{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:49:05Z","timestamp":1770749345642,"version":"3.50.0"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JSPS KAKENHI","award":["23H01647"],"award-info":[{"award-number":["23H01647"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Impaired glucose metabolism, a known precursor to type 2 diabetes, is associated with dysregulation of the autonomic nervous system. To assess such autonomic states, consumer wearable devices provide continuous, non-invasive physiological monitoring and may capture autonomic signatures related to metabolic status. This exploratory study examined whether dynamic features of heart rate (HR) and heart rate variability (HRV) during sleep\u2014derived from a consumer wrist-worn device (Fitbit)\u2014are associated with glucose metabolism status in free-living adults. We analyzed 189 nights from 18 participants (7 participants in the higher-glycemic-risk group, estimated glycated hemoglobin (HbA1c) \u2265 5.5%; 11 participants in the lower-glycemic-risk group, estimated HbA1c &lt; 5.5%). From 28 candidate HR\/HRV variables, Elastic Net regression (\u03b1=0.5) was applied to identify features associated with nocturnal mean glucose. Fourteen features retained non-zero coefficients; notably, dynamic features capturing overnight trends and variability patterns showed stronger associations than conventional static mean values. The nocturnal trends of within-window standard deviation and variance of ln(RMSSD) (root mean square of successive differences between consecutive RR intervals, estimated here from PPG-derived inter-beat intervals; RMSSD) emerged as prominent candidates, alongside HR variability indices. Independent between-group comparisons further confirmed that two dynamic HRV features differed significantly between the lower- and higher-glycemic-risk groups (both p&lt;0.05; Cohen\u2019s |d|&gt;1.1). Specifically, the lower-glycemic-risk group exhibited decreasing overnight trends in HRV variability, consistent with progressive autonomic stabilization during sleep. In contrast, the higher-glycemic-risk group showed increasing variability trends, suggestive of persistent autonomic instability. These directional patterns are consistent with prior evidence linking autonomic dysfunction to impaired glucose metabolism. We characterize these findings as hypothesis-generating. The identified dynamic HR\/HRV features represent physiologically plausible candidate correlates of glycemic status and warrant confirmatory investigation in larger, independent cohorts with laboratory-measured HbA1c. More broadly, this work highlights the potential of widely available, consumer-grade wearable devices to move beyond activity tracking and support continuous, real-world assessment of cardiometabolic health, thereby expanding their utility in everyday health monitoring and preventive medicine.<\/jats:p>","DOI":"10.3390\/s26041118","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T12:42:42Z","timestamp":1770640962000},"page":"1118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Sleep-Derived Heart Rate and Heart Rate Variability Features Associated with Glucose Metabolism Status: An Exploratory Feature-Selection Study Using Consumer Wearables"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8023-5671","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"first","affiliation":[{"name":"Graduate School of Engineering Science, The University of Osaka, Osaka 560-8531, Japan"},{"name":"IntaSect Communications, Inc., 3-1 Kanda Ogawamachi, Chiyoda-ku, Tokyo 101-0052, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syarifah Nabilah","family":"Syed Taha","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering Science, The University of Osaka, Osaka 560-8531, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshiyuki","family":"Nishinaka","sequence":"additional","affiliation":[{"name":"IntaSect Communications, Inc., 3-1 Kanda Ogawamachi, Chiyoda-ku, Tokyo 101-0052, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Tan","sequence":"additional","affiliation":[{"name":"IntaSect Communications, Inc., 3-1 Kanda Ogawamachi, Chiyoda-ku, Tokyo 101-0052, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8048-281X","authenticated-orcid":false,"given":"Hajime","family":"Ohtsu","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering Science, The University of Osaka, Osaka 560-8531, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1861-2881","authenticated-orcid":false,"given":"Sinyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering Science, The University of Osaka, Osaka 560-8531, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5433-7002","authenticated-orcid":false,"given":"Ken","family":"Kiyono","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering Science, The University of Osaka, Osaka 560-8531, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"ref_1","unstructured":"International Diabetes Federation (2025). IDF Diabetes Atlas, International Diabetes Federation. [11th ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1056\/NEJMoa012512","article-title":"Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin","volume":"346","author":"Knowler","year":"2002","journal-title":"N. Engl. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"273","DOI":"10.4093\/dmj.2015.39.4.273","article-title":"Glycemic variability: How do we measure it and why is it important?","volume":"39","author":"Suh","year":"2015","journal-title":"Diabetes Metab. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1177\/193229680800200618","article-title":"Glycemic Variability: The Third Component of the Dysglycemia in Diabetes. Is It Important? How to Measure It?","volume":"2","author":"Monnier","year":"2008","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1253\/circj.CJ-17-0192","article-title":"Low Heart Rate Variability and Sympathetic Dominance Modifies the Association Between Insulin Resistance and Metabolic Syndrome\u2014The Toon Health Study","volume":"81","author":"Saito","year":"2017","journal-title":"Circ. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1007\/s00125-020-05316-z","article-title":"The prevalence of cardiac autonomic neuropathy in prediabetes: A systematic review","volume":"64","author":"Eleftheriadou","year":"2021","journal-title":"Diabetologia"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1007\/s00125-015-3534-7","article-title":"Increased prevalence of cardiac autonomic dysfunction at different degrees of glucose intolerance in the general population: The KORA S4 survey","volume":"58","author":"Ziegler","year":"2015","journal-title":"Diabetologia"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1007\/s13300-019-00693-0","article-title":"Cardiac Autonomic Neuropathy in Obesity, the Metabolic Syndrome and Prediabetes: A Narrative Review","volume":"10","author":"Williams","year":"2019","journal-title":"Diabetes Ther."},{"key":"ref_9","unstructured":"Task Force of the European Society of Cardiology and the European Society of Pacing and Electrophysiology (1996). Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation, 93, 1043\u20131065."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shaffer, F., and Ginsberg, J.P. (2017). An overview of heart rate variability metrics and norms. Front. Public Health, 5.","DOI":"10.3389\/fpubh.2017.00258"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Benichou, T., Pereira, B., Mermillod, M., Tauveron, I., Pfabigan, D., Maqdasy, S., and Dutheil, F. (2018). Heart rate variability in type 2 diabetes mellitus: A systematic review and meta-analysis. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0195166"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ortiz-Guzm\u00e1n, J.E., Moll\u00e0-Casanova, S., Serra-A\u00f1\u00f3, P., Arias-Mutis, \u00d3.J., Calvo, C., Bizy, A., Alberola, A., Chorro, F.J., and Zarzoso, M. (2023). Short-Term Heart Rate Variability in Metabolic Syndrome: A Systematic Review and Meta-Analysis. J. Clin. Med., 12.","DOI":"10.3390\/jcm12186051"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.2337\/dc19-2367","article-title":"Both Prediabetes and Type 2 Diabetes Are Associated With Lower Heart Rate Variability: The Maastricht Study","volume":"43","author":"Coopmans","year":"2020","journal-title":"Diabetes Care"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1210\/clinem\/dgad200","article-title":"Heart Rate Variability and Incident Type 2 Diabetes in the General Population","volume":"108","author":"Wang","year":"2023","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e3164","DOI":"10.1002\/dmrr.3175","article-title":"Decreased heart rate variability as a predictor for diabetes: A prospective study of the Brazilian Longitudinal Study of Adult Health","volume":"35","author":"Hoshi","year":"2019","journal-title":"Diabetes\/Metab. Res. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1073\/pnas.0706446105","article-title":"Slow-wave sleep and the risk of type 2 diabetes in humans","volume":"105","author":"Tasali","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1378\/chest.09-0791","article-title":"Effects of sleep fragmentation on glucose metabolism in normal subjects","volume":"137","author":"Stamatakis","year":"2010","journal-title":"Chest"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.smrv.2016.02.001","article-title":"The impact of sleep amount and sleep quality on glycemic control in type 2 diabetes: A systematic review and meta-analysis","volume":"31","author":"Lee","year":"2017","journal-title":"Sleep Med. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"529","DOI":"10.2337\/dc14-2073","article-title":"Sleep duration and risk of type 2 diabetes: A meta-analysis of prospective studies","volume":"38","author":"Shan","year":"2015","journal-title":"Diabetes Care"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cheng, W., Chen, H., Tian, L., Ma, Z., and Cui, X. (2023). Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus. Front. Physiol., 14.","DOI":"10.3389\/fphys.2023.1157270"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1038\/s41591-022-02012-w","article-title":"Association of step counts over time with the risk of chronic disease in the All of Us Research Program","volume":"28","author":"Master","year":"2022","journal-title":"Nat. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"429","DOI":"10.2217\/pme-2018-0044","article-title":"Wearables and the medical revolution","volume":"15","author":"Dunn","year":"2018","journal-title":"Pers. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ijcard.2012.03.119","article-title":"How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram","volume":"166","author":"Vagedes","year":"2013","journal-title":"Int. J. Cardiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e17355","DOI":"10.2196\/17355","article-title":"Measuring heart rate variability in free-living conditions using consumer-grade photoplethysmography: Validation study","volume":"5","author":"Lam","year":"2020","journal-title":"JMIR Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"04NT01","DOI":"10.1088\/1361-6579\/ab840a","article-title":"Feasible assessment of recovery and cardiovascular health: Accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG","volume":"41","author":"Kinnunen","year":"2020","journal-title":"Physiol. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e70527","DOI":"10.14814\/phy2.70527","article-title":"Validation of Nocturnal Resting Heart Rate and Heart Rate Variability in Consumer Wearables","volume":"13","author":"Dial","year":"2025","journal-title":"Physiol. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.numecd.2018.01.003","article-title":"Head-to-head comparison of the accuracy of Abbott FreeStyle Libre and Dexcom G5 mobile","volume":"28","author":"Boscari","year":"2018","journal-title":"Nutr. Metab. Cardiovasc. Dis."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"S51","DOI":"10.1089\/dia.2017.0050","article-title":"Clinical implications of accuracy measurements of continuous glucose sensors","volume":"19","author":"Bailey","year":"2017","journal-title":"Diabetes Technol. Ther."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13987","DOI":"10.1038\/s41598-023-40949-1","article-title":"Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: A functional data analysis","volume":"13","author":"Matabuena","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s10916-010-9506-z","article-title":"The Advantages of Wearable Green Reflected Photoplethysmography","volume":"35","author":"Maeda","year":"2011","journal-title":"J. Med. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"282","DOI":"10.3390\/electronics3020282","article-title":"Wearable Photoplethysmographic Sensors\u2014Past and Present","volume":"3","author":"Tamura","year":"2014","journal-title":"Electronics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41746-020-0226-6","article-title":"Investigating sources of inaccuracy in wearable optical heart rate sensors","volume":"3","author":"Bent","year":"2020","journal-title":"npj Digit. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1109\/JPROC.2022.3149785","article-title":"Wearable photoplethysmography for cardiovascular monitoring","volume":"110","author":"Charlton","year":"2022","journal-title":"Proc. IEEE"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e16273","DOI":"10.2196\/16273","article-title":"Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis","volume":"21","author":"Haghayegh","year":"2019","journal-title":"J. Med. Internet Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.2337\/dc08-0545","article-title":"Translating the A1C Assay Into Estimated Average Glucose Values","volume":"31","author":"Nathan","year":"2008","journal-title":"Diabetes Care"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.2337\/dc18-1581","article-title":"Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring","volume":"41","author":"Bergenstal","year":"2018","journal-title":"Diabetes Care"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1089\/dia.2020.0501","article-title":"HbA1c and glucose management indicator discordance: A real-world analysis","volume":"23","author":"Perlman","year":"2021","journal-title":"Diabetes Technol. Ther."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1111\/j.1464-5491.2011.03378.x","article-title":"Cut-off values of fasting and post-load plasma glucose and HbA1c for predicting Type 2 diabetes in community-dwelling Japanese subjects: The Hisayama Study","volume":"29","author":"Mukai","year":"2012","journal-title":"Diabet. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/S0140-6736(11)60472-8","article-title":"HbA1c 5.7\u20136.4% and impaired fasting plasma glucose for diagnosis of prediabetes and risk of progression to diabetes in Japan (TOPICS 3): A longitudinal cohort study","volume":"378","author":"Heianza","year":"2011","journal-title":"Lancet"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.2337\/dc09-1939","article-title":"A1C level and future risk of diabetes: A systematic review","volume":"33","author":"Zhang","year":"2010","journal-title":"Diabetes Care"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"American Diabetes Association Professional Practice Committee (2025). 2. Diagnosis and classification of diabetes: Standards of Care in Diabetes\u20142025. Diabetes Care, 48, S27\u2013S49.","DOI":"10.2337\/dc25-S002"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neubiorev.2016.07.004","article-title":"Sleep, sleep deprivation, autonomic nervous system and cardiovascular diseases","volume":"74","author":"Tobaldini","year":"2017","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.metabol.2018.02.010","article-title":"Sleep influences on obesity, insulin resistance, and risk of type 2 diabetes","volume":"84","author":"Reutrakul","year":"2018","journal-title":"Metabolism"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1007\/s11892-014-0562-5","article-title":"Metabolic and glycemic sequelae of sleep disturbances in children and adults","volume":"15","author":"Koren","year":"2015","journal-title":"Curr. Diabetes Rep."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"zsad013","DOI":"10.1093\/sleep\/zsad013","article-title":"The role of sleep and heart rate variability in metabolic syndrome: Evidence from the Midlife in the United States study","volume":"46","author":"Nevels","year":"2023","journal-title":"Sleep"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Piersanti, A., Littero, M., Del Giudice, L.L., Marcantoni, I., Burattini, L., Tura, A., and Morettini, M. (2025). Variability of the Skin Temperature from Wrist-Worn Device for Definition of Novel Digital Biomarkers of Glycemia. Sensors, 25.","DOI":"10.3390\/s25134038"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T12:50:39Z","timestamp":1770641439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26041118"],"URL":"https:\/\/doi.org\/10.3390\/s26041118","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]}}}