ArticleJMIR mental health2022
Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping.
Article in JMIR mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Remote Monitoring in Myasthenia Gravis: Exploring Symptom Variability.Annals of clinical and translational neurology · 2026Trial
- Incremental Value of Smartphone Sensing for Monitoring Momentary Affect Intensity in Adults Using Transformer-Based Models: Observational Study.JMIR mHealth and uHealth · 2026Observational
- Plasmaphosphorylated tau as biomarkers for multiple sclerosis diagnosis, subtyping, and prognosis.Brain communications · 2026Article
- Systematic review: digital biomarkers of fatigue in chronic diseases.NPJ digital medicine · 2025Article
- Aging-dependent change in Th17 and cytokine response in multiple sclerosis.Journal of neuroinflammation · 2025Article
- Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation.Journal of medical Internet research · 2025Article
- Modeling multiple sclerosis using mobile and wearable sensor data.NPJ digital medicine · 2024Article
- 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 · 2024Article
- Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective.Journal of medical Internet research · 2023Article
- Management of multiple sclerosis fatigue in the digital age: from assessment to treatment.Frontiers in neuroscience · 2023Review
- Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS).
objectiveWe presented a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated stay-at-home period due to a global pandemic.
methodsFirst, we extracted features that capture behavior changes due to the stay-at-home order. Then, we adapted and applied an existing algorithm to these behavior-change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the stay-at-home period.
resultsUsing data collected between November 2019 and May 2020, the algorithm detected depression with an accuracy of 82.5% (65% improvement over baseline; F
conclusionsOur approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics, which would cause drastic behavior changes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.