Digital Physiological Self-Monitoring in Pre-Service Physical Education Teacher Education: A Conceptual Mind-Body Model for Motivation, Self-Regulation, and Professional Competence
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Objective: This article proposes a conceptual mind-body learning model for integrating digital physiological self-monitoring into pre-service physical education teacher education.
Methods and Materials: A narrative conceptual synthesis used purposive theoretical sampling across literature on wearable monitoring, embodied cognition, interoceptive awareness, self-determination theory, self-regulated learning, teacher competence, learning analytics ethics, and digital pedagogy. No original empirical data were collected, and no effectiveness claims are made.
Findings: Educational value lies in guided comparison of device feedback with subjective bodily experience, motivational meaning, context, and pedagogical judgment. A five-phase cycle links bodily observation, subjective interpretation, motivational meaning, self-regulatory action, and pedagogical translation. The phases are iterative rather than linear.
Conclusion: The model defines its boundaries, three indicator-sensitivity tiers, no-device alternatives, cultural adaptation requirements, minimum safeguards, and indicators for future testing. Physiological self-monitoring should function as a human-centered reflective resource rather than a fitness-ranking or grading tool. Its effects on motivation, self-regulation, well-being, equity, and professional competence require empirical testing before it can be recommended as evidence-based practice.
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Achten, J., & Jeukendrup, A. E. (2003). Heart rate monitoring: Applications and limitations. Sports Medicine, 33(7), 517-538. https://doi.org/10.2165/00007256-200333070-00004
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman. https://books.google.com/books?vid=ISBN9780716728504
Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617-645. https://doi.org/10.1146/annurev.psych.59.103006.093639
Blömeke, S., Gustafsson, J.-E., & Shavelson, R. J. (2015). Beyond dichotomies: Competence viewed as a continuum. Zeitschrift für Psychologie, 223(1), 3-13. https://doi.org/10.1027/2151-2604/a000194
Borresen, J., & Lambert, M. I. (2008). Autonomic control of heart rate during and after exercise: Measurements and implications for monitoring training status. Sports Medicine, 38(8), 633-646. https://doi.org/10.2165/00007256-200838080-00002
Brickwood, K.-J., Watson, G., O'Brien, J., & Williams, A. D. (2019). Consumer-based wearable activity trackers increase physical activity participation: Systematic review and meta-analysis. JMIR mHealth and uHealth, 7(4), e11819. https://doi.org/10.2196/11819
Cardinale, M., & Varley, M. C. (2017). Wearable training-monitoring technology: Applications, challenges, and opportunities. International Journal of Sports Physiology and Performance, 12(Suppl 2), S2-55-S2-62. https://doi.org/10.1123/ijspp.2016-0423
Chu, S. L., Garcia, B. M., & Rani, N. (2023). Research on wearable technologies for learning: A systematic review. Frontiers in Education, 8, Article 1270389. https://doi.org/10.3389/feduc.2023.1270389
Craig, A. D. (2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews Neuroscience, 3(8), 655-666. https://doi.org/10.1038/nrn894
Cunningham-Rose, R., Garrett, N., Lonsdale, C., & Harris, N. (2025). The acute effect of heart rate monitor projection on exercise effort in school students. European Physical Education Review, 31(3), 415-425. https://doi.org/10.1177/1356336X241280834
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum. https://doi.org/10.1007/978-1-4899-2271-7
Drachsler, H., & Greller, W. (2016). Privacy and analytics: It's a DELICATE issue: A checklist for trusted learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 89-98). ACM. https://doi.org/10.1145/2883851.2883893
Düking, P., Fuss, F. K., Holmberg, H.-C., & Sperlich, B. (2018). Recommendations for assessment of the reliability, sensitivity, and validity of data provided by wearable sensors designed for monitoring physical activity. JMIR mHealth and uHealth, 6(4), e102. https://doi.org/10.2196/mhealth.9341
Francis, M., Avoseh, M., Card, K., Newland, L., & Streff, K. (2023). Student privacy and learning analytics: Investigating the application of privacy within a student success information system in higher education. Journal of Learning Analytics, 10(3), 102-114. https://doi.org/10.18608/jla.2023.7975
Gallagher, S. (2005). How the body shapes the mind. Oxford University Press. https://doi.org/10.1093/0199271941.001.0001
Halson, S. L. (2014). Monitoring training load to understand fatigue in athletes. Sports Medicine, 44(Suppl 2), S139-S147. https://doi.org/10.1007/s40279-014-0253-z
Hernández-Mustieles, M. A., Lima-Carmona, Y. E., Pacheco-Ramírez, M. A., Mendoza-Armenta, A. A., Romero-Gómez, J. E., Cruz-Gómez, C. F., Rodríguez-Alvarado, D. C., Arceo, A., Cruz-Garza, J. G., Ramírez-Moreno, M. A., & Lozoya-Santos, J. de J. (2024). Wearable biosensor technology in education: A systematic review. Sensors, 24(8), 2437. https://doi.org/10.3390/s24082437
Hong, H., Dai, L., & Zheng, X. (2025). Advances in wearable sensors for learning analytics: Trends, challenges, and prospects. Sensors, 25(9), 2714. https://doi.org/10.3390/s25092714
Impellizzeri, F. M., Marcora, S. M., & Coutts, A. J. (2019). Internal and external training load: 15 years on. International Journal of Sports Physiology and Performance, 14(2), 270-273. https://doi.org/10.1123/ijspp.2018-0935
Jones, K. M. L. (2019). Learning analytics and higher education: A proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16, Article 24. https://doi.org/10.1186/s41239-019-0155-0
Jovanovic, P., & Kay, R. (2022). Exploring ethical issues with using wearable technology in K-12 classrooms: A review of the literature. Journal of Digital Life and Learning, 2(2), 3-11. https://doi.org/10.51357/jdll.v2i2.203
Lupton, D. (2016). The quantified self: A sociology of self-tracking. Polity Press. https://researchprofiles.canberra.edu.au/en/publications/the-quantified-self-a-sociology-of-self-tracking/
Martín-Rodríguez, A., & Madrigal-Cerezo, R. (2025). Technology-enhanced pedagogy in physical education: Bridging engagement, learning, and lifelong activity. Education Sciences, 15(4), 409. https://doi.org/10.3390/educsci15040409
Mehling, W. E., Price, C., Daubenmier, J. J., Acree, M., Bartmess, E., & Stewart, A. (2012). The Multidimensional Assessment of Interoceptive Awareness (MAIA). PLOS ONE, 7(11), e48230. https://doi.org/10.1371/journal.pone.0048230
Ntoumanis, N. (2005). A prospective study of participation in optional school physical education using a self-determination theory framework. Journal of Educational Psychology, 97(3), 444-453. https://doi.org/10.1037/0022-0663.97.3.444
Nurhayati, F., Tuasikal, A. R. S., Nurhasan, N., Muhammad, H. N., Indahwati, N., Kartiko, D. C., & Pranoto, A. (2025). Analysis of pre-service teachers’ 21st century skills in Indonesia by gender. International Journal of Body, Mind and Culture, 12(4), 64-71. https://doi.org/10.61838/ijbmc.v12i4.895
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438-450. https://doi.org/10.1111/bjet.12152
Peake, J. M., Kerr, G., & Sullivan, J. P. (2018). A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations. Frontiers in Physiology, 9, Article 743. https://doi.org/10.3389/fphys.2018.00743
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385-407. https://doi.org/10.1007/s10648-004-0006-x
Ridwan, M., Aswanda, N., Sumarno, S., Ahmedov, F., & Pranoto, A. (2025). Augmented reality’s impact on learning motivation in physical education: A systematic review. International Journal of Body, Mind and Culture, 12(2), 5-12. https://doi.org/10.61838/ijbmc.v12i2.838
Rubel, A., & Jones, K. M. L. (2016). Student privacy in learning analytics: An information ethics perspective. The Information Society, 32(2), 143-159. https://doi.org/10.1080/01972243.2016.1130502
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. https://doi.org/10.1037/0003-066X.55.1.68
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. The Guilford Press. https://www.guilford.com/books/Self-Determination-Theory/Ryan-Deci/9781462538966
Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books. https://www.hachettebookgroup.com/titles/donald-a-schon/the-reflective-practitioner/9780465068784/
Schunk, D. H., & Greene, J. A. (Eds.). (2018). Handbook of self-regulation of learning and performance (2nd ed.). Routledge. https://doi.org/10.4324/9781315697048
Selwyn, N. (2019). What's the problem with learning analytics? Journal of Learning Analytics, 6(3), 11-19. https://doi.org/10.18608/jla.2019.63.3
Seyed Alitabar, S. H. (2025). The biopsychosocial model in modern healthcare: Overcoming barriers to holistic patient care. International Journal of Body, Mind and Culture, 12(2), 1–4. https://doi.org/10.61838/ijbmc.v12i2.950
Shapiro, S. L., Carlson, L. E., Astin, J. A., & Freedman, B. (2006). Mechanisms of mindfulness. Journal of Clinical Psychology, 62(3), 373-386. https://doi.org/10.1002/jclp.20237
Shilling, C. (2017). Body pedagogics: Embodiment, cognition and cultural transmission. Sociology, 51(6), 1205-1221. https://doi.org/10.1177/0038038516641868
Skulmowski, A., & Rey, G. D. (2018). Embodied learning: Introducing a taxonomy based on bodily engagement and task integration. Cognitive Research: Principles and Implications, 3, Article 6. https://doi.org/10.1186/s41235-018-0092-9
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. https://doi.org/10.1177/0002764213479366
Spittle, M., Jackson, K., & Casey, M. (2009). Applying self-determination theory to understand the motivation for becoming a physical education teacher. Teaching and Teacher Education, 25(1), 190-197. https://doi.org/10.1016/j.tate.2008.07.005
Spittle, S., Spittle, M., Encel, K., & Itoh, S. (2022). Confidence and motivation to teach primary physical education: A survey of specialist primary physical education pre-service teachers in Australia. Frontiers in Education, 7, Article 1061099. https://doi.org/10.3389/feduc.2022.1061099
Standage, M., Duda, J. L., & Ntoumanis, N. (2003). A model of contextual motivation in physical education: Using constructs from self-determination and achievement goal theories to predict physical activity intentions. Journal of Educational Psychology, 95(1), 97-110. https://doi.org/10.1037/0022-0663.95.1.97
Standage, M., Duda, J. L., & Ntoumanis, N. (2005). A test of self-determination theory in school physical education. British Journal of Educational Psychology, 75(3), 411-433. https://doi.org/10.1348/000709904X22359
Stoddart, A. L., & Selanders, K. P. (2022). Preparing for physical literacy: Exploring pre-service teachers’ training and understanding. Teaching and Teacher Education, 120, Article 103886. https://doi.org/10.1016/j.tate.2022.103886
Teixeira, P. J., Carraça, E. V., Markland, D., Silva, M. N., & Ryan, R. M. (2012). Exercise, physical activity, and self-determination theory: A systematic review. International Journal of Behavioral Nutrition and Physical Activity, 9, Article 78. https://doi.org/10.1186/1479-5868-9-78
Theobald, M. (2021). Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 66, Article 101976. https://doi.org/10.1016/j.cedpsych.2021.101976
Tschannen-Moran, M., & Woolfolk Hoy, A. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783-805. https://doi.org/10.1016/S0742-051X(01)00036-1
Vallerand, R. J., Pelletier, L. G., Blais, M. R., Brière, N. M., Senécal, C., & Vallières, E. F. (1992). The Academic Motivation Scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003-1017. https://doi.org/10.1177/0013164492052004025
Whitehead, M. (Ed.). (2010). Physical literacy: Throughout the life course. Routledge. https://doi.org/10.4324/9780203881903
Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE. https://uk.sagepub.com/en-gb/eur/big-data-in-education/book245288
Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625-636. https://doi.org/10.3758/BF03196322
Zhao, Z., & Su, Y. (2026). Wearable technologies in school physical education: A systematic review of pedagogical implications. Physical Education Theory and Methodology, 26(1), 79-89. https://doi.org/10.17309/tmfv.2026.1.07
Zhou, T. (2024). The role of pre-service physical education teachers in physical education: A bibliometric and systematic review. Heliyon, 10(7), Article e28702. https://doi.org/10.1016/j.heliyon.2024.e28702
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70. https://doi.org/10.1207/s15430421tip4102_2
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