Barrett, S., Midgley, A., & Lovell, R. (2014). PlayerLoad™: Reliability, convergent validity, and influence of unit position during treadmill running. International Journal of Sports Physiology and Performance, 9, 945-952.10.1123/ijspp.2013-041824622625
Boyd, L., Ball, K., & Aughey, R. (2011). The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. International Journal of Sports Physiology and Performance, 6, 311-321.10.1123/ijspp.6.3.31121911857
Blanch, P. & Gabbett, T. (2015). Has the athlete trained enough to return to play safely? The acute:chronic workload ratio permits clinicians to quantify a player’s risk of subsequent injury. British Journal of Sports Medicine, 50, 471-475.10.1136/bjsports-2015-09544526701923
Biermann, A. (1987). Fundamental mechanisms in machine learning and inductive inference: Part 2. Advanced Topics in Artificial Intelligence 345, 125-145.10.1007/978-3-662-40145-3_4
Bittencourt, N., Meeuwise, W., Mendonca, L., Nettel-Aguirre, A., Ocarino, L., & Fonseca, S. (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, 1309-1314.10.1136/bjsports-2015-09585027445362
Buchheit, M. (2014). Monitoring training status with HR measures: Do all roads lead to Rome? Frontiers in Physiology, 5, 1-19.10.3389/fphys.2014.00073393618824578692
Buchheit, M., Chivot, A., Parouty, J., Mercier, D., Haddad, A.H., Laursen, P.B., & Ahmaid i, S. (2009). Monitoring endurance running performance using cardiac parasympathetic function. European Journal of Applied Physiology, 108(6), 1153–1167.10.1007/s00421-009-1317-x20033207
Cook, C. (2016). Predicting future physical injury in sports: It’s a complicated dynamic system. British Journal of Sports Medicine, 50, 1356-1357.10.1136/bjsports-2016-09644527288514
Drew, M. & Finch, C. (2016). The relationship between training load and injury, illness andsoreness: A systematic review. Sports Medicine, 46, 861-883.10.1007/s40279-015-0459-826822969
Dye, S.F. (2001). Therapeutic implications of a tissue homeostasis approach to patellofemoral pain. Sports Medicine and Arthroscopy Review, 9(4), 306–311.10.1097/00132585-200110000-00008
Dye, S.F. (2005). The pathophysiology of patellofemoral pain. Clinical Orthopaedics and Related Research, 436, 100–110.10.1097/01.blo.0000172303.74414.7d
Foster, C. (1998). Monitoring training in athletes with reference to overtraining syndrome. Medicine & Science in Sports & Exercise, 30, 1164-1168.10.1097/00005768-199807000-00023
Friedman, N., Murphy, K., & Russell, S. (1998). Learning the structure of dynamic probabilistic networks. In G. Cooper, & S. Moral (Eds.), Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, (139-147). San Francisco, CA: Morgan Kaufmann Publishers, Inc.
Fuster-Parra, P., Garcia-Mas, A., Ponseti, F., Palou, P., & Cruz, J. (2014). A bayesian network to discover relationships between negative features in sport: A case study of teen players. Quality & Quantity 48, 1473-1491.10.1007/s11135-013-9848-y
Gabbett, T. (2010). The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. Journal of Strength and Conditioning Research, 24, 2593-2603.10.1519/JSC.0b013e3181f19da4
Gabbett, T. (2016). The training-injury prevention paradox: Should athletes be training smarter and harder?” British Journal of Sport Medicine, 50, 273-280.10.1136/bjsports-2015-095788
Galea, S., Riddle, M., & Kaplan, G. (2010). Causal thinking and complex system approaches in epidemiology. International Journal of Epidemiology, 39, 97-106.10.1093/ije/dyp296
Gisselman, A.S., Baxter, G.D., Wright, A., Hegedus, E., & Tumilty, E. (2016). Musculoskeletal overuse injuries and heart rate variability: Is there a link? Medical Hypotheses, 87(C), 1–7.10.1016/j.mehy.2015.12.003
Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers and Operations Research, 13, 533-549.10.1016/0305-0548(86)90048-1
Holme, B.R. (2015). Wearable microsensor technology to measure physical activity demands in handball: A reliability study of Inertial Movement Analysis and PlayerLoad (master’s thesis). Norwegian School of Sport Sciences, Oslo, Norway.
Hooper, K., Mackinnon, L., Howard, A., Gordon, R., & Bachman, A. (1995). Markers for monitoring overtraining and recovery. Medicine & Science in Sports & Exercise, 27, 106-112.10.1249/00005768-199501000-00019
Hulin, B., Gabbett, T., Lawson, D., Captui, P., & Sampson, J. (2015). The acute:chronic workload ratio predicts injury: High chronic workload may decrease injury risk in elite rugby league players. British Journal of Sports Medicine, 50, 231-236.10.1136/bjsports-2015-09481726511006
Ilyukhina, V. A. (2011). Continuity and prospects of research in systemic integrative psychophysiology of functional states and cognitive activity. Human Physiology, 37(4), 484–499.10.1134/S0362119711040098
Ilyukhina, V. A. (2013). Ultraslow information control systems in the integration of life activity processes in the brain and body. Human Physiology, 39(3), 323–333.10.1134/S0362119713030092
Ivarsson, A. & Johnson, U. (2010). Psychological factors as predictors of injuries among senior soccer players. A prospective study. Journal of Sport Science and Medicine, 9, 347-352.
Ivarsson, A., Johnson, U., & Poglog, L. (2013). Psychological predictors of injury occurrence: A prospective investigation of professional Swedish soccer players.” Journal of Sport Rehabilitation, 22, 19-26.10.1123/jsr.22.1.1923404909
Jouffe, L. & Munteanu, P. (2001). New search strategies for learning bayesian networks. Proceedings of the Tenth International Symposium on Applied Stochastic Models and Data Analysis, 2, 591-596.
Korb, K. & Nicholson, A. (2011). Bayesian artificial intelligence. In D. Blei, D. Madigan, M. Meila, & F. Murtagh (Eds.). Boca Raton, FL: Taylor & Francis Group, LLC.
Lam W. & Bacchus, F. (1994). Learning bayesian belief networks: An approach based on MDL principle. Computational Intelligence, 10(4), 271-293.10.1111/j.1467-8640.1994.tb00166.x
Laux, P., Krumm, B, Diers, D.M., & Flor, H. (2015). Recovery-stress balance and injury risk in professional football players: A prospective study. Journal of Sports Sciences, 33, 2140-2148.10.1080/02640414.2015.1064538467355926168148
Lucas, P., van der Gaag, L., & Abu-Hanna, A. (2004). Bayesian networks in biomedicine and healthcare. Artificial Intelligence in Medicine, 30, 201-214.10.1016/j.artmed.2003.11.00115081072
Nicholson, J., Holmes, E., Lindon, J., & Wilson, I. (2004). The challenge of modelling mammalian biocomplexity. Nature Biotechnology, 22, 1268-1274.10.1038/nbt101515470467
Mah, C.D., Mah, K.E., Kezirian, E.J., & Dement, W. (2011). The effects of sleep extension on the athletic performance of collegiate basketball players. Sleep, 34(7), 943–950.10.5665/SLEEP.1132311983621731144
Meeuwisse, W. (1994). Causation in sports injury: A multifactorial model. Clinical Journal of Sport Medicine, 4, 166-170.10.1097/00042752-199407000-00004
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann Publishers, Inc.10.1016/B978-0-08-051489-5.50008-4
Philippe, P. & Mansi, O. (1998). Nonlinearity in the epidemiology of complex health and disease process. Theoretical Medicine and Bioethics, 19, 591-607.10.1023/A:1009979306346
Quatman, C., Quatman, C., & Hewett, T. (2009). Prediction and prevention of musculoskeletal injury: A paradigm shift in methodology. British Journal of Sports Medicine, 43, 1100-1107.10.1136/bjsm.2009.065482403427619884108
Reyner, L.A., & Horne, J.A. (2013). Sleep restriction and serving accuracy in performance tennis players, and effects of caffeine. Physiology and Behavior, 120, 93–96.10.1016/j.physbeh.2013.07.00223916998
Rodgers, T. & Landers, D. (2005). Mediating effects of peripheral vision in the life event stress/athletic injury relationship. Sport Psychology, 27, 271-288.10.1123/jsep.27.3.271
Soligard, T., Schwellnus, M., Alonso, J., Bahr, R., Clarsen, B., Dijkstra, H., Gabbett, T., Gleeson, M., Hagglund, M., Hutchinson, M., Janse van Rensburg, C., Khan, K., Meeusen, R., Orchard, J., Pluim, B., Raftery, M., Budgett, R., & Engebretsen, L. (2016). How much is too Much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury. British Journal of Sports Medicine, 50, 1030-1041.10.1136/bjsports-2016-09658127535989
Timpka T., Jacobsson, J., Dahlström, Ö., Kowalski, J., Bargoria, V., Ekberg, J., Nilsson, S., & Renström, P. (2015). The psychological factor ‘self-blame’ predicts overuse injury among top-level Swedish track and field athletes: A 12-month cohort study. British Journal of Sports Medicine, 49, 1472-1477.10.1136/bjsports-2015-09462226373585
Vilamitjana, J.J., Lentini, N.A., Perez, M.F.J, & Verde, P.E. (2014). Heart rate variability as biomarker of training load in professional soccer players. Medicine and Science in Sports and Exercise, 46(5), 842–843.10.1249/01.mss.0000496026.80272.dd
Williams, J., Tonymon, P., & Anderson, M. (1991). Effects of stressors and coping resources on anxiety and peripheral narrowing. Journal of Applied Sport Psychology, 16, 174-181.10.1080/10413209108406439
Williams, S., West, S., Cross, M., & Stokes, K. (2017). Better way to determine the acute:chronic workload ratio?. British Journal of Sports Medicine, 51, 209-210.10.1136/bjsports-2016-09658927650255
Williamson, L. (2005). Bayesian nets and causality: Philosophical and computational foundations. Oxford, England: Oxford University Press.10.1093/acprof:oso/9780198530794.001.0001
Zebis, M. K., Bencke, L., Andersen, L.L., Alkjaer, T., Suetta, C., Mortensen, P., Kjaer, M., & Aagaard, P. (2010). Acute fatigue impairs neuromuscular activity of anterior cruciate ligament-agonist muscles in female team handball players. Scandinavian Journal of Medicine and Science in Sports, 21(6), 833–840.10.1111/j.1600-0838.2010.01052.x20500560
Zou, M., & Conzen, S. (2005). A new dynamic bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 21, 71-79.10.1093/bioinformatics/bth46315308537