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The N-Pact Factor, Replication, Power, and Quantitative Research in Adapted Physical Activity Quarterly

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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Urine Specific Gravity as a Practical Marker for Identifying Suboptimal Fluid Intake of Runners ∼12-hr Postexercise

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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Evaluating the Performance of Sensor-Based Bout Detection Algorithms: The Transition Pairing Method

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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Preliminary Baseline Vestibular Ocular Motor Screening Scores in Pediatric Soccer Athletes

Morgan Anderson, Christopher P. Tomczyk, Aaron J. Zynda, Alyssa Pollard-McGrandy, Megan C. Loftin, and Tracey Covassin

with a history of concussion was less than 5% and when analyses were re-examined excluding participants with missing demographic information, the false-positive rate for VOMS total score (17.8%) and change score (35.6%), and the internal consistency of VOMS total scores and change scores were similar

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Overcoming the “Ostrich Effect”: A Narrative Review on the Incentives and Consequences of Questionable Research Practices in Kinesiology

Nicholas B. Tiller and Panteleimon Ekkekakis

sports medicine; physical education; and the sports, health, and exercise sciences) may have broad consequences: from increasing the frequency of false positives in the published literature to diminishing scientific quality and rigor and inhibiting scientific progress and the attainment of replicable

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Validity of a Microsensor-Based Algorithm for Detecting Scrum Events in Rugby Union

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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Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults

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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Spot Urine Concentrations Should Not Be Used for Hydration Assessment: A Methodology Review

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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Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning

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


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.


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


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.


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.


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.

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Congruence of Three Risk Indices for Obesity in a Population of Adults with Mental Retardation

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.