research-article

Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners

Authors:

Aonghus Lawlor,

Jakim Berndsen,

Ciara FeelyAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 32, Issue 5

Pages 787 - 838

https://doi.org/10.1007/s11257-021-09299-3

Published: 01 November 2022 Publication History

Abstract

Every year millions of people, from all walks of life, spend months training to run a traditional marathon. For some it is about becoming fit enough to complete the gruelling 26.2 mile (42.2 km) distance. For others, it is about improving their fitness, to achieve a new personal-best finish-time. In this paper, we argue that the complexities of training for a marathon, combined with the availability of real-time activity data, provide a unique and worthwhile opportunity for machine learning and for recommender systems techniques to support runners as they train, race, and recover. We present a number of case studies—a mix of original research plus some recent results—to highlight what can be achieved using the type of activity data that is routinely collected by the current generation of mobile fitness apps, smart watches, and wearable sensors.

References

[1]

Abt G and Lovell R The use of individualized speed and intensity thresholds for determining the distance run at high-intensity in professional soccer J. Sports Sci. 2009 27 9 893-898

[2]

Abut F, Akay MF, and George J Developing new VO2max prediction models from maximal, submaximal and questionnaire variables using support vector machines combined with feature selection Comput. Biol. Med. 2016 79 182-192

[3]

Agresta CE, Peacock J, Housner J, Zernicke RF, and Zendler JD Experience does not influence injury-related joint kinematics and kinetics in distance runners Gait Posture 2018 61 13-18

[4]

Akay, M.F., Aktürk, E., Balıkçı, A.: VO2max prediction from submaximal exercise test using artificial neural network. In: Proceedings of the 21st Signal Processing and Communications Applications Conference (SIU), pp. 1–3 (2013).

[5]

Akay MF, Inan C, Bradshaw DI, and George JD Support vector regression and multilayer feed forward neural networks for non-exercise prediction of VO2max Exp. Syst. Appl. 2009 36 6 10112-10119

[6]

Akay MF, Zayid EIM, Aktürk E, and George JD Artificial neural network-based model for predicting VO2max from a submaximal exercise test Exp. Syst. Appl. 2011 38 3 2007-2010

[7]

Akay MF, Abut F, Cetin E, Yarim I, and Sow B Support vector machines for predicting the hamstring and quadriceps muscle strength of college-aged athletes Turk. J. Elect. Eng. Comput. Sci. 2017 25 4 2567-2582

[8]

Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L.A.S., Kay, J., Rej, T.: CCR: a content-collaborative reciprocal recommender for online dating. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Catalonia, Spain, pp. 2199–2204. AAAI Press, Palo Alto, CA, USA (2011).

[9]

Álvarez, P., Guiu, A., Beltrán, J.R., de Quirós, J.G., Baldassarri, S.: Dj-running: an emotion-based system for recommending spotify songs to runners. In: Proceedings of the 7th International Conference on Sports Science research and Technology Support (icSPORTS), pp. 55–63. ScitePress (2019).

[10]

Álvarez P, Zarazaga-Soria F, and Baldassarri S Mobile music recommendations for runners based on location and emotions: the dj-running system Pervasive Mob. Comput. 2020 67 101242

[11]

Anderson T Biomechanics and running economy Sports Med. 1996 22 2 76-89

[12]

Barros ES, Nascimento DC, Prestes J, Nóbrega OT, Córdova C, Sousa F, and Boullosa DA Acute and chronic effects of endurance running on inflammatory markers: a systematic review Front. Physiol. 2017 8 779

[13]

Bartolucci F and Murphy TB A finite mixture latent trajectory model for modeling ultrarunners behavior in a 24-hour race J. Quant. Anal. Sports 2015 11 4 193-203

[14]

Bassler T Rehabilitation through marathon running West. J. Med. 1979 130 5 466

[15]

Bauer, J.S., Consolvo, S., Greenstein, B., Schooler, J., Wu, E., Watson, N.F., Kientz, J.: Shuteye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'12), pp. 1401–1410. ACM, New York, NY, USA (2012).

[16]

Berlin, E., Laerhoven, K.V.: Detecting leisure activities with dense motif discovery. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Ubicomp ’12, Pittsburgh, PA, USA, September 5–8, 2012, pp. 250–259 ACM, New York, NY, USA (2012).

[17]

Berndsen, J., Lawlor, A., Smyth, B.: Running with recommendation. In: Proceedings of the 2nd International Workshop on Health Recommender Systems (HealthRecSys 2017) co-located with the 11th International Conference on Recommender Systems (RecSys 2017), Como, Italy, August 31, 2017, CEUR Workshop Proceedings, vol. 1953, pp. 18–21 (2017)

[18]

Berndsen, J., Smyth, B., Lawlor, A.: A collaborative filtering approach to successfully completing the marathon. In: Proceedings of the 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 653–658. IEEE (2020).

[19]

Berndsen, J., Smyth, B., Lawlor, A.: Fit to run: Personalised recommendations for marathon training. In: RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22–26, 2020, pp. 480–485. ACM, New York, NY, USA (2020).

[20]

Berndsen, J., Smyth, B., Lawlor, A.: Mining marathon training data to generate useful user profiles. In: International Workshop on Machine Learning and Data Mining for Sports Analytics, pp. 113–125. Springer (2020).

[21]

Berndsen, J., Smyth, B., Lawlor, A.: Pace my race: recommendations for marathon running. In: T. Bogers, A. Said, P. Brusilovsky, D. Tikk (eds.) Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16–20, 2019, pp. 246–250. ACM (2019).

[22]

Berndsen J, Lawlor A, and Smyth B Exploring the wall in marathon running J. Sports Anal. 2020 6 3 173-186

[23]

Billat V, Bernard O, Pinoteau J, Petit B, and Koralsztein J Time to exhaustion at vo2max and lactate steady state velocity in sub elite long-distance runners Arch. Int. Physiol. Biochim. Biophys. 1994 102 3 215-219

[24]

Billat V, Hill D, Pinoteau J, Petit B, and Koralsztein JP Effect of protocol on determination of velocity at vo2 max and on its time to exhaustion Arch. Physiol. Biochem. 1996 104 3 313-321

[25]

Billat VL, Sirvent P, Py G, Koralsztein JP, and Mercier J The concept of maximal lactate steady state Sports Med. 2003 33 6 407-426

[26]

Boratto L, Carta S, Fenu G, Manca M, Mulas F, and Pilloni P The role of social interaction on users motivation to exercise: a persuasive web framework to enhance the self-management of a healthy lifestyle Pervasive Mob. Comput. 2017 36 98-114

[27]

Bornn, L., Ward, P., Norman, D.: Training schedule confounds the relationship between acute: chronic workload ratio and injury. In: Proceedings of the MIT Sloan Sports Analytics Conference, Boston, MA, USA (2019)

[28]

Borràs J, Moreno A, and Valls A Intelligent tourism recommender systems: a survey Exp. Syst. Appl. 2014 41 16 7370-7389

[29]

Bowen L, Gross AS, Gimpel M, Bruce-Low S, and Li FX Spikes in acute:chronic workload ratio (ACWR) associated with a 5–7 times greater injury rate in English Premier League football players: a comprehensive 3-year study Br. J. Sports Med. 2019 54 731-738

[30]

Boyd, K., Costa, V.S., Davis, J., Page, C.D.: Unachievable region in precision-recall space and its effect on empirical evaluation. In: Proceedings of the 29th International Conference Machine Learning, pp. 1619–1626. OmniPress (2012)

[31]

Bridge D, Göker MH, McGinty L, and Smyth B Case-based recommender systems Knowl. Eng. Rev. 2005 20 3 315-320

[32]

Buman MP, Brewer BW, Cornelius AE, Van Raalte JL, and Petitpas AJ Hitting the wall in the marathon: phenomenological characteristics and associations with expectancy, gender, and running history Psychol. Sport Exerc. 2008 9 2 177-190

[33]

Buman MP, Omli JW, Giacobbi PR Jr, and Brewer BW Experiences and coping responses of hitting the wall for recreational marathon runners J. Appl. Sport Psychol. 2008 20 3 282-300

[34]

Buman MP, Brewer BW, and Cornelius AE A discrete-time hazard model of hitting the wall in recreational marathon runners Psychol. Sport Exerc. 2009 10 6 662-666

[35]

Burke R Hybrid recommender systems: survey and experiments User Model. User Adap. Inter. 2002 12 4 331-370

[36]

Carey, D.L., Ong, K.L., Whiteley, R., Crossley, K.M., Crow, J., Morris, M.E.: Predictive modelling of training loads and injury in australian football. arXiv:1706.04336 (2017)

[37]

Caulfield, B., Blood, J., Smyth, B., Kelly, D.: Rehabilitation exercise feedback on android platform. In: Proceedings of the 2nd Conference on Wireless Health, pp. 1–2. ACM (2011).

[38]

Chakraborty, B.: Integrating awareness in user oriented route recommendation system. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–5. IEEE (2012).

[39]

Chawla NV Maimon O and Rokach L Data mining for imbalanced datasets: an overview Data mining and knowledge discovery handbook 2010 Boston, MA Springer 875-886

[40]

Chen, C.W., Yang, L., Wen, H., Jones, R., Radosavljevic, V., Bouchard, H.: Podrecs: Workshop on podcast recommendations. In: Fourteenth ACM Conference on Recommender Systems, pp. 621–622. ACM. (2020)

[41]

Cheng, H.T.: Learning and recognizing the hierarchical and sequential structure of human activities. Ph.D. thesis, Carnegie Mellon University, Carnegie Mellon University, Pittsburgh, PA, USA (2013)

[42]

Cheung KL, Durusu D, Sui X, and de Vries H How recommender systems could support and enhance computer-tailored digital health programs: a scoping review Dig. Health 2019 5 2055207618824727

[43]

Claudino JG, Capanema DdO, de Souza TV, Serrão JC, Machado Pereira AC, and Nassis GP Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review Sports Med. Open 2019 5 1 28

[44]

Clough PJ, Dutch S, Maughan RJ, and Shepherd J Pre-race drop-out in marathon runners: reasons for withdrawal and future plans Br. J. Sports Med. 1987 21 4 148-149

[45]

Cornforth D, Campbell P, Nesbitt K, Robinson D, and Jelinek HF Prediction of game performance in Australian football using heart rate variability measures Int. J. Signal Imag. Syst. Eng. 2015 8 1–2 80-88

[46]

Damsted C, Glad S, Nielsen RO, Sørensen H, and Malisoux L Is there evidence for an association between changes in training load and running-related injuries? A systematic review Int. J. Sports Phys. Ther. 2018 13 6 931-942

[47]

Daniels, J.T.: Daniels’ running formula. Human Kinetics (2013)

[48]

Daskalova, N., Metaxa-Kakavouli, D., Tran, A., Nugent, N., Boergers, J., McGeary, J., Huang, J.: Sleepcoacher: A personalized automated self-experimentation system for sleep recommendations. In: Proceedings of the 29th Annual Symposium on User Interface Software and Technology, pp. 347–358. ACM (2016).

[49]

Daskalova N, Lee B, Huang J, Ni C, and Lundin J Investigating the effectiveness of cohort-based sleep recommendations Proc. ACM Interact. Mob. Wear. Ubiquit. Technol. 2018 2 3 1-19

[50]

Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd international conference on Machine Learning, pp. 233–240. ACM (2006).

[51]

De Brabandere A, De Beeck TO, Schütte KH, Meert W, Vanwanseele B, and Davis J Data fusion of body-worn accelerometers and heart rate to predict vo2max during submaximal running PLoS ONE 2018 13 6 e0199509

[52]

Deaner RO More males run fast: a stable sex difference in competitiveness in us distance runners Evol. Hum. Behav. 2006 27 1 63-84

[53]

Ding, Y., Zhang, Y., Li, L., Xu, W., Wang, H.: A reciprocal recommender system for graduates’ recruitment. In: Proceedings of the 2016 8th International Conference on Information Technology in Medicine and Education (ITME), pp. 394–398. IEEE (2016)

[54]

Direito A, Carraça E, Rawstorn J, Whittaker R, and Maddison R mhealth technologies to influence physical activity and sedentary behaviors: behavior change techniques, systematic review and meta-analysis of randomized controlled trials Ann. Behav. Med. 2017 51 2 226-239

[55]

Doherty C, Keogh A, Davenport J, Lawlor A, Smyth B, and Caulfield B An evaluation of the training determinants of marathon performance: a meta-analysis with meta-regression J. Sci. Med. Sport 2019 23 2 182-188

[56]

Doherty C, Keogh A, Smyth B, Megyesi P, and Caulfield B Devising a pace-based definition for the wall: an observational analysis of marathoners subjective experiences of fatigue J. Athletic Train. 2020 55 5 494-500

[57]

Donohue B, Miller A, Beisecker M, Houser D, Valdez R, Tiller S, and Taymar T Effects of brief yoga exercises and motivational preparatory interventions in distance runners: results of a controlled trial Br. J. Sports Med. 2006 40 1 60-63

[58]

Dunne, L.E., Smyth, B.: Psychophysical elements of wearability. In: Rosson, M.B., Gilmore, D.J. (eds.) Proceedings of the 2007 Conference on Human Factors in Computing Systems, CHI 2007, San Jose, California, USA, April 28–May 3, 2007, pp. 299–302. ACM (2007).

[59]

Dunne, L.E., Walsh, P., Smyth, B., Caulfield, B.: A system for wearable monitoring of seated posture in computer users. In: Leonhardt, S., Falck, T., Mähönen, P. (eds.) 4th International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2007, March 26–28, 2007, RWTH Aachen University, Germany, pp. 203–207. Springer (2007).

[60]

Dunne LE, Walsh P, Hermann S, Smyth B, and Caulfield B Wearable monitoring of seated spinal posture IEEE Trans. Biomed. Circuits Syst. 2008 2 2 97-105

[61]

Ely MR, Martin DE, Cheuvront SN, and Montain SJ Effect of ambient temperature on marathon pacing is dependent on runner ability Med. Sci. Sports Exerc. 2008 40 9 1675-1680

[62]

Emig T and Peltonen J Human running performance from real-world big data Nat. Commun. 2020 11 1 1-9

[63]

Faude O, Kindermann W, and Meyer T Lactate threshold concepts Sports Med. 2009 39 6 469-490

[64]

Fawcett T Mining the quantified self: personal knowledge discovery as a challenge for data science Big Data 2015 3 4 249-266

[65]

Feely, C., Caulfield, B., Lawlor, A., Smyth, B.: Providing explainable race-time predictions and training plan recommendations to marathon runners. In: Proceedings of the Fourteenth ACM Conference on Recommender Systems, pp. 539–544. ACM (2020).

[66]

Feely, C., Caulfield, B., Lawlor, A., Smyth, B.: Using case-based reasoning to predict marathon performance and recommend tailored training plans. In: Proceedings of the International Conference on Case-Based Reasoning, pp. 67–81. Springer (2020).

[67]

Fister I, Rauter S, Yang XS, Ljubič K, and Fister I Planning the sports training sessions with the bat algorithm Neurocomputing 2015 149 PB 993-1002

[68]

Fitzgerald, D., Trakarnratanakul, N., Dunne, L., Smyth, B., Caulfield, B.: Development and user evaluation of a virtual rehabilitation system for wobble board balance training. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4194–4198. IEEE (2008)

[69]

Fitzgerald D, Trakarnratanakul N, Smyth B, and Caulfield B Effects of a wobble board-based therapeutic exergaming system for balance training on dynamic postural stability and intrinsic motivation levels J. Orthopaed. Sports Phys. Therapy 2010 40 1 11-19

[70]

Florence Sl and Weir JP Relationship of critical velocity to marathon running performance Eur. J. Appl. Physiol. 1997 75 3 274-278

[71]

Frejlichowski, D., Czapiewski, P., Hofman, R.: Finding similar clothes based on semantic description for the purpose of fashion recommender system. In: Asian Conference on Intelligent Information and Database Systems (ACIIDS), pp. 13–22. Springer (2016).

[72]

Fry R, Morton A, and Keast D Periodisation and the prevention of overtraining Can. J. Sport Sci. J. Can. des Sci. du Sport 1992 17 3 241-248

[73]

Fry R, Morton A, and Keast D Periodisation of training stress: a review Can. J. Sport Sci. 1992 17 3 234-240

[74]

Fullagar HH, Skorski S, Duffield R, Hammes D, Coutts AJ, and Meyer T Sleep and athletic performance: the effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise Sports Med. 2015 45 2 161-186

[75]

Gabbett TJ The training-injury prevention paradox: should athletes be training smarter and harder? Br. J. Sports Med. 2016 50 5 273-280

[76]

Gavalas D, Konstantopoulos C, Mastakas K, and Pantziou G Mobile recommender systems in tourism J. Netw. Comput. Appl. 2014 39 319-333

[77]

Ge, M., Ricci, F., Massimo, D.: Health-aware food recommender system. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 333–334. ACM (2015)

[78]

Glaros, C., Fotiadis, D.I., Likas, A., Stafylopatis, A.: A wearable intelligent system for monitoring health condition and rehabilitation of running athletes. In: Proceedings of the 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003, pp. 276–279. IEEE (2003).

[79]

Goyal, P., Sapienza, A., Ferrara, E.: Recommending teammates with deep neural networks. In: Proceedings of the 29th International Conference on Hypertext and Social Media (HT), pp. 57–61. ACM (2018).

[80]

Hammer, C., Podlog, L.: Motivation and marathon running. In: Marathon running: Physiology, psychology, nutrition and training aspects, pp. 107–124. Springer (2016)

[81]

Han, Z., Xu, J.: Recommending sports instructional content based on motion sensor data (2016). US Patent 9409074

[82]

Hanks S and Weld DS A domain-independent algorithm for plan adaptation J. Artif. Intel. Res. 1994 2 319-360

[83]

He H and Garcia EA Learning from imbalanced data IEEE Trans. Knowl. Data Eng. 2009 21 9 1263-1284

[84]

Hong W, Zheng S, Wang H, and Shi J A job recommender system based on user clustering JCP 2013 8 8 1960-1967

[85]

Hosseinpour M and Terlutter R Your personal motivator is with you: a systematic review of mobile phone applications aiming at increasing physical activity Sports Med. 2019 49 9 1425-1447

[86]

Hulme A, Nielsen RO, Timpka T, Verhagen E, and Finch C Risk and protective factors for middle- and long-distance running-related injury Sports Med. (Auckland, N.Z.) 2017 47 5 869-886

[87]

Hung SY, Yen DC, and Wang HY Applying data mining to telecom churn management Expert Syst. Appl. 2006 31 3 515-524

[88]

Hwangbo H, Kim YS, and Cha KJ Recommendation system development for fashion retail e-commerce Electron. Commer. Res. Appl. 2018 28 94-101

[89]

Jelinek HF, Kelarev A, Robinson DJ, Stranieri A, and Cornforth DJ Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football Appl. Soft Comput. 2014 14 81-87

[90]

Jeukendrup AE Nutrition for endurance sports: marathon, triathlon, and road cycling J. Sports Sci. 2011 29 sup1 S91-S99

[91]

Kampakis, S.: Predictive modelling of football injuries. arXiv preprint arXiv:1609.07480 (2016)

[92]

Kelly D, Smyth B, and Caulfield B Uncovering measurements of social and demographic behavior from smartphone location data IEEE Trans. Hum. Mach. Syst. 2013 43 2 188-198

[93]

Keogh A, Smyth B, Caulfield B, Lawlor A, Berndsen J, and Doherty C Prediction equations for marathon performance: a systematic review Int. J. Sports Physiol. Perform. 2019 14 9 1159-1169

[94]

Khan, M.A., Smyth, B., Coyle, D.: Addressing the complexity of personalized, context-aware and health-aware food recommendations: an ensemble topic modelling based approach. Journal of Intelligent Information Systems (2021).

[95]

King AC, Hekler EB, Grieco LA, Winter SJ, Sheats JL, Buman MP, Banerjee B, Robinson TN, and Cirimele J Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults PLoS ONE 2013 8 4 e62613

[96]

Knijnenburg BP and Kobsa A Making decisions about privacy: information disclosure in context-aware recommender systems ACM Trans. Interact. Intel. Syst. (TiiS) 2013 3 3 20

[97]

Krouse RZ, Ransdell LB, Lucas SM, and Pritchard ME Motivation, goal orientation, coaching, and training habits of women ultrarunners J. Strength Condit. Res. 2011 25 10 2835-2842

[98]

Kurade NP An intelligent method for selecting and recommending best players to help build sports team Int. J. Comput. Appl. 2014 105 7

[99]

Lam, S., Frankowski, D., Riedl, J.: Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In: Proceedings of Emerging trends in information and communication security (ETRICS) pp. 14–29. Springer (2006).

[100]

Lazarus BH, Stewart AM, White KM, Rowell AE, Esmaeili A, Hopkins WG, and Aughey RJ Proposal of a global training load measure predicting match performance in an elite team sport Front. Physiol. 2017 8 930

[101]

Li, L., Li, T.: Meet: a generalized framework for reciprocal recommender systems. In: Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM), pp. 35–44. ACM (2012).

[102]

Lister C, West JH, Cannon B, Sax T, and Brodegard D Just a fad? Gamification in health and fitness apps JMIR Serious Games 2014 2 2 e9

[103]

Loepp, B., Ziegler, J.: Recommending running routes: framework and demonstrator. In: Workshop on Recommendation in Complex Scenarios co-located with the 12th ACM Conference on Recommender Systems (2018)

[104]

López-Valenciano A, Ayala F, Puerta JM, DE Ste Croix MBA, Vera-Garcia FJ, Hernández-Sánchez S, Ruiz-Pérez I, and Myer GD A preventive model for muscle injuries: a novel approach based on learning algorithms Med. Sci. Sports Exerc. 2018 50 5 915-927

[105]

Macedo, A.Q., Marinho, L.B., Santos, R.L.: Context-aware event recommendation in event-based social networks. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 123–130. ACM (2015).

[106]

Maier T, Meister D, Trösch S, and Wehrlin JP Predicting biathlon shooting performance using machine learning J. Sports Sci. 2018 36 20 2333-2339

[107]

Malisoux L, Nielsen RO, Urhausen A, and Theisen D A step towards understanding the mechanisms of running-related injuries J. Sci. Med. Sport 2015 18 5 523-528

[108]

Marks, W.H.: Footwear recommendations from foot scan data describing feet of a user (2017). US Patent 9648926

[109]

Masters KS, Ogles BM, and Jolton JA The development of an instrument to measure motivation for marathon running: the motivations of marathoners scales (moms) Res. Q. Exerc. Sport 1993 64 2 134-143

[110]

Mayer-Schönberger V and Cukier K Big data: a revolution that will transform how we live, work, and think 2013 Boston Houghton Mifflin Harcourt

[111]

McConnell, C., Smyth, B.: Going further with cases: Using case-based reasoning to recommend pacing strategies for ultra-marathon runners. In: Proceedings of the 27th International Conference on Case-Based Reasoning (ICCBR), pp. 358–372. Springer (2019).

[112]

McGinty, L., Smyth, B.: Collaborative case-based reasoning: Applications in personalised route planning. In: Proceedings of the 4th International Conference on Case-Based Reasoning (ICCBR), pp. 362–376. Springer (2001).

[113]

Mika, S.: Challenges for nutrition recommender systems. In: Proceedings of the 2nd Workshop on Context Aware Intelligent Assistance, Berlin, Germany, pp. 25–33. CEUR Workshop Proceedings (2011)

[114]

Millington B Smartphone apps and the mobile privatization of health and fitness Crit. Stud. Media Commun. 2014 31 5 479-493

[115]

Minkov, E., Charrow, B., Ledlie, J., Teller, S., Jaakkola, T.: Collaborative future event recommendation. In: Proceedings of the 19th ACM international conference on Information and knowledge management, pp. 819–828. ACM (2010).

[116]

Mulas, F., Carta, S., Pilloni, P., Manca, M.: Everywhere run: a virtual personal trainer for supporting people in their running activity. In: Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, ACE 2011, Lisbon, Portugal, November 8–11, 2011, p. 70. ACM (2011).

[117]

Mulas, F., Pilloni, P., Manca, M., Boratto, L., Carta, S.: Using new communication technologies and social media interaction to improve the motivation of users to exercise. In: Second International Conference on Future Generation Communication Technologies (FGCT 2013), London, UK, November 12–14, 2013, pp. 87–92. IEEE (2013).

[118]

Muniz-Pumares D, Karsten B, Triska C, and Glaister M Methodological approaches and related challenges associated with the determination of critical power and curvature constant J. Strength Condit. Res. 2019 33 2 584-596

[119]

Munoz-Avila H and Cox MT Case-based plan adaptation: an analysis and review IEEE Intell. Syst. 2008 23 4 75-81

[120]

Napier C, MacLean CL, Maurer J, Taunton JE, and Hunt MA Kinetic risk factors of running-related injuries in female recreational runners Scand. J. Med. Sci. Sports 2018 28 10 2164-2172

[121]

Ni, J., Muhlstein, L., McAuley, J.: Modeling heart rate and activity data for personalized fitness recommendation. In: Proceedings of the World Wide Web Conference, WWW-19, pp. 1343–1353. ACM (2019).

[122]

Nicolas M, Banizette M, and Millet GY Stress and recovery states after a 24 h ultra-marathon race: a one-month follow-up study Psychol. Sport Exerc. 2011 12 4 368-374

[123]

Noakes, T.: Lore of running. Human Kinetics (2003)

[124]

Noakes T Physiological models to understand exercise fatigue and the adaptations that predict or enhance athletic performance Scand. J. Med. Sci. Sports Rev. Article 2000 10 3 123-145

[125]

O’Donovan, J., Gretarsson, B., Bostandjiev, S., Hollerer, T., Smyth, B.: A visual interface for social information filtering. In: Proceedings of the 2009 International Conference on Computational Science and Engineering, vol. 4, pp. 74–81. IEEE (2009)

[126]

O’Donovan, J., Smyth, B., Gretarsson, B., Bostandjiev, S., Höllerer, T.: Peerchooser: visual interactive recommendation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1085–1088. ACM (2008).

[127]

O’Huiginn, B., Smyth, B., Coughlan, G., Fitzgerald, D., Caulfield, B.: Therapeutic exergaming. In: Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, pp. 273–277. IEEE (2009).

[128]

Panjan A, Sarabon N, and Filipčič A Prediction of the successfulness of tennis players with machine learning methods Kinesiology 2010 42 1 98-106

[129]

Piatetsky-Shapiro, G., Masand, B.: Estimating campaign benefits and modeling lift. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’99, pp. 185–193. ACM (1999).

[130]

Pilloni, P., Mulas, F., Piredda, L., Carta, S.: Assessing the user experience design as a persuasive methodology in a real world sport application. In: The 11th International Conference on Advances in Mobile Computing and Multimedia, MoMM ’13, Vienna, Austria, December 2–4, 2013, p. 387. ACM (2013).

[131]

Pilloni, P., Piras, L., Boratto, L., Carta, S., Fenu, G., Mulas, F.: Recommendation in Persuasive eHealth Systems: an Effective Strategy to Spot Users’ Losing Motivation to Exercise. In: Elsweiler, D., Hors-Fraile, S., Ludwig, B., Said, A., Schäfer, H., Trattner, C., Torkamaan, H., Valdez, A.C. (eds.). Proceedings of the 2nd International Workshop on Health Recommender Systems co-located with the 11th International Conference on Recommender Systems (RecSys 2017), Como, Italy, August 31, 2017, CEUR Workshop Proceedings, vol. 1953, pp. 6–9. http://ceur-ws.org/ (2017)

[132]

Pilloni P, Piras L, Carta S, Fenu G, Mulas F, and Boratto L Recommender system lets coaches identify and help athletes who begin losing motivation IEEE Comput. 2018 51 3 36-42

[133]

Pizzato, L., Rej, T., Chung, T., Koprinska, I., Kay, J.: Recon: a reciprocal recommender for online dating. In: Proceedings of the fourth ACM conference on Recommender systems, pp. 207–214. ACM (2010).

[134]

Pizzato, L., Rej, T., Chung, T., Koprinska, I., Yacef, K., Kay, J.: Reciprocal recommender system for online dating. In: Proceedings of the fourth ACM conference on Recommender systems, pp. 353–354. ACM (2010).

[135]

Poole DC, Wilkerson DP, and Jones AM Validity of criteria for establishing maximal o2 uptake during ramp exercise tests Eur. J. Appl. Physiol. 2008 102 4 403-410

[136]

Prabhakar, S., Spanakis, G., Zaiane, O.: Reciprocal recommender system for learners in massive open online courses (moocs). In: International Conference on Web-Based Learning, pp. 157–167. Springer (2017).

[137]

Pulkkinen, A., Saarikoski, E.: System for monitoring and predicting physiological state under physical exercise (2010). US Patent 7805186

[138]

Qiao, Z., Zhang, P., Zhou, C., Cao, Y., Guo, L., Zhang, Y.: Event recommendation in event-based social networks. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 3131–3131. AAAI Press (2014)

[139]

Rae DE, Chin T, Dikgomo K, Hill L, McKune AJ, Kohn TA, and Roden LC One night of partial sleep deprivation impairs recovery from a single exercise training session Eur. J. Appl. Physiol. 2017 117 4 699-712

[140]

Rauter S New approach for planning the mountain bike training with virtual coach TRENDS Sport Sci. 2018 2 25 69-74

[141]

Ribeiro, D., Machado, J., Ribeiro, J., Vasconcelos, M.J.M., Vieira, E.F., de Barros, A.C.: Souschef: Mobile meal recommender system for older adults. In: Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AgeingWell), pp. 36–45. SCITEPRESS (2017).

[142]

Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook. Springer, New York (2015).

[143]

Ricci F Travel recommender systems IEEE Intell. Syst. 2002 17 6 55-57

[144]

Riegel PS Athletic records and human endurance: a time-vs.-distance equation describing world-record performances may be used to compare the relative endurance capabilities of various groups of people Am. Sci. 1981 69 3 285-290

[145]

Rolf C Overuse injuries of the lower extremity in runners Scand. J. Med. Sci. Sports 1995 5 4 181-190

[146]

Rooksby, J., Rost, M., Morrison, A., Chalmers, M.C.: Personal tracking as lived informatics. In: Proceedings of the 32nd annual ACM conference on Human factors in computing systems, pp. 1163–1172. ACM (2014).

[147]

Rossi A, Pappalardo L, Cintia P, Iaia FM, Fernàndez J, and Medina D Effective injury forecasting in soccer with gps training data and machine learning PLoS ONE 2018 13 7 e0201264

[148]

Ryan MB, Valiant GA, McDonald K, and Taunton JE The effect of three different levels of footwear stability on pain outcomes in women runners: a randomised control trial Br. J. Sports Med. 2011 45 9 715-721

[149]

Saito T and Rehmsmeier M The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets PLoS ONE 2015 10 3 e0118432

[150]

Saragiotto BT, Yamato TP, Hespanhol Junior LC, Rainbow MJ, Davis IS, and Lopes AD What are the main risk factors for running-related injuries? Sports Med. (Auckland, N.Z.) 2014 44 8 1153-1163

[151]

Schmid W, Knechtle B, Knechtle P, Barandun U, Rüst CA, Rosemann T, and Lepers R Predictor variables for marathon race time in recreational female runners Asian J. Sports Med. 2012 3 2 90

[152]

Schoeppe S, Alley S, Van Lippevelde W, Bray NA, Williams SL, Duncan MJ, and Vandelanotte C Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review Int. J. Behav. Nutr. Phys. Act. 2016 13 1 127

[153]

Scott, D., Gurrin, C., Johansen, D., Johansen, H.: Searching and recommending sports content on mobile devices. In: Proceedings of the International Conference on Multimedia Modeling, pp. 779–781. Springer (2010).

[154]

Senin P, Lin J, Wang X, Oates T, Gandhi S, Boedihardjo AP, Chen C, and Frankenstein S GrammarViz 3.0: interactive discovery of variable-length time series patterns ACM Trans. Knowl. Discov. Data 2018 12 1 10:1-10:28

[155]

Shin D The effects of explainability and causability on perception, trust, and acceptance: implications for explainable AI Int. J. Hum. Comput. Stud. 2021 146 102551

[156]

Smyth, B., Cunningham, P.: A novel recommender system for helping marathoners to achieve a new personal-best. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, August 27-31, 2017, pp. 116–120. ACM (2017).

[157]

Smyth, B., Cunningham, P.: An analysis of case representations for marathon race prediction and planning. In: Proceedings of the 26th International Conference on Case-Based Reasoning (ICCBR), Stockholm, Sweden, July 9–12, 2018, pp. 369–384. Springer (2018).

[158]

Smyth, B., Cunningham, P.: Marathon race planning: a case-based reasoning approach. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13–19, 2018, Stockholm, Sweden, pp. 5364–5368. AAAI Press (2018).

[159]

Smyth, B., Cunningham, P.: Running with cases: a CBR approach to running your best marathon. In: Proceedings of the 25th International Conference (ICCBR), Trondheim, Norway, June 26–28, 2017, pp. 360–374. Springer (2017).

[160]

Smyth, B., Muniz-Pumares, D.: Calculation of critical speed from raw training data in recreational marathon runners. Med. Sci. Sports Exercise. 52(12), 2637-2645 (2020)

[161]

Smyth, B., Willemsen, M.: Predicting the personal-best times of speed skaters using case-based reasoning. In: Proceedings of the 28th International Conference (ICCBR), Salamanca, Spain, June 8–12, 2020, pp. 112–126. Springer (2020).

[162]

Smyth, B.: Case-based recommendation. In: The Adaptive Web, Methods and Strategies of Web Personalization, pp. 342–376. Springer (2007).

[163]

Smyth B Fast starters and slow finishers: a large-scale data analysis of pacing at the beginning and end of the marathon for recreational runners J. Sports Anal. 2018 4 3 229-242

[164]

Smyth B Recommender systems: a healthy obsession Proc. AAAI Conf. Artif. Intel. 2019 33 9790-9794

[165]

Stevinson CD and Biddle SJ Cognitive orientations in marathon running and hitting the wall Br. J. Sports Med. 1998 32 3 229-234

[166]

Sullivan, A.N., Lachman, M.E.: Behavior change with fitness technology in sedentary adults: a review of the evidence for increasing physical activity. Front. Public Health 4, 289 (2017). https://www.frontiersin.org/article/10.3389/fpubh.2016.00289

[167]

Sundar, S.S., Bellur, S., Jia, H.: Motivational technologies: a theoretical framework for designing preventive health applications. In: Proceedings of the International conference on persuasive technology, pp. 112–122. Springer (2012).

[168]

Taha Z, Musa RM, Majeed APA, Alim MM, and Abdullah MR The identification of high potential archers based on fitness and motor ability variables: a support vector machine approach Hum. Mov. Sci. 2018 57 184-193

[169]

Tang J, Hu X, and Liu H Social recommendation: a review Soc. Netw. Anal. Min. 2013 3 4 1113-1133

[170]

Thornton HR, Delaney JA, Duthie GM, and Dascombe BJ Importance of various training-load measures in injury incidence of professional rugby league athletes Int. J. Sports Physiol. Perform. 2017 12 6 819-824

[171]

Trubee, N.W.: The effects of age, sex, heat stress, and finish time on pacing in the marathon. Ph.D. thesis, University of Dayton, Dayton, OH, USA (2011)

[172]

Vall A, Dorfer M, Eghbal-Zadeh H, Schedl M, Burjorjee K, and Widmer G Feature-combination hybrid recommender systems for automated music playlist continuation User Model. User Adap. Inter. 2019 29 2 527-572

[173]

van der Worp MP, ten Haaf DSM, van Cingel R, de Wijer A, Nijhuis-van der Sanden MWG, and Staal JB Injuries in runners; a systematic review on risk factors and sex differences PLoS ONE 2015 10 2 e0114937

[174]

Vandelanotte C, Müller AM, Short CE, Hingle M, Nathan N, Williams SL, Lopez ML, Parekh S, and Maher CA Past, present, and future of ehealth and mhealth research to improve physical activity and dietary behaviors J. Nutr. Educ. Behav. 2016 48 3 219-228

[175]

Vickey T, Breslin J, and Williams A Fitness-theres an app for that: review of mobile fitness apps Int. J. Sport Soc. 2012 3 4

[176]

Wakita, Y., Oku, K., Huang, H.H., Kawagoe, K.: A fashion-brand recommender system using brand association rules and features. In: Proceedings of the 2015 IIAI 4th International Congress on Advanced Applied Informatics, pp. 719–720. IEEE (2015).

[177]

Webb C, Vehrs PR, George JD, and Hager R Estimating vo2max using a personalized step test Meas. Phys. Educ. Exerc. Sci. 2014 18 3 184-197

[178]

Werthner H and Ricci F E-commerce and tourism Commun. ACM 2004 47 12 101-105

[179]

Whiteside D, Cant O, Connolly M, and Reid M Monitoring hitting load in tennis using inertial sensors and machine learning Int. J. Sports Physiol. Perform. 2017 12 9 1212-1217

[180]

Yingying, L., Chiusano, S., D’Elia, V.: Modeling athlete performance using clustering techniques. In: Proceedings of the Third International Symposium on Electronic Commerce and Security Workshops (ISECS 2010), p. 169 IEEE (2010)

[181]

Zhang, F.: Development of a performance prediction model for college athletes based on chaos theory and machine learning algorithms. In: Proceedings of the 2019 5th International Conference on Education and Training Technologies, pp. 146–150. ACM (2019).

[182]

Zhao J, Freeman B, and Li M Can mobile phone apps influence peoples health behavior change? an evidence review J. Med. Internet Res. 2016 18 11 e287

[183]

Zrenner, M., Ullrich, M., Zobel, P., Jensen, U., Laser, F., Groh, B.H., Duemler, B., Eskofier, B.M.: Kinematic parameter evaluation for the purpose of a wearable running shoe recommendation. In: Proceedings of the 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 106–109. IEEE (2018).

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Sahdra SDuthie GKay JYacef KKostakos VKay JHoang T(2024)Monitoring Physical Health, Mental Health, Nutrition, and Sleep in Athletes to Improve Performance: Workshop Position Paper: Multimodal Sports Interaction: Wearables and HCI in MotionCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678499(444-449)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678499
Sahdra SDuthie GKay JYacef KKostakos VKay JHoang T(2024)Understanding the Person-specific Predictors of Athlete Performance: Ubicomp/ISWC 2024 Doctoral ColloquiumCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678362(249-255)Online publication date: 5-Oct-2024
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Sahdra SDuthie GKay JYacef KKostakos VKay JHoang T(2024)Maximising the Usefulness of Wearable Data for AthletesCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3677600(131-136)Online publication date: 5-Oct-2024
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Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners

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cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 32, Issue 5

Nov 2022

279 pages

ISSN:0924-1868

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© The Author(s) 2021.

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Kluwer Academic Publishers

United States

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Published: 01 November 2022

Accepted: 22 July 2021

Received: 19 June 2020

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Sahdra SDuthie GKay JYacef KKostakos VKay JHoang T(2024)Monitoring Physical Health, Mental Health, Nutrition, and Sleep in Athletes to Improve Performance: Workshop Position Paper: Multimodal Sports Interaction: Wearables and HCI in MotionCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678499(444-449)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678499
Sahdra SDuthie GKay JYacef KKostakos VKay JHoang T(2024)Understanding the Person-specific Predictors of Athlete Performance: Ubicomp/ISWC 2024 Doctoral ColloquiumCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678362(249-255)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678362
Sahdra SDuthie GKay JYacef KKostakos VKay JHoang T(2024)Maximising the Usefulness of Wearable Data for AthletesCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3677600(131-136)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3677600
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Coppens IDe Pessemier TMartens L(2024)Connecting physical activity with context and motivation: a user study to define variables to integrate into mobile health recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-023-09368-934:1(147-181)Online publication date: 1-Mar-2024
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Felfernig AWundara MTran TLe VLubos SPolat-Erdeniz S(2024)Sports recommender systems: overview and research directionsJournal of Intelligent Information Systems10.1007/s10844-024-00857-w62:4(1125-1164)Online publication date: 1-Aug-2024
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Feely CCaulfield BLawlor ASmyth B(2024)A Case-Based Reasoning Approach to Post-injury Training Recommendations for Marathon RunnersCase-Based Reasoning Research and Development10.1007/978-3-031-63646-2_22(338-353)Online publication date: 1-Jul-2024
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Feely CCaulfield BLawlor ASmyth B(2023)Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training RecommendationsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592952(183-193)Online publication date: 18-Jun-2023
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