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Jeffrey Martin and Drew Martin

, 2015 ; Martin et al., 2019 ; Morin, 2016 ; Mulkay & Gilbert, 1986 ). If an effect is “real,” the same or a similar effect should be observed in a replication study ( Simons, 2014 ). If a replication fails, it may suggest that a false positive was observed in the initial results (i.e., the conclusion

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Eric Kyle O’Neal, Samantha Louise Johnson, Brett Alan Davis, Veronika Pribyslavska, and Mary Caitlin Stevenson-Wilcoxson

.005) in the case of false negatives or prerun dehydration (USG ≥ 1.025) in the case of false positives. All data: n  = 132; r  = −.58, p  < .001. Sweat loss = 2.00–2.99% body mass: n  = 72; r  = −.55, p  < .001. Sweat loss = ≥3.00% body mass: n  = 60; r  = −.70, p  < .001. USG = urine specific

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Paul R. Hibbing, Samuel R. LaMunion, Haileab Hilafu, and Scott E. Crouter

, Collier, & Sugiyama, 2013 ). Specifically, each criterion transition (or “change point”) is paired to the earliest-occurring prediction that falls within some allowable lag time window (i.e., within ±δ). Any subsequent predictions within ±δ are ignored (i.e., labeled false positives) once the criterion

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Ryan M. Chambers, Tim J. Gabbett, and Michael H. Cole

detected. Scrum algorithm confidence scoring was set to the lowest possible setting (0%), thereby incorporating all 4833 instances. Each instance was then matched with the relevant time stamp, and false positives were thoroughly checked against video-coded scrum events. Statistical Analysis True positive

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Chelsea Steel, Katie Crist, Amanda Grimes, Carolina Bejarano, Adrian Ortega, Paul R. Hibbing, Jasper Schipperijn, and Jordan A. Carlson

with the trip log if at least 1 min of the logged trip was detected by the test measure. A “false trip” (i.e., false positive) was defined as any trip detected by the test measure that did not have at least 1 min of overlap with a logged trip. A “missed trip” (i.e., false negative) was defined as any

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Samuel N. Cheuvront, Robert W. Kenefick, and Edward J. Zambraski

A common practice in sports science is to assess hydration status using the concentration of a single spot urine collection taken at any time of day for comparison against concentration (specific gravity, osmolality, color) thresholds established from first morning voids. There is strong evidence that this practice can be confounded by fluid intake, diet, and exercise, among other factors, leading to false positive/negative assessments. Thus, the purpose of this paper is to provide a simple explanation as to why this practice leads to erroneous conclusions and should be curtailed in favor of consensus hydration assessment recommendations.

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Shannon David, Kim Gray, Jeffrey A. Russell, and Chad Starkey

The original and modified Ottawa Ankle Rules (OARs) were developed as clinical decision rules for use in emergency departments. However, the OARs have not been evaluated as an acute clinical evaluation tool.

Objective:

To evaluate the measures of diagnostic accuracy of the OARs in the acute setting.

Methods:

The OARs were applied to all appropriate ankle injuries at 2 colleges (athletics and club sports) and 21 high schools. The outcomes of OARs, diagnosis, and decision for referral were collected by the athletic trainers (ATs) at each of the locations. Contingency tables were created for evaluations completed within 1 h for which radiographs were obtained. From these data the sensitivity, specificity, positive and negative likelihood ratios, and positive and negative predictive values were calculated.

Results:

The OARs met the criteria for radiographs in 100 of the 124 cases, of which 38 were actually referred for imaging. Based on radiographic findings in an acute setting, the OARs (n = 38) had a high sensitivity (.88) and are good predictors to rule out the presence of a fracture. Low specificity (0.00) results led to a high number of false positives and low positive predictive values (.18).

Conclusion:

When applied during the first hour after injury the OARs significantly overestimate the need for radiographs. However, a negative finding rules out the need to obtain radiographs. It appears the AT’s decision making based on the totality of the examination findings is the best filter in determining referral for radiographs.

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James H. Rimmer, David Braddock, and Glenn Fujiura

A body mass index (BMI) greater than 27 has been cited as a risk factor for heart disease and diabetes mellitus resulting from excess weight. The purpose of this study was to determine the association between BMI (>27) and two other obesity indices–height-weight and percent body fat–as well as to investigate the relationship between BMI and three blood lipid parameters–total cholesterol, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in 329 adults with mental retardation (MR). Males were significantly taller and heavier than females, but females had a significantly higher BMI. Kendall’s Tau-C revealed a significant association between BMI and each of the following: height-weight, percent body fat, LDL-C, and HDL-C. However, there were a significant number of false negatives and false positives on each of the criteria. The congruence between at-risk BMI and two other obesity parameters (height-weight and percent body fat) in a population of adults with MR is not strong. Professionals should employ the BMI along with skinfold measures to assess a person’s at-risk status for excess weight.

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Dean J. McNamara, Tim J. Gabbett, Paul Chapman, Geraldine Naughton, and Patrick Farhart

Purpose:

Bowling workload is linked to injury risk in cricket fast bowlers. This study investigated the validity of microtechnology in the automated detection of bowling counts and events, including run-up distance and velocity, in cricket fast bowlers.

Method:

Twelve highly skilled fast bowlers (mean ± SD age 23.5 ± 3.7 y) performed a series of bowling, throwing, and fielding activities in an outdoor environment during training and competition while wearing a microtechnology unit (MinimaxX). Sensitivity and specificity of a bowling-detection algorithm were determined by comparing the outputs from the device with manually recorded bowling counts. Run-up distance and run-up velocity were measured and compared with microtechnology outputs.

Results:

No significant differences were observed between direct measures of bowling and nonbowling events and true positive and true negative events recorded by the MinimaxX unit (P = .34, r = .99). The bowling-detection algorithm was shown to be sensitive in both training (99.0%) and competition (99.5%). Specificity was 98.1% during training and 74.0% during competition. Run-up distance was accurately recorded by the unit, with a percentage bias of 0.8% (r = .90). The final 10-m (–8.9%, r = .88) and 5-m (–7.3%, r = .90) run-up velocities were less accurate.

Conclusions:

The bowling-detection algorithm from the MinimaxX device is sensitive to detect bowling counts in both cricket training and competition. Although specificity is high during training, the number of false positive events increased during competition. Additional bowling workload measures require further development.

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David Whiteside, Olivia Cant, Molly Connolly, and Machar Reid

Context:

Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players’ heavy travel schedules.

Purpose:

To develop an automated stroke-classification system to help quantify hitting load in tennis.

Methods:

Nineteen athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash, or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine-learning models were then constructed to classify true shot type from the IMU signals.

Results:

Across 10-fold cross-validation, a cubic-kernel support vector machine classified binned shots (overhead, forehand, or backhand) with an accuracy of 97.4%. A second cubic-kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types.

Conclusions:

With a view to monitoring external load, the combination of miniature inertial sensors and machine learning offers a practical and automated method of quantifying shot counts and discriminating shot types in elite tennis players.