ArticleNPJ digital medicine2022
A machine-learning based objective measure for ALS disease severity.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06885918 (ALS Translational Research Program), which is not on this map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
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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.
ALS Translational Research Program
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- The Use of Digital Devices to Monitor Physical Behavior in Motor Neuron Disease: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review.Orphanet journal of rare diseases · 2023Pooled it
- A systematic review of digital technology to evaluate motor function and disease progression in motor neuron disease.Journal of neurology · 2022Pooled it
- Knowledge mapping of biomarkers in amyotrophic lateral sclerosis: a comprehensive bibliometric and visual analysis.Neurodegenerative disease management · 2026Review
- Short prescribed exercises can quantify upper limb functioning in neurodegenerative disease.Journal of neuroengineering and rehabilitation · 2026Article
- Analytical and clinical validation of step counting method in people living with amyotrophic lateral sclerosis.Scientific reports · 2025Article
- Review
- Multimodal Digital Phenotyping of Behavior in a Neurology Clinic: Development of the Neurobooth Platform and the First Two Years of Data Collection.medRxiv : the preprint server for health sciences · 2025Article
- Listener effort measures clinically meaningful change of dysarthria in amyotrophic lateral sclerosis.Brain communications · 2025Article
- Article
- A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis.PloS one · 2025Article
- Free-living monitoring of ALS progression in upper limbs using wearable accelerometers.Journal of neuroengineering and rehabilitation · 2024Observational
- Predictive modeling of ALS progression: an XGBoost approach using clinical features.BioData mining · 2024Article
- Multimodal Technologies for Remote Assessment of Neurological and Mental Health.Journal of speech, language, and hearing research : JSLHR · 2024Review
- Exploratory study to evaluate the acceptability of a wearable accelerometer to assess motor progression in motor neuron disease.Journal of neurology · 2024Article
- Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Estimating Sample Size and Reducing Overfitting.Journal of speech, language, and hearing research : JSLHR · 2024Article
- At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis.Nature communications · 2023Article
- Telehealth-based exercise in amyotrophic lateral sclerosis.Frontiers in neurology · 2023Article
- HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.Frontiers in digital health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amyotrophic Lateral Sclerosis (ALS) disease severity is usually measured using the subjective, questionnaire-based revised ALS Functional Rating Scale (ALSFRS-R). Objective measures of disease severity would be powerful tools for evaluating real-world drug effectiveness, efficacy in clinical trials, and for identifying participants for cohort studies. We developed a machine learning (ML) based objective measure for ALS disease severity based on voice samples and accelerometer measurements from a four-year longitudinal dataset. 584 people living with ALS consented and carried out prescribed speaking and limb-based tasks. 542 participants contributed 5814 voice recordings, and 350 contributed 13,009 accelerometer samples, while simultaneously measuring ALSFRS-R scores. Using these data, we trained ML models to predict bulbar-related and limb-related ALSFRS-R scores. On the test set (n = 109 participants) the voice models achieved a multiclass AUC of 0.86 (95% CI, 0.85-0.88) on speech ALSFRS-R prediction, whereas the accelerometer models achieved a median multiclass AUC of 0.73 on 6 limb-related functions. The correlations across functions observed in self-reported ALSFRS-R scores were preserved in ML-derived scores. We used these models and self-reported ALSFRS-R scores to evaluate the real-world effects of edaravone, a drug approved for use in ALS. In the cohort of 54 test participants who received edaravone as part of their usual care, the ML-derived scores were consistent with the self-reported ALSFRS-R scores. At the individual level, the continuous ML-derived score can capture gradual changes that are absent in the integer ALSFRS-R scores. This demonstrates the value of these tools for assessing disease severity and, potentially, drug effects.
Identifiers
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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.