This paper is in the following e-collection/theme issue:

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1732)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21543, first published .
Smartphone-Based Monitoring of Parkinson Disease: Quasi-Experimental Study to Quantify Hand Tremor Severity and Medication Effectiveness

Smartphone-Based Monitoring of Parkinson Disease: Quasi-Experimental Study to Quantify Hand Tremor Severity and Medication Effectiveness

Smartphone-Based Monitoring of Parkinson Disease: Quasi-Experimental Study to Quantify Hand Tremor Severity and Medication Effectiveness

Authors of this article:

Elina Kuosmanen1 Author Orcid Image ;   Florian Wolling2 Author Orcid Image ;   Julio Vega3 Author Orcid Image ;   Valerii Kan1 Author Orcid Image ;   Yuuki Nishiyama4 Author Orcid Image ;   Simon Harper3 Author Orcid Image ;   Kristof Van Laerhoven2 Author Orcid Image ;   Simo Hosio1 Author Orcid Image ;   Denzil Ferreira1 Author Orcid Image
Article Authors Cited by (26) Tweetations (2) Metrics

Journals

  1. Vescio B, Quattrone A, Nisticò R, Crasà M, Quattrone A. Wearable Devices for Assessment of Tremor. Frontiers in Neurology 2021;12 View
  2. Martín-Ávila G, Vieira-Campos A, Labrador-Marcos S, Zheng X, Méndez-Burgos A, Thuissard I, Andreu-Vázquez C, Ordieres-Meré J, Aladro Y. Patients' self-assessment of essential tremor severity by a validated scale: A useful tool in telemedicine?. Parkinsonism & Related Disorders 2022;96:22 View
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  9. Zhang H, Parsia B, Poliakoff E, Harper S. Tracking social behaviour with smartphones in people with Parkinson's: a longitudinal study. Behaviour & Information Technology 2024;43(11):2323 View
  10. Ferreira V, Metting E, Schauble J, Seddighi H, Beumeler L, Gallo V. eHealth tools to assess the neurological function for research, in absence of the neurologist – a systematic review, part I (software). Journal of Neurology 2024;271(1):211 View
  11. Bui T, Thangavel B, Sharipov M, Chen K, Shin J. Smartphone-Based Portable Bio-Chemical Sensors: Exploring Recent Advancements. Chemosensors 2023;11(9):468 View
  12. Paredes-Acuna N, Utpadel-Fischler D, Ding K, Thakor N, Cheng G. Upper limb intention tremor assessment: opportunities and challenges in wearable technology. Journal of NeuroEngineering and Rehabilitation 2024;21(1) View
  13. Zhang Y, Zeng Z, Mirian M, Yen K, Park K, Doo M, Ji J, Shen Z, McKeown M. Investigating the efficacy and importance of mobile-based assessments for Parkinson's disease: uncovering the potential of novel digital tests. Scientific Reports 2024;14(1) View
  14. He T, Chen J, Hossain M, Lyu Z. Enhanced detection of early Parkinson’ s disease through multi-sensor fusion on smartphone-based IoMT platforms. Information Fusion 2025;117:102889 View
  15. Harland T, Sabourin S, Mayer R, Durphy J, Adam O, Pilitsis J. In Reply: Longevity of Deep Brain Stimulation in Essential Tremor: A Comparison of Omnidirectional and Directional Leads. Neurosurgery 2025;97(1):e15 View
  16. Liu X, Zhang L. Letter: Longevity of Deep Brain Stimulation in Essential Tremor: A Comparison of Omnidirectional and Directional Leads. Neurosurgery 2025;97(1):e13 View
  17. Zawada S, Acosta J, Collins C, Dumitrascu O, Harahsheh E, Hagen C, Ganjizadeh A, Mahmoudi E, Erickson B, Demaerschalk B. Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study. Mayo Clinic Proceedings: Digital Health 2025;3(3):100240 View
  18. Zhu Z, Wu E, Leng P, Sun J, Ma M, Pan Z, Borzì L. Finger drawing on smartphone screens enables early Parkinson’s disease detection through hybrid 1D-CNN and BiGRU deep learning architecture. PLOS One 2025;20(7):e0327733 View
  19. Negi A, Karjagi S, Parisi L, Daley K, Abay A, Gala A, Wilkins K, Hoffman S, Ferris M, Zahed H, Chattree G, Palushaj B, Bronte-Stewart H. Remote real time digital monitoring fills a critical gap in the management of Parkinson’s disease. npj Parkinson's Disease 2025;11(1) View

Books/Policy Documents

  1. Nair S, Muddapu V, Sriram M, Aditya R, Gupta R, Chakravarthy S. Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation. View
  2. Ymeri G, Salvi D, Olsson C. Pervasive Computing Technologies for Healthcare. View
  3. Sarmah A, Kanagaraj S, Unal A. Artificial Intelligence in Tissue and Organ Regeneration. View
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Conference Proceedings

  1. Fiska V, Gakilazou V, Katertsidis N, Tzallas A, Giannakeas N, Tsipouras M. 2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM). Automatic Parkinson's tremor assessment with data analysis from daily activities View
  2. Amprimo G, Rechichi I, Ferraris C, Olmo G. 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). Objective Assessment of the Finger Tapping Task in Parkinson's Disease and Control Subjects using Azure Kinect and Machine Learning View
  3. Vieira R, Arrais A, Dias D, Soares C, Massano J, Cunha J. 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Creating the Next Digital Telemedicine Tool for Parkinson's Disease Management with AI View