Evidence map›Paper›PMID 39039102›Full record

ArticleScientific reports2024

A novel digital tool for detection and monitoring of amyotrophic lateral sclerosis motor impairment and progression via keystroke dynamics.

Alejandro Acien, Narghes Calcagno, Katherine M Burke, Ijah Mondesire-Crump, Ashley A Holmes, Sri Mruthik, Ben Goldy, Janina E Syrotenko, Zoe Scheier, Amrita Iyer and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Observational
  2. Article
  3. A comprehensive guide to digital neurology. An update on current development on digital neurology derived from the Digital Neuro Hub project.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
    Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Alejandro Acien *Area 2 AI Corporation, 245 Main St, Cambridge, MA, 02142, USA.
Narghes Calcagno *Department of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA.
Katherine M BurkeDepartment of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA.
Ijah Mondesire-CrumpnQ Medical, Cambridge, MA, USA.
Ashley A HolmesnQ Medical, Cambridge, MA, USA.
Sri MruthiknQ Medical, Cambridge, MA, USA.
Ben GoldynQ Medical, Cambridge, MA, USA.
Janina E SyrotenkonQ Medical, Cambridge, MA, USA.
Zoe ScheierDepartment of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA.
Amrita IyerDepartment of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA.
Alison ClarkDepartment of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA.
Mackenzie KeeganDepartment of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA.
Yoshiteru UshirogawaMitsubishi Tanabe Pharma Corporation, Osaka, Osaka, Japan.
Atsushi KatoMitsubishi Tanabe Pharma Corporation, Osaka, Osaka, Japan.
Taku YasudaMitsubishi Tanabe Pharma Corporation, Osaka, Osaka, Japan.
Amir LahavMitsubishi Tanabe Pharma America, Inc, Jersey City, NJ, USA.
Satoshi IwasakiMitsubishi Tanabe Pharma America, Inc, Jersey City, NJ, USA.
Mark PascarellaArea 2 AI Corporation, 245 Main St, Cambridge, MA, 02142, USA.
Stephen A JohnsonDepartment of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA.
Teresa Arroyo-GallegoArea 2 AI Corporation, 245 Main St, Cambridge, MA, 02142, USA. teresa@area2.ai.
James D BerryDepartment of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative condition leading to progressive muscle weakness, atrophy, and ultimately death. Traditional ALS clinical evaluations often depend on subjective metrics, making accurate disease detection and monitoring disease trajectory challenging. To address these limitations, we developed the nQiALS toolkit, a machine learning-powered system that leverages smartphone typing dynamics to detect and track motor impairment in people with ALS. The study included 63 ALS patients and 30 age- and sex-matched healthy controls. We introduce the three core components of this toolkit: the nQiALS-Detection, which differentiated ALS from healthy typing patterns with an AUC of 0.89; the nQiALS-Progression, which separated slow and fast progression at specific thresholds with AUCs ranging between 0.65 and 0.8; and the nQiALS-Fine Motor, which identified subtle progression in fine motor dysfunction, suggesting earlier prediction than the state-of-the-art assessment. Together, these tools represent an innovative approach to ALS assessment, offering a complementary, objective metric to traditional clinical methods and which may reshape our understanding and monitoring of ALS progression.

Indexed as

Amyotrophic Lateral SclerosisDisease ProgressionSmartphoneAgedCase-Control StudiesFemaleHumansMachine LearningMaleMiddle AgedALSFRS-RAmyotrophic lateral sclerosisDigital biomarkersFine motorKeystroke dynamicsMachine learningMotor impairment

Identifiers

PMID39039102
PMCPMC11263713

What OpenQuestion holds

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Registered trials

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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.