Evidence map›Paper›PMID 39870826›Full record

ArticleNPJ digital medicine2025

A systematic review of portable technologies for the early assessment of motor development in infants.

Weiyang Deng, Megan K O'Brien, Rachel A Andersen, Richa Rai, Erin Jones, Arun Jayaraman

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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  6. The future of follow-up: technology-enabled medical and developmental follow-up care for NICU graduates.Journal of perinatology : official journal of the California Perinatal Association · 2026
    Review
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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

6 authors.

Weiyang Deng *Technology & Innovation Hub, Shirley Ryan AbilityLab, Chicago, IL, USA.
Megan K O'Brien *Technology & Innovation Hub, Shirley Ryan AbilityLab, Chicago, IL, USA.ORCID http://orcid.org/0000-0001-8069-365X
Rachel A AndersenTechnology & Innovation Hub, Shirley Ryan AbilityLab, Chicago, IL, USA.
Richa RaiTechnology & Innovation Hub, Shirley Ryan AbilityLab, Chicago, IL, USA.
Erin JonesTechnology & Innovation Hub, Shirley Ryan AbilityLab, Chicago, IL, USA.
Arun JayaramanTechnology & Innovation Hub, Shirley Ryan AbilityLab, Chicago, IL, USA. ajayaraman@sralab.org.ORCID http://orcid.org/0000-0002-9302-6693

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early screening and evaluation of infant motor development are crucial for detecting motor deficits and enabling timely interventions. Traditional clinical assessments are often subjective, without fully capturing infants' "real-world" behavior. This has sparked interest in portable, low-cost technologies to objectively and precisely measure infant motion at home, with a goal of enhancing ecological validity. In this systematic review, we explored the current landscape of portable, technology-based solutions to assess early motor development (within the first year), outlining the prevailing challenges and future directions. We reviewed 66 publications, which utilized video, sensors, or a combination of technologies. There were three key applications of these technologies: (1) automating clinical assessments, (2) illuminating new measures of motor development, and (3) predicting developmental outcomes. There was a promising trend toward earlier and more accurate detection using portable technologies. Additional research and demographic diversity are needed to develop fully automated, robust, and user-friendly tools. Registration & Protocol OSF Registries https://doi.org/10.17605/OSF.IO/R6JAE .

Identifiers

PMID39870826
PMCPMC11772671

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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.