Evidence map›Paper›PMID 32212384›Full record

ArticleAutism research : official journal of the International Society for Autism Research2020

A Six-Minute Measure of Vocalizations in Toddlers with Autism Spectrum Disorder.

Elena J Tenenbaum, Kimberly L H Carpenter, Maura Sabatos-DeVito, Jordan Hashemi, Saritha Vermeer, Guillermo Sapiro, Geraldine Dawson

Open access · greenAbstract read
In one paragraph

Article in Autism research : official journal of the International Society for Autism Research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.5field-weighted citation impact, top 19% of its field
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

11 citing papers in PubMed, 21 citations in OpenAlex.

  1. Neurobehavioral Assessment of Sensorimotor Function in Autism Using Smartphone Technology.Autism research : official journal of the International Society for Autism Research · 2026
    Article
  2. Article
  3. Distinct early development trajectories in Nf1Journal of neurodevelopmental disorders · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism.Autism research : official journal of the International Society for Autism Research · 2021
    Article
  11. 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 at 1 institution in 1 country.

Elena J TenenbaumDuke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA.ORCID 0000-0002-0973-142X
Kimberly L H CarpenterDuke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA.
Maura Sabatos-DeVitoDuke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA.
Jordan HashemiDuke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA.
Saritha VermeerDuke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA.
Guillermo SapiroElectrical and Computer Engineering, Duke University, Durham, North Carolina, USA.
Geraldine DawsonDuke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA.
Duke University · US

Funding

Recruitment and Assessment CoreP50HD093074 · NICHD · DUKE UNIVERSITY · PI Geraldine Dawson · 2017 to 2026
$27.6M
Novel Approaches to Infant Screening for ASD in Pediatric Primary CareR01MH121329 · NIMH · DUKE UNIVERSITY · PI DAWSON, GERALDINE · 2019 to 2023
$3.9M
Scalable Computational Platform For Active Closed-Loop Behavioral Coding in Autism Spectrum DisorderR01MH120093 · NIMH · DUKE UNIVERSITY · PI DAWSON, GERALDINE, SAPIRO, GUILLERMO R · 2019 to 2022
$1.6M
SCH: INT: Computational Tools for Avoidaint/Restrictive Food Intake Disorder R01MH122370 · NIMH · DUKE UNIVERSITY · PI SAPIRO, GUILLERMO R, ZUCKER, NANCY L · 2019 to 2021
$860k
Eunice Kennedy Shriver National Institute of Child Health and Human Development P50HD093074NICHD NIH HHS P50 HD093074NIMH NIH HHS R01 MH120093NIMH NIH HHS R01 MH121329NIMH NIH HHS R01 MH122370
6 · The paper itself

Abstract

To improve early identification of autism spectrum disorder (ASD), we need objective, reliable, and accessible measures. To that end, a previous study demonstrated that a tablet-based application (app) that assessed several autism risk behaviors distinguished between toddlers with ASD and non-ASD toddlers. Using vocal data collected during this study, we investigated whether vocalizations uttered during administration of this app can distinguish among toddlers aged 16-31 months with typical development (TD), language or developmental delay (DLD), and ASD. Participant's visual and vocal responses were recorded using the camera and microphone in a tablet while toddlers watched movies designed to elicit behaviors associated with risk for ASD. Vocalizations were then coded offline. Results showed that (a) children with ASD and DLD were less likely to produce words during app administration than TD participants; (b) the ratio of syllabic vocalizations to all vocalizations was higher among TD than ASD or DLD participants; and (c) the rates of nonsyllabic vocalizations were higher in the ASD group than in either the TD or DLD groups. Those producing more nonsyllabic vocalizations were 24 times more likely to be diagnosed with ASD. These results lend support to previous findings that early vocalizations might be useful in identifying risk for ASD in toddlers and demonstrate the feasibility of using a scalable tablet-based app for assessing vocalizations in the context of a routine pediatric visit. LAY SUMMARY: Although parents often report symptoms of autism spectrum disorder (ASD) in infancy, we are not yet reliably diagnosing ASD until much later in development. A previous study tested a tablet-based application (app) that recorded behaviors we know are associated with ASD to help identify children at risk for the disorder. Here we measured how children vocalize while they watched the movies presented on the tablet. Children with ASD were less likely to produce words, less likely to produce speechlike sounds, and more likely to produce atypical sounds while watching these movies. These measures, combined with other behaviors measured by the app, might help identify which children should be evaluated for ASD. Autism Res 2020, 13: 1373-1382. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

Indexed as

SpeechAutism Spectrum DisorderChild, PreschoolFemaleHumansInfantMaleParentsdevelopmental psychologyearly detectionearly signsinfantslanguage

Identifiers

PMID32212384
PMCPMC7881362
OpenAlexW3013251200

What OpenQuestion holds

Textmetadata
LicenceTDM
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.