse-response model and data collected via a novel smartphone application can be used to model HRV responses to swimming training and nontraining-related stressors. Large relationships between seasonal changes in measured HRV parameters and CS provide further evidence for incorporating a HRV-guided training approach. [ABSTRACT FROM AUTHOR]

Purpose: Critical speed (CS) and supra-CS distance capacity (D') are useful metrics for monitoring changes in swimmers' physiological and performance capacities. However, the utility of these metrics across a season has not been systematically evaluated in high-level swimmers. Methods: A total of 27 swimmers (mean [SD]: 18 females, age= 19.1 [2.9] y, and 9 males, age = 19.5 [1.9] y) completed the 12 x 25-m swimming test multiple times (4 [3] tests/swimmer) across a 2-y period. Season-best times in all distances for the test stroke were sourced from publicly available databases. Swimmers' distance speciality was determined as the event with the time closest to world record. Four metrics were calculated from the 12 x 25-m lest: CS, D', peak speed, and drop-off %. Results: Guyatt responsiveness index values were calculated to ascertain the practically relevant sensitivity of each 12 x 25-m metric: CS = 1.5, peak speed = 2.3, D' = 2.1, and drop-off % = 2.6. These values are modified effect sizes; all are large effects. Bayesian mixed modeling showed substantial between-subjects differences between genders and strokes for each variable but minimal within-subject changes across the season. Drop-off % was lower in 200-m swimmers (14.0% [3.3%]) than in 100-m swimmers (18.1% [4.1%], P - .003, effect size = 1.10). Conclusions: The 12x25-m test is best suited to differentiating between swimmers ol different strokes and events. Further development is needed to improve its utility in quantifying meaningful changes over a season for individual swimmers. [ABSTRACT FROM AUTHOR]

Davies, TB, Halaki, M, Orr, R, Mitchell, L, Helms, ER, Clarke, J, and Hackett, DA. Effect of set structure on upper-body muscular hypertrophy and performance in recreationally trained men and women. J Strength Cond Res 36(8): 2176–2185, 2022—This study explored the effect of volume-equated traditional-set and cluster-set structures on muscular hypertrophy and performance after high-load resistance training manipulating the bench press exercise. Twenty-one recreationally trained subjects (12 men and 9 women) performed a 3-week familiarization phase and were then randomized into one of two 8-week upper-body and lower-body split programs occurring over 3 and then progressing to 4 sessions per week. Subjects performed 4 sets of 5 repetitions at 85% one repetition maximum (1RM) using a traditional-set structure (TRAD, n = 10), which involved 5 minutes of interset rest only, or a cluster-set structure, which included 30-second inter-repetition rest and 3 minutes of interset rest (CLUS, n = 11). A 1RM bench press, repetitions to failure at 70% 1RM, regional muscle thickness, and dual-energy x-ray absorptiometry were used to estimate changes in muscular strength, local muscular endurance, regional muscular hypertrophy, and body composition, respectively. Velocity loss was assessed using a linear position transducer at the intervention midpoint. TRAD demonstrated a significantly greater velocity loss magnitude (g = 1.50) and muscle thickness of the proximal pectoralis major (g = −0.34) compared with CLUS. There were no significant differences between groups for the remaining outcomes, although a small effect size favoring TRAD was observed for the middle region of the pectoralis major (g = −0.25). It seems that the greater velocity losses during sets observed in traditional-set compared with cluster-set structures may promote superior muscular hypertrophy within specific regions of the pectoralis major in recreationally trained subjects. [ABSTRACT FROM AUTHOR]

Dietary intake plays a significant role in athletic performance and is influenced by several factors, including nutrition knowledge. Gaelic footballers are amateur athletes who conduct high-intensity, intermittent activity during training and competition, and have previously demonstrated insufficient dietary intake. This study aimed to examine nutrition knowledge in elite and non-elite Gaelic footballers. An online survey was distributed to competitive Gaelic footballers, examining nutrition knowledge using the Abridged Nutrition for Sport Knowledge Questionnaire. Total, general, and sport nutrition knowledge were compared between elite and non-elite athletes, and those who had and had not previously received nutrition education, using Mann-Whitney U-tests. A total of 190 participants (15.3% women) completed the survey. No differences between elite and non-elite athletes in nutrition knowledge were identified (p > 0.05). Athletes with previous nutrition education scored higher than those without previous nutrition education in total (54.0 ± 4.9% vs 46.8 ± 9.6%; p = 0.002) and sport (51.9 ± 12.5% vs 43.4 ± 11.8%; p = 0.005) nutrition knowledge. Findings suggest an importance of nutrition education at all levels of athletic competition to improve nutrition knowledge, which may empower athletes to make appropriate dietary decisions to support training and competition demands. [ABSTRACT FROM AUTHOR]

To develop a statistical model of winning times for international swimming events with the aim of predicting winning time distributions and the probability of winning for the 2020 and 2024 Olympic Games. The data set included first and third place times from all individual swimming events from the Olympics and World Championships from 1990 to 2019. We compared different model formulations fitted with Bayesian inference to obtain predictive distributions; comparisons were based on mean percentage error in out-of-sample predictions of Olympics and World Championships winning swim times from 2011 to 2019. The Bayesian time series regression model, comprising auto-regressive and moving average terms and other predictors, had the smallest mean prediction error of 0.57% (CI 0.46–0.74%). For context, using the respective previous Olympics or World Championships winning time resulted in a mean prediction error of 0.70% (CI 0.59–0.82%). The Olympics were on average 0.5% (CI 0.3–0.7%) faster than World Championships over the study period. The model computes the posterior predictive distribution, which allows coaches and athletes to evaluate the probability of winning given an individual's swim time, and the probability of being faster or slower than the previous winning time or even the world record. [ABSTRACT FROM AUTHOR]