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Comparison of Six Data Cleaning Methods for Determining Repetitive Head Impact Exposure in Youth Tackle Football.

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Title: Comparison of Six Data Cleaning Methods for Determining Repetitive Head Impact Exposure in Youth Tackle Football.
Authors: DeAngelo, Samantha1,2 (AUTHOR) Samantha.deangelo@osumc.edu, Culiver, Adam3,4 (AUTHOR), Le Flao, Enora1,2,5 (AUTHOR), Shoaf, Nick1 (AUTHOR), Doshi, Durshil1 (AUTHOR), Tracy, Ryan1 (AUTHOR), Aryeetey, Nii-Ayi6 (AUTHOR), Quatrale, Anna6 (AUTHOR), Smith, Carly1,2 (AUTHOR), Ma, Jianing7 (AUTHOR), Pan, Jeff7 (AUTHOR), Yang, Jingzhen2,8 (AUTHOR), Rose, Sean C2,9 (AUTHOR), Onate, James1,2,5 (AUTHOR), Edwards, Nathan5 (AUTHOR), Saygin, Zeynep2,6 (AUTHOR), Caccese, Jaclyn B.1,2,10 (AUTHOR) jaclyn.caccese@osumc.edu
Source: Annals of Biomedical Engineering. May2026, Vol. 54 Issue 5, p1494-1503. 10p.
Subjects: Data scrubbing, Classification algorithms, Football, Head injuries, Mouth protectors
Abstract: Purpose: Instrumented mouthguards (iMGs) are commonly used to quantify head acceleration event (HAE) exposure, but accurate interpretation requires rigorous data cleaning methods. This study compared six data cleaning methods for determining HAE rates and magnitudes, as well as cleaning method validity compared to the 5th method video verification in youth tackle football. Methods: Fifty athletes (ages 8-12) wore Impact Monitoring Mouthguards during games across one season. Six data cleaning methods were applied to HAEs, including uncleaned data, time-windowing, proprietary classification algorithms, video verification, and combinations thereof. Impact rate, peak linear acceleration (PLA), and peak rotational velocity (PRV) were compared across methods using rate ratios, and intra-class correlation coefficients (ICCs), and non-parametric analyses. Results: Data cleaning methods significantly influenced HAE rate but had minimal effect on magnitude. The uncleaned dataset produced the highest HAE rate (67.75 per athlete exposure), while the most stringent method (i.e., time-windowed, proprietary algorithm-classified, video-verified data) yielded the lowest (0.70 per athlete exposure). Although the time-windowed, proprietary algorithm-classified data demonstrated high specificity (0.96), it demonstrated low sensitivity (0.37) and positive predictive value (0.39) when compared to video-verified data. Differences in PLA across methods were not significant; only one significant difference in PRV was observed. Conclusions: These findings highlight the impact of data cleaning on HAE quantification in youth tackle football. Although video verification remains best practice, it is resource intensive. Time-windowed, algorithm-classified data may serve as an efficient proxy in similar cohorts, though researchers should recognize its limitations. Findings support the need for standardized data cleaning methods and transparent reporting to ensure accurate and comparable HAE exposure estimates. [ABSTRACT FROM AUTHOR]
Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: Comparison of Six Data Cleaning Methods for Determining Repetitive Head Impact Exposure in Youth Tackle Football.
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  Data: <searchLink fieldCode="AR" term="%22DeAngelo%2C+Samantha%22">DeAngelo, Samantha</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> Samantha.deangelo@osumc.edu</i><br /><searchLink fieldCode="AR" term="%22Culiver%2C+Adam%22">Culiver, Adam</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Le+Flao%2C+Enora%22">Le Flao, Enora</searchLink><relatesTo>1,2,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shoaf%2C+Nick%22">Shoaf, Nick</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Doshi%2C+Durshil%22">Doshi, Durshil</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tracy%2C+Ryan%22">Tracy, Ryan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Aryeetey%2C+Nii-Ayi%22">Aryeetey, Nii-Ayi</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Quatrale%2C+Anna%22">Quatrale, Anna</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Smith%2C+Carly%22">Smith, Carly</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Jianing%22">Ma, Jianing</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Jeff%22">Pan, Jeff</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jingzhen%22">Yang, Jingzhen</searchLink><relatesTo>2,8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rose%2C+Sean+C%22">Rose, Sean C</searchLink><relatesTo>2,9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Onate%2C+James%22">Onate, James</searchLink><relatesTo>1,2,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Edwards%2C+Nathan%22">Edwards, Nathan</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Saygin%2C+Zeynep%22">Saygin, Zeynep</searchLink><relatesTo>2,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Caccese%2C+Jaclyn+B%2E%22">Caccese, Jaclyn B.</searchLink><relatesTo>1,2,10</relatesTo> (AUTHOR)<i> jaclyn.caccese@osumc.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Annals+of+Biomedical+Engineering%22">Annals of Biomedical Engineering</searchLink>. May2026, Vol. 54 Issue 5, p1494-1503. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Data+scrubbing%22">Data scrubbing</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Football%22">Football</searchLink><br /><searchLink fieldCode="DE" term="%22Head+injuries%22">Head injuries</searchLink><br /><searchLink fieldCode="DE" term="%22Mouth+protectors%22">Mouth protectors</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Instrumented mouthguards (iMGs) are commonly used to quantify head acceleration event (HAE) exposure, but accurate interpretation requires rigorous data cleaning methods. This study compared six data cleaning methods for determining HAE rates and magnitudes, as well as cleaning method validity compared to the 5th method video verification in youth tackle football. Methods: Fifty athletes (ages 8-12) wore Impact Monitoring Mouthguards during games across one season. Six data cleaning methods were applied to HAEs, including uncleaned data, time-windowing, proprietary classification algorithms, video verification, and combinations thereof. Impact rate, peak linear acceleration (PLA), and peak rotational velocity (PRV) were compared across methods using rate ratios, and intra-class correlation coefficients (ICCs), and non-parametric analyses. Results: Data cleaning methods significantly influenced HAE rate but had minimal effect on magnitude. The uncleaned dataset produced the highest HAE rate (67.75 per athlete exposure), while the most stringent method (i.e., time-windowed, proprietary algorithm-classified, video-verified data) yielded the lowest (0.70 per athlete exposure). Although the time-windowed, proprietary algorithm-classified data demonstrated high specificity (0.96), it demonstrated low sensitivity (0.37) and positive predictive value (0.39) when compared to video-verified data. Differences in PLA across methods were not significant; only one significant difference in PRV was observed. Conclusions: These findings highlight the impact of data cleaning on HAE quantification in youth tackle football. Although video verification remains best practice, it is resource intensive. Time-windowed, algorithm-classified data may serve as an efficient proxy in similar cohorts, though researchers should recognize its limitations. Findings support the need for standardized data cleaning methods and transparent reporting to ensure accurate and comparable HAE exposure estimates. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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