We are updating our website on Thursday, December 2 from 9 AM – 5 PM EST. During this time, users may experience some disruptions while using the site. We apologize for the inconvenience.

Do Athlete Monitoring Tools Improve a Coach’s Understanding of Performance Change?

in International Journal of Sports Physiology and Performance
Restricted access

Purchase article

USD  $24.95

Student 1 year online subscription

USD  $114.00

1 year online subscription

USD  $152.00

Student 2 year online subscription

USD  $217.00

2 year online subscription

USD  $289.00

Purpose: To assess a coach’s subjective assessment of their athletes’ performances and whether the use of athlete-monitoring tools could improve on the coach’s prediction to identify performance changes. Methods: Eight highly trained swimmers (7 male and 1 female, age 21.6 [2.0] y) recorded perceived fatigue, total quality recovery, and heart-rate variability over a 9-month period. Prior to each race of the swimmers’ main 2 events, the coach (n = 1) was presented with their previous race results and asked to predict their race time. All race results (n = 93) with aligning coach’s predictions were recorded and classified as a dichotomous outcome (0 = no change; 1 = performance decrement or improvement [change +/− > or < smallest meaningful change]). A generalized estimating equation was used to assess the coach’s accuracy and the contribution of monitoring variables to the model fit. The probability from generalized estimating equation models was assessed with receiver operating characteristic curves to identify the model’s accuracy from the area under the curve analysis. Results: The coach’s predictions had the highest diagnostic accuracy to identify both decrements (area under the curve: 0.93; 95% confidence interval, 0.88–0.99) and improvements (area under the curve: 0.89; 95% confidence interval, 0.83–0.96) in performance. Conclusions: These findings highlight the high accuracy of a coach’s subjective assessment of performance. Furthermore, the findings provide a future benchmark for athlete-monitoring systems to be able to improve on a coach’s existing understanding of swimming performance.

The authors are with the Sport and Exercise Discipline Group, Faculty of Health, University of Technology Sydney, Sydney, NSW, Australia. Crowcroft and Slattery are also with the New South Wales Inst of Sport, Sydney, NSW, Australia. McCleave is also with Rowing Australia, Yarralumla, ACT, Australia.

Crowcroft (stephen.crowcroft@uts.edu.au) is corresponding author.
  • 1.

    Kiely J. Periodization paradigms in the 21st century: evidence-led or tradition-driven. Int J Sports Physiol Perform. 2012;7(3):242250. PubMed ID: 22356774 doi:10.1123/ijspp.7.3.242

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Abraham A, Collins D. Taking the next step: ways forward for coaching science. Quest. 2011;63(4):366384. doi:10.1080/00336297.2011.10483687

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    Collins D, Collins L, Carson HJ. “If it feels right, do it:” intuitive decision making in a sample of high-level sport coaches. Front Psychol. 2016;7:504. PubMed ID: 27148116

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Kahneman D, Klein G. Conditions for intuitive expertise: a failure to disagree. Am Psychol. 2009;64(6):515526. PubMed ID: 19739881 doi:10.1037/a0016755

  • 5.

    Borresen J, Lambert MI. The quantification of training load, the training response and the effect on performance. Sports Med. 2009;39(9):779795. doi:10.2165/11317780-000000000-00000

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Taylor K, Chapman DW, Cronin JB, Newton MJ, Gill N. Fatigue monitoring in high performance sport: a survey of current trends. J Aus Strength Cond. 2012;20(1):1223.

    • Search Google Scholar
    • Export Citation
  • 7.

    Grove WM, Zald DH, Lebow BS, Snitz BE, Nelson C. Clinical versus mechanical prediction: a meta-analysis. Psychol Assess. 2000;12(1):1930. PubMed ID: 10752360 doi:10.1037/1040-3590.12.1.19

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Calvert TW, Banister EW, Savage MV, Bach T. A systems model of the effects of training on physical performance. IEEE Trans Syst Man Cybern Syst. 1976;SMC-6(2):94102. doi:10.1109/TSMC.1976.5409179

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Hellard P, Avalos M, Lacoste L, Barale F, Chatard JC, Millet GP. Assessing the limitations of the Banister model in monitoring training. J Sports Sci. 2006;24(5):509520. PubMed ID: 16608765 doi:10.1080/02640410500244697

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Mann TN, Lamberts RP, Lambert MI. High responders and low responders: factors associated with individual variation in response to standardized training. Sports Med. 2014;44(8):11131124. PubMed ID: 24807838 doi:10.1007/s40279-014-0197-3

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Bellenger CR, Karavirta L, Thomson RL, Robertson EY, Davison K, Buckley JD. Contextualising parasympathetic hyperactivity in functionally overreached athletes with perceptions of training tolerance. Int J Sports Physiol Perform. 2016;11(5):685692. doi:10.1123/ijspp.2015-0495

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12.

    Aubry A, Hausswirth C, Louis J, Coutts AJ, Le Meur Y. Functional overreaching: the key to peak performance during the taper? Med Sci Sports Exerc. 2014;46(9):17691777. PubMed ID: 25134000 doi:10.1249/MSS.0000000000000301

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Crowcroft S, McCleave E, Slattery K, Coutts AJ. Assessing the measurement sensitivity and diagnostic characteristics of athlete-monitoring tools in national swimmers. Int J Sports Physiol Perform. 2017;12(suppl 2):S2-95S2-100.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Kenttä G, Hassmén P. Overtraining and recovery: a conceptual model. Sports Med. 1998;26(1):116. PubMed ID: 9739537 doi:10.2165/00007256-199826010-00001

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Plews DJ, Laursen PB, Le Meur Y, Hausswirth C, Kilding AE, Buchheit M. Monitoring training with heart rate variability: how much compliance is needed for valid assessment? Int J Sports Physiol Perform. 2013;9(5):783790. PubMed ID: 24334285 doi:10.1123/ijspp.2013-0455

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Wallace LK, Slattery KM, Coutts AJ. Establishing the criterion validity and reliability of common methods for quantifying training load. J Strength Cond Res. 2014;28(8):23302337. PubMed ID: 24662229 doi:10.1519/JSC.0000000000000416

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Foster C, Daines E, Hector L, Snyder AC, Welsh R. Athletic performance in relation to training load. Wis Med J. 1996;95(6):370374. PubMed ID: 8693756

  • 18.

    Hopkins WG, Hawley JA, Burke LM. Design and analysis of research on sport performance enhancement. Med Sci Sports Exerc. 1999;31(3):472485. PubMed ID: 10188754 doi:10.1097/00005768-199903000-00018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Pettitt RW. The standard difference score: a new statistic for evaluating strength and conditioning programs. J Strength Cond Res. 2010;24(1):287291. PubMed ID: 19924005 doi:10.1519/JSC.0b013e3181c3b99b

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Zeger SL, Liang K, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988;44(4):10491060. doi:10.2307/2531734.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Pan WK. Akaike’s information criterion in generalized estimating equations. Biometrics. 2001;57(1):120125. PubMed ID: 11252586 doi:10.1111/j.0006-341X.2001.00120.x

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27(8):861874. doi:10.1016/j.patrec.2005.10.010

  • 23.

    Shanteau J. Competence in experts: the role of task characteristics. Organ Behav Hum Decis Process. 1992;53:252266. doi:10.1016/0749-5978(92)90064-E

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24.

    Hooper SL, Mackinnon LT, Howard A, Gordon RD, Bachmann AW. Markers for monitoring overtraining and recovery. Med Sci Sports Exerc. 1995;27(1):106112. PubMed ID: 7898325 doi:10.1249/00005768-199501000-00019

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Buchheit M. Sensitivity of monthly heart rate and psychometric measures for monitoring physical performance in highly trained young handball players. J Sports Med. 2015;36(5):351356.

    • Search Google Scholar
    • Export Citation
  • 26.

    Robertson S, Bartlett JD, Gastin PB. Red, amber or green? Athlete monitoring in team sport: the need for decision support systems. Int J Sports Physiol Perform. 2017;12(suppl 2):S2-73S2-79.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27.

    Bourdon PC, Cardinale M, Murray A, et al. Monitoring athlete training loads: consensus statement. Int J Sports Physiol Perform. 2017;12(suppl 2):S2-161S2-170.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28.

    Saw AE, Main LC, Gastin PB. Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med. 2016;50(5):281291. PubMed ID: 26423706 doi:10.1136/bjsports-2015-094758

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 29.

    Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol. 2014;5:73. PubMed ID: 24578692 doi:10.3389/fphys.2014.00073

  • 30.

    Chalencon S, Busso T, Lacour JR, et al. A model for the training effects in swimming demonstrates a strong relationship between parasympathetic activity, performance and index of fatigue. PLoS One. 2012;7(12):e52636. PubMed ID: 23285121 doi:10.1371/journal.pone.0052636

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31.

    Wallace LK, Slattery KM, Coutts AJ. A comparison of methods for quantifying training load: relationships between modelled and actual training responses. Eur J Appl Physiol. 2014;114(1):1120. PubMed ID: 24104194 doi:10.1007/s00421-013-2745-1

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Gilbert WD, Trudel P. Learning to coach through experience: reflection in model youth sport coaches. J Teach Phys Educ. 2001;21(1):1634. doi:10.1123/jtpe.21.1.16

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 33.

    Goldberg LR. Man versus model of man: a rationale, plus some evidence, for a method of improving on clinical inferences. Psychol Bull. 1970;73(6):422432. doi:10.1037/h0029230

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 2586 1493 87
Full Text Views 115 47 6
PDF Downloads 116 60 9