ArticleMultiple sclerosis (Houndmills, Basingstoke, England)2025
From "invisible" to "audible": Features extracted during simple speech tasks classify patient-reported fatigue in multiple sclerosis.
Article in Multiple sclerosis (Houndmills, Basingstoke, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07222618 ("Selfie" Videos), which is not on this map. Cited by 3 papers.
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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.
"Selfie" Videos: A Novel, Patient-centered, Comprehensive Approach to Measuring Function in MS
Who cites it
3 citing papers in PubMed.
- Listening to MS: AI-assisted speech analysis for diagnosis and fatigue prediction (COMMITMENT).Frontiers in digital health · 2026Article
- MSPEECH (multiple sclerosis monitoring through speech interaction in clinic and at home): a Living Lab study protocol for co-created, speech-based digital biomarkers in multiple sclerosis.Frontiers in digital health · 2026Article
- Facial Expression Metrics as Digital Biomarkers of Neurologic Disease.Neurology open access · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFatigue is a major "invisible" symptom in people with multiple sclerosis (PwMS), which may affect speech. Automated speech analysis is an objective, rapid tool to capture digital speech biomarkers linked to functional outcomes.
objectiveTo use automated speech analysis to assess multiple sclerosis (MS) fatigue metrics.
methodsEighty-four PwMS completed scripted and spontaneous speech tasks; fatigue was assessed with Modified Fatigue Impact Scale (MFIS). Speech was processed using an automated speech analysis pipeline (ki elements: SIGMA speech processing library) to transcribe speech and extract features. Regression models assessed associations between speech features and fatigue and validated in a separate set of 30 participants.
resultsCohort characteristics were as follows: mean age 49.8 (standard deviation (
conclusionFatigue may be assessed using simple, low-burden speech tasks that correlate with gold-standard subjective fatigue measures.
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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 Socratic graph.