{"id":{"repo_id":"calgary","oai_identifier":"oai:ucalgary.scholaris.ca:1880/121543"},"canonical_url":"https://search.dev.ndltd.org/etd/calgary/oai:ucalgary.scholaris.ca:1880/121543","repository":{"repo_id":"calgary","name":"University of Calgary","base_url":"https://ucalgary.scholaris.ca/server/oai/request"},"display":{"title":"Novel Wearable Technology Methods to Guide and Monitor Endurance Training","abstract":"To maximize the performance and health benefits of endurance exercise, training must be prescribed strategically—scaling exercise intensities to an individual’s physical capabilities and balancing training with sufficient rest and recovery. To accomplish this goal, contemporary best practice emphasizes exercise intensity domain and threshold identification to structure training strategies. Although several laboratory- and field-based approaches aim to support this framework, they often fail to accurately match work rates with intended physiological thresholds and are limited by their reliance on multiple exhaustive exercise tests and specialized equipment. Most critically, these approaches are impractical for capturing day-to-day fluctuations in performance and fatigue accumulation, limiting their utility to inform short-term training strategies. This dissertation aimed to address these limitations by refining contemporary testing protocols and integrating wearable technologies to develop novel methods for prescribing training intensity and monitoring training responses. In Chapter Two, we validated the accuracy of an innovative Step-Ramp-Step (SRS) protocol to match running speed and wearable-derived running power with critical physiological thresholds, highlighting its practical utility for exercise intensity prescription. In Chapter Three, we demonstrated strong agreement between critical speed (CS) and wearable-derived critical power (CP), uncovering advantages of wearable running sensors for guiding real-time training intensity. In Chapter Four, we explored non-linear analyses of heart rate variability (HRV-DFAα1) and found that, although it did not effectively indicate metabolic thresholds, HRV-DFAα1 was sensitive to both exercise intensity and duration, demonstrating its potential value to monitor fatigue accumulation during prolonged exercise. Finally in Chapter Five, we identified key associations between HRV-DFAα1 and exercise performance outcomes, supporting its utility in monitoring short-term training responses and providing insights into exercise readiness-to-train, durability, and training load. Collectively, our findings demonstrate the value of integrating wearable sensors with innovative testing protocols to enhance endurance training strategies. These innovative methods address several limitations of contemporary training approaches by offering practical, scalable, and non-invasive assessments to accurately identify exercise intensity, guide training in real-world settings, and monitor short-term training responses.","abstract_html":"To maximize the performance and health benefits of endurance exercise, training must be prescribed strategically—scaling exercise intensities to an individual’s physical capabilities and balancing training with sufficient rest and recovery. To accomplish this goal, contemporary best practice emphasizes exercise intensity domain and threshold identification to structure training strategies. Although several laboratory- and field-based approaches aim to support this framework, they often fail to accurately match work rates with intended physiological thresholds and are limited by their reliance on multiple exhaustive exercise tests and specialized equipment. Most critically, these approaches are impractical for capturing day-to-day fluctuations in performance and fatigue accumulation, limiting their utility to inform short-term training strategies. This dissertation aimed to address these limitations by refining contemporary testing protocols and integrating wearable technologies to develop novel methods for prescribing training intensity and monitoring training responses. In Chapter Two, we validated the accuracy of an innovative Step-Ramp-Step (SRS) protocol to match running speed and wearable-derived running power with critical physiological thresholds, highlighting its practical utility for exercise intensity prescription. In Chapter Three, we demonstrated strong agreement between critical speed (CS) and wearable-derived critical power (CP), uncovering advantages of wearable running sensors for guiding real-time training intensity. In Chapter Four, we explored non-linear analyses of heart rate variability (HRV-DFAα1) and found that, although it did not effectively indicate metabolic thresholds, HRV-DFAα1 was sensitive to both exercise intensity and duration, demonstrating its potential value to monitor fatigue accumulation during prolonged exercise. Finally in Chapter Five, we identified key associations between HRV-DFAα1 and exercise performance outcomes, supporting its utility in monitoring short-term training responses and providing insights into exercise readiness-to-train, durability, and training load. Collectively, our findings demonstrate the value of integrating wearable sensors with innovative testing protocols to enhance endurance training strategies. These innovative methods address several limitations of contemporary training approaches by offering practical, scalable, and non-invasive assessments to accurately identify exercise intensity, guide training in real-world settings, and monitor short-term training responses.","abstract_has_math":false,"creators":["van Rassel, Cody Ray"],"institution":"Kinesiology","degree_name":"Doctor of Philosophy (PhD)","degree_level":null,"degree_discipline":"Kinesiology","degree_department":null,"school":null,"contributors":[],"advisors":["MacInnis, Martin"],"committee_chairs":[],"committee_members":["Din, Cari","Eves, Neil","Leguillette, Renaud","Lee, Joon","Edwards, Brent","Clermont, Christian"],"year":2025,"date_issued":"2025-05-13","date_published":"2025-05-13","updated_at":"2026-07-24T01:30:29Z","subjects":["Wearable technology","Exercise","Exercise intensity","Exercise performance","Non-linear analyses","Heart rate variability","Exercise training","Running power","Endurance training"],"languages":["en"],"rights":["Unless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://dx.doi.org/10.11575/PRISM/49133"],"render_values":[{"text":"https://dx.doi.org/10.11575/PRISM/49133","href":"https://dx.doi.org/10.11575/PRISM/49133","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1880/121543","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["MacInnis, Martin"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Din, Cari","Eves, Neil","Leguillette, Renaud","Lee, Joon","Edwards, Brent","Clermont, Christian"]},{"key":"dc:creator","label":"Author","values":["van Rassel, Cody Ray"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-11"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-05-14T18:08:16Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-05-14T18:08:16Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05-13"]},{"key":"dc:type","label":"Dc Type","values":["doctoral thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Kinesiology"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Calgary"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Wearable technology","Exercise","Exercise intensity","Exercise performance","Non-linear analyses","Heart rate variability","Exercise training","Running power","Endurance training"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Unless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. 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To accomplish this goal, contemporary best practice emphasizes exercise intensity domain and threshold identification to structure training strategies. Although several laboratory- and field-based approaches aim to support this framework, they often fail to accurately match work rates with intended physiological thresholds and are limited by their reliance on multiple exhaustive exercise tests and specialized equipment. Most critically, these approaches are impractical for capturing day-to-day fluctuations in performance and fatigue accumulation, limiting their utility to inform short-term training strategies. This dissertation aimed to address these limitations by refining contemporary testing protocols and integrating wearable technologies to develop novel methods for prescribing training intensity and monitoring training responses. In Chapter Two, we validated the accuracy of an innovative Step-Ramp-Step (SRS) protocol to match running speed and wearable-derived running power with critical physiological thresholds, highlighting its practical utility for exercise intensity prescription. In Chapter Three, we demonstrated strong agreement between critical speed (CS) and wearable-derived critical power (CP), uncovering advantages of wearable running sensors for guiding real-time training intensity. In Chapter Four, we explored non-linear analyses of heart rate variability (HRV-DFAα1) and found that, although it did not effectively indicate metabolic thresholds, HRV-DFAα1 was sensitive to both exercise intensity and duration, demonstrating its potential value to monitor fatigue accumulation during prolonged exercise. Finally in Chapter Five, we identified key associations between HRV-DFAα1 and exercise performance outcomes, supporting its utility in monitoring short-term training responses and providing insights into exercise readiness-to-train, durability, and training load. Collectively, our findings demonstrate the value of integrating wearable sensors with innovative testing protocols to enhance endurance training strategies. These innovative methods address several limitations of contemporary training approaches by offering practical, scalable, and non-invasive assessments to accurately identify exercise intensity, guide training in real-world settings, and monitor short-term training responses."]},{"key":"dc:title","label":"Title","values":["Novel Wearable Technology Methods to Guide and Monitor Endurance Training"]}]}],"canonical_facts":{"dc:contributor.advisor":["MacInnis, Martin"],"dc:contributor.committeemember":["Din, Cari","Eves, Neil","Leguillette, Renaud","Lee, Joon","Edwards, Brent","Clermont, Christian"],"dc:creator":["van Rassel, Cody Ray"],"dc:date":["2025-11"],"dc:date.accessioned":["2025-05-14T18:08:16Z"],"dc:date.available":["2025-05-14T18:08:16Z"],"dc:date.issued":["2025-05-13"],"dc:description.abstract":["To maximize the performance and health benefits of endurance exercise, training must be prescribed strategically—scaling exercise intensities to an individual’s physical capabilities and balancing training with sufficient rest and recovery. 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In Chapter Two, we validated the accuracy of an innovative Step-Ramp-Step (SRS) protocol to match running speed and wearable-derived running power with critical physiological thresholds, highlighting its practical utility for exercise intensity prescription. In Chapter Three, we demonstrated strong agreement between critical speed (CS) and wearable-derived critical power (CP), uncovering advantages of wearable running sensors for guiding real-time training intensity. In Chapter Four, we explored non-linear analyses of heart rate variability (HRV-DFAα1) and found that, although it did not effectively indicate metabolic thresholds, HRV-DFAα1 was sensitive to both exercise intensity and duration, demonstrating its potential value to monitor fatigue accumulation during prolonged exercise. Finally in Chapter Five, we identified key associations between HRV-DFAα1 and exercise performance outcomes, supporting its utility in monitoring short-term training responses and providing insights into exercise readiness-to-train, durability, and training load. Collectively, our findings demonstrate the value of integrating wearable sensors with innovative testing protocols to enhance endurance training strategies. These innovative methods address several limitations of contemporary training approaches by offering practical, scalable, and non-invasive assessments to accurately identify exercise intensity, guide training in real-world settings, and monitor short-term training responses."],"dc:identifier.doi":["https://dx.doi.org/10.11575/PRISM/49133"],"dc:identifier.uri":["https://hdl.handle.net/1880/121543"],"dc:language.iso":["en"],"dc:rights":["Unless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. 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