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Monitoring External Workloads and Co...

Van Dyke, Michelle Lynn,

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Monitoring External Workloads and Countermovement Jump Performance Throughout a Preseason in Division 1 Collegiate Women's Basketball Players /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Monitoring External Workloads and Countermovement Jump Performance Throughout a Preseason in Division 1 Collegiate Women's Basketball Players // Michelle Lynn Van Dyke.
Author:	Van Dyke, Michelle Lynn,
Description:	1 electronic resource (155 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Sports management. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30970358
ISBN:	9798380866118

Monitoring External Workloads and Countermovement Jump Performance Throughout a Preseason in Division 1 Collegiate Women's Basketball Players /
Van Dyke, Michelle Lynn,

Monitoring External Workloads and Countermovement Jump Performance Throughout a Preseason in Division 1 Collegiate Women's Basketball Players /Michelle Lynn Van Dyke. - 1 electronic resource (155 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Monitoring external workloads and countermovement jump performance may be useful for coaches. PURPOSE: The purpose of this study was to determine the effects of external load on player performance as measured by a CMJ and specific blood biomarkers throughout the preseason. METHODS:10 female division 1 basketball athletes had Player: LoadTM (PL) monitored for all mandatory basketball training during six weeks of the preseason and CMJs were performed weekly. Blood biomarkers were collected before preseason and at the end of preseason. Data were analyzed via the Catapult Sport software (Openfield, Catapult, Innovations, Melbourne, VIC, Australia) to quantify all participant movement. Data from CMJs were analyzed via Sparta Science technology (Sparta: Trac; SPARTA Performance Science, v1.2.4). Cumulative effect of physical activity (CTPL) was estimated as a sum of total PL up to each jump testing session divided by the number of days. Linear mixed-effects models were used to model data related to the efficacy of PL and CTPL. Athletes (id) and their positions were examined as potential random effects. RESULTS: The best fit model suggested a high-order polynomial pattern between PL and the number of days since the first jump testing session with a random effect for the intercept (marginal R2 = 0.290; conditional R2 = 0.471). The fixed effect for the slope of the first order term was found to be positive. There was a significant negative effect of CTPL on JH (p = 0.0037). The boot strapped model showed a marginal R2 of 0.0183 (95% CI [0.000952, 0.0744]) and a conditional R2 of 0.884 (95% CI [0.762, 0.956]). For RSImod, a significant negative association between RSImod and CTPL (p = 0.0039, 95% CI [-0.0002214, -4.597081e-05]). CONCLUSION: Workloads increase during preseason. CMJ height and RSImod may have limited utility in displaying the effects cumulative workloads. Position played did not impact workload or the impact of that workload on the player. PRACTICAL APPLICATION: Cumulative effect of physical activity may be tracked using CTPL derived from PL. Practitioners may be encouraged to monitor alternative countermovement variables to better understand performance response to the cumulative effect of physical activity.

English

ISBN: 9798380866118Subjects--Topical Terms:

3423935
Sports management.
Subjects--Index Terms:

External workloads

Monitoring External Workloads and Countermovement Jump Performance Throughout a Preseason in Division 1 Collegiate Women's Basketball Players /
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100 1 $a Van Dyke, Michelle Lynn, $e author. $3 3823347
245 1 0 $a Monitoring External Workloads and Countermovement Jump Performance Throughout a Preseason in Division 1 Collegiate Women's Basketball Players / $c Michelle Lynn Van Dyke.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (155 pages)
336 $a text $b txt $2 rdacontent
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338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisors: Ramsey, Michael; Mizuguchi, Satoshi; Carroll, Kevin; Burland, Julie.
502 $b Ph.D. $c East Tennessee State University $d 2023.
520 $a Monitoring external workloads and countermovement jump performance may be useful for coaches. PURPOSE: The purpose of this study was to determine the effects of external load on player performance as measured by a CMJ and specific blood biomarkers throughout the preseason. METHODS:10 female division 1 basketball athletes had Player: LoadTM (PL) monitored for all mandatory basketball training during six weeks of the preseason and CMJs were performed weekly. Blood biomarkers were collected before preseason and at the end of preseason. Data were analyzed via the Catapult Sport software (Openfield, Catapult, Innovations, Melbourne, VIC, Australia) to quantify all participant movement. Data from CMJs were analyzed via Sparta Science technology (Sparta: Trac; SPARTA Performance Science, v1.2.4). Cumulative effect of physical activity (CTPL) was estimated as a sum of total PL up to each jump testing session divided by the number of days. Linear mixed-effects models were used to model data related to the efficacy of PL and CTPL. Athletes (id) and their positions were examined as potential random effects. RESULTS: The best fit model suggested a high-order polynomial pattern between PL and the number of days since the first jump testing session with a random effect for the intercept (marginal R2 = 0.290; conditional R2 = 0.471). The fixed effect for the slope of the first order term was found to be positive. There was a significant negative effect of CTPL on JH (p = 0.0037). The boot strapped model showed a marginal R2 of 0.0183 (95% CI [0.000952, 0.0744]) and a conditional R2 of 0.884 (95% CI [0.762, 0.956]). For RSImod, a significant negative association between RSImod and CTPL (p = 0.0039, 95% CI [-0.0002214, -4.597081e-05]). CONCLUSION: Workloads increase during preseason. CMJ height and RSImod may have limited utility in displaying the effects cumulative workloads. Position played did not impact workload or the impact of that workload on the player. PRACTICAL APPLICATION: Cumulative effect of physical activity may be tracked using CTPL derived from PL. Practitioners may be encouraged to monitor alternative countermovement variables to better understand performance response to the cumulative effect of physical activity.
546 $a English
590 $a School code: 0069
650 4 $a Sports management. $3 3423935
650 4 $a Physical education. $3 635343
650 4 $a Kinesiology. $3 517627
653 $a External workloads
653 $a Countermovement
653 $a Jump performance
653 $a Basketball players
653 $a Catapult Sport software
690 $a 0575
690 $a 0523
690 $a 0430
710 2 $a East Tennessee State University. $e degree granting institution. $3 3771801
720 1 $a Ramsey, Michael $e degree supervisor.
720 1 $a Mizuguchi, Satoshi $e degree supervisor.
720 1 $a Carroll, Kevin $e degree supervisor.
720 1 $a Burland, Julie $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-06B.
790 $a 0069
791 $a Ph.D.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30970358