Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload.
Missing data can influence calculations of accumulated athlete workload. The objectives were to identify the best single imputation methods and examine workload trends using multiple imputation. External (jumps per hour) and internal (rating of perceived exertion; RPE) workload were recorded for 93...
| Publicado en: | Journal of Sports Science & Medicine Vol. 20; no. 2; pp. 188 - 197 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Hakan Gur, Journal of Sports Science & Medicine
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149972228&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149972228 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13032968 FYN jtl: Journal of Sports Science & Medicine issn: 13032968 maglogo: N pubinfo: dt: Jun2021 vid: 20 iid: 2 pid: 26030 pub: Hakan Gur, Journal of Sports Science & Medicine artinfo: ui: 149972228 149972228 149972228 10.52082/jssm.2021.188 149972228 ppf: 188 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload. aug: au: Benson, Lauren C. Stilling, Carlyn Owoeye, Oluwatoyosi B. A. Emery, Carolyn A. affil: United States Olympic & Paralympic Committee, Colorado Springs, CO, United States sug: subj: Athletes Workload Data Analysis, Statistical Human Secondary Analysis Descriptive Statistics Data Analysis Software Prospective Studies Male Female Adolescence Machine Learning Chi Square Test Adolescent: 13-18 years Male Female ab: Missing data can influence calculations of accumulated athlete workload. The objectives were to identify the best single imputation methods and examine workload trends using multiple imputation. External (jumps per hour) and internal (rating of perceived exertion; RPE) workload were recorded for 93 (45 females, 48 males) high school basketball players throughout a season. Recorded data were simulated as missing and imputed using ten imputation methods based on the context of the individual, team and session. Both single imputation and machine learning methods were used to impute the simulated missing data. The difference between the imputed data and the actual workload values was computed as root mean squared error (RMSE). A generalized estimating equation determined the effect of imputation method on RMSE. Multiple imputation of the original dataset, with all known and actual missing workload data, was used to examine trends in longitudinal workload data. Following multiple imputation, a Pearson correlation evaluated the longitudinal association between jump count and sRPE over the season. A single imputation method based on the specific context of the session for which data are missing (team mean) was only outperformed by methods that combine information about the session and the individual (machine learning models). There was a significant and strong association between jump count and sRPE in the original data and imputed datasets using multiple imputation. The amount and nature of the missing data should be considered when choosing a method for single imputation of workload data in youth basketball. Multiple imputation using several predictor variables in a regression model can be used for analyses where workload is accumulated across an entire season. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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