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

Ecological Momentary Assessment (EMA) (309) Neurology and Neurosciences (185) mHealth for Data Collection and Research (955) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1399) Cognitive and Neurorehabilitation (262)

Published on in Vol 8, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15628, first published .
Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments

Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments

Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments

Authors of this article:

Jeroen Habets1 Author Orcid Image ;   Margot Heijmans1 Author Orcid Image ;   Christian Herff1 Author Orcid Image ;   Claudia Simons2, 3 Author Orcid Image ;   Albert FG Leentjens2 Author Orcid Image ;   Yasin Temel1 Author Orcid Image ;   Mark Kuijf4 Author Orcid Image ;   Pieter Kubben1, 5 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

Journals

  1. Hssayeni M, Ghoraani B. Multi-Modal Physiological Data Fusion for Affect Estimation Using Deep Learning. IEEE Access 2021;9:21642 View
  2. Habets J, Heijmans M, Leentjens A, Simons C, Temel Y, Kuijf M, Kubben P, Herff C. A Long-Term, Real-Life Parkinson Monitoring Database Combining Unscripted Objective and Subjective Recordings. Data 2021;6(2):22 View
  3. Lin Z, Zhou L, Li Y, Liu S, Xie Q, Xu X, Wu J. Identification of Potential Genomic Biomarkers for Parkinson’S Disease Using Data Pooling of Gene Expression Microarrays. Biomarkers in Medicine 2021;15(8):585 View
  4. De Calheiros Velozo J, Habets J, George S, Niemeijer K, Minaeva O, Hagemann N, Herff C, Kuppens P, Rintala A, Vaessen T, Riese H, Delespaul P. Designing daily-life research combining experience sampling method with parallel data. Psychological Medicine 2024;54(1):98 View
  5. Lee J, Yeom I, Chung M, Kim Y, Yoo S, Kim E. Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review. JMIR mHealth and uHealth 2022;10(1):e33944 View
  6. Carpi M, Pierantozzi M, Cofano S, Fernandes M, Cerroni R, De Cillis F, Mercuri N, Stefani A, Liguori C. Both Motor and Non-Motor Fluctuations Matter in the Clinical Management of Patients with Parkinson’s Disease: An Exploratory Study. Journal of Personalized Medicine 2023;13(2):242 View
  7. Weintraub D, Aarsland D, Chaudhuri K, Dobkin R, Leentjens A, Rodriguez-Violante M, Schrag A. The neuropsychiatry of Parkinson's disease: advances and challenges. The Lancet Neurology 2022;21(1):89 View
  8. 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
  9. Pons P, Navas-Medrano S, Soler-Dominguez J. Extended reality for mental health: Current trends and future challenges. Frontiers in Computer Science 2022;4 View
  10. Feldmann L, Roudini J, Kühn A, Habets J. Improving naturalistic neuroscience with patient engagement strategies. Frontiers in Human Neuroscience 2024;17 View
  11. Lee J, Suh Y, Kim E, Yoo S, Kim Y. A Mobile App for Comprehensive Symptom Management in People With Parkinson’s Disease. CIN: Computers, Informatics, Nursing 2024 View
  12. Dietvorst E, Hillegers M, Legerstee J, Vries L, Vreeker A, Keijsers L. Real-time personalized feedback in mHealth for adolescents. DIGITAL HEALTH 2024;10 View

Books/Policy Documents

  1. Vizcarra J. Handbook of Digital Technologies in Movement Disorders. View
  2. Alor-Hernández G, Sánchez-Morales L, García-Dimas F, Cruz-Ramos N, Sánchez-Cervantes J. Artificial Intelligence in Prescriptive Analytics. View