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

Wearables and MHealth Reviews (260) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1317) Quality Evaluation and Descriptive Analysis/Reviews of Multiple Existing Mobile Apps (363) mHealth for Wellness, Behavior Change and Prevention (2754)

Published on in Vol 10, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33944, first published .
Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review

Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review

Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review

Authors of this article:

JuHee Lee1, 2 Author Orcid Image ;   Insun Yeom2 Author Orcid Image ;   Misook L Chung3, 4 Author Orcid Image ;   Yielin Kim4 Author Orcid Image ;   Subin Yoo2 Author Orcid Image ;   Eunyoung Kim2 Author Orcid Image
Article Authors Cited by (9) Tweetations (15) Metrics

Journals

  1. Petutschnig A, Reichel S, Měchurová K, Resch B. An eDiary App Approach for Collecting Physiological Sensor Data from Wearables together with Subjective Observations and Emotions. Sensors 2022;22(16):6120 View
  2. Maduraiveeran G. Nanomaterials-based portable electrochemical sensing and biosensing systems for clinical and biomedical applications. Journal of Analytical Science and Technology 2022;13(1) View
  3. Khachian A, Zeighami K, Khoubbin Khoshnazar T, Haghani H. The Effectiveness of Self-management Education Based on the Mobile Application on the Quality of Life of People with Parkinson’s Disease. Middle East Journal of Rehabilitation and Health Studies 2023;10(2) View
  4. Bernard R, Seijas V, Davis M, Volkova A, Diviani N, Lüscher J, Sabariego C. Mobile Health Self-management Support for Spinal Cord Injury: Systematic Literature Review. JMIR mHealth and uHealth 2023;11:e42679 View
  5. 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;42(4):289 View
  6. 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
  7. Di Cesare M, Perpetuini D, Cardone D, Merla A. Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson’s Disease: A Study on Speaker Diarization and Classification Techniques. Sensors 2024;24(5):1499 View

Books/Policy Documents

  1. Rochester L, Del Din S, Hu M, Morgan C, Carroll C. Digital Technologies in Movement Disorders. View