{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:33:20Z","timestamp":1762353200839,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T00:00:00Z","timestamp":1653350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MDS"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>VO2max index has a significant impact on overall health. Its estimation through wearables notifies the user of his level of fitness but cannot provide a detailed analysis of the time intervals in which heartbeat dynamics are changed and\/or fatigue is emerging. Here, we developed a multiple modality biosignal processing method to investigate running sessions to characterize in real time heartbeat dynamics in response to external energy demand. We isolated dynamic regimes whose fraction increases with the VO2max and with the emergence of neuromuscular fatigue. This analysis can be extremely valuable by providing personalized feedback about the user\u2019s fitness level improvement that can be realized by developing personalized exercise plans aimed to target a contextual increase in the dynamic regime fraction related to VO2max increase, at the expense of the dynamic regime fraction related to the emergence of fatigue. These strategies can ultimately result in the reduction in cardiovascular risk.<\/jats:p>","DOI":"10.3390\/s22113974","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:14:14Z","timestamp":1653437654000},"page":"3974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Unsupervised Clustering of Heartbeat Dynamics Allows for Real Time and Personalized Improvement in Cardiovascular Fitness"],"prefix":"10.3390","volume":"22","author":[{"given":"Cassandra","family":"Serantoni","sequence":"first","affiliation":[{"name":"Department of Neuroscience, Biophysics Sections, Universit\u00e0 Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy"},{"name":"Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS, 00168 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6009-4465","authenticated-orcid":false,"given":"Giovanna","family":"Zimatore","sequence":"additional","affiliation":[{"name":"Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi, 10, 22060 Novedrate, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1257-2295","authenticated-orcid":false,"given":"Giada","family":"Bianchetti","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Biophysics Sections, Universit\u00e0 Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy"},{"name":"Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS, 00168 Rome, Italy"}]},{"given":"Alessio","family":"Abeltino","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Biophysics Sections, Universit\u00e0 Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy"},{"name":"Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS, 00168 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4260-5107","authenticated-orcid":false,"given":"Marco","family":"De Spirito","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Biophysics Sections, Universit\u00e0 Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy"},{"name":"Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS, 00168 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2154-319X","authenticated-orcid":false,"given":"Giuseppe","family":"Maulucci","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Biophysics Sections, Universit\u00e0 Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy"},{"name":"Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS, 00168 Rome, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"ref_1","first-page":"757","article-title":"The Maximum Oxygen Intake","volume":"38","author":"Shephard","year":"1968","journal-title":"Bull. World Health Organ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1093\/qjmed\/os-16.62.135","article-title":"Muscular Exercise, Lactic Acid, and the Supply and Utilization of Oxygen","volume":"62","author":"Hill","year":"1923","journal-title":"QJM Int. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1123\/pes.2013-0153","article-title":"A Systematic Review and Meta-Analysis of Submaximal Exercise-Based Equations to Predict Maximal Oxygen Uptake in Young People","volume":"26","author":"Ferrar","year":"2014","journal-title":"Pediatr. Exerc. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20151028","DOI":"10.1098\/rspb.2015.1028","article-title":"Variation in the Link between Oxygen Consumption and ATP Production, and Its Relevance for Animal Performance","volume":"282","author":"Salin","year":"2015","journal-title":"Proc. R. Soc. B Biol. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e653","DOI":"10.1161\/CIR.0000000000000461","article-title":"Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association","volume":"134","author":"Ross","year":"2016","journal-title":"Circulation"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.1001\/jama.2009.681","article-title":"Cardiorespiratory Fitness as a Quantitative Predictor of All-Cause Mortality and Cardiovascular Events in Healthy Men and Women: A Meta-Analysis","volume":"301","author":"Kodama","year":"2009","journal-title":"JAMA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e000854","DOI":"10.1136\/bmjsem-2020-000854","article-title":"Scaling VO2max to Body Size Differences to Evaluate Associations to CVD Incidence and All-Cause Mortality Risk","volume":"7","author":"Ekblom","year":"2021","journal-title":"BMJ Open Sport Exerc. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bianchetti, G., Abeltino, A., Serantoni, C., Ardito, F., Malta, D., De Spirito, M., and Maulucci, G. (2022). Personalized Self-Monitoring of Energy Balance through Integration in a Web-Application of Dietary, Anthropometric, and Physical Activity Data. J. Pers. Med., 12.","DOI":"10.3390\/jpm12040568"},{"key":"ref_9","first-page":"346","article-title":"The Prediction of Vo2max: A Comparison of 7 Indirect Tests of Aerobic Power","volume":"13","author":"Grant","year":"1999","journal-title":"J. Strength Cond. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1055\/s-0030-1255110","article-title":"A Simple Approach to Assess VT during a Field Walk Test","volume":"31","author":"Dourado","year":"2010","journal-title":"Int. J. Sports Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1519\/JSC.0000000000001502","article-title":"Heart-Rate Variability Threshold as an Alternative for Spiro-Ergometry Testing: A Validation Study","volume":"31","author":"Mankowski","year":"2017","journal-title":"J. Strength Cond. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e18907","DOI":"10.2196\/18907","article-title":"Wearable Health Devices in Health Care: Narrative Systematic Review","volume":"8","author":"Lu","year":"2020","journal-title":"JMIR MHealth UHealth"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1177\/0141076816663560","article-title":"A Review of Wearable Technology in Medicine","volume":"109","author":"Iqbal","year":"2016","journal-title":"J. R. Soc. Med."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Adesida, Y., Papi, E., and McGregor, A.H. (2019). Exploring the Role of Wearable Technology in Sport Kinematics and Kinetics: A Systematic Review. Sensors, 19.","DOI":"10.3390\/s19071597"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aroganam, G., Manivannan, N., and Harrison, D. (2019). Review on Wearable Technology Sensors Used in Consumer Sport Applications. Sensors, 19.","DOI":"10.3390\/s19091983"},{"key":"ref_16","unstructured":"POLAR (2022, April 21). Polar-Fitness-Test-White-Paper.Pdf. Available online: https:\/\/www.polar.com\/sites\/default\/files\/static\/science\/white-papers\/polar-fitness-test-white-paper.pdf."},{"key":"ref_17","unstructured":"FIRSTBEAT (2022, April 06). White_paper_VO2max_30.6.2017.Pdf. Available online: https:\/\/assets.firstbeat.com\/firstbeat\/uploads\/2017\/06\/white_paper_VO2max_30.6.2017.pdf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Passler, S., Bohrer, J., Bl\u00f6chinger, L., and Senner, V. (2019). Validity of Wrist-Worn Activity Trackers for Estimating VO2max and Energy Expenditure. Int. J. Environ. Res. Public. Health, 16.","DOI":"10.3390\/ijerph16173037"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"63","DOI":"10.31686\/ijier.vol5.iss2.619","article-title":"Validation of the Garmin Forerunner 920XT Fitness Watch VO2peak Test","volume":"5","author":"Kraft","year":"2017","journal-title":"Int. J. Innov. Educ. Res."},{"key":"ref_20","unstructured":"APPLE (2022, April 21). Using Apple Watch to Estimate Cardio Fitness with VO2Max. Available online: https:\/\/www.apple.com\/healthcare\/docs\/site\/Using_Apple_Watch_to_Estimate_Cardio_Fitness_with_VO2_max.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bacon, A.P., Carter, R.E., Ogle, E.A., and Joyner, M.J. (2013). VO2max Trainability and High Intensity Interval Training in Humans: A Meta-Analysis. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0073182"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Moore, S.C., Patel, A.V., Matthews, C.E., Berrington de Gonzalez, A., Park, Y., Katki, H.A., Linet, M.S., Weiderpass, E., Visvanathan, K., and Helzlsouer, K.J. (2012). Leisure Time Physical Activity of Moderate to Vigorous Intensity and Mortality: A Large Pooled Cohort Analysis. PLoS Med., 9.","DOI":"10.1371\/journal.pmed.1001335"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.annepidem.2009.01.019","article-title":"Healthy Hearts\u2013and the Universal Benefits of Being Physically Active: Physical Activity and Health","volume":"19","author":"Blair","year":"2009","journal-title":"Ann. Epidemiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5551","DOI":"10.1113\/jphysiol.2009.179432","article-title":"Exercise Protects the Cardiovascular System: Effects beyond Traditional Risk Factors: Exercise Protects the Cardiovascular System","volume":"587","author":"Joyner","year":"2009","journal-title":"J. Physiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1016\/j.cmet.2017.04.030","article-title":"Sprinting Toward Fitness","volume":"25","author":"Gibala","year":"2017","journal-title":"Cell Metab."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0735-1097(00)01054-8","article-title":"Age-Predicted Maximal Heart Rate Revisited","volume":"37","author":"Tanaka","year":"2001","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_27","unstructured":"Box, G.E.P., Jenkins, G.M., and Reinsel, G.C. (1994). Time Series Analysis: Forecasting and Control, Prentice Hall."},{"key":"ref_28","unstructured":"Lazzeri, F. (2022, May 10). Machine Learning for Time Series Forecasting with Python|Wiley. Available online: https:\/\/www.wiley.com\/en-us\/Machine+Learning+for+Time+Series+Forecasting+with+Python-p-9781119682387."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1111\/j.1467-9892.2009.00623.x","article-title":"Bartlett\u2019s Formula for a General Class of Nonlinear Processes","volume":"30","author":"Francq","year":"2009","journal-title":"J. Time Ser. Anal."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array Programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1080\/00031305.1986.10475349","article-title":"Unit Roots in Time Series Models: Tests and Implications","volume":"40","author":"Dickey","year":"1986","journal-title":"Am. Stat."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1177\/002224378302000204","article-title":"Cluster analysis in marketing research: Review and suggestions for application","volume":"20","author":"Punj","year":"1983","journal-title":"J. Mark. Res."},{"key":"ref_33","unstructured":"Theodoridis, S., and Koutroumbas, K. (2009). Pattern Recognition, Elsevier."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/3-540-47887-6_4","article-title":"On Data Clustering Analysis: Scalability, Constraints, and Validation","volume":"Volume 2336","author":"Goos","year":"2002","journal-title":"Advances in Knowledge Discovery and Data Mining"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"238173","DOI":"10.1016\/j.aca.2020.12.048","article-title":"Label-Free Metabolic Clustering through Unsupervised Pixel Classification of Multiparametric Fluorescent Images","volume":"1148","author":"Bianchetti","year":"2021","journal-title":"Anal. Chim. Acta"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5728","DOI":"10.1364\/BOE.399655","article-title":"Unsupervised Clustering of Multiparametric Fluorescent Images Extends the Spectrum of Detectable Cell Membrane Phases with Sub-Micrometric Resolution","volume":"11","author":"Bianchetti","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"ref_37","first-page":"100","article-title":"Algorithm AS 136: A K-Means Clustering Algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J. R. Stat. Soc. Ser. C Appl. Stat."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kogan, J., Nicholas, C., and Teboulle, M. (2006). Grouping Multidimensional Data, Springer.","DOI":"10.1007\/3-540-28349-8"},{"key":"ref_39","unstructured":"Arthur, D., and Vassilvitskii, S. (2006, January 5\u20137). How Slow Is the k-Means Method?. Proceedings of the Twenty-Second Annual Symposium on Computational Geometry-SCG\u201906, Sedona, AZ, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing Partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J. Classif."},{"key":"ref_41","unstructured":"Van Rossum, G., and Drake, F.L. (2009). Python 3 Reference Manual, CreateSpace."},{"key":"ref_42","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Webber, C.L., and Marwan, N. (2015). Recurrence Quantification Analysis: Theory and Best Practices, Springer International Publishing. Understanding Complex Systems.","DOI":"10.1007\/978-3-319-07155-8"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.physrep.2006.11.001","article-title":"Recurrence Plots for the Analysis of Complex Systems","volume":"438","author":"Marwan","year":"2007","journal-title":"Phys. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/S1350-4533(01)00112-6","article-title":"Recurrence Quantification Analysis as a Tool for Nonlinear Exploration of Nonstationary Cardiac Signals","volume":"24","author":"Zbilut","year":"2002","journal-title":"Med. Eng. Phys."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Webber, C., and Marwan, N. (2015). Recurrences Analysis of Otoacoustic Emissions. Recurrence Quantification Analysis, Springer. Chapter 8: Theory and Best Practices.","DOI":"10.1007\/978-3-319-07155-8"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"927","DOI":"10.2147\/CIA.S252837","article-title":"Detection of Age-Related Hearing Losses (ARHL) via Transient-Evoked Otoacoustic Emissions","volume":"15","author":"Zimatore","year":"2020","journal-title":"Clin. Interv. Aging"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"033135","DOI":"10.1063\/1.5140455","article-title":"Recurrence Quantification Analysis of Heart Rate Variability during Continuous Incremental Exercise Test in Obese Subjects","volume":"30","author":"Zimatore","year":"2020","journal-title":"Chaos Woodbury N"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zimatore, G., Falcioni, L., Gallotta, M.C., Bonavolont\u00e0, V., Campanella, M., De Spirito, M., Guidetti, L., and Baldari, C. (2021). Recurrence Quantification Analysis of Heart Rate Variability to Detect Both Ventilatory Thresholds. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0249504"},{"key":"ref_51","first-page":"20110624","article-title":"Estimating Coupling Directions in the Cardiorespiratory System Using Recurrence Properties","volume":"371","author":"Marwan","year":"2013","journal-title":"Philos. Transact. A Math. Phys. Eng. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4246","DOI":"10.1016\/j.physleta.2009.09.042","article-title":"Complex Network Approach for Recurrence Analysis of Time Series","volume":"373","author":"Marwan","year":"2009","journal-title":"Phys. Lett. A"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s11207-007-0405-5","article-title":"Synchronization in Sunspot Indices in the Two Hemispheres","volume":"243","author":"Zolotova","year":"2007","journal-title":"Sol. Phys."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"043101","DOI":"10.1063\/1.4979351","article-title":"The Remarkable Coherence between Two Italian Far Away Recording Stations Points to a Role of Acoustic Emissions from Crustal Rocks for Earthquake Analysis","volume":"27","author":"Zimatore","year":"2017","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s11071-020-05511-y","article-title":"Recurrence Quantification Analysis on a Kaldorian Business Cycle Model","volume":"100","author":"Orlando","year":"2020","journal-title":"Nonlinear Dyn."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"083129","DOI":"10.1063\/5.0015916","article-title":"Business Cycle Modeling between Financial Crises and Black Swans: Ornstein-Uhlenbeck Stochastic Process vs Kaldor Deterministic Chaotic Model","volume":"30","author":"Orlando","year":"2020","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1142\/S0218127411028957","article-title":"Measuring the intermittent synchronicity of macroeconomic growth in Europe","volume":"21","author":"Crowley","year":"2011","journal-title":"Int. J. Bifurc. Chaos"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.cageo.2016.11.016","article-title":"PyRQA\u2014Conducting Recurrence Quantification Analysis on Very Long Time Series Efficiently","volume":"104","author":"Rawald","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.cmet.2018.04.014","article-title":"Maximizing Cellular Adaptation to Endurance Exercise in Skeletal Muscle","volume":"27","author":"Hawley","year":"2018","journal-title":"Cell Metab."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/3974\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:17:36Z","timestamp":1760138256000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/3974"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,24]]},"references-count":59,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22113974"],"URL":"https:\/\/doi.org\/10.3390\/s22113974","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,5,24]]}}}