The influence of dietary carbohydrate on perceived recovery status differs at the group and individual level
evidence of nonergodicity among endurance athletes
DOI:
https://doi.org/10.51224/SRXIV.196Keywords:
intraindividual variability, monitoring, carbohydrate, training load monitoring, decision tree, machine learningAbstract
Purpose: Research findings are typically reported at the group level but applied to individuals. However, an emerging issue in sports science concerns nonergodicity — whereby group-level data cannot be generalized to individuals. The purpose of this study was to determine if the relationship between daily carbohydrate intake and perceived recovery status displays nonergodicity.
Methods: Fifty-five endurance athletes recorded daily measures of self-selected dietary intake, training, sleep, and subjective wellbeing for 12 weeks. We constructed linear models to measure the influence of daily carbohydrate intake on perceived recovery status while accounting for training load, sleep duration, sleep quality, and muscle soreness. Using linear model coefficients for carbohydrate intake we tested whether the distributions (mean and SD) differed at the group and individual levels (indicating nonergodicity). Additionally, a decision tree was created to explore factors that could provide an indication of an individual athlete’s relationship between carbohydrate intake and perceived recovery status.
Results: Mean values were not different between group- and individual-level analyses, but SDs at the individual level were ~2.5 times larger than at the group level, indicating nonergodicity. Model coefficients for carbohydrate intake were negative for five participants, positive for four participants, and non-significant for 31 participants. The Kappa value measuring accuracy of the decision tree was 0.54, indicating moderate prediction accuracy.
Conclusion: For most individuals, carbohydrate intake did not influence recovery status. However, the influence of dietary carbohydrate intake on daily recovery differs at the group and individual level. Therefore, practical recommendations should be based on individual-level analysis.
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References
Achten, J., Halson, S. L., Moseley, L., Rayson, M. P., Casey, A., & Jeukendrup, A. E. (2004). Higher dietary carbohydrate content during intensified running training results in better maintenance of performance and mood state. J Appl Physiol (1985), 96(4), 1331-1340. doi:10.1152/japplphysiol.00973.2003
Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 2: Predictive values. BMJ, 309(6947), 102. doi:10.1136/bmj.309.6947.102
Bakdash, J. Z., & Marusich, L. R. (2017). Repeated Measures Correlation. Front Psychol, 8, 456. doi:10.3389/fpsyg.2017.00456
Bartlett, J. D., Hawley, J. A., & Morton, J. P. (2015). Carbohydrate availability and exercise training adaptation: too much of a good thing? Eur J Sport Sci, 15(1), 3-12. doi:10.1080/17461391.2014.920926
Chee, N., Ghorbani, S., Golkashani, H. A., Leong, R. L. F., Ong, J. L., & Chee, M. W. L. (2021). Multi-Night Validation of a Sleep Tracking Ring in Adolescents Compared with a Research Actigraph and Polysomnography. Nat Sci Sleep, 13, 177-190. doi:10.2147/NSS.S286070
Chinoy, E. D., Cuellar, J. A., Huwa, K. E., Jameson, J. T., Watson, C. H., Bessman, S. C., . . . Markwald, R. R. (2021). Performance of seven consumer sleep-tracking devices compared with polysomnography. Sleep, 44(5). doi:10.1093/sleep/zsaa291
Chrzanowski-Smith, O. J., Piatrikova, E., Betts, J. A., Williams, S., & Gonzalez, J. T. (2020). Variability in exercise physiology: Can capturing intra-individual variation help better understand true inter-individual responses? Eur J Sport Sci, 20(4), 452-460. doi:10.1080/17461391.2019.1655100
Clemente, F. M., Rabbani, A., & Araujo, J. P. (2019). Ratings of perceived recovery and exertion in elite youth soccer players: Interchangeability of 10-point and 100-point scales. Physiol Behav, 210, 112641. doi:10.1016/j.physbeh.2019.112641
Evenepoel, C., Clevers, E., Deroover, L., Van Loo, W., Matthys, C., & Verbeke, K. (2020). Accuracy of Nutrient Calculations Using the Consumer-Focused Online App MyFitnessPal: Validation Study. J Med Internet Res, 22(10), e18237. doi:10.2196/18237
Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018). Lack of group-to-individual generalizability is a threat to human subjects research. Proc Natl Acad Sci U S A, 115(27), E6106-E6115. doi:10.1073/pnas.1711978115
Foster, C., Boullosa, D., McGuigan, M., Fusco, A., Cortis, C., Arney, B. E., . . . Porcari, J. P. (2021). 25 Years of Session Rating of Perceived Exertion: Historical Perspective and Development. Int J Sports Physiol Perform, 16(5), 612-621. doi:10.1123/ijspp.2020-0599
Fullagar, H. H. K., Harper, L. D., Govus, A., McCunn, R., Eisenmann, J., & McCall, A. (2019). Practitioner Perceptions of Evidence-Based Practice in Elite Sport in the United States of America. J Strength Cond Res, 33(11), 2897-2904. doi:10.1519/JSC.0000000000003348
Gastin, P. B., Meyer, D., & Robinson, D. (2013). Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football. J Strength Cond Res, 27(9), 2518-2526. doi:10.1519/JSC.0b013e31827fd600
Glazier, P. S., & Mehdizadeh, S. (2019). Challenging Conventional Paradigms in Applied Sports Biomechanics Research. Sports Med, 49(2), 171-176. doi:10.1007/s40279-018-1030-1
Haddad, M., Stylianides, G., Djaoui, L., Dellal, A., & Chamari, K. (2017). Session-RPE Method for Training Load Monitoring: Validity, Ecological Usefulness, and Influencing Factors. Front Neurosci, 11, 612. doi:10.3389/fnins.2017.00612
Halson, S. L., Lancaster, G. I., Achten, J., Gleeson, M., & Jeukendrup, A. E. (2004). Effects of carbohydrate supplementation on performance and carbohydrate oxidation after intensified cycling training. J Appl Physiol (1985), 97(4), 1245-1253. doi:10.1152/japplphysiol.01368.2003
Hill, Y., Meijer, R. R., Van Yperen, N. W., Michelakis, G., Barisch, S., & Den Hartigh, R. J. (2021). Nonergodicity in protective factors of resilience in athletes. Sport Exerc Perform Psychol, 10(2), 217.
Hooper, S. L., & Mackinnon, L. T. (1995). Monitoring overtraining in athletes. Recommendations. Sports Med, 20(5), 321-327. doi:10.2165/00007256-199520050-00003
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. J Stat Softw, 27, 1-22.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning : with applications in R. New York: Springer.
Killer, S. C., Svendsen, I. S., Jeukendrup, A. E., & Gleeson, M. (2017). Evidence of disturbed sleep and mood state in well-trained athletes during short-term intensified training with and without a high carbohydrate nutritional intervention. J Sports Sci, 35(14), 1402-1410. doi:10.1080/02640414.2015.1085589
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (Vol. 26): Springer.
Kuhn, M., & Wickham, H. (2020). Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. Retrieved from https://www.tidymodels.org
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/843571
Laurent, C. M., Green, J. M., Bishop, P. A., Sjokvist, J., Schumacker, R. E., Richardson, M. T., & Curtner-Smith, M. (2011). A practical approach to monitoring recovery: development of a perceived recovery status scale. J Strength Cond Res, 25(3), 620-628. doi:10.1519/JSC.0b013e3181c69ec6
Mangalam, M., & Kelty-Stephen, D. G. (2021). Point estimates, Simpson's paradox, and nonergodicity in biological sciences. Neurosci Biobehav Rev, 125, 98-107. doi:10.1016/j.neubiorev.2021.02.017
Melin, A. K., Heikura, I. A., Tenforde, A., & Mountjoy, M. (2019). Energy Availability in Athletics: Health, Performance, and Physique. Int J Sport Nutr Exerc Metab, 29(2), 152-164. doi:10.1123/ijsnem.2018-0201
Miller, D. J., Lastella, M., Scanlan, A. T., Bellenger, C., Halson, S. L., Roach, G. D., & Sargent, C. (2020). A validation study of the WHOOP strap against polysomnography to assess sleep. J Sports Sci, 38(22), 2631-2636. doi:10.1080/02640414.2020.1797448
Molenaar, P. C., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current directions in psychological science, 18(2), 112-117.
Neumann, N. D., Van Yperen, N. W., Brauers, J. J., Frencken, W., Brink, M. S., Lemmink, K., . . . Den Hartigh, R. J. R. (2022). Nonergodicity in Load and Recovery: Group Results Do Not Generalize to Individuals. Int J Sports Physiol Perform, 17(3), 391-399. doi:10.1123/ijspp.2021-0126
Roberts, D. M., Schade, M. M., Mathew, G. M., Gartenberg, D., & Buxton, O. M. (2020). Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography. Sleep, 43(7). doi:10.1093/sleep/zsaa045
Rothschild, J., Kilding, A. E., & Plews, D. J. (2020). Prevalence and Determinants of Fasted Training in Endurance Athletes: A Survey Analysis. Int J Sport Nutr Exerc Metab, 30(5), 345-356. doi:10.1123/ijsnem.2020-0109
Rothschild, J. A., Kilding, A. E., & Plews, D. J. (2021). Pre-Exercise Nutrition Habits and Beliefs of Endurance Athletes Vary by Sex, Competitive Level, and Diet. J Am Coll Nutr, 40(6), 517-528. doi:10.1080/07315724.2020.1795950
Rothschild, J. A., Morton, J. P., Stewart, T., Kilding, A. E., & Plews, D. J. (2022). The quantification of daily carbohydrate periodization among endurance athletes during 12 weeks of self-selected training: presentation of a novel Carbohydrate Periodization Index. medRxiv.
Rothschild, J. A., Stewart, T., Kilding, A. E., & Plews, D. J. (2022). Predicting daily recovery during long-term endurance training using machine learning analysis. SportRχiv.
Sawczuk, T., Jones, B., Scantlebury, S., & Till, K. (2018). The influence of training load, exposure to match play and sleep duration on daily wellbeing measures in youth athletes. J Sports Sci, 36(21), 2431-2437. doi:10.1080/02640414.2018.1461337
Sawczuk, T., Jones, B., Scantlebury, S., & Till, K. (2021). Influence of Perceptions of Sleep on Well-Being in Youth Athletes. J Strength Cond Res, 35(4), 1066-1073. doi:10.1519/JSC.0000000000002857
Schaffer, A. L., Dobbins, T. A., & Pearson, S. A. (2021). Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol, 21(1), 58. doi:10.1186/s12874-021-01235-8
Sousa, M., Madsen, K., Simões, H., Pereira, R., Negrão, C., Mendonça, R., . . . Silva, M. (2010). Effects of carbohydrate supplementation on competitive runners undergoing overload training followed by a session of intermittent exercise. Eur J Appl Physiol, 109(3), 507-516. doi:10.1007/s00421-010-1388-8
Thomas, D. T., Erdman, K. A., & Burke, L. M. (2016). American College of Sports Medicine Joint Position Statement. Nutrition and Athletic Performance. Medicine and Science in Sports and Exercise, 48(3), 543-568. doi:10.1249/MSS.0000000000000852
Thorpe, R. T., Strudwick, A. J., Buchheit, M., Atkinson, G., Drust, B., & Gregson, W. (2016). Tracking Morning Fatigue Status Across In-Season Training Weeks in Elite Soccer Players. Int J Sports Physiol Perform, 11(7), 947-952. doi:10.1123/ijspp.2015-0490
Zaffaroni, A., Coffey, S., Dodd, S., Kilroy, H., Lyon, G., O'Rourke, D., . . . Penzel, T. (2019). Sleep Staging Monitoring Based on Sonar Smartphone Technology. Annu Int Conf IEEE Eng Med Biol Soc, 2019, 2230-2233. doi:10.1109/EMBC.2019.8857033
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