The Concurrent Validity of Session-Rating of Perceived Exertion Workload Obtained Face-to-Face Versus Via an Online Application: A Team Case Study

in International Journal of Sports Physiology and Performance
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Purpose: To compare the concurrent validity of session-rating of perceived exertion (sRPE) workload determined face-to-face and via an online application in basketball players. Methods: Sixteen semiprofessional, male basketball players (21.8 [4.3] y, 191.2 [9.2] cm, 85.0 [15.7] kg) were monitored during all training sessions across the 2018 (8 players) and 2019 (11 players) seasons in a state-level Australian league. Workload was reported as accumulated PlayerLoad (PL), summated-heart-rate-zones (SHRZ) workload, and sRPE. During the 2018 season, rating of perceived exertion (RPE) was determined following each session via individualized face-to-face reporting. During the 2019 season, RPE was obtained following each session via a phone-based, online application. Repeated-measures correlations with 95% confidence intervals were used to determine the relationships between sRPE collected using each method and other workload measures (PL and SHRZ) as indicators of concurrent validity. Results: Although all correlations were significant (P < .05), sRPE obtained using face-to-face reporting demonstrated stronger relationships with PL (r = .69 [.07], large) and SHRZ (r = .74 [.06], very large) compared with the online application (r = .29 [.25], small [PL] and r = .34 [.22], moderate [SHRZ]). Conclusions: Concurrent validity of sRPE workload was stronger when players reported RPE in an individualized, face-to-face manner compared with using a phone-based online application. Given the weaker relationships with other workload measures, basketball practitioners should be cautious when using player training workloads predicated on RPE obtained via online applications.

The authors are with the School of Health, Medical, and Applied Sciences and the Human Exercise and Training Laboratory, Central Queensland University, Rockhampton, QLD, Australia.

Fox (j.fox2@cqu.edu.au) is corresponding author.
  • 1.

    Fox JL, Scanlan AT, Stanton R. A review of player monitoring approaches in basketball: current trends and future directions. J Strength Cond Res. 2017;31:20212029. PubMed ID: 28445227 doi:

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

    Fox JL, Scanlan AT, Sargent C, Stanton R. A survey of player monitoring approaches and microsensor use in basketball. J Hum Sport Exerc. 2020;15(1):230240. doi:

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

    Scanlan AT, Wen N, Tucker PS, Borges NR, Dalbo VJ. Training mode’s influence on the relationships between training-load models during basketball conditioning. Int J Sports Physiol Perform. 2014;9:853859. PubMed ID: 24434042 doi:

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

    Coyne JO, Haff GG, Coutts AJ, Newton RU, Nimphius S. The current state of subjective training load monitoring—A practical perspective and call to action. Sports Med Open. 2018;4:58. PubMed ID: 30570718

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Menaspà MJ, Menaspà P, Clark SA, Fanchini M. Validity of the online athlete management system to assess training load. Int J Sports Physiol Perform. 2018;13:750754.

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

    Roos L, Taube W, Tuch C, Frei KM, Wyss T. Factors that influence the rating of perceived exertion after endurance training. Int J Sports Physiol Perform. 2018;13:10421049. PubMed ID: 29543071 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Pino-Ortega J, Bastida-Castillo A, Lozano JMO, Rojas-Valverde D, Reche-Soto P, Gomez-Carmona CD. Comparison of two methods for recording heart rate telemetry: chest band vs technical shirt. Retos. 2019;36:469473.

    • Search Google Scholar
    • Export Citation
  • 8.

    Barrett S, Midgley A, Lovell R. PlayerLoad: reliability, convergent validity, and influence of unit position during treadmill running. Int J Sports Physiol Perform. 2014;9:945952. PubMed ID: 24622625 doi:

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

    Edwards S. The Heart Rate Monitor Book. Sacramento, CA: Fleet Feet Press; 1993.

  • 10.

    Berkelmans DM, Dalbo VJ, Fox JL, et al. Influence of different methods to determine maximum heart rate on training load outcomes in basketball players. J Strength Cond Res. 2018;32:31773185. PubMed ID: 30540282 doi:

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

    Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15:109115 PubMed ID: 11708692

  • 12.

    Saw AE, Kellmann M, Main LC, Gastin PB. Athlete self-report measures in research and practice: considerations for the discerning reader and fastidious practitioner. Int J Sports Physiol Perform. 2017;12:S2-127S2-135. PubMed ID: 27834546 doi:

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

    Holbrook AL, Green MC, Krosnick JA. Telephone versus face-to-face interviewing of national probability samples with long questionnaires: comparisons of respondent satisficing and social desirability response bias. Public Opin Q. 2003;67:79125.

    • Crossref
    • Search Google Scholar
    • Export Citation
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