Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19068, first published .
Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

Journals

  1. Del Din S, Kirk C, Yarnall A, Rochester L, Hausdorff J, Mirelman A, Dorsey E, Brundin P, Bloem B. Body-Worn Sensors for Remote Monitoring of Parkinson’s Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead. Journal of Parkinson's Disease 2021;11(s1):S35 View
  2. Williamson J, Telfer B, Mullany R, Friedl K. Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank. Sensors 2021;21(6):2047 View
  3. Riggare S, Stamford J, Hägglund M, Mirelman A, Dorsey E, Brundin P, Bloem B. A Long Way to Go: Patient Perspectives on Digital Health for Parkinson’s Disease. Journal of Parkinson's Disease 2021;11(s1):S5 View
  4. Jeong H, Jeong Y, Park Y, Kim K, Park J, Kang D. Applications of deep learning methods in digital biomarker research using noninvasive sensing data. DIGITAL HEALTH 2022;8:205520762211366 View
  5. Huang C, Zhang F, Xu Z, Wei J. The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds. Sensors 2022;22(4):1678 View
  6. Guo C, Chiesa P, de Moor C, Fazeli M, Schofield T, Hofer K, Belachew S, Scotland A. Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review. Journal of Medical Internet Research 2022;24(11):e37683 View
  7. Atrsaei A, Hansen C, Elshehabi M, Solbrig S, Berg D, Liepelt-Scarfone I, Maetzler W, Aminian K. Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson’s Disease. Frontiers in Aging Neuroscience 2021;13 View
  8. Morgan C, Jameson J, Craddock I, Tonkin E, Oikonomou G, Isotalus H, Heidarivincheh F, McConville R, Tourte G, Kinnunen K, Whone A. Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset. Parkinsonism & Related Disorders 2022;105:114 View
  9. Rodríguez-Martín D, Cabestany J, Pérez-López C, Pie M, Calvet J, Samà A, Capra C, Català A, Rodríguez-Molinero A. A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM. Frontiers in Neurology 2022;13 View
  10. Krokidis M, Dimitrakopoulos G, Vrahatis A, Tzouvelekis C, Drakoulis D, Papavassileiou F, Exarchos T, Vlamos P. A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes. Sensors 2022;22(2):409 View
  11. Maremmani C, Rovini E, Salvadori S, Pecori A, Pasquini J, Ciammola A, Rossi S, Berchina G, Monastero R, Cavallo F. Hands–feet wireless devices: Test–retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring. Acta Neurologica Scandinavica 2022;146(3):304 View
  12. Denk D, Herman T, Zoetewei D, Ginis P, Brozgol M, Cornejo Thumm P, Decaluwe E, Ganz N, Palmerini L, Giladi N, Nieuwboer A, Hausdorff J. Daily-Living Freezing of Gait as Quantified Using Wearables in People With Parkinson Disease: Comparison With Self-Report and Provocation Tests. Physical Therapy 2022;102(12) View
  13. Schütz N, Knobel S, Botros A, Single M, Pais B, Santschi V, Gatica-Perez D, Buluschek P, Urwyler P, Gerber S, Müri R, Mosimann U, Saner H, Nef T. A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust. npj Digital Medicine 2022;5(1) View
  14. Raykov Y, Evers L, Badawy R, Bloem B, Heskes T, Meinders M, Claes K, Little M. Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life. IEEE Journal of Biomedical and Health Informatics 2021;25(6):2293 View
  15. Peraza L, Kinnunen K, McNaney R, Craddock I, Whone A, Morgan C, Joules R, Wolz R. An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson’s Disease. Sensors 2021;21(24):8286 View
  16. Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson’s Disease: Towards a New Era of Research and Clinical Care. Phenomics 2022;2(5):349 View
  17. Habets J, Herff C, Kubben P, Kuijf M, Temel Y, Evers L, Bloem B, Starr P, Gilron R, Little S. Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson’s Disease Using a Wrist-Worn Accelerometer. Sensors 2021;21(23):7876 View
  18. Virmani T, Lotia M, Glover A, Pillai L, Kemp A, Iyer A, Farmer P, Syed S, Larson-Prior L, Prior F. Feasibility of telemedicine research visits in people with Parkinson’s disease residing in medically underserved areas. Journal of Clinical and Translational Science 2022;6(1) View
  19. Rastegari E, Ali H, Marmelat V. Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring. Sensors 2022;22(23):9122 View
  20. Chandrabhatla A, Pomeraniec I, Ksendzovsky A. Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson’s disease motor symptoms. npj Digital Medicine 2022;5(1) View
  21. Fröhlich H, Bontridder N, Petrovska-Delacréta D, Glaab E, Kluge F, Yacoubi M, Marín Valero M, Corvol J, Eskofier B, Van Gyseghem J, Lehericy S, Winkler J, Klucken J. Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease. Frontiers in Neurology 2022;13 View
  22. Rehman R, Guan Y, Shi J, Alcock L, Yarnall A, Rochester L, Del Din S. Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson’s Disease Classification Using Machine Learning. Frontiers in Aging Neuroscience 2022;14 View
  23. Sakamaki T, Furusawa Y, Hayashi A, Otsuka M, Fernandez J. Remote Patient Monitoring for Neuropsychiatric Disorders: A Scoping Review of Current Trends and Future Perspectives from Recent Publications and Upcoming Clinical Trials. Telemedicine and e-Health 2022;28(9):1235 View
  24. Sieberts S, Borzymowski H, Guan Y, Huang Y, Matzner A, Page A, Bar-Gad I, Beaulieu-Jones B, El-Hanani Y, Goschenhofer J, Javidnia M, Keller M, Li Y, Saqib M, Smith G, Stanescu A, Venuto C, Zielinski R, Jayaraman A, Evers L, Foschini L, Mariakakis A, Pandey G, Shawen N, Synder P, Omberg L, Grosan C. Developing better digital health measures of Parkinson’s disease using free living data and a crowdsourced data analysis challenge. PLOS Digital Health 2023;2(3):e0000208 View
  25. Colón-Semenza C, Zajac J, Schwartz A, Darbandsari P, Ellis T. Experiences from the implementation of physical therapy via telehealth for individuals with Parkinson disease during the COVID-19 pandemic. Disability and Rehabilitation 2024;46(8):1593 View
  26. ZhuParris A, de Goede A, Yocarini I, Kraaij W, Groeneveld G, Doll R. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. Sensors 2023;23(11):5243 View
  27. Morgan C, Tonkin E, Masullo A, Jovan F, Sikdar A, Khaire P, Mirmehdi M, McConville R, Tourte G, Whone A, Craddock I. A multimodal dataset of real world mobility activities in Parkinson’s disease. Scientific Data 2023;10(1) View
  28. Lukac M, Luben H, Martin A, Simmons Z, Geronimo A. Spatial-Temporal Analysis of Gait in Amyotrophic Lateral Sclerosis Using Foot-Worn Inertial Sensors: An Observational Study. Digital Biomarkers 2023;8(1):22 View
  29. Goldman J, Volpe D, Ellis T, Hirsch M, Johnson J, Wood J, Aragon A, Biundo R, Di Rocco A, Kasman G, Iansek R, Miyasaki J, McConvey V, Munneke M, Pinto S, St. Clair K, Toledo S, York M, Todaro R, Yarab N, Wallock K. Delivering Multidisciplinary Rehabilitation Care in Parkinson’s Disease: An International Consensus Statement. Journal of Parkinson's Disease 2024;14(1):135 View
  30. Virmani T, Pillai L, Smith V, Glover A, Abrams D, Farmer P, Syed S, Spencer H, Kemp A, Barron K, Murray T, Morris B, Bowers B, Ward A, Imus T, Larson-Prior L, Lotia M, Prior F. Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson’s disease. Journal of Clinical and Translational Science 2024;8(1) View
  31. Bacon K, Felson D, Jafarzadeh S, Kolachalama V, Hausdorff J, Gazit E, Stefanik J, Corrigan P, Segal N, Lewis C, Nevitt M, Kumar D. Gait Alterations and Association With Worsening Knee Pain and Physical Function: A Machine Learning Approach With Wearable Sensors in the Multicenter Osteoarthritis Study. Arthritis Care & Research 2024 View

Books/Policy Documents

  1. Munoz Ospina B, Quintana-Peña V, Alvarez D, A. Valderrama J, Takeuchi Y, L. Orozco J. Dementia in Parkinson’s Disease - Everything you Need to Know. View
  2. Bianchini E, Maetzler W. Digital Technologies in Movement Disorders. View
  3. Bouça-Machado R, Kauppila L, Guerreiro T, Ferreira J. Digital Technologies in Movement Disorders. View
  4. Rochester L, Del Din S, Hu M, Morgan C, Carroll C. Digital Technologies in Movement Disorders. View