Evidence map›Paper›PMID 36506490›Full record

ArticleDigital health

Associations between smartphone keystroke dynamics and cognition in MS.

Michelle H Chen, Alex Leow, Mindy K Ross, John DeLuca, Nancy Chiaravalloti, Silvana L Costa, Helen M Genova, Erica Weber, Faraz Hussain, Alexander P Demos

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
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  11. Unobtrusive inference of circadian rhythms from smartphone data.medRxiv : the preprint server for health sciences · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2024
    Review
  19. Article
  20. Article
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

10 authors.

Michelle H ChenInstitute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA.ORCID https://orcid.org/0000-0002-5689-0044
Alex LeowDepartment of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
Mindy K RossDepartment of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
John DeLucaKessler Foundation, East Hanover, NJ, USA.
Nancy ChiaravallotiKessler Foundation, East Hanover, NJ, USA.ORCID https://orcid.org/0000-0003-2943-7567
Silvana L CostaKessler Foundation, East Hanover, NJ, USA.
Helen M GenovaKessler Foundation, East Hanover, NJ, USA.
Erica WeberKessler Foundation, East Hanover, NJ, USA.
Faraz HussainDepartment of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
Alexander P DemosDepartment of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Examine the associations between smartphone keystroke dynamics and cognitive functioning among persons with multiple sclerosis (MS). Methods: Sixteen persons with MS with no self-reported upper extremity or typing difficulties and 10 healthy controls (HCs) completed six weeks of remote monitoring of their keystroke dynamics (i.e., how they typed on their smartphone keyboards). They also completed a comprehensive neuropsychological assessment and symptom ratings about fatigue, depression, and anxiety at baseline. Results: A total of 1,335,787 keystrokes were collected, which were part of 30,968 typing sessions. The MS group typed slower ( Conclusion: Typing speed may be sensitive to cognitive functions subserved by the frontal-subcortical brain circuits. Individuals with better cognitive functioning and less severe symptoms may be better at monitoring their typing errors. Keystroke dynamics have the potential to be used as an unobtrusive remote monitoring method for real-life cognitive functioning among persons with MS, which may improve the detection of relapses, evaluate treatment efficacy, and track disability progression.

Indexed as

digital phenotypingmHealthMultiple sclerosispassive monitoringtyping

Identifiers

PMID36506490
PMCPMC9730018

What OpenQuestion holds

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Registered trials

None linked

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.