Evidence map›Paper›PMID 35546606›Full record

SynthesisScientific reports2022

Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis.

Hessa Alfalahi, Ahsan H Khandoker, Nayeefa Chowdhury, Dimitrios Iakovakis, Sofia B Dias, K Ray Chaudhuri, Leontios J Hadjileontiadis

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 2 pooled it
–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

35 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Smartphone-Based Hand Function Assessment: Systematic Review.Journal of medical Internet research · 2024
    Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Observational
  15. Article
  16. Article
  17. Article
  18. Article
  19. Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  20. 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
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

7 authors.

Hessa AlfalahiDepartment of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates. hessa.alfalahi@ku.ac.ae.
Ahsan H KhandokerDepartment of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
Nayeefa ChowdhuryDepartment of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
Dimitrios IakovakisDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
Sofia B DiasDepartment of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
K Ray ChaudhuriParkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, United Kingdom.
Leontios J HadjileontiadisDepartment of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I

Indexed as

Alzheimer DiseaseCognitive DysfunctionParkinson DiseaseBiomarkersFingersHumansMotor SkillsSensitivity and SpecificityBiomarkers

Identifiers

PMID35546606
PMCPMC9095860

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.