参考文献
[1] Guo H, Wang S, Peng H, et al. Life′s essential 8 and cardiovascular diseases progression among adults in the United Kingdom[J]. Metabolism, 2025,162:156031. DOI: 10.1016/j.metabol.2024.156031.
[2] Li H, Zhong W, Cheng H, et al. Association between weight‑adjusted‑waist index and long‑term prognostic outcomes in cardiovascular disease patients: results from the NHANES 1999‑2018 study[J]. Diabetol Metab Syndr, 2025, 17(1):19. DOI: 10.1186/s13098‑025‑01590‑2.
[3] Pentikäinen H, Toivo K, Kokko S, et al. Resting electrocardiogram and blood pressure in young endurance and nonendurance athletes and nonathletes[J]. J Athl Train, 2021, 56(5):484‑490. DOI: 10.4085/78‑20.
[4] Saiz‑Vivo J, Chakravarthy N, Ho W, et al. Artificial intelligence for atrial fibrillation prediction while in sinus rhythm: a narrative review[J]. Eur Heart J Digit Health, 2026, 7(2):ztaf144. DOI: 10.1093/ehjdh/ztaf144.
[5] Williams GJ, Al‑Baraikan A, Rademakers FE, et al. Wearable technology and the cardiovascular system: the future of patient assessment[J]. Lancet Digit Health, 2023, 5(7):e467‑476. DOI: 10.1016/S2589‑7500(23)00087‑0.
[6] Hughes A, Shandhi M, Master H, et al. Wearable devices in cardiovascular medicine[J]. Circ Res, 2023, 132(5):652‑670. DOI: 10.1161/CIRCRESAHA.122.322389.
[7] Tu J, Min J, Song Y, et al. A wireless patch for the monitoring of C‑reactive protein in sweat[J]. Nat Biomed Eng, 2023, 7(10):1293‑1306. DOI: 10.1038/s41551‑023‑ 01059‑5.
[8] Sempionatto JR, Lin M, Yin L, et al. An epidermal patch for the simultaneous monitoring of haemodynamic and metabolic biomarkers[J]. Nat Biomed Eng, 2021, 5(7):737‑748. DOI: 10.1038/s41551‑021‑00685‑1.
[9] Vasudevan S, Saha A, Tarver ME, et al. Digital biomarkers: convergence of digital health technologies and biomarkers[J]. NPJ Digit Med, 2022, 5(1):36. DOI: 10.1038/s41746‑022‑00583‑z.
[10] Zhu H, Luo Q, Wang Y, et al. Artificial intelligence‑enabled 8‑channel ECG diagnosing of abnormalities with wide QRS complexes[J]. Health Data Sci, 2026,6:0265. DOI: 10.34133/hds.0265.
[11] Zhang P, Lin F, Ma F, et al. Clinician‑artificial intelligence collaboration: a win‑win solution for efficiency and reliability in atrial fibrillation diagnosis[J]. Med, 2025, 6(7):100668. DOI: 10.1016/j.medj.2025.100668.
[12] Orini M, van Duijvenboden S, Young WJ, et al. Long‑term association of ultra‑short heart rate variability with cardiovascular events[J]. Sci Rep, 2023, 13(1):18966. DOI: 10.1038/s41598‑023‑45988‑2.
[13] Wang BX, Brennand E, Le Page P, et al. Heart rate variability in cardiovascular disease diagnosis, prognosis and management[J]. Front Cardiovasc Med, 2025,12:1680783. DOI: 10.3389/fcvm.2025.1680783.
[14] Ziegler KA, Engelhardt S, Carnevale D, et al. Neural mechanisms in cardiovascular health and disease[J]. Circ Res, 2025, 136(11):1233‑1261. DOI: 10.1161/CIRCRESAHA.125.325580.
[15] Oosterhoff P, Tereshchenko LG, van der Heyden MA, et al. Short‑term variability of repolarization predicts ventricular tachycardia and sudden cardiac death in patients with structural heart disease: a comparison with QT variability index[J]. Heart Rhythm, 2011, 8(10):1584‑1590. DOI: 10.1016/j.hrthm.2011.04.033.
[16] Wu Y, Asvadi S, van der Ven M, et al. Overnight maternal heart rate variability for early prediction of gestational diabetes mellitus: a retrospective cohort study[J]. NPJ Womens Health,2025,3:37.
[17] Zhang L, Li L, Andell P, et al. Attention‑deficit/hyperactivity disorder medications and long‑term risk of cardiovascular diseases[J]. JAMA Psychiatry, 2024, 81(2):178‑187. DOI: 10.1001/jamapsychiatry.2023.4294.
[18] Zhao Q, Geng S, Wang B, et al. Deep learning in heart sound analysis: from techniques to clinical applications[J]. Health Data Sci, 2024,4:0182. DOI: 10.34133/hds.0182.
[19] Zhang J, Li J, Huang Z, et al. Recent progress in wearable brain‑computer interface (BCI) devices based on electroencephalogram (EEG) for medical applications: a review[J]. Health Data Sci, 2023,3:0096. DOI: 10.34133/hds.0096.
[20] Keshet A, Reicher L, Bar N, et al. Wearable and digital devices to monitor and treat metabolic diseases[J]. Nat Metab, 2023, 5(4):563‑571. DOI: 10.1038/s42255‑023‑ 00778‑y.
[21] Stamatakis E, Ahmadi MN, Gill J, et al. Association of wearable device‑measured vigorous intermittent lifestyle physical activity with mortality[J]. Nat Med, 2022, 28(12):2521‑2529. DOI: 10.1038/s41591‑022‑02100‑x.
[22] Terentes‑Printzios D, Vlachopoulos C, Korogiannis L, et al. Low heart rate variability is associated with future arrhythmic events in hypertension[J]. Eur Heart J, 2019, 40 (Suppl 1):2420.
[23] Lei J, Zhou Y, Tian X, et al. A deep learning method for beat‑level risk analysis and interpretation of atrial fibrillation patients during sinus rhythm[J]. Biomedical Signal Processing and Control, 2025(100):107028.
[24] Aggarwal B, Gao Y, Alfini A, et al. Sleep and circadian rhythms in cardiovascular resilience: mechanisms, implications, and a Roadmap for research and interventions[J]. Nat Rev Cardiol, 2026, 23(2):116‑130. DOI: 10.1038/s41569‑025‑01188‑1.
[25] Knutson KL, Dixon DD, Grandner MA, et al. Role of circadian health in cardiometabolic health and disease risk: a scientific statement from the American Heart Association[J]. Circulation, 2025, 152(21):e408‑419. DOI: 10.1161/CIR.0000000000001388.
[26] Franklin D, Tzavelis A, Lee JY, et al. Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography[J]. Nat Biomed Eng, 2023, 7(10):1229‑1241. DOI: 10.1038/s41551‑023‑01098‑y.
[27] Wang H, Guo Y. Atrial fibrillation screening driven by monitoring technologies: reducing stroke burden in Asian populations[J]. JACC Asia, 2025, 5(11):1425‑1427. DOI: 10.1016/j.jacasi.2025.09.003.
[28] Feli M, Kazemi K, Azimi I, et al. End‑to‑end ppg processing pipeline for wearables: from quality assessment and motion artifacts removal to hr/hrv feature extraction[C/OL]//2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM),Turkiye, 2023: 1895‑1900[2026‑03‑31].https://ieeexplore.ieee.org/document/10385998
[29] Argüello‑Prada EJ, Castillo García JF. Machine learning applied to reference signal‑less detection of motion artifacts in photoplethysmographic signals: a review[J]. Sensors (Basel), 2024, 24(22): 7193. DOI: 10.3390/s24227193.
[30] McKenna S, McCord N, Diven J, et al. Evaluating the impacts of digital ECG denoising on the interpretive capabilities of healthcare professionals[J]. Eur Heart J Digit Health, 2024, 5(5):601‑610. DOI: 10.1093/ehjdh/ztae063.
[31] Li K, Cardoso C, Moctezuma‑Ramirez A, et al. Heart rate variability measurement through a smart wearable device: another breakthrough for personal health monitoring?[J]. Int J Environ Res Public Health, 2023, 20(24):7146. DOI: 10.3390/ijerph20247146.
[32] Nayak SK, Pradhan B, Mohanty B, et al. A review of methods and applications for a heart rate variability analysis[J]. Algorithms, 2023,16:433.
[33] Shim J, Fleisch E, Barata F. Circadian rhythm analysis using wearable‑based accelerometry as a digital biomarker of aging and healthspan[J]. NPJ Digit Med, 2024, 7(1):146. DOI: 10.1038/s41746‑024‑01111‑x.
[34] Kim DW, Mayer C, Lee MP, et al. Efficient assessment of real‑world dynamics of circadian rhythms in heart rate and body temperature from wearable data[J]. J R Soc Interface, 2023, 20(205):20230030. DOI: 10.1098/rsif.2023.0030.
[35] Bayoumy K, Gaber M, Elshafeey A, et al. Smart wearable devices in cardiovascular care: where we are and how to move forward[J]. Nat Rev Cardiol, 2021, 18(8):581‑599. DOI: 10.1038/s41569‑021‑00522‑7.
[36] Lin F, Zhang P, Chen Y, et al. Artificial‑intelligence‑based risk prediction and mechanism discovery for atrial fibrillation using heart beat‑to‑beat intervals[J]. Med, 2024, 5(5):414‑431.e5. DOI: 10.1016/j.medj.2024.02.006.
[37] Cheng X, Song C, Ouyang F, et al. Systolic blood pressure variability: risk of cardiovascular events, chronic kidney disease, dementia, and death[J]. Eur Heart J, 2025, 46(27):2673‑2687. DOI: 10.1093/eurheartj/ehaf256.
[38] Hippisley‑Cox J, Coupland C, Bafadhel M, et al. Development and validation of a new algorithm for improved cardiovascular risk prediction[J]. Nat Med, 2024, 30(5):1440‑1447. DOI: 10.1038/s41591‑024‑02905‑y.
[39] Lu JK, Wang W, Mahadzir M, et al. Digital biomarkers of ageing for monitoring physiological systems in community‑dwelling adults[J]. Lancet Healthy Longev, 2025, 6(6):100725. DOI: 10.1016/j.lanhl.2025.100725.
[40] Kalmady SV, Salimi A, Sun W, et al. Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level[J]. NPJ Digit Med, 2024, 7(1):133. DOI: 10.1038/s41746‑024‑01130‑8.
[41] Al‑Zaiti SS, Martin‑Gill C, Zègre‑Hemsey JK, et al. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction[J]. Nat Med, 2023, 29(7):1804‑1813. DOI: 10.1038/s41591‑023‑02396‑3.
[42] Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice[J]. BMC Med Educ, 2023, 23(1):689. DOI: 10.1186/s12909‑023‑04698‑z.
[43] Chen H, Cao Z, Zhang J, et al. Accelerometer‑measured physical activity and neuroimaging‑driven brain age[J]. Health Data Sci, 2025,5:0257. DOI: 10.34133/hds.0257.
[44] Tao Y, Zhang D, Pang N, et al. Multi‑modal artificial intelligence algorithm for the prediction of left atrial low‑voltage areas in atrial fibrillation patient based on sinus rhythm electrocardiogram and clinical characteristics: a retrospective, multicentre study[J]. Eur Heart J Digit Health, 2025, 6(2):200‑208. DOI: 10.1093/ehjdh/ztae095.
[45] Hempel P, Ribeiro AH, Vollmer M, et al. Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study[J]. NPJ Digit Med, 2025, 8(1):25. DOI: 10.1038/s41746‑024‑01428‑7.
[46] Martínez‑Ibáñez P, Marco‑Moreno I, García‑Sempere A, et al. Long‑term effect of home blood pressure self‑monitoring plus medication self‑titration for patients with hypertension: a secondary analysis of the ADAMPA Randomized Clinical Trial[J]. JAMA Netw Open, 2024, 7(5):e2410063. DOI: 10.1001/jamanetworkopen.2024.10063.
[47] Perez MV, Mahaffey KW, Hedlin H, et al. Large‑scale assessment of a smartwatch to identify atrial fibrillation[J]. N Engl J Med, 2019, 381(20):1909‑1917. DOI: 10.1056/NEJMoa1901183.
[48] Spatz ES, Ginsburg GS, Rumsfeld JS, et al. Wearable digital health technologies for monitoring in cardiovascular medicine[J]. N Engl J Med, 2024, 390(4):346‑356. DOI: 10.1056/NEJMra2301903.
[49] Stehlik J, Schmalfuss C, Bozkurt B, et al. Continuous wearable monitoring analytics predict heart failure hospitalization: the LINK‑HF Multicenter Study[J]. Circ Heart Fail, 2020, 13(3):e006513. DOI: 10.1161/CIRCHEARTFAILURE.119.006513.
[50] Boehmer JP, Cremer S, Abo‑Auda WS, et al. Impact of a novel wearable sensor on heart failure rehospitalization: an open‑label concurrent‑control clinical trial[J]. JACC Heart Fail, 2024, 12(12):2011‑2022. DOI: 10.1016/j.jchf.2024.07.022.
[51] Natarajan A, Pantelopoulos A, Emir‑Farinas H, et al. Heart rate variability with photoplethysmography in 8 million individuals: a cross‑sectional study[J]. Lancet Digit Health, 2020, 2(12):e650‑657. DOI: 10.1016/S2589‑7500(20)30246‑6.
[52] Sridhar AR, Cheung JW, Lampert R, et al. State of the art of mobile health technologies use in clinical arrhythmia care[J]. Commun Med (Lond), 2024, 4(1):218. DOI: 10.1038/s43856‑024‑00618‑4.
[53] Kelters IR, Koop Y, Young ME, et al. Circadian rhythms in cardiovascular disease[J]. Eur Heart J, 2025, 46(36):3532‑3545. DOI: 10.1093/eurheartj/ehaf367.
[54] Daniore P, Nittas V, Haag C, et al. From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal[J]. NPJ Digit Med, 2024, 7(1):161. DOI: 10.1038/s41746‑024‑01151‑3.
[55] Guo Y, Wang H, Zhang H, et al. Mobile photoplethysmographic technology to detect atrial fibrillation[J]. J Am Coll Cardiol, 2019, 74(19):2365‑2375. DOI: 10.1016/j.jacc.2019.08.019.
[56] Wang H, Wang J, Jing W, et al. Reliability and validity of a novel single‑lead portable electrocardiogram device for pregnant women: a comparative study[J]. BMC Med Inform Decis Mak, 2025, 25(1):108. DOI: 10.1186/s12911‑025‑02952‑6.
[57] Armoundas AA, Ahmad FS, Bennett DA, et al. Data interoperability for ambulatory monitoring of cardiovascular disease: a scientific statement from the American Heart Association[J]. Circ Genom Precis Med, 2024, 17(3):e000095. DOI: 10.1161/HCG.0000000000000095.
[58] Pedroso AF, Khera R. Leveraging AI‑enhanced digital health with consumer devices for scalable cardiovascular screening, prediction, and monitoring[J]. NPJ Cardiovasc Health, 2025, 2(1):34. DOI: 10.1038/s44325‑025‑00071‑9.
[59] Andreoletti M, Haller L, Vayena E, et al. Mapping the ethical landscape of digital biomarkers: a scoping review[J]. PLOS Digit Health, 2024, 3(5):e0000519. DOI: 10.1371/journal.pdig.0000519.
[60] Olawade DB, Fidelis SC, Marinze S, et al. Artificial intelligence in clinical trials: a comprehensive review of opportunities, challenges, and future directions[J]. Int J Med Inform, 2026,206:106141. DOI: 10.1016/j.ijmedinf.2025.106141.
[61] Yang K, Hong M, Zhang J, et al. ECG‑LM: understanding electrocardiogram with a large language model[J]. Health Data Sci, 2025,5:0221. DOI: 10.34133/hds.0221.