Smartwatch-Driven Biofeedback for Personalised Physical Education Interventions

Thesis Abstract

Physical education increasingly demands individualized engagement strategies to maximize student motivation, skill acquisition, and health outcomes; however, traditional classroom approaches often fail to deliver timely, personalised feedback within dynamic activity contexts. This study addresses the gap by examining how smartwatch-driven biofeedback can support personalised physical education interventions, enhancing students’ aerobic fitness, movement competence, and self-regulated learning. The aim is to evaluate whether real-time physiological and biomechanical feedback delivered via wearables improves engagement, target achievement, and instructional efficacy in secondary-school PE settings. Specific objectives are (1) to determine the effect of smartwatch biofeedback on students’ moderate-to-vigorous physical activity (MVPA) levels during PE lessons; (2) to assess changes in movement efficiency and technique accuracy using accelerometer-derived metrics and teacher-rated checklists; (3) to explore the impact of biofeedback on students’ motivation, self-efficacy, and autonomous learning dispositions; (4) to examine teachers’ perceived feasibility, readiness, and instructional adaptation when integrating smartwatch-driven feedback into lesson design; and (5) to identify contextual factors mediating the effectiveness of biofeedback (e.g., lesson type, class size, and equipment availability). A mixed-methods design will be employed in three phases over a 12-month period. Phase I adopts a quasi-experimental design with two secondary-school PE cohorts (n = 240 students; ages 12–15) assigned to an intervention condition receiving smartwatch-based biofeedback integrated into standard curricula and a control condition following traditional instruction for 12 weeks. Phase II uses a within-subjects crossover for a subsample (n = 60) to compare outcomes across biofeedback and non-biofeedback cycles, controlling for maturation effects. Phase III comprises qualitative interviews and focus groups with PE teachers (n = 12) and student focus groups (n = 24) to elucidate mechanisms and contextual influences. Data collection instruments include (a) wrist-worn wearables providing heart rate, active minutes, and estimated VO2max proxies; (b) accelerometers for movement pattern analysis; (c) validated questionnaires measuring motivation (Intrinsic Motivation Inventory), self-efficacy (General Self-Efficacy Scale), and autonomous learning (Alfrey Adaptive Learning Scale); (d) teacher checklists for technique assessment and lesson fidelity; (e) semi-structured interviews and focus groups; and (f) lesson observation protocols. Data analysis will proceed as follows descriptive statistics to profile baselines; multilevel linear modeling to assess intervention effects on MVPA, fitness proxies, and technique metrics while accounting for nested data (students within classes); repeated-measures ANOVA for crossover outcomes; regression analyses to identify mediators and moderators (motivation, self-efficacy, class size, and lesson type); and thematic analysis of qualitative data to triangulate quantitative findings and reveal implementation barriers and enablers. Instrument validity and reliability will be established through pilot testing (n = 30) and Cronbach’s alpha assessments (target ? ? .80 for psychometric scales). Ethical approval will be obtained from the university's Human Research Ethics Committee, with informed consent from guardians and assent from students, and data handling will comply with privacy regulations. The study is expected to show that smartwatch-driven biofeedback yields statistically significant improvements in MVPA participation (p < .05), higher precision in movement technique (p < .05), and enhanced autonomous motivation and self-efficacy (p < .05) compared with conventional PE instruction. Qualitative results are anticipated to reveal that real-time feedback fosters self-regulation, goal-setting, and reflective practice, while teacher experiences will highlight feasible integration strategies, required teacher training, and equipment considerations. Theoretical framing will draw on Self-Determination Theory to interpret motivational shifts and the Bioecological Model to understand contextual influences, supplemented by motor learning theory in interpreting technique improvements. The study contributes to knowledge by providing rigorous, empirically grounded evidence on the efficacy and conditions under which wearable biofeedback can personalise PE interventions, informing policy, curriculum design, and teacher professional development. Practical implications include guidelines for implementing wearable-assisted pedagogy at scale, recommendations for data governance, and a framework for integrating biofeedback into lesson planning without compromising inclusivity or data privacy. The main conclusion is that smartwatch-driven biofeedback can meaningfully enhance personalised PE interventions when integrated with clear instructional goals, teacher support, and context-aware adaptation; future research should explore long-term impacts on lifelong physical activity habits and equity across diverse school settings.