Evidence map›Paper›PMID 41063162›Full record

ArticleBMC oral health2025

Development of a non-contact screening approach for identifying oral function high-risk older adults using jaw movement and diadochokinetic performance.

Chen-Fu Hung, Min Hsu, Hsiu-Yueh Liu, Mong-Fong Horng, Chia-Chi Yang, Lan-Yuen Guo

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Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chen-Fu HungPh. D. Program in Biomedical Engineering, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Min HsuDepartment of Sports Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Hsiu-Yueh LiuDepartment of Oral Hygiene, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Mong-Fong HorngDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan.
Chia-Chi YangMaster Program of Long-Term Care in Aging, College of Nursing, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Lan-Yuen GuoPh. D. Program in Biomedical Engineering, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan. yuen@kmu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying older adults at high risk of progressive oral functional decline is a critical public-health priority, as early decline often manifests initially as subtle swallowing difficulties. Standard diagnostic tools (e.g., VFSS, FEES) are invasive, whereas most existing screening methods assess only a single functional dimension. Therefore, this study aimed to develop and validate a fully non-invasive, multidimensional screening approach that integrates high-resolution video-based jaw-movement analysis with diadochokinetic (DDK) performance to enable the early detection of older adults at risk.

methodEighty-nine participants aged 65-95 years were recruited from community care stations in Taiwan. Participants were classified as high risk for oral functional decline if they performed fewer than three swallows within 30 s on the repetitive saliva swallowing test (RSST). The assessment included DDK performance and jaw movement analysis using video tracking. Logistic regression was used to identify significant predictors, followed by receiver operating characteristic (ROC) curve analysis to evaluate diagnostic performance.

resultsAmong the 89 participants (mean age = 77.6 years; 22 men), 41 (46%) met the RSST high-risk criterion. Female participants exhibited significantly faster jaw-movement velocities compared to male participants (p < 0.05). Multivariate logistic regression confirmed age, DDK performance, and jaw-kinematic variables as independent predictors of high-risk status. A combined model of key DDK measures and average jaw-closing angular velocity achieved optimal discrimination (AUC = 0.83) and high sensitivity (0.95), outperforming individual predictors.

conclusionThis study presents a practical, cost-effective, and non-contact screening method for early identification of oral function high risk in older adults. Integrating jaw movement with DDK performance offers high sensitivity, supporting potential use in early clinical interventions and community-based screening programs.

trial registrationISRCTN11010971 (https//www.isrctn.com/ISRCTN11010971). Registered on 17 April 2025.

Indexed as

Deglutition DisordersGeriatric AssessmentJawMass ScreeningAgedAged, 80 and overDeglutitionFemaleHumansMaleMovementTaiwanVideo RecordingJaw movementNon-invasive screeningOlder adultsOral functionOral motor function

Identifiers

PMID41063162
PMCPMC12509351

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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.