Evidence map›Paper›PMID 37759106›Full record

ArticleBehavior research methods2024

Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration.

Adolfo M García, Fernando Johann, Raúl Echegoyen, Cecilia Calcaterra, Pablo Riera, Laouen Belloli, Facundo Carrillo

Abstract read
In one paragraph

Article in Behavior research methods, 2024. 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
  2. Article
  3. Article
  4. Article
  5. The include network: Advancing cross-linguistic equity in brain health research.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  6. Benchmarking speech biomarkers of Alzheimer's against cognitive and neural measures.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  7. The landscape of dementia research, diagnosis, treatment, and care in Latin America.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Review
  8. Article
  9. Review
  10. The two faces of elderspeak.NPJ dementia · 2026
    Review
  11. Observational
  12. Cognitive Phenotyping of Parkinson's Disease Patients Via Digital Analysis of Spoken Word Properties.Movement disorders : official journal of the Movement Disorder Society · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Gaps in biomedical research in frontotemporal dementia: A call for diversity and disparities focused research.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Review
  18. Review
  19. Review
  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

7 authors.

Adolfo M GarcíaGlobal Brain Health Institute, University of California, 505 Parnassus Ave, San Francisco, CA, 94143, USA. adolfo.garcia@gbhi.org.
Fernando JohannCognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina.
Raúl EchegoyenCognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina.
Cecilia CalcaterraCognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina.
Pablo RieraInstituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina.
Laouen BelloliInstituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina.
Facundo CarrilloInstituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina.

Funding

TDP-43 Loss-of-Function: Biology to BiomarkersP01AG019724 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Jennifer Merrilees · 2002 to 2026
$67.2M
An automated machine learning approach to language changes in Alzheimer’s disease and frontotemporal dementia across Latino and English-speaking populationsR01AG075775 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MARIA LUISA GORNO TEMPINI, Adolfo Martin Garcia · 2023 to 2026
$7.2M
US-South American Initiative for Genetic-Neural-Behavioral Interactions in Human Neurodegenerative ResearchR01AG057234 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Claudia Duran-Aniotz, Agustin M. Ibanez · 2019 to 2026
$6.1M
NIA NIH HHS P01 AG019724NIA NIH HHS R01 AG057234NIA NIH HHS R01 AG075775
6 · The paper itself

Abstract

Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL's current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here:  https://demo.sci.tellapp.org/ .

Indexed as

Mobile ApplicationsNeurodegenerative DiseasesHumansLanguageSpeechAutomated toolsMulticentric researchNeurodegenerative diseasesSpeech and languageTranslational science

Identifiers

PMID37759106
PMCPMC11200269

What OpenQuestion holds

Textmetadata
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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.