Published on in Vol 19, No 3 (2017): March

Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System

Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System

Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System

Journals

  1. Vitacca M, Montini A, Comini L. How will telemedicine change clinical practice in chronic obstructive pulmonary disease?. Therapeutic Advances in Respiratory Disease 2018;12:175346581875477 View
  2. Gálvez-Barrón C, Villar-Álvarez F, Ribas J, Formiga F, Chivite D, Boixeda R, Iborra C, Rodríguez-Molinero A. Effort Oxygen Saturation and Effort Heart Rate to Detect Exacerbations of Chronic Obstructive Pulmonary Disease or Congestive Heart Failure. Journal of Clinical Medicine 2019;8(1):42 View
  3. Battineni G, Sagaro G, Chinatalapudi N, Amenta F. Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis. Journal of Personalized Medicine 2020;10(2):21 View
  4. Buekers J, De Boever P, Vaes A, Aerts J, Wouters E, Spruit M, Theunis J. Oxygen saturation measurements in telemonitoring of patients with COPD: a systematic review. Expert Review of Respiratory Medicine 2018;12(2):113 View
  5. Farmer A, Williams V, Velardo C, Shah S, Yu L, Rutter H, Jones L, Williams N, Heneghan C, Price J, Hardinge M, Tarassenko L. Self-Management Support Using a Digital Health System Compared With Usual Care for Chronic Obstructive Pulmonary Disease: Randomized Controlled Trial. Journal of Medical Internet Research 2017;19(5):e144 View
  6. van der Burg J, Aziz N, Kaptein M, Breteler M, Janssen J, van Vliet L, Winkeler D, van Anken A, Kasteleyn M, Chavannes N. Long-term effects of telemonitoring on healthcare usage in patients with heart failure or COPD. Clinical eHealth 2020;3:40 View
  7. Gonem S, Janssens W, Das N, Topalovic M. Applications of artificial intelligence and machine learning in respiratory medicine. Thorax 2020;75(8):695 View
  8. Orchard P, Agakova A, Pinnock H, Burton C, Sarran C, Agakov F, McKinstry B. Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data. Journal of Medical Internet Research 2018;20(9):e263 View
  9. Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease. Current Opinion in Pulmonary Medicine 2018;24(2):117 View
  10. Soler J, Alves Pegoraro J, Le X, Nguyen D, Grassion L, Antoine R, Guerder A, Gonzalez-Bermejo J. Validation of respiratory rate measurements from remote monitoring device in COPD patients. Respiratory Medicine and Research 2019;76:1 View
  11. Lee C, Ho K. Knowledge to action framework for home health monitoring. Healthcare Management Forum 2019;32(4):183 View
  12. Rodriguez Hermosa J, Fuster Gomila A, Puente Maestu L, Amado Diago C, Callejas González F, Malo De Molina Ruiz R, Fuentes Ferrer M, Álvarez Sala-Walther J, Calle Rubio M. Compliance and Utility of a Smartphone App for the Detection of Exacerbations in Patients With Chronic Obstructive Pulmonary Disease: Cohort Study. JMIR mHealth and uHealth 2020;8(3):e15699 View
  13. Luks A, Swenson E. Pulse Oximetry for Monitoring Patients with COVID-19 at Home. Potential Pitfalls and Practical Guidance. Annals of the American Thoracic Society 2020;17(9):1040 View
  14. Houben-Wilke S, Augustin I, Wouters B, Stevens R, Janssen D, Spruit M, Vanfleteren L, Franssen F, Wouters E. The patient with a complex chronic respiratory disease: a specialist of his own life?. Expert Review of Respiratory Medicine 2017:1 View
  15. Fan K, Mandel J, Agnihotri P, Tai-Seale M. Remote Patient Monitoring Technologies for Predicting Chronic Obstructive Pulmonary Disease Exacerbations: Review and Comparison. JMIR mHealth and uHealth 2020;8(5):e16147 View
  16. Antonelli A, Guilizzoni D, Angelucci A, Melloni G, Mazza F, Stanzi A, Venturino M, Kuller D, Aliverti A. Comparison between the Airgo™ Device and a Metabolic Cart during Rest and Exercise. Sensors 2020;20(14):3943 View
  17. Blakey J, Bender B, Dima A, Weinman J, Safioti G, Costello R. Digital technologies and adherence in respiratory diseases: the road ahead. European Respiratory Journal 2018;52(5):1801147 View
  18. Buekers J, Theunis J, De Boever P, Vaes A, Koopman M, Janssen E, Wouters E, Spruit M, Aerts J. Wearable Finger Pulse Oximetry for Continuous Oxygen Saturation Measurements During Daily Home Routines of Patients With Chronic Obstructive Pulmonary Disease (COPD) Over One Week: Observational Study. JMIR mHealth and uHealth 2019;7(6):e12866 View
  19. Morelli D, Bartoloni L, Colombo M, Plans D, Clifton D. Profiling the propagation of error from PPG to HRV features in a wearable physiological‐monitoring device. Healthcare Technology Letters 2018;5(2):59 View
  20. Snyder C, Dorsey E, Atreja A. The Best Digital Biomarkers Papers of 2017. Digital Biomarkers 2018;2(2):64 View
  21. Luo G, Stone B, Koebnick C, He S, Au D, Sheng X, Murtaugh M, Sward K, Schatz M, Zeiger R, Davidson G, Nkoy F. Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis. JMIR Research Protocols 2019;8(6):e13783 View
  22. Wang C, Chen X, Du L, Zhan Q, Yang T, Fang Z. Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease. Computer Methods and Programs in Biomedicine 2020;188:105267 View
  23. Cooper C, Sirichana W, Arnold M, Neufeld E, Taylor M, Wang X, Dolezal B. <p>Remote Patient Monitoring for the Detection of COPD Exacerbations</p>. International Journal of Chronic Obstructive Pulmonary Disease 2020;Volume 15:2005 View
  24. Barbosa M, Sousa C, Morais-Almeida M, Simões M, Mendes P. Telemedicine in COPD: An Overview by Topics. COPD: Journal of Chronic Obstructive Pulmonary Disease 2020;17(5):601 View
  25. Wageck B, Cox N, Holland A. Recovery Following Acute Exacerbations of Chronic Obstructive Pulmonary Disease – A Review. COPD: Journal of Chronic Obstructive Pulmonary Disease 2019;16(1):93 View
  26. Tomasic I, Tomasic N, Trobec R, Krpan M, Kelava T. Continuous remote monitoring of COPD patients—justification and explanation of the requirements and a survey of the available technologies. Medical & Biological Engineering & Computing 2018;56(4):547 View
  27. Miłkowska-Dymanowska J, Białas A, Obrębski W, Górski P, Piotrowski W. A pilot study of daily telemonitoring to predict acute exacerbation in chronic obstructive pulmonary disease. International Journal of Medical Informatics 2018;116:46 View
  28. Charlton P, Birrenkott D, Bonnici T, Pimentel M, Johnson A, Alastruey J, Tarassenko L, Watkinson P, Beale R, Clifton D. Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review. IEEE Reviews in Biomedical Engineering 2018;11:2 View
  29. Bugajski A, Lengerich A, Koerner R, Szalacha L. Utilizing an Artificial Neural Network to Predict Self‐Management in Patients With Chronic Obstructive Pulmonary Disease: An Exploratory Analysis. Journal of Nursing Scholarship 2021;53(1):16 View
  30. Kronborg T, Hangaard S, Cichosz S, Hejlesen O. A two-layer probabilistic model to predict COPD exacerbations for patients in telehealth. Computers in Biology and Medicine 2021;128:104108 View
  31. Nicolò A, Massaroni C, Schena E, Sacchetti M. The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise. Sensors 2020;20(21):6396 View
  32. Liao J, Liu D, Su G, Liu L. Recognizing diseases with multivariate physiological signals by a DeepCNN-LSTM network. Applied Intelligence 2021;51(11):7933 View
  33. Saberi-Karimian M, Khorasanchi Z, Ghazizadeh H, Tayefi M, Saffar S, Ferns G, Ghayour-Mobarhan M. Potential value and impact of data mining and machine learning in clinical diagnostics. Critical Reviews in Clinical Laboratory Sciences 2021;58(4):275 View
  34. Kirszenblat R, Edouard P. Validation of the Withings ScanWatch as a Wrist-Worn Reflective Pulse Oximeter: Prospective Interventional Clinical Study. Journal of Medical Internet Research 2021;23(4):e27503 View
  35. Wu C, Li G, Huang C, Cheng Y, Chen C, Chien J, Kuo P, Kuo L, Lai F. Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study. JMIR mHealth and uHealth 2021;9(5):e22591 View
  36. Polsky M, Moraveji N. Early Identification of COVID-19 using Remote Cardiorespiratory Monitoring: Three Case Reports (Preprint). Interactive Journal of Medical Research 2021 View
  37. Patel N, Kinmond K, Jones P, Birks P, Spiteri M. Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations. International Journal of Chronic Obstructive Pulmonary Disease 2021;Volume 16:1887 View
  38. Mehdipour A, Wiley E, Richardson J, Beauchamp M, Kuspinar A. The Performance of Digital Monitoring Devices for Oxygen Saturation and Respiratory Rate in COPD: A Systematic Review. COPD: Journal of Chronic Obstructive Pulmonary Disease 2021;18(4):469 View
  39. Polsky M, Moraveji N. Early identification and treatment of COPD exacerbation using remote respiratory monitoring. Respiratory Medicine Case Reports 2021;34:101475 View
  40. Yamagami K, Nomura A, Kometani M, Shimojima M, Sakata K, Usui S, Furukawa K, Takamura M, Okajima M, Watanabe K, Yoneda T. Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY): Pilot Evaluation. JMIR Formative Research 2021;5(9):e30819 View
  41. Pullen R, Miravitlles M, Sharma A, Singh D, Martinez F, Hurst J, Alves L, Dransfield M, Chen R, Muro S, Winders T, Blango C, Muellerova H, Trudo F, Dorinsky P, Alacqua M, Morris T, Carter V, Couper A, Jones R, Kostikas K, Murray R, Price D. CONQUEST Quality Standards: For the Collaboration on Quality Improvement Initiative for Achieving Excellence in Standards of COPD Care. International Journal of Chronic Obstructive Pulmonary Disease 2021;Volume 16:2301 View
  42. Abineza C, Balas V, Nsengiyumva P, Balas V. A machine-learning-based prediction method for easy COPD classification based on pulse oximetry clinical use. Journal of Intelligent & Fuzzy Systems 2022;43(2):1683 View
  43. Gelbman B, Reed C. An Integrated, Multimodal, Digital Health Solution for Chronic Obstructive Pulmonary Disease: Prospective Observational Pilot Study. JMIR Formative Research 2022;6(3):e34758 View
  44. Wei S, Lu R, Zhang Z, Wang F, Tan H, Wang X, Ma J, Zhang Y, Deng N, Chen J. MRI-assessed diaphragmatic function can predict frequent acute exacerbation of COPD: a prospective observational study based on telehealth-based monitoring system. BMC Pulmonary Medicine 2022;22(1) View
  45. Angelucci A, Kuller D, Aliverti A. A Home Telemedicine System for Continuous Respiratory Monitoring. IEEE Journal of Biomedical and Health Informatics 2021;25(4):1247 View
  46. Shah S, Nwaru B, Sheikh A, Simpson C, Kotz D. Development and validation of a multivariable mortality risk prediction model for COPD in primary care. npj Primary Care Respiratory Medicine 2022;32(1) View
  47. Owiti J, Benson M, Maplanka M, Oluseye L, Carvalho D. Is Methadone Safe for Patients With Opioid Use Disorder and Coronavirus Disease 2019 Infection?. Journal of Addictions Nursing 2022;33(2):86 View
  48. Althobiani M, Evans R, Alqahtani J, Aldhahir A, Russell A, Hurst J, Porter J. Home monitoring of physiology and symptoms to detect interstitial lung disease exacerbations and progression: a systematic review. ERJ Open Research 2021;7(4):00441-2021 View
  49. Tsang K, Pinnock H, Wilson A, Shah S. Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. Journal of Asthma and Allergy 2022;Volume 15:855 View
  50. Watson A, Wilkinson T. Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research. Therapeutic Advances in Respiratory Disease 2022;16:175346662210754 View
  51. Exarchos K, Aggelopoulou A, Oikonomou A, Biniskou T, Beli V, Antoniadou E, Kostikas K. Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease. IEEE Journal of Biomedical and Health Informatics 2022;26(5):2331 View
  52. Hawthorne G, Richardson M, Greening N, Esliger D, Briggs-Price S, Chaplin E, Clinch L, Steiner M, Singh S, Orme M. A proof of concept for continuous, non-invasive, free-living vital signs monitoring to predict readmission following an acute exacerbation of COPD: a prospective cohort study. Respiratory Research 2022;23(1) View
  53. Althobiani M, Alqahtani J, Hurst J, Russell A, Porter J. Telehealth for patients with interstitial lung diseases (ILD): results of an international survey of clinicians. BMJ Open Respiratory Research 2021;8(1):e001088 View
  54. Hayward N, Shaban M, Badger J, Jones I, Wei Y, Spencer D, Isichei S, Knight M, Otto J, Rayat G, Levett D, Grocott M, Akerman H, White N. A capaciflector provides continuous and accurate respiratory rate monitoring for patients at rest and during exercise. Journal of Clinical Monitoring and Computing 2022;36(5):1535 View
  55. Ranjan Y, Althobiani M, Jacob J, Orini M, Dobson R, Porter J, Hurst J, Folarin A. Remote Assessment of Lung Disease and Impact on Physical and Mental Health (RALPMH): Protocol for a Prospective Observational Study. JMIR Research Protocols 2021;10(10):e28873 View
  56. Bandera-Barros J, Méndez-Hernández J, Wilches-Visbal J. Oximetría de pulso en enfermedades respiratorias. Nova 2022;20(39):95 View
  57. Galbraith M, Kelso P, Levine M, Wasserman R, Sikka J, Read J. Addressing silent hypoxemia with COVID-19: Implementation of an outpatient pulse oximetry program in Vermont. Public Health in Practice 2021;2:100186 View
  58. Song X, Liu X, Dong R, Kummer K, Wang C. Implementation of Tele-Intensive Care Unit Services During the COVID-19 Pandemic: A Systematic Literature Review and Updated Experience from Shandong Province. Telemedicine and e-Health 2023;29(5):646 View
  59. Davies H, Bachtiger P, Williams I, Molyneaux P, Peters N, Mandic D. Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD. IEEE Transactions on Biomedical Engineering 2022;69(7):2390 View
  60. Charlton P, Kyriacou P, Mant J, Marozas V, Chowienczyk P, Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. Proceedings of the IEEE 2022;110(3):355 View
  61. Yin Y, Xu J, Cai S, Chen Y, Chen Y, Li M, Zhang Z, Kang J. Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study. International Journal of Chronic Obstructive Pulmonary Disease 2022;Volume 17:2093 View
  62. Liao K, Liu C, Chen C, Shen Y. Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease. Diagnostics 2021;11(12):2396 View
  63. Janjua S, Carter D, Threapleton C, Prigmore S, Disler R. Telehealth interventions: remote monitoring and consultations for people with chronic obstructive pulmonary disease (COPD). Cochrane Database of Systematic Reviews 2021;2021(7) View
  64. Liu Y, Arnaert A, da Costa D, Sumbly P, Debe Z, Charbonneau S. Experiences of Patients With Chronic Obstructive Pulmonary Disease Using the Apple Watch Series 6 Versus the Traditional Finger Pulse Oximeter for Home SpO2 Self-Monitoring: Qualitative Study Part 2. JMIR Aging 2023;6:e41539 View
  65. Serna-Pascual M, D'Cruz R, Volovaya M, Jolley C, Hart N, Rafferty G, Steier J, Aston P, Nandi M. Novel breathing pattern analysis: Symmetric Projection Attractor Reconstruction improves identification of impending COPD re-exacerbations – a retrospective cohort analysis. ERJ Open Research 2023;9(4):00164-2023 View
  66. Coutu F, Iorio O, Ross B. Remote patient monitoring strategies and wearable technology in chronic obstructive pulmonary disease. Frontiers in Medicine 2023;10 View
  67. Arslan B, Sener K, Guven R, Kapci M, Korkut S, Sutasir M, Tekindal M. Accuracy of the Apple Watch in measuring oxygen saturation: comparison with pulse oximetry and ABG. Irish Journal of Medical Science (1971 -) 2024;193(1):477 View
  68. Althobiani M, Khan B, Shah A, Ranjan Y, Mendes R, Folarin A, Mandal S, Porter J, Hurst J. Clinicians’ Perspectives of Wearable Technology to Detect and Monitor Exacerbations of Chronic Obstructive Pulmonary Disease: Mixed-Method Survey. International Journal of Chronic Obstructive Pulmonary Disease 2023;Volume 18:1401 View
  69. Litvinova O, Hammerle F, Stoyanov J, Ksepka N, Matin M, Ławiński M, Atanasov A, Willschke H. Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine. Healthcare 2023;11(22):3003 View
  70. Khukhlina O, Dudka I, Dudka T. Oxidative and nitrosative stress — the leading mechanisms of chronic pancreatitis and chronic obstructive pulmonary disease interaction and inducers of their progression. GASTROENTEROLOGY 2023;57(4):195 View
  71. Alexiadis A, Tsanas A, Shtika L, Efopoulos V, Votis K, Tzovaras D, Triantafyllidis A. Next-Day Prediction of Hypoglycaemic Episodes Based on the Use of a Mobile App for Diabetes Self-Management. IEEE Access 2024;12:7469 View
  72. Barata F, Shim J, Wu F, Langer P, Fleisch E. The Bitemporal Lens Model—toward a holistic approach to chronic disease prevention with digital biomarkers. JAMIA Open 2024;7(2) View
  73. Tian H, Su X, Hou Y. Feedback stabilization of probabilistic finite state machines based on deep Q-network. Frontiers in Computational Neuroscience 2024;18 View
  74. Luckett T, Phillips J, Johnson M, Garcia M, Bhattarai P, Carrieri-Kohlman V, Hutchinson A, Disler R, Currow D, Agar M, Ivynian S, Chye R, Newton P, Davidson P. Insights from Australians with respiratory disease living in the community with experience of self-managing through an emergency department ‘near miss’ for breathlessness: a strengths-based qualitative study. BMJ Open 2017;7(12):e017536 View
  75. Glyde H, Blythin A, Wilkinson T, Nabney I, Dodd J. Exacerbation predictive modelling using real-world data from the myCOPD app. Heliyon 2024;10(10):e31201 View

Books/Policy Documents

  1. Levin-Zamir D, Parial L. Encyclopedia of Gerontology and Population Aging. View
  2. Sumilang C. Advancing Mobile Learning in Contemporary Educational Spaces. View
  3. . Body Sensor Networking, Design and Algorithms. View
  4. do Amaral J, de Melo P. Artificial Intelligence in Precision Health. View
  5. Nunavath V, Goodwin M, Fidje J, Moe C. Engineering Applications of Neural Networks. View
  6. Das N, Topalovic M, Janssens W. Artificial Intelligence in Medicine. View
  7. Levin-Zamir D, Parial L. Encyclopedia of Gerontology and Population Aging. View
  8. Nerella S, Gonzalez K, Cupka J, Ruppert M, Loftus T, Bihorac A, Rashidi P. Encyclopedia of Sensors and Biosensors. View
  9. Das N, Topalovic M, Janssens W. Artificial Intelligence in Medicine. View
  10. Gonem S. Artificial Intelligence in Clinical Practice. View
  11. Upson S, Kusupati V, Bime C. Comprehensive Precision Medicine. View
  12. Fielding K, Subbaraman R, Khan A, Celan C, Charalambous S, Franke M, Huddart S, Katamba A, Law S, Stagg H. Digital Respiratory Healthcare. View
  13. Abineza C, Balas V, Nsengiyumva P. Data Analytics in System Engineering. View