Ben Liang (252969), Sohom Saha (22521704), Hanlei Miao (22521707) et al. · Figshare · 2025
<div> Objectives This study aimed to develop and evaluate a real-time sensor-based monitoring and feedback system for enhancing four core football performance metrics, passing accuracy, sprint speed, agility, and shot power, each defined and quantified using validated wearable sensors and baseline‐referenced improvement thresholds. Methods A randomized controlled trial (RCT) was conducted over eight weeks with 30 university-level male football players (aged 21.70 ± 1.28 years) from Zhengzhou University. Participants were randomly assigned to an experimental group (n = 15), which trained using the real-time monitoring system, or a control group (n = 15), which followed traditional training methods. The wearable system integrated accelerometers, gyroscopes, and magnetometers to provide real-time, skill-specific feedback during drills. Performance data were collected weekly and analyzed using repeated measures ANOVA with effect sizes calculated via partial eta squared (η²p). Results The results demonstrated statistically and practically significant improvements in the experimental group across all measured parameters. Notably, the effect sizes ranged from large to very large (η²p = .59 to.89), indicating that the improvements were not only statistically reliable but also substantial enough to have meaningful impact on the players’ performance. Passing accuracy increased by 10.21% (F(1,27) = 210.02, p < 0.001, η²p = .88), sprint speed improved by 17.33% (F(1,27) = 92.00, p < 0.001, η²p = .76), agility improved by 10.90% (F(1,27) = 41.75, p < 0.001, η²p = .59), and shot power increased by 10.75% (F(1,27) = 247.32, p < 0.001, η²p = .89). The control group showed negligible or negative changes in all performance metrics. Performance improvements in the experimental group were progressive and sustained across the 8-week training period, with weekly data showing steady gains in passing accuracy, sprint speed, agility, and shot power. No performance regressions or plateaus were observed during the intervention period. Conclusion By delivering instantaneous, sensor-validated feedback on precisely defined performance metrics, the system accelerated improvements in both technical and physical skills. These findings support the integration of wearable sensor technology into football training to achieve data-driven, individualized skill development. Future work should explore AI-driven personalization and long-term retention of gains. </div>
Cited by 0Open access
Marko Poženel (14019907), Aljaž Zrnec (14019910), Dejan Lavbič (14019913) · Figshare · 2022
<p>The y-axis represents the ICC, while the x-axis portrays four groups of participants. The groups are divided into quartiles according to the points that participants scored when performing the gamified process. The first column AKR-11 contains results of a similar IQ study, in which [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0274811#pone.0274811.ref002" target="_blank">2</a>] studied the measurability of IQ on the same set of IQ dimensions used in our study. The four highlighted columns (rightmost) exhibit the results of our study, where each column represents one group of players.</p>
Cited by 0Open access
Itziar Ubillos (5314415), Ruth Aguilar (17510), Hector Sanz (4592026) et al. · Figshare · 2018
<p>A) ICC for the replicates of the positive control serial dilutions (2.5-fold starting at 1:50 with replicates in alternate positions); and B) ICC for the three blanks per plate. ICCs are assessed for each antigen and all dilutions combined.</p>
Cited by 0Open access
Mukul Srivastava, K. J. Keen · Biometrika · 1988
A noniterative method for estimating the interclass correlation coefficient is derived from the technique of weighted sums of squares. The asymptotic variance of this estimator is derived under the assumption of normality. Through extensive Monte Carlo simulations, the sample variance of this estimator is found not to differ greatly from that of the maximum likelihood estimator.
Cited by 8
Shingo Shirahata · Communication in Statistics- Theory and Methods · 1982
Abstract A nonparametric measure of interclass correlation is considered and its unbiased estimator and a test based on the estimator are studied. Hie measure is an analogue of the Kendall's measure of dependence. It is shown that the variance of the estimator is small and the information loss of the test based on the estimator is not serious relative to a standard parametric test in the sense of the Pitman asymptotic relative efficiency. Furthermore, the approximate variance of the estimator is given in the normal model. Keywords: Kendall's measure of dependenceconcordancerank statisticsunbiased estimatorasymptotic normalityefficiencyapproximate variance
Cited by 1
Hidemi Okuma (463936), Mazen Sudah (714801), Tiia Kettunen (9028046) et al. · Figshare · 2020
Cited by 0Open access
Muni S. Srivastava, Kevin J. Keen · Biometrika · 1988
A noniterative method for estimating the interclass correlation coefficient is derived from the technique of weighted sums of squares. The asymptotic variance of this estimator is derived under the assumption of normality. Through extensive Monte Carlo simulations, the sample variance of this estimator is found not to differ greatly from that of the maximum likelihood estimator.
Cited by 47
Akiko Imura (7245170), Hiroyuki Nagaki (13977813), Takahiro Higuch (13977816) · Figshare · 2022
Cited by 0Open access
Sydney P. Rudko (8837366), Ronald L. Reimink (8837369), Bradley Peter (1858381) et al. · Figshare · 2020
<p>Comparing the reproducibility of samples run on the Chaibio Open qPCR thermocycler and the ABI 7500 thermocycler/QuantStudio 3.</p>
Cited by 0Open access
Bernard Rosner, Allan Donner, C H Hennekens · Journal of the Royal Statistical Society Series C (Applied Statistics) · 1977
B. Rosner, A. Donner, C. H. Hennekens, Estimation of Interclass Correlation from Familial Data, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 26, No. 2 (1977), pp. 179-187
Cited by 79
Ravindra Khattree, Dayanand N. Naik · Biometrika · 1994
Cited by 0
藤澤洋徳, Hironori Fujisawa · Tokyo Tech Research Repository (Tokyo Institute of Technology) · 2006
Cited by 0
Ajinkya R. Limkar (11268936), Simran M. Sharma (22272108), William A. Ricke (279015) · Figshare · 2025
Interclass Correlation Coefficients (ICC) with corresponding p-values assessing inter-rater reliability for quantitative measurements of the mouse prostatic urethra. ICC values were calculated from three independent raters. Interpretation thresholds are as follows: < 0.5 = poor, 0.5–0.75 = moderate, 0.75–0.9 = good, and >0.9 = excellent reliability. (DOCX)
Cited by 0Open access
Jae Hun Oh (11564882), Seung Pill Choi (8614758), Jong Ho Zhu (11564885) et al. · Figshare · 2021
Cited by 0Open access
Tripdatabase · Zenodo (CERN European Organization for Nuclear Research) · 2025
Intraclass correlation coefficient (ICC) values for retinal OCT angiography indicate good repeatability and reliability, especially for small scan sizes, but may vary with larger scans or different measurement conditions.
Cited by 0Open access
Thomas Demoor, Joris Walraevens, Dieter Fiems et al. · Lecture notes in computer science · 2008
Cited by 5
Tripdatabase · Zenodo (CERN European Organization for Nuclear Research) · 2025
Intraclass correlation coefficient (ICC) values for retinal OCT angiography indicate good repeatability and reliability, especially for small scan sizes, but may vary with larger scans or different measurement conditions.
Cited by 0Open access
Sanjaya K. Satapathy (8188650), Humberto C. Gonzalez (8843084), Jason Vanatta (8843087) et al. · Figshare · 2020
<p>Interclass correlation coefficient for Histology reading by two independent readers.</p>
Cited by 0Open access
Michael Eliasziw, Allan Donner · Biometrics · 1990
The asymptotic and finite-sample properties of several recent estimators of interclass correlation are compared to more traditional estimators in the case of a variable number of siblings per family. It is shown that Karlin's family-weighted pairwise estimator (Karlin, Cameron, and Williams, 1981, Proceedings of the National Academy of Science 78, 2664-2668) is virtually equivalent to the ensemble estimator (Rosner, Donner, and Hennekens, 1977, Applied Statistics 26, 179-187), thus suggesting an estimator of the former's asymptotic variance. Further, an estimator proposed by Srivastava (1984, Biometrika 71, 177-185) is shown to be identical to the modified sib-mean estimator (Konishi, 1982, Annals of the Institute of Statistical Mathematics 34, 505-515) when the sib-sib correlation is estimated by the method of unweighted group means. Although the estimator due to Srivastava has smaller asymptotic variance than the other two, the gain in efficiency is slight, for familial data, both asymptotically and in finite samples.
Cited by 15
Іhor Zanevskyy, Lyudmyla Zanevska · Journal of Testing and Evaluation · 2015
Abstract A mean-score reliability of a test in a form of repeated trials with the same measurement equipment is estimated using an intraclass correlation coefficient varied within -∞ and 1. The research aims to outline a method of estimation of the retest mean-score reliability coefficient adequate to its theoretical model, i.e., without negative values. The idea is: (1) to include all the scores obtained in the test–retest of the group of subjects to a united sample, and (2) to estimate corresponding true scores as individual means scores and include them to another sample. A proposed reliability score coefficient is derived as a ratio of a sum of squares of the individual means scores multiplied by a number of repeated trials to a total sum of squares. The method proposed in the work brings values of the mean-score reliability coefficient into the interval defined in the classic theory of test reliability: 0 to 1. The approach is illustrated with numerical examples.
Cited by 4