Rolling Average Research Articles

Relationships Among PlayerLoad, High-Intensity Intermittent Running Ability, and Injury Risk in Professional Rugby League Players.

To examine relationships among physical performance, workload, and injury risk in professional rugby league players. Maximal-effort (n = 112) and submaximal (n = 1084) running performances of 45 players were recorded from 1 club over 2 consecutive seasons. Poorer and better submaximal running performance was determined by higher and lower exercise heart rates, respectively. Exponentially weighted moving averages and daily rolling averages were used to assess microtechnology-derived acute and chronic field-based workloads. The associations among within-individual submaximal running performance, workload, and noncontact lower-limb injury were then investigated. The injury risk associated with poorer submaximal performance was "likely" greater than stable (relative risk = 1.8; 90% confidence interval, 0.9-3.7) and better submaximal performance (relative risk = 2.0; 90% confidence interval, 0.9-4.4). Compared with greater submaximal performance, poorer performance was associated with lower chronic workloads (effect size [d] = 0.82 [0.13], large) and higher acute:chronic workload ratios (d = 0.49 [0.14], small). Chronic workload demonstrated a "nearly perfect" positive relationship with maximal-effort running performance (exponentially weighted moving average, R2 = .91 [.15]; rolling average, R2 = .91 [.14]). At acute:chronic workload ratios >1.9, no differences in injury risk were found between rolling average and exponentially weighted moving average methods (relative risk = 1.1; 90% confidence interval, 0.3-3.8; unclear). Reductions in submaximal running performance are related with low chronic workloads, high acute:chronic workload ratios, and increased injury risk. These findings demonstrate that a submaximal running assessment can be used to provide information on physical performance and injury risk in professional rugby league players.

Methods for Estimating Resting Energy Expenditure Using Indirect Calorimetry in Adults with Overweight and Obesity (P13-030-19)

ObjectivesStandard procedures to estimate resting energy expenditure (REE) using indirect calorimetry are time-consuming, and may be unnecessary. Indeed, the guidelines recommend a pre-test resting period of 30-minutes, followed by a 5-minute stabilization period, and then waiting until the first steady state period (SS), defined as a 5-minute period with a coefficient of variance (CV) of <10% for VO2 and VCO2, to estimate REE. The aim of the study was to evaluate alternative procedures for estimating REE in adults with overweight and obesity. MethodsIndirect calorimetry was performed in 37 adults enrolled in a weight loss trial using a metabolic cart (Quark RMR, COSMED). The volume of oxygen (VO2) and volume of carbon dioxide (VCO2) were collected every 10 sec for ≥20-minutes following pre-test resting (10-mins) and stabilization (5-mins) periods. The measurement period was segmented into five-minute (REE6–10, REE11–15, REE16–20, and REE21–25) and rolling (REE6–15, REE6–20, and REE6–25) periods, and VO2, VCO2, and CV were calculated for each period. REE was calculated using standard criteria (REESS). Alternative SS periods of 3- and 4-minutes (REE3 and REE4) were applied to those who did not achieve REESS. REESS estimates were compared to the other estimates of REE using paired t-tests. ResultsParticipants were 51 ± 14SD yo, primarily women (78%), and had a BMI of 35.4 ± 5.5SD kg/m2. REESS was achieved by 81% (n = 30) of all participants, and 54% (n = 20) achieved REESS during the first 5-minute period (REE6–10) following stabilization. Applying REE3 and REE4 criteria, those who did not reach REESS increased to 92% (n = 34). There were no significant differences between REESS, and REE3 (P = 0.21), REE4 (P = 0.40), REE6–10 (P = 0.38), REE6–15 (P = 0.15) or REE6–20 (P = 0.05). ConclusionsThe majority of adults with overweight and obesity met the standard criteria for SS following a reduced pre-test resting period. However, the non-significant difference between REESS and rolling averages suggest the standard criteria may be unnecessary in a group setting. Funding SourcesAmerican Heart Association.

Heart Rate Variability Monitoring During Strength and High-Intensity Interval Training Overload Microcycles.

Objective: In two independent study arms, we determine the effects of strength training (ST) and high-intensity interval training (HIIT) overload on cardiac autonomic modulation by measuring heart rate (HR) and vagal heart rate variability (HRV).Methods: In the study, 37 well-trained athletes (ST: 7 female, 12 male; HIIT: 9 female, 9 male) were subjected to orthostatic tests (HR and HRV recordings) each day during a 4-day baseline period, a 6-day overload microcycle, and a 4-day recovery period. Discipline-specific performance was assessed before and 1 and 4 days after training.Results: Following ST overload, supine HR, and vagal HRV (Ln RMSSD) were clearly increased and decreased (small effects), respectively, and the standing recordings remained unchanged. In contrast, HIIT overload resulted in decreased HR and increased Ln RMSSD in the standing position (small effects), whereas supine recordings remained unaltered. During the recovery period, these responses were reversed (ST: small effects, HIIT: trivial to small effects). The correlations between changes in HR, vagal HRV measures, and performance were weak or inconsistent. At the group and individual levels, moderate to strong negative correlations were found between HR and Ln RMSSD when analyzing changes between testing days (ST: supine and standing position, HIIT: standing position) and individual time series, respectively. Use of rolling 2–4-day averages enabled more precise estimation of mean changes with smaller confidence intervals compared to single-day values of HR or Ln RMSSD. However, the use of averaged values displayed unclear effects for evaluating associations between HR, vagal HRV measures, and performance changes, and have the potential to be detrimental for classification of individual short-term responses.Conclusion: Measures of HR and Ln RMSSD during an orthostatic test could reveal different autonomic responses following ST or HIIT which may not be discovered by supine or standing measures alone. However, these autonomic changes were not consistently related to short-term changes in performance and the use of rolling averages may alter these relationships differently on group and individual level.

Calculating reserve power requirements from wind–power forecasts

Forecasting the output power of large wind farms is helpful for planning reserve capacity in a mixed-generation power supply system. After classifying the reserves needed in a power system, this research focuses on the reserve components that are affected by wind–power fluctuations, the regulating, and load-following reserves. Considering the uncertainties in system load forecasts and wind–power forecasts, a general model for calculating the reserve requirements of a power system is proposed. Then, ultra-short-term forecasts of wind–power output 1 h into the future were studied, and a practical method that uses 10-min data to forecast wind output with a rolling average was proposed to predict the amount of power generation in the next hour. Then, using the RTS-96 dataset from the IEEE as an example, the reserve capacity requirement for the power system is determined according to the forecast uncertainty. Accurate predictive models for wind forecasting can reduce the needed reserve capacity of the system while guaranteeing the reliability and safety of the grid.

The detection of intramammary infections using online somatic cell counts

Timely and accurate identification of cows with intramammary infections is essential for optimal udder health management. Various sensor systems have been developed to provide udder health information that can be used as a decision support tool for the farmer. Among these sensors, the DeLaval Online Cell Counter (DeLaval, Tumba, Sweden) provides somatic cell counts from every milking at cow level. Our aim was to describe and evaluate diagnostic sensor properties of these online cell counts (OCC) for detecting an intramammary infection, defined as an episode of subclinical mastitis or a new case of clinical mastitis. The predictive abilities of a single OCC value, rolling averages of OCC values, and an elevated mastitis risk (EMR) variable were compared for their accuracy in identifying cows with episodes of subclinical mastitis or new cases of clinical mastitis. Detection of subclinical mastitis episodes by OCC was performed in 2 separate groups of different mastitis pathogens, Pat 1 and Pat 2, categorized by their known ability to increase somatic cell count. The data for this study were obtained in a field trial conducted in the dairy herd of the Norwegian University of Life Sciences. Altogether, 173 cows were sampled at least once during a 17-mo study period. The total number of quarter milk cultures was 5,330. The most common Pat 1 pathogens were Staphylococcus epidermidis, Staphylococcus aureus, and Streptococcus dysgalactiae. The most common Pat 2 pathogens were Corynebacterium bovis, Staphylococcus chromogenes, and Staphylococcus haemolyticus. The OCC were successfully recorded from 82,182 of 96,542 milkings during the study period. For episodes of subclinical mastitis the rolling 7-d average OCC and the EMR approach performed better than a single OCC value for detection of Pat 1 subclinical mastitis episodes. The EMR approach outperformed the OCC approaches for detection of Pat 2 subclinical mastitis episodes. For the 2 pathogen groups, the sensitivity of detection of subclinical mastitis episodes was 69% (Pat 1) and 31% (Pat 2), respectively, at a predefined specificity of 80% (EMR). All 3 approaches were equally good at detecting new cases of clinical mastitis, with an optimum sensitivity of 80% and specificity of 90% (single OCC value).

Prevalence of childhood lead poisoning and respiratory disease associated with lead smelter emissions

BackgroundThe city of Port Pirie in South Australia has been a world leading centre for lead and zinc smelting and processing since 1889 that continues to cause contamination of its environment and resident population. This study quantifies the effect of lead and SO2 emissions from Nyrstar Port Pirie Pty Ltd's smelter on blood lead and respiratory health outcomes, respectively, and establishes what air quality values are required to better protect human health. MethodBlood lead and emergency department presentation data collected by South Australia Health (SA Health) and lead in air and SO2 data collected by the South Australian Environment Protection Authority (SAEPA) were obtained and analysed to quantify health outcomes due to smelter emissions in Port Pirie. Regression analysis was used to assess the relationship between the concentration of lead in air and children's blood lead levels between the years of available data: 2003 to 2017. Ambient SO2 concentrations (SAEPA) measured continuously between 2008 and 2018 were 24-hour averaged and compared to daily local emergency department respiratory presentation rates (available from July 2012 to October 2018). Rates of emergency department respiratory presentations at Port Pirie and regional comparators were calculated as age-standardised rates. ResultsThe data show that increases in ambient SO2 concentrations are associated with increased rates of emergency department respiratory presentations of Port Pirie residents, in which children are over-represented. The 30-day rolling average of respiratory presentations was significantly associated (p < 0.05) with incremental increases in SO2. Analysis of the relationship between lead in air and blood lead shows that annual geometric mean air lead concentrations need to be <0.11 μg/m3 to ensure the geometric mean blood lead of Port Pirie children under 5 years is ≤5 μg/dL. For children aged 24 months, lead in air needs to be no greater than 0.082 μg/m3 (annual geometric mean) to ensure geometric mean blood lead does not exceed 5 μg/dL. ConclusionCurrent smelting emissions continue to pose a clear risk of harm to Port Pirie children. Allowable emissions must be lowered significantly to limit adverse childhood health outcomes including respiratory illness and IQ, academic achievement and socio-behavioural problems that are associated with lead exposure at levels experienced by Port Pirie children. Current SO2 levels are likely to be responsible for increased rates of emergency department respiratory presentations in Port Pirie compared with other South Australian locations. As a minimum, Australian SO2 air quality standards need to be enforced in Port Pirie to better protect human health. Lead in air needs to be approximately 80% lower than the current national standard (0.5 μg/m3) to ensure that the geometric blood lead of children under 5 years is less than or equal to 5 μg/dL.

Interest rates forecasting and stress testing in India: a PCA-ARIMA approach

In the Basel III era, measuring, managing, mitigating and forecasting interest rate risk has become significant. In the first study of its kind, this paper develops a principal component analysis-based forecasting of interest rates of different maturities and stress testing approach in a univariate auto-regressive integrated moving average (ARIMA) framework in the context of India. Debating the existence of multiple representations of interest rates in the Indian market, the study broke down all the short-term, as well as long-term interest rates to derive the optimal principal component of unique interest rates. These unique interest rates are further exercised to forecast the future interest rates through the ARIMA model. The rolling average method was applied to the recently created Indian volatility index (VIX) to place the stress points. The model performance was examined over this stress point of building multiple scenario analysis. The study found that ARIMA (2-1-1) forecasting model of interest rates produced a better forecast result, both in the case of in-sample and out-of-sample performances as well as in stress periods. From the forecasting results, the study found that the proportionate gain in yield is higher as the maturity increases. By examining the stress period on the basis of eight quarters (2007: Q4 of 2009: Q3) rolling average on Indian volatility Indices, the survey concluded that the Indian economy will reach a stable situation signalled by less volatile interest rates post the second quarter of 2018.

Workloads placed on adolescent cricket players: A pilot study

Cricket players nowadays are faced with increased physical demands, and as a result, it is important to manage their workload, particularly to control and predict risk of injury. While this has been investigated at an elite level, few studies have looked at the workloads placed on adolescent cricket players. The purpose of this study was therefore to determine the workloads placed on school boy cricketers, specifically within a South African context. Twelve male school boy cricketers between the ages of 16 and 19 years participated in the study. Match and practice data were collected over a period of 74 days and included number of shuttles run (batsmen), number of deliveries bowled (bowlers) as well as central ratings of perceived exertion (RPE). Injury data were also collected. These data were then used to determine the acute:chronic (a:c) workload ratio (two-week rolling average) as well as session RPE (sRPE). Fast bowlers delivered more balls during matches compared to practices, whereas batsmen ran more shuttles at practices compared to matches. Session RPE was higher for matches compared to practices. There did not appear to be a relationship between workload and injury risk; however, this may have been due to the small sample size. It was concluded that it is important to monitor individual workloads of players. Also, intensities of practices need to be increased to match game demands. Lastly, the study design was effective and the methods used were found to be appropriate for a larger population.

Strategies to Ensure Accurate Calculation of Parameters of the VO2 Response Profile During Heavy Intensity Cycle Ergometer Exercise

Background: The parameters of the VO2 response profile are obtained by fitting breath-by-breath VO2 data from an exercise test to an appropriate mathematical model. Several strategies have been recommended to ensure, or at least improve, the accuracy of the values. Objectives: The purpose of this study was to evaluate two strategies to enhance the accuracy of parameter estimates that describe the two-component VO2 response during heavy intensity exercise. The first was to use data from a number of tests rather than just one. The second was to ‘smooth’ the data, using three-breath, five-breath, or seven-breath rolling averages of the breath-by-breath VO2 data prior to fitting the data to the two-component model. Methods: Twenty participants (eight women and twelve men) performed six 6-min heavy-intensity (midway between the ventilatory threshold and VO2max) cycle ergometer tests. Breath-by-breath data and smoothed data from each test were fit to a two-component model. The parameter estimates from the first test, and the average of the values from the first two, first three, first four, first five, and all six tests were compared against the criterion value, which was the average of all six values obtained using five-breath averages. Results: Modeling five-breath averages of data from the first test generated values for the parameters that were closely related to the criterion values. Modeling data from two or three tests improved the accuracy slightly, but improvements were small, and negligible when more than three tests were included. Conclusions: Depending upon the accuracy required, that is depending upon how close each and every participant’s value must be to his or her ‘true’ value, smoothed data from one or two tests is sufficient to calculate the values that describe the two-parameter VO2 response profile in heavy intensity cycling exercise.

A retrospective analysis of seasonal growth patterns of nursery and finishing pigs in commercial production

Objective: Determine seasonal patterns of nursery and finisher growth performance in 3 commercial US production systems located in the midwest. Materials and methods: Five years of production records, including 5039 nursery and 5354 finisher production batches, were collected from 3 production systems. Explanatory variables include system, site, pig-flow type, feeder type, batch size, week of placement, average days-on-feed, fill length, number of sow farm sources, dietary energy, mortality, and initial body weight. Week of placement served as the unit for seasonal patterns. Nursery and finisher performance (average daily gain [ADG], average daily feed intake [ADFI], and gain to feed ratio [G:F]) were analyzed in separate datasets using multi-level linear mixed models. A guided stepwise selection approach was used to select fixed variables and their interactions. Seasonality curves were generated using rolling averages of least squares means with a 5-week window and 1-week step-size. Results: For nursery, the seasonality effect was significant (P &lt; .001) for ADG, ADFI, but not for G:F. Nursery ADG and ADFI decreased as week of placement progressed from the 1st to 20th week of a year but increased thereafter. All finisher growth responses were affected by week of placement (P &lt; .001) but the pattern and magnitude of seasonal variability differed among systems (system × week interactions, P &lt; .02). Implications: Seasonal variability of nursery and finisher performance can be quantified using production records in a multi-level linear mixed model. Seasonality effects on finisher performance were system dependent, while nursery seasonality shared more similarity among investigated systems.

Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times

Business processes are dynamic and change due to diverse factors. While existing approaches aim to detect drifts in the process structure, Tesseract looks for temporal drifts in activity interim times. This orthogonal view on the process extends the traditional data cube of events – case id, activities and timestamps – by a fourth dimension and improves the operational support by a visualization of temporal drifts in real-time.Insights about temporal deviations lead to an augmented awareness of imminent failures or improved service times. The detection of related structural concept drifts can be improved by early warning, as operation times of critical parts often increase before they catastrophically fail.

Enhancing the Evaluation and Interpretation of Fitness Testing Data Within Youth Athletes

ABSTRACT FITNESS TESTING IS COMMON PRACTICE WITHIN YOUTH ATHLETES. HOWEVER, THE INTERPRETATION OF FITNESS DATA OFTEN OCCURS WITHIN CHRONOLOGICAL ANNUAL-AGE CATEGORIES, RESULTING IN ATHLETES BEING (DIS)ADVANTAGED DUE TO AGE OR MATURITY DISCREPANCIES. INSTEAD, EVALUATING FITNESS PERFORMANCE AGAINST ROLLING AVERAGES MAY BE MORE APPROPRIATE. THIS ARTICLE PRESENTS A NOVEL METHOD FOR ANALYZING FITNESS TESTING DATA IN YOUTH ATHLETES USING Z-SCORES ACCORDING TO ROLLING AVERAGES FOR BOTH CHRONOLOGICAL AGE AND MATURITY STATUS. THIS ANALYSIS TECHNIQUE ALLOWS FOR THE DUAL ABILITY TO INTERPRET YOUTH FITNESS PERFORMANCE ACCORDING TO AGE AND MATURATION, ENHANCING ACCURACY OF DATA INTERPRETATION FOR TALENT IDENTIFICATION, DEVELOPMENT, AND STRENGTH AND CONDITIONING PROGRAMMING.

The Individual and Combined Effects of Multiple Factors on the Risk of Soft Tissue Non-contact Injuries in Elite Team Sport Athletes.

Aim: Relationships between athlete monitoring-derived variables and injury risk have been investigated predominantly in isolation. The aim of this study was to evaluate the individual and combined effects of multiple factors on the risk of soft-tissue non-contact injuries in elite team sport athletes.Methods: Fifty-five elite Australian footballers were prospectively monitored over two consecutive seasons. Internal and external training load was quantified using the session rating of perceived exertion and GPS/accelerometry, respectively. Cumulative load and acute-to-chronic workload ratios were derived using rolling averages and exponentially weighted moving averages. History of injuries in the current and previous seasons was recorded along with professional experience, weekly musculoskeletal screening, and subjective wellness scores for individual athletes. Individual and combined effects of these variables on injury risk were evaluated with generalized linear mixed models.Results: High cumulative loads and acute-to-chronic workload ratios were associated with increased risk of injuries. The effects for measures derived using exponentially weighted moving averages were greater than those for rolling averages. History of a recent injury, long-term experience at professional level, and substantial reductions in a selection of musculoskeletal screening and subjective wellness scores were associated with increased risk. The effects of high cumulative loads were underestimated by ~20% before adjusting for previous injuries, whereas the effects of high acute-to-chronic workload ratios were overestimated by 10–15%. Injury-prone players, identified via player identity in the mixed model, were at > 5 times higher risk of injuries compared to robust players (hazard ratio 5.4, 90% confidence limits 3.6–12) despite adjusting for training load and previous injuries. Combinations of multiple risk factors were associated with extremely large increases in risk; for example, a hazard ratio of 22 (9.7–52) was observed for the combination of high acute load, recent history of a leg injury, and a substantial reduction in the adductor squeeze test score.Conclusion: On the basis of our findings with an elite team of Australian footballers, the information from athlete monitoring practices in team sports should be interpreted collectively and used as a part of the injury prevention decision-making process along with consideration of individual differences in risk.

Continuous surface EMG power reflects the metabolic cost of shivering during targeted temperature management after cardiac arrest

AimShivering may interfere with targeted temperature management (TTM) after cardiac arrest, contributing to secondary brain injury. Early identification of shivering is challenging with existing tools. We hypothesized that shivering detected by continuous surface sEMG monitoring would be validated with calorimetry and detected earlier than by intermittent clinical observation. MethodsThis prospective observational study enrolled a convenience sample of comatose adult cardiac arrest patients treated with TTM at 33 °C. Clinical shivering was monitored hourly using the Bedside Shivering Assessment Scale (BSAS) by bedside nurses who administered intermittent neuromuscular blockade (NMB) when BSAS ≥ 1. The research team monitored independently for shivering with BSAS every 15 min during continuous blinded monitoring of oxygen consumption (VO2) via indirect calorimetry and sEMG power during the maintenance phase of TTM. A sustained 20% increase in the 5-min rolling average of VO2 above baseline identified the Gold Standard shivering threshold (VO2-20). ResultsAmong 18 patients, clinical shivering was detected 23 times in 14 patients. Hierarchical models to predict a shiver event determined by the VO2-20 for sEMG power and BSAS revealed an AUC for sEMG power of 0.92 (95%CI = 0.88–0.95), and 0.90 (CI = 0.87–0.94) for BSAS. The optimal threshold of sEMG to predict VO2-20 was 32 decibels (dB), and this was exceeded 38 (29–56) min before nurse-detected shivering. ConclusionsShivering was detected by sEMG power earlier than by clinical assessment with BSAS, with similar accuracy compared to the indirect calorimetry gold standard. Continuous sEMG monitoring appears useful for clinical assessment and research for shivering during TTM.

Impact of a program ensuring consistent response to acute drops in lung function in children with cystic fibrosis

BackgroundVariation in CF pulmonary outcomes is multifactorial, but a significant component appears to be dependent upon differences in CF Center care. Previous investigations suggest that high performing CF centers are more consistent and proactive in the treatment of pulmonary exacerbations. We incorporated this approach into a program that could be bundled and shared with other CF Centers. MethodsThe reorganization of CF pulmonary care at the Children's Hospital of Richmond included the development of a pulmonary algorithm to define a standard response to changes in lung function and run charts to track process and outcome measures. We calculated the rolling average of the best percent predicted FEV1 (ppFEV1) over the previous 12 months as our primary outcome measure. ResultsThe mean of the best ppFEV1 in the previous 12 months rose from 87% predicted (65% predicted for those 13–18 years, 97% predicted for those 6–13 years) in January 2013 to 98% predicted (95% predicted for those 13–18 years, 110% predicted for those 6–13 years) in January 2018. The ppFEV1 difference between children 6–13 years and adolescents 13–18 years dropped from 34 to 14 during that time. ConclusionsImprovements in pulmonary outcomes can be accomplished rapidly using basic quality improvement principles, including interdisciplinary team goal setting, standardized and proactive approaches that ensure consistent recognition and treatment of pulmonary exacerbations, and the use of data to follow the effectiveness of the process. We believe that the steps involved would be easy for other CF Centers to adapt to their own settings.

Reference‐Dependent Hedging: Theory and Evidence from Iowa Corn Producers

AbstractWe develop a theoretical model of optimal hedging that nests expected utility and expected target utility theories. We use this model to characterize optimal hedging with and without reference price dependence. The model's theoretical predictions are tested with a unique database consisting of every forward contract written with a major grain marketing firm by Iowa corn producers over a five‐year period. Our results suggest that a current December futures price higher than a reference price triggers hedging activity. A likely candidate for producers’ reference price is a rolling average of the current futures price. We then use trading activity implied by the producers to determine if they benefit from the way they hedge. The evidence is mixed. Finally, we compare the producer forward contract data to the only publicly available data on producer hedging: The Commodity Futures Trading Commission Disaggregated Commitment of Traders Report (DCOT) for Short Hedgers. A hedge ratio constructed from the open interest in new futures contracts of the DCOT report is highly correlated with the producer hedge series in the Iowa data, providing evidence that DCOT data represent farmers’ hedging behavior reasonably well. This work has important implications for future research that uses the DCOT data, and provides new evidence about producers’ hedging behavior that marketing specialists and extension agents can use to enhance their educational efforts related to risk management.

Evaluation of the use of bias factors with water monitoring data.

Aquatic exposure assessments using surface water quality monitoring data are often challenged by missing extreme concentrations if sampling frequency is less than daily. A bias factor method has been previously proposed to address this concern for peak concentrations, where a bias factor is a multiplicative quantity to upwardly adjust estimates so that the true value is exceeded 95% of the time. In other words, bias factors are statistically protective adjustments. We evaluate this method using a research data set of 69 near-daily sampled site-years from the Atrazine Ecological Monitoring Program, dividing the data set into 23 reference and 46 validation site-years. Bias factors calculated from the reference data set are used to evaluate the method using the validation set for 1) point estimation, 2) interval estimation, and 3) decision-making. Sampling designs are every 7, 14, 28, and 90 d; and target quantities of assessment interest are the 90th and 95th percentiles and maximum m-day rolling averages (m = 1, 7, 21, 60, 90). We find that bias factors are poor point estimators in comparison with alternative methods. For interval estimation, average coverage is less than nominal, with coverage at individual site-years sometimes very low. Positive correlation of bias factors and target quantities, where present, adversely affects method performance. For decision rules or screening, the method typically shows very low false-negative rates but at the cost of extremely high false-positive rates. Environ Toxicol Chem 2018;37:1864-1876. © 2018 SETAC.

Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs.

The assessment of competitive movement demands in team sports has traditionally relied upon global positioning system (GPS) analyses presented as fixed-time epochs (e.g., 5–40 min). More recently, presenting game data as a rolling average has become prevalent due to concerns over a loss of sampling resolution associated with the windowing of data over fixed periods. Accordingly, this study compared rolling average (ROLL) and fixed-time (FIXED) epochs for quantifying the peak movement demands of international rugby union match-play as a function of playing position. Elite players from three different squads (n = 119) were monitored using 10 Hz GPS during 36 matches played in the 2014–2017 seasons. Players categorised broadly as forwards and backs, and then by positional sub-group (FR: front row, SR: second row, BR: back row, HB: half back, MF: midfield, B3: back three) were monitored during match-play for peak values of high-speed running (>5 m·s-1; HSR) and relative distance covered (m·min-1) over 60–300 s using two types of sample-epoch (ROLL, FIXED). Irrespective of the method used, as the epoch length increased, values for the intensity of running actions decreased (e.g., For the backs using the ROLL method, distance covered decreased from 177.4 ± 20.6 m·min-1 in the 60 s epoch to 107.5 ± 13.3 m·min-1 for the 300 s epoch). For the team as a whole, and irrespective of position, estimates of fixed effects indicated significant between-method differences across all time-points for both relative distance covered and HSR. Movement demands were underestimated consistently by FIXED versus ROLL with differences being most pronounced using 60 s epochs (95% CI HSR: -6.05 to -4.70 m·min-1, 95% CI distance: -18.45 to -16.43 m·min-1). For all HSR time epochs except one, all backs groups increased more (p < 0.01) from FIXED to ROLL than the forward groups. Linear mixed modelling of ROLL data highlighted that for HSR (except 60 s epoch), SR was the only group not significantly different to FR. For relative distance covered all other position groups were greater than the FR (p < 0.05). The FIXED method underestimated both relative distance (~11%) and HSR values (up to ~20%) compared to the ROLL method. These differences were exaggerated for the HSR variable in the backs position who covered the greatest HSR distance; highlighting important consideration for those implementing the FIXED method of analysis. The data provides coaches with a worst-case scenario reference on the running demands required for periods of 60–300 s in length. This information offers novel insight into game demands and can be used to inform the design of training games to increase specificity of preparation for the most demanding phases of matches.

Exercise Oscillatory Ventilation: Interreviewer Agreement and a Novel Determination.

Determination of exercise oscillatory ventilation (EOV) is subjective, and the interreviewer agreement has not been reported. The purposes of this study were, among patients with heart failure (HF), as follows: 1) to determine the interreviewer agreement for EOV and 2) to describe a novel, objective, and quantifiable measure of EOV. This was a secondary analysis of the HEART Camp: Promoting Adherence to Exercise in Patients with Heart Failure study. EOV was determined through a blinded review by six individuals on the basis of their interpretation of the EOV literature. Interreviewer agreement was assessed using Fleiss kappa (κ). Final determination of EOV was based on agreement by four of the six reviewers. A new measure (ventilation dispersion index; VDI) was calculated for each test, and its ability to predict EOV was assessed with the receiver operator characteristics curve. Among 243 patients with HF (age, 60 ± 12 yr; 45% women), the interreviewer agreement for EOV was fair (κ = 0.303) with 10-s discrete data averages and significantly better, but only moderate (κ = 0.429) with 30-s rolling data averages. Prevalence rates of positive and indeterminate EOVs were 18% and 30% with the 10-s discrete averages and 14% and 13% with the 30-s rolling averages, respectively. VDI was strongly associated with EOV, with areas under the receiver operator characteristics curve of 0.852 to 0.890. Interreviewer agreement for EOV in patients with HF is fair to moderate, which can negatively affect risk stratification. VDI has strong predictive validity with EOV; as such, it might be a useful measure of prognosis in patients with HF.

Glycaemic State Analysis from Continuous Glucose Monitoring Measurements in Infants

Abstract Neonatal hypoglycaemia is common in at-risk infants and can cause adverse neurologic outcomes in later life. Continuous glucose monitoring (CGM) technology offers a way to continuously monitor patient condition, helping to detect hypoglycaemia as well as provide insight into the general glycaemic state of the patient. Characterising Glycaemic States can be easily done by eye, but no simple, clinically relevant algorithm exists to do this characterisation analytically or computationally. This paper presents such an algorithm to characterise Glycaemic States and detect State Changes. This algorithm was developed on a cohort of 366 infants, using a total of 12356 hours of CGM sensor data. State Changes were defined as an intersection between a 6-hour rolling average of the CGM trace and the average of the whole interstitial glucose CGM trace, with a 5 hour minimum crossover threshold defining a single State. The majority of infants were found to have experienced less than 2 State Changes in the first 48 hours of birth (279 of 366 patients, 76%). The median number of State Changes per day was 0.68 [IQR: 0.60, 1.14], while the median absolute change in IG over a State Change was 0.6 mmol/L [IQR: 0.4, 0.9 mmol/L]. Visually, the majority of algorithmically characterised State Changes matched CGM traces characterised by eye. Future use of the algorithm could associate the State Changes with clinical outcomes.