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

Methods and New Tools in Mental Health Research (428) Mobile Health (mhealth) (2777) JMIR Theme Issue: COVID-19 Special Issue (2394) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions (374) Outbreak and Pandemic Preparedness and Management (1679) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484) Digital Biomarkers and Digital Phenotyping (178) Machine Learning (1400) Fitness Trackers and Smart Pedometers/Accelerometers (543) Wearable Devices and Sensors (825) mHealth for Wellness, Behavior Change and Prevention (2754) Neurology and Neurosciences (150)

Published on in Vol 9, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38495, first published .
Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping

Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping

Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping

Authors of this article:

Prerna Chikersal1 Author Orcid Image ;   Shruthi Venkatesh2 Author Orcid Image ;   Karman Masown2 Author Orcid Image ;   Elizabeth Walker2 Author Orcid Image ;   Danyal Quraishi2 Author Orcid Image ;   Anind Dey3 Author Orcid Image ;   Mayank Goel1 Author Orcid Image ;   Zongqi Xia2 Author Orcid Image
Article Authors Cited by (8) Tweetations (5) Metrics

Journals

  1. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278 View
  2. Triantafyllidis A, Segkouli S, Zygouris S, Michailidou C, Avgerinakis K, Fappa E, Vassiliades S, Bougea A, Papagiannakis N, Katakis I, Mathioudis E, Sorici A, Bajenaru L, Tageo V, Camonita F, Magga-Nteve C, Vrochidis S, Pedullà L, Brichetto G, Tsakanikas P, Votis K, Tzovaras D. Mobile App Interventions for Parkinson’s Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review. Sensors 2023;23(7):3396 View
  3. Voigt I, Inojosa H, Wenk J, Akgün K, Ziemssen T. Building a monitoring matrix for the management of multiple sclerosis. Autoimmunity Reviews 2023;22(8):103358 View
  4. Woelfle T, Bourguignon L, Lorscheider J, Kappos L, Naegelin Y, Jutzeler C. Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective. Journal of Medical Internet Research 2023;25:e44428 View
  5. Pinarello C, Elmers J, Inojosa H, Beste C, Ziemssen T. Management of multiple sclerosis fatigue in the digital age: from assessment to treatment. Frontiers in Neuroscience 2023;17 View
  6. Yang W, Yang L, Mao S, Liu D, Wang L. Analysis of the effect of nursing care based on action research method on the prevention of postoperative lymphedema in breast cancer patients. Medicine 2023;102(52):e36743 View
  7. Riley C, Venkatesh S, Dhand A, Doshi N, Kavak K, Levit E, Perrone C, Weinstock-Guttman B, Longbrake E, De Jager P, Xia Z. Impact of the COVID-19 Pandemic on the Personal Networks and Neurological Outcomes of People With Multiple Sclerosis: Cross-Sectional and Longitudinal Case-Control Study. JMIR Public Health and Surveillance 2024;10:e45429 View
  8. Gashi S, Oldrati P, Moebus M, Hilty M, Barrios L, Ozdemir F, Kana V, Lutterotti A, Rätsch G, Holz C. Modeling multiple sclerosis using mobile and wearable sensor data. npj Digital Medicine 2024;7(1) View