INFORMAZIONI SU QUESTO ARTICOLO

Cita

Andrade, R., Wik, E. H., Rebelo-Marques, A., Blanch, P., Whiteley, R., Espregueira-Mendes, J., & Gabbett, T. J. (2020). Is the acute: Chronic workload ratio (ACWR) associated with risk of time-loss injury in professional team sports? A systematic review of methodology, variables and injury risk in practical situations. Sports medicine, 1–23.10.1007/s40279-020-01308-632572824 Search in Google Scholar

Ayala, F., López-Valenciano, A., Jose, A., De Ste Croix, M. B., Vera-García, F., García-Vaquero, M., … Myer, G. (2019). A preventive model for hamstring injuries in professional soccer: Learning algorithms. International journal of sports medicine, 40(5), 344–353.10.1055/a-0826-195530873572 Search in Google Scholar

Bacon, C. S., & Mauger, A. R. (2017). Prediction of overuse injuries in professional u18-u21 footballers using metrics of training distance and intensity. The Journal of Strength & Conditioning Research, 31(11), 3067–3076.10.1519/JSC.000000000000174427930446 Search in Google Scholar

Bahr, R., & Holme, I. (2003). Risk factors for sports injuries—A methodological approach. British journal of sports medicine, 37(5), 384–392.10.1136/bjsm.37.5.384175135714514527 Search in Google Scholar

Bhardwaj, B. K., & Pal, S. (2012). Data Mining: A prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418. Search in Google Scholar

Bittencourt, N. F. N., Meeuwisse, W. H., Mendonça, L. D., Nettel-Aguirre, A., Ocarino, J. M., & Fonseca, S. T. (2016). Complex systems approach for sports injuries: Moving from risk factor identification to injury pattern recognition—Narrative review and new concept. British journal of sports medicine, 50(21), 1309–1314.10.1136/bjsports-2015-09585027445362 Search in Google Scholar

Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., … Gregson, W. (2017). Monitoring athlete training loads: Consensus statement. International journal of sports physiology and performance, 12(s2), S2-161-S2-170.10.1123/IJSPP.2017-020828463642 Search in Google Scholar

Bowen, L., Gross, A. S., Gimpel, M., & Li, F.-X. (2017). Accumulated workloads and the acute: Chronic workload ratio relate to injury risk in elite youth football players. British journal of sports medicine, 51(5), 452–459.10.1136/bjsports-2015-095820546066327450360 Search in Google Scholar

Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press. Search in Google Scholar

Brink, M. S., Visscher, C., Arends, S., Zwerver, J., Post, W. J., & Lemmink, K. A. (2010). Monitoring stress and recovery: New insights for the prevention of injuries and illnesses in elite youth soccer players. British journal of sports medicine, 44(11), 809–815.10.1136/bjsm.2009.06947620511621 Search in Google Scholar

Bult, H. J., Barendrecht, M., & Tak, I. J. R. (2018). Injury risk and injury burden are related to age group and peak height velocity among talented male youth soccer players. Orthopaedic journal of sports medicine, 6(12), 2325967118811042.10.1177/2325967118811042629337430560140 Search in Google Scholar

Carey, D. L., Ong, K., Whiteley, R., Crossley, K. M., Crow, J., & Morris, M. E. (2018). Predictive modelling of training loads and injury in Australian football. International Journal of Computer Science in Sport, 17(1), 49–66.10.2478/ijcss-2018-0002 Search in Google Scholar

Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28.10.1016/j.compeleceng.2013.11.024 Search in Google Scholar

Chawla, N. V. (2005). Data Mining for Imbalanced Datasets: An Overview. In O. Maimon & L. Rokach (A c. Di), Data Mining and Knowledge Discovery Handbook (pagg. 853–867). Boston, MA: Springer US. https://doi.org/10.1007/0-387-25465-X_4010.1007/0-387-25465-X_40 Search in Google Scholar

Cima, G. (2017). Preliminary results on ontology-based open data publishing. In A. Artale, B. Glimm, & R. Kontchakov (A c. Di), Proceedings of the 30th international workshop on description logics, montpellier, france, july 18-21, 2017. CEUR-WS.org. Recuperato da http://ceur-ws.org/Vol-1879/paper24.pdf Search in Google Scholar

Cima, G., Lenzerini, M., & Poggi, A. (2017). Semantic technology for open data publishing. Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 1–1.10.1145/3102254.3102255 Search in Google Scholar

Cortez, P., & Embrechts, M. J. (2013). Using sensitivity analysis and visualization techniques to open black box data mining models. Information Sciences, 225, 1–17.10.1016/j.ins.2012.10.039 Search in Google Scholar

De Ridder, R., Witvrouw, E., Dolphens, M., Roosen, P., & Van Ginckel, A. (2017). Hip strength as an intrinsic risk factor for lateral ankle sprains in youth soccer players: A 3-season prospective study. The American journal of sports medicine, 45(2), 410–416.10.1177/036354651667265027852594 Search in Google Scholar

Delecroix, B., Mccall, A., Dawson, B., Berthoin, S., & Dupont, G. (2019). Workload monotony, strain and non-contact injury incidence in professional football players. Science and Medicine in Football, 3(2), 105–108.10.1080/24733938.2018.1508881 Search in Google Scholar

Fanchini, M., Rampinini, E., Riggio, M., Coutts, A. J., Pecci, C., & McCall, A. (2018). Despite association, the acute: Chronic work load ratio does not predict non-contact injury in elite footballers. Science and Medicine in Football, 2(2), 108–114.10.1080/24733938.2018.1429014 Search in Google Scholar

Foster, C. (1998). Monitoring training in athletes with reference to overtraining syndrome. Medicine and Science in Sports and Exercise, 30(7), 1164–1168. https://doi.org/10.1097/00005768-199807000-0002310.1097/00005768-199807000-000239662690 Search in Google Scholar

Foster, C., Florhaug, J. A., Franklin, J., Gottschall, L., Hrovatin, L. A., Parker, S., … Dodge, C. (2001). A new approach to monitoring exercise training. The Journal of Strength & Conditioning Research, 15(1), 109–115.10.1519/00124278-200102000-00019 Search in Google Scholar

Fuller, C. W., Ekstrand, J., Junge, A., Andersen, T. E., Bahr, R., Dvorak, J., … Meeuwisse, W. H. (2006). Consensus statement on injury definitions and data collection procedures in studies of football (soccer) injuries. Scandinavian journal of medicine & science in sports, 16(2), 83–92.10.1111/j.1600-0838.2006.00528.x16533346 Search in Google Scholar

Gabbett, T. J. (2016). The training—Injury prevention paradox: Should athletes be training smarter and harder? British journal of sports medicine, 50(5), 273–280.10.1136/bjsports-2015-095788478970426758673 Search in Google Scholar

Gjaka, M., Tschan, H., Francioni, F. M., Tishkuaj, F., & Tessitore, A. (2016). MONITORING OF LOADS AND RECOVERY PERCEIVED DURING WEEKS WITH DIFFERENT SCHEDULE IN YOUNG SOCCER PLAYERS. Kinesiologia Slovenica, 22(1). Search in Google Scholar

Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.10.1002/9781118548387 Search in Google Scholar

Hulin, B. T., Gabbett, T. J., Blanch, P., Chapman, P., Bailey, D., & Orchard, J. W. (2014). Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. British journal of sports medicine, 48(8), 708–712.10.1136/bjsports-2013-09252423962877 Search in Google Scholar

Impellizzeri, F. M., Rampinini, E., Coutts, A. J., Sassi, A., & Marcora, S. M. (2004). Use of RPE-based training load in soccer. Medicine & Science in sports & exercise, 36(6), 1042–1047.10.1249/01.MSS.0000128199.23901.2F Search in Google Scholar

Impellizzeri, F. M., Woodcock, S., Coutts, A. J., Fanchini, M., McCall, A., & Vigotsky, A. D. (2021). What Role Do Chronic Workloads Play in the Acute to Chronic Workload Ratio? Time to Dismiss ACWR and Its Underlying Theory. Sports Medicine, 51(3), 581–592. https://doi.org/10.1007/s40279-020-01378-610.1007/s40279-020-01378-633332011 Search in Google Scholar

Jaspers, A., Kuyvenhoven, J. P., Staes, F., Frencken, W. G., Helsen, W. F., & Brink, M. S. (2018). Examination of the external and internal load indicators’ association with overuse injuries in professional soccer players. Journal of science and medicine in sport, 21(6), 579–585.10.1016/j.jsams.2017.10.00529079295 Search in Google Scholar

Johnson, D. M., Williams, S., Bradley, B., Sayer, S., Murray Fisher, J., & Cumming, S. (2020). Growing pains: Maturity associated variation in injury risk in academy football. European journal of sport science, 20(4), 544–552.10.1080/17461391.2019.163341631215359 Search in Google Scholar

Johnson, L. L., Borkowf, C., & Albert, P. (2007). An Introduction to Biostatistics: Randomization, Hypothesis Testing, and Sample Size Estimation.10.1016/B978-012369440-9/50019-0 Search in Google Scholar

Kenttä, G., & Hassmén, P. (1998). Overtraining and recovery. Sports medicine, 26(1), 1–16.10.2165/00007256-199826010-000019739537 Search in Google Scholar

Ko, J., Rosen, A. B., & Brown, C. N. (2018). Functional performance tests identify lateral ankle sprain risk: A prospective pilot study in adolescent soccer players. Scandinavian Journal of Medicine & Science in Sports, 28(12), 2611–2616.10.1111/sms.1327930120831 Search in Google Scholar

Kofotolis, N. (2014). Ankle sprain injuries in soccer players aged 7-15 years during a one-year season. Biology of exercise, 10(2).10.4127/jbe.2014.0077 Search in Google Scholar

Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (Vol. 26). Springer.10.1007/978-1-4614-6849-3 Search in Google Scholar

Malina, R. M., Bouchard, C., & Bar-Or, O. (2004). Growth, maturation, and physical activity. Human kinetics.10.5040/9781492596837 Search in Google Scholar

Malone, S., Owen, A., Newton, M., Mendes, B., Collins, K. D., & Gabbett, T. J. (2017). The acute: Chonic workload ratio in relation to injury risk in professional soccer. Journal of science and medicine in sport, 20(6), 561–565.10.1016/j.jsams.2016.10.01427856198 Search in Google Scholar

Marshall, D. A., Lopatina, E., Lacny, S., & Emery, C. A. (2016). Economic impact study: Neuromuscular training reduces the burden of injuries and costs compared to standard warm-up in youth soccer. British journal of sports medicine, 50(22), 1388–1393.10.1136/bjsports-2015-09566627034127 Search in Google Scholar

McCall, A., Dupont, G., & Ekstrand, J. (2016). Injury prevention strategies, coach compliance and player adherence of 33 of the UEFA Elite Club Injury Study teams: A survey of teams’ head medical officers. British journal of sports medicine, 50(12), 725–730.10.1136/bjsports-2015-09525926795611 Search in Google Scholar

McCall, A., Dupont, G., & Ekstrand, J. (2018). Internal workload and non-contact injury: A one-season study of five teams from the UEFA Elite Club Injury Study. British journal of sports medicine, 52(23), 1517–1522.10.1136/bjsports-2017-09847329626055 Search in Google Scholar

Meeuwisse, W. H., Tyreman, H., Hagel, B., & Emery, C. (2007). A dynamic model of etiology in sport injury: The recursive nature of risk and causation. Clinical Journal of Sport Medicine, 17(3), 215–219.10.1097/JSM.0b013e3180592a4817513916 Search in Google Scholar

Mirwald, R. L., Baxter-Jones, A. D., Bailey, D. A., & BEUNEN, G. P. (2002). An assessment of maturity from anthropometric measurements. Medicine & science in sports & exercise, 34(4), 689–694.10.1249/00005768-200204000-00020 Search in Google Scholar

Montella, A., de Oña, R., Mauriello, F., Riccardi, M. R., & Silvestro, G. (2020). A data mining approach to investigate patterns of powered two-wheeler crashes in Spain. Accident Analysis & Prevention, 134, 105251.10.1016/j.aap.2019.07.02731402051 Search in Google Scholar

Oliver, J. L., Ayala, F., Croix, M. B. D. S., Lloyd, R. S., Myer, G. D., & Read, P. J. (2020). Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players. Journal of science and medicine in sport, 23(11), 1044–1048.10.1016/j.jsams.2020.04.02132482610 Search in Google Scholar

Petticrew, M. P., Sowden, A. J., Lister-Sharp, D., & Wright, K. (2000). False-negative results in screening programmes: Systematic review of impact and implications. Health technology assessment (Winchester, England), 4(5), 1–120.10.3310/hta4050 Search in Google Scholar

Philippaerts, R. M., Vaeyens, R., Janssens, M., Van Renterghem, B., Matthys, D., Craen, R., … Malina, R. M. (2006). The relationship between peak height velocity and physical performance in youth soccer players. Journal of sports sciences, 24(3), 221–230.10.1080/0264041050018937116368632 Search in Google Scholar

Polinder, S., Haagsma, J., Panneman, M., Scholten, A., Brugmans, M., & Van Beeck, E. (2016). The economic burden of injury: Health care and productivity costs of injuries in the Netherlands. Accident Analysis & Prevention, 93, 92–100.10.1016/j.aap.2016.04.00327177394 Search in Google Scholar

Read, P. J., Oliver, J. L., De Ste Croix, M. B. A., Myer, G. D., & Lloyd, R. S. (2018). A prospective investigation to evaluate risk factors for lower extremity injury risk in male youth soccer players. Scandinavian journal of medicine & science in sports, 28(3), 1244–1251.10.1111/sms.13013655676929130575 Search in Google Scholar

Richardson, A., Clarsen, B., Verhagen, E., & Stubbe, J. H. (2017). High prevalence of self-reported injuries and illnesses in talented female athletes. BMJ open sport & exercise medicine, 3(1), e000199.10.1136/bmjsem-2016-000199553025828761701 Search in Google Scholar

Rommers, N., Rössler, R., Verhagen, E., Vandecasteele, F., Verstockt, S., Vaeyens, R., … Witvrouw, E. (2020). A machine learning approach to assess injury risk in elite youth football players. Medicine and science in sports and exercise, 52(8), 1745–1751.10.1249/MSS.000000000000230532079917 Search in Google Scholar

Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernández, J., & Medina, D. (2018). Effective injury forecasting in soccer with GPS training data and machine learning. PloS one, 13(7), e0201264.10.1371/journal.pone.0201264605946030044858 Search in Google Scholar

Ruddy, J., Shield, A., Maniar, N., Williams, M., Duhig, S., Timmins, R., … Opar, D. (2018). Predictive modeling of hamstring strain injuries in elite Australian footballers. Medicine and science in sports and exercise, 50(5), 906–914.10.1249/MSS.000000000000152729266094 Search in Google Scholar

Sansone, P., Tschan, H., Foster, C., & Tessitore, A. (2020). Monitoring training load and perceived recovery in female basketball: Implications for training design. The Journal of Strength & Conditioning Research.10.1519/JSC.000000000000297130589724 Search in Google Scholar

Seshadri, D. R., Thom, M. L., Harlow, E. R., Gabbett, T. J., Geletka, B. J., Hsu, J. J., … Voos, J. E. (2021). Wearable technology and analytics as a complementary toolkit to optimize workload and to reduce injury burden. Frontiers in sports and active living, 2, 228.10.3389/fspor.2020.630576785963933554111 Search in Google Scholar

Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524.10.1016/j.asoc.2019.105524 Search in Google Scholar

Singh, S., & Gupta, P. (2014). Comparative study ID3, cart and C4. 5 decision tree algorithm: A survey. International Journal of Advanced Information Science and Technology (IJAIST), 27(27), 97–103. Search in Google Scholar

Timpka, T., Risto, O., & Björmsjö, M. (2008). Boys soccer league injuries: A community-based study of time-loss from sports participation and long-term sequelae. European journal of public health, 18(1), 19–24.10.1093/eurpub/ckm05017569703 Search in Google Scholar

Towlson, C., Salter, J., Ade, J. D., Enright, K., Harper, L. D., Page, R. M., & Malone, J. J. (2020). Maturity-associated considerations for training load, injury risk, and physical performance within youth soccer: One size does not fit all. Journal of Sport and Health Science.10.1016/j.jshs.2020.09.003 Search in Google Scholar

Vallance, E., Sutton-Charani, N., Imoussaten, A., Montmain, J., & Perrey, S. (2020). Combining Internal-and External-Training-Loads to Predict Non-Contact Injuries in Soccer. Applied Sciences, 10(15), 5261.10.3390/app10155261 Search in Google Scholar

van der Sluis, A., Elferink-Gemser, M. T., Coelho-e-Silva, M. J., Nijboer, J. A., Brink, M. S., & Visscher, C. (2014). Sport injuries aligned to peak height velocity in talented pubertal soccer players. International journal of sports medicine, 35(04), 351–355.10.1055/s-0033-1349874 Search in Google Scholar

Vänttinen, T., Blomqvist, M., Nyman, K., & Häkkinen, K. (2011). Changes in body composition, hormonal status, and physical fitness in 11-, 13-, and 15-year-old Finnish regional youth soccer players during a two-year follow-up. The Journal of Strength & Conditioning Research, 25(12), 3342–3351.10.1519/JSC.0b013e318236d0c221921822 Search in Google Scholar

Venturelli, M., Schena, F., Zanolla, L., & Bishop, D. (2011). Injury risk factors in young soccer players detected by a multivariate survival model. Journal of science and medicine in sport, 14(4), 293–298.10.1016/j.jsams.2011.02.01321474378 Search in Google Scholar

Wang, C., Stokes, T., Steele, R., Wedderkopp, N., & Shrier, I. (2020). Injury risk increases minimally over a large range of the acute: Chronic workload ratio in children. arXiv preprint arXiv:2010.02952. Search in Google Scholar

Watson, A., Brickson, S., Brooks, A., & Dunn, W. (2017). Subjective well-being and training load predict in-season injury and illness risk in female youth soccer players. British journal of sports medicine, 51(3), 194–199.10.1136/bjsports-2016-09658427919919 Search in Google Scholar

Windt, J., & Gabbett, T. J. (2017). How do training and competition workloads relate to injury? The workload—Injury aetiology model. British Journal of Sports Medicine, 51(5), 428–435.10.1136/bjsports-2016-09604027418321 Search in Google Scholar

Winter, E. M., & Maughan, R. J. (2009). Requirements for ethics approvals. Journal of sports sciences, 27(10), 985.10.1080/0264041090317834419847681 Search in Google Scholar

Zouhal, H., Boullosa, D., Ramirez-Campillo, R., Ali, A., & Granacher, U. (2021). Acute: Chronic Workload Ratio: Is There Scientific Evidence? Frontiers in Physiology, 12.10.3389/fphys.2021.669687813856934025457 Search in Google Scholar

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