{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T01:13:43Z","timestamp":1777511623957,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA-a1) of heart rate variability (HRV) has been shown to be a sensitive marker for assessing global organismic demands. The wide dynamic range within the exercise intensity spectrum and the relationship to established physiologic threshold boundaries potentially allow in-field use and also open opportunities to provide real-time feedback. The present study expands the idea of using everyday workout data from the AI Endurance app to obtain the relationship between cycling power and DFA-a1. Collected data were imported between September 2021 and August 2023 with an initial pool of 3123 workouts across 21 male users. The aim of this analysis was to further apply a new method of implementing workout group data considering representative values of DFA-a1 segmentation compared to single workout data and including all data points to enhance the validity of the internal-to-external load relationship. The present data demonstrate a universal relationship between cycling power and DFA-a1 from everyday workout data that potentially allows accessible and regular tracking of intensity zone demarcation information. The analysis highlights the superior efficacy of the representative-based approach of included data in most cases. Validation data of the performance level and the up-to-date relationship are still pending.<\/jats:p>","DOI":"10.3390\/s24144468","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T15:22:05Z","timestamp":1720624925000},"page":"4468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Relationship of Cycling Power and Non-Linear Heart Rate Variability from Everyday Workout Data: Potential for Intensity Zone Estimation and Monitoring"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0965-3967","authenticated-orcid":false,"given":"Stefano","family":"Andriolo","sequence":"first","affiliation":[{"name":"AI Endurance Inc., Hamilton, ON L8P 0A1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2490-8331","authenticated-orcid":false,"given":"Markus","family":"Rummel","sequence":"additional","affiliation":[{"name":"AI Endurance Inc., Hamilton, ON L8P 0A1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5610-6013","authenticated-orcid":false,"given":"Thomas","family":"Gronwald","sequence":"additional","affiliation":[{"name":"Institute of Interdisciplinary Exercise Science and Sports Medicine, MSH Medical School Hamburg, 20457 Hamburg, Germany"},{"name":"G-Lab, Faculty of Applied Sport Sciences and Personality, BSP Business and Law School, 12247 Berlin, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1055\/s-2004-830514","article-title":"A Conceptual Framework for Performance Diagnosis and Training Prescription from Submaximal Gas Exchange Parameters\u2014Theory and Application","volume":"26","author":"Meyer","year":"2005","journal-title":"Int. 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