Published on in Vol 5, No 3 (2016): Jul-Sept

Large-Scale Wearable Sensor Deployment in Parkinson’s Patients: The Parkinson@Home Study Protocol

Large-Scale Wearable Sensor Deployment in Parkinson’s Patients: The Parkinson@Home Study Protocol

Large-Scale Wearable Sensor Deployment in Parkinson’s Patients: The Parkinson@Home Study Protocol

Journals

  1. Lim S, Tan A. Historical perspective: The pros and cons of conventional outcome measures in Parkinson's disease. Parkinsonism & Related Disorders 2018;46:S47 View
  2. Aşuroğlu T, Açıcı K, Berke Erdaş Ç, Kılınç Toprak M, Erdem H, Oğul H. Parkinson's disease monitoring from gait analysis via foot-worn sensors. Biocybernetics and Biomedical Engineering 2018;38(3):760 View
  3. Antonini A, Gentile G, Giglio M, Marcante A, Gage H, Touray M, Fotiadis D, Gatsios D, Konitsiotis S, Timotijevic L, Egan B, Hodgkins C, Biundo R, Pellicano C. Acceptability to patients, carers and clinicians of an mHealth platform for the management of Parkinson’s disease (PD_Manager): study protocol for a pilot randomised controlled trial. Trials 2018;19(1) View
  4. Klucken J, Krüger R, Schmidt P, Bloem B, Brundin P, Langston J, Bloem B. Management of Parkinson’s Disease 20 Years from Now: Towards Digital Health Pathways. Journal of Parkinson's Disease 2018;8(s1):S85 View
  5. López-Blanco R, Velasco M, Méndez-Guerrero A, Romero J, del Castillo M, Serrano J, Benito-León J, Bermejo-Pareja F, Rocon E. Essential tremor quantification based on the combined use of a smartphone and a smartwatch: The NetMD study. Journal of Neuroscience Methods 2018;303:95 View
  6. Turner M, Nieuwenhuijsen M, Anderson K, Balshaw D, Cui Y, Dunton G, Hoppin J, Koutrakis P, Jerrett M. Assessing the Exposome with External Measures: Commentary on the State of the Science and Research Recommendations. Annual Review of Public Health 2017;38(1):215 View
  7. Zeissler M, Li V, Parmar M, Carroll C. Is It Possible to Conduct a Multi-Arm Multi-Stage Platform Trial in Parkinson’s Disease: Lessons Learned from Other Neurodegenerative Disorders and Cancer. Journal of Parkinson's Disease 2020;10(2):413 View
  8. Jauhiainen M, Puustinen J, Mehrang S, Ruokolainen J, Holm A, Vehkaoja A, Nieminen H. Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study. JMIR Research Protocols 2019;8(3):e12808 View
  9. Izmailova E, Wagner J, Perakslis E. Wearable Devices in Clinical Trials: Hype and Hypothesis. Clinical Pharmacology & Therapeutics 2018;104(1):42 View
  10. Rovini E, Maremmani C, Cavallo F. Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review. Telemedicine and e-Health 2019;25(3):167 View
  11. Elm J, Daeschler M, Bataille L, Schneider R, Amara A, Espay A, Afek M, Admati C, Teklehaimanot A, Simuni T. Feasibility and utility of a clinician dashboard from wearable and mobile application Parkinson’s disease data. npj Digital Medicine 2019;2(1) View
  12. Taylor K, Staunton H, Lipsmeier F, Nobbs D, Lindemann M. Outcome measures based on digital health technology sensor data: data- and patient-centric approaches. npj Digital Medicine 2020;3(1) View
  13. Kononova A, Li L, Kamp K, Bowen M, Rikard R, Cotten S, Peng W. The Use of Wearable Activity Trackers Among Older Adults: Focus Group Study of Tracker Perceptions, Motivators, and Barriers in the Maintenance Stage of Behavior Change. JMIR mHealth and uHealth 2019;7(4):e9832 View
  14. Tosserams A, de Vries N, Bloem B, Nonnekes J. Multidisciplinary Care to Optimize Functional Mobility in Parkinson Disease. Clinics in Geriatric Medicine 2020;36(1):159 View
  15. Radder D, Sturkenboom I, van Nimwegen M, Keus S, Bloem B, de Vries N. Physical therapy and occupational therapy in Parkinson's disease. International Journal of Neuroscience 2017;127(10):930 View
  16. Prashanth R, Dutta Roy S. Early detection of Parkinson’s disease through patient questionnaire and predictive modelling. International Journal of Medical Informatics 2018;119:75 View
  17. Iannone L, Preda A, Blottière H, Clarke G, Albani D, Belcastro V, Carotenuto M, Cattaneo A, Citraro R, Ferraris C, Ronchi F, Luongo G, Santocchi E, Guiducci L, Baldelli P, Iannetti P, Pedersen S, Petretto A, Provasi S, Selmer K, Spalice A, Tagliabue A, Verrotti A, Segata N, Zimmermann J, Minetti C, Mainardi P, Giordano C, Sisodiya S, Zara F, Russo E, Striano P. Microbiota-gut brain axis involvement in neuropsychiatric disorders. Expert Review of Neurotherapeutics 2019;19(10):1037 View
  18. Lipsmeier F, Taylor K, Kilchenmann T, Wolf D, Scotland A, Schjodt‐Eriksen J, Cheng W, Fernandez‐Garcia I, Siebourg‐Polster J, Jin L, Soto J, Verselis L, Boess F, Koller M, Grundman M, Monsch A, Postuma R, Ghosh A, Kremer T, Czech C, Gossens C, Lindemann M. Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Movement Disorders 2018;33(8):1287 View
  19. Cohen S, Waks Z, Elm J, Gordon M, Grachev I, Navon-Perry L, Fine S, Grossman I, Papapetropoulos S, Savola J. Characterizing patient compliance over six months in remote digital trials of Parkinson’s and Huntington disease. BMC Medical Informatics and Decision Making 2018;18(1) View
  20. Kovalchick C, Sirkar R, Regele O, Kourtis L, Schiller M, Wolpert H, Alden R, Jones G, Wright J. Can composite digital monitoring biomarkers come of age? A framework for utilization. Journal of Clinical and Translational Science 2017;1(6):373 View
  21. Zajki-Zechmeister T, Kögl M, Kalsberger K, Franthal S, Homayoon N, Katschnig-Winter P, Wenzel K, Zajki-Zechmeister L, Schwingenschuh P. Quantification of tremor severity with a mobile tremor pen. Heliyon 2020;6(8):e04702 View
  22. AlMahadin G, Lotfi A, Zysk E, Siena F, Carthy M, Breedon P. Parkinson’s disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms - a patient and healthcare professional perspective. BMC Neurology 2020;20(1) View
  23. Eggers C, Wellach I, Groppa S, Strothjohann M, Klucken J. Versorgung von Parkinson-Patienten in Deutschland: Status quo und Perspektiven im Spiegel des digitalen Wandels. Der Nervenarzt 2021;92(6):602 View
  24. Dockendorf M, Hansen B, Bateman K, Moyer M, Shah J, Shipley L. Digitally Enabled, Patient‐Centric Clinical Trials: Shifting the Drug Development Paradigm. Clinical and Translational Science 2021;14(2):445 View
  25. Ahamed F, Shahrestani S, Cheung H. Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia. Sensors 2020;20(21):6031 View
  26. 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
  27. AlMahadin G, Lotfi A, Carthy M, Breedon P. Enhanced Parkinson’s Disease Tremor Severity Classification by Combining Signal Processing with Resampling Techniques. SN Computer Science 2022;3(1) View
  28. Anbalagan B, Karnam Anantha S, Kalpana R. Novel Approach to Prognosis Parkinson’s Disease with Wireless Technology Using Resting Tremors. Wireless Personal Communications 2022;125(4):2985 View
  29. 岳 星. Parkinson’s Tremor Rating System Based on MLP. Software Engineering and Applications 2022;11(01):16 View
  30. Liu W, Tung T, Zhang C, Shi L. Systematic review for the prevention and management of falls and fear of falling in patients with Parkinson's disease. Brain and Behavior 2022;12(8) View
  31. 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
  32. Aşuroğlu T, Oğul H. A deep learning approach for parkinson’s disease severity assessment. Health and Technology 2022;12(5):943 View
  33. Gaßner H, Friedrich J, Masuch A, Jukic J, Stallforth S, Regensburger M, Marxreiter F, Winkler J, Klucken J. The Effects of an Individualized Smartphone-Based Exercise Program on Self-defined Motor Tasks in Parkinson Disease: Pilot Interventional Study. JMIR Rehabilitation and Assistive Technologies 2022;9(4):e38994 View
  34. Masi G, Amprimo G, Priano L, Ferraris C. New Perspectives in Nonintrusive Sleep Monitoring for Neurodegenerative Diseases—A Narrative Review. Electronics 2023;12(5):1098 View
  35. Barracca A, Ledda S, Mancosu G, Pintore G, Quintaliani G, Ronco C, Kashani K. Digital Health: A New Frontier. Journal of Translational Critical Care Medicine 2023;5(1) View
  36. Barracca A, Ledda S, Mancosu G, Pintore G, Quintaliani G, Ronco C, Kashani K. Digital Health: A New Frontier. Journal of Translational Critical Care Medicine 2023;5(2) View
  37. Lützow L, Teckenburg I, Koch V, Marxreiter F, Jukic J, Stallforth S, Regensburger M, Winkler J, Klucken J, Gaßner H. The effects of an individualized smartphone-based exercise program on self-defined motor tasks in Parkinson’s disease: a long-term feasibility study. Journal of Patient-Reported Outcomes 2023;7(1) View
  38. Tam W, Alajlani M, Abd-alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review. Journal of Medical Internet Research 2023;25:e42950 View
  39. Wright J, Regele O, Kourtis L, Pszenny S, Sirkar R, Kovalchick C, Jones G. Evolution of the digital biomarker ecosystem. Digital Medicine 2017;3(4):154 View
  40. Bridges B, Taylor J, Weber J. Evaluation of the Parkinson’s Remote Interactive Monitoring System in a Clinical Setting: Usability Study. JMIR Human Factors 2024;11:e54145 View

Books/Policy Documents

  1. López Blanco R, Sánchez Ferro Á. Pathology, Prevention and Therapeutics of Neurodegenerative Disease. View
  2. Salami M. Comprehensive Gut Microbiota. View
  3. Intas G, Platis C, Stergiannis P. Handbook of Computational Neurodegeneration. View