Evidence map›Paper›PMID 41433269›Full record

ArticlePloS one2025

Reliability, validity, and correlates of an AI voice emotion recognition app among nurses.

Chu-Ying Huang, Wen-Pei Chang

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Article in PloS one, 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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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Chu-Ying HuangTaiwan Association of Technological and Economic Cooperation, Taoyuan City, Taiwan.
Wen-Pei ChangSchool of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan.ORCID https://orcid.org/0000-0002-4906-2136

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital tools are increasingly widespread in healthcare, particularly in the fields of emotion recognition and mental health assessment.

objectivesThis study evaluated whether an artificial intelligence (AI) voice emotion recognition (VER) app could identify nurses' emotions and explored its associations with their background and health conditions.

methodsThe emotions of 349 clinical nurses at a medical center in northern Taiwan were analyzed using an AI VER app and several standardized psychological questionnaires. To control for potential confounding variables, demographic and health-related factors including age, gender, work experience, exercise habits, and history of physical symptoms were collected and statistically adjusted in correlation analyses. Convergent validity was tested with Pearson's correlations, and test-retest reliability was evaluated in 30 nurses using intraclass correlation coefficients (ICCs).

resultsSignificant correlations were observed between app-derived emotions and standard scales (anger: Novaco Anger Inventory-Short Form, r = .42; fear: Perceived Stress Scale, r = .41; happiness: Oxford Happiness Questionnaire, r = .45; and sadness: Beck Depression Inventory-II, r = .47; all p < .001). Multiple regression identified significant lifestyle and health predictors of emotions: less exercise predicted higher anger (β = .11, p = .025), peptic ulcers predicted greater fear (β = .19, p < .001), daily coffee predicted higher happiness (β = .11, p = .041), and irregular menstrual cycles predicted lower happiness (β = -.13, p = .014) and greater sadness (β = .30, p < .001). The AI VER app demonstrated good test-retest reliability (ICC = 0.73-0.80).

conclusionPeptic ulcers, irregular menstrual cycles, and lack of exercise were associated with negative emotions such as fear, sadness, and anger. The AI VER app could objectively detect these emotional patterns in nurses, helping to identify emotional fluctuations early and support timely mental healthcare.

Indexed as

Artificial IntelligenceEmotionsMobile ApplicationsNursesVoiceAdultFemaleHumansMaleMiddle AgedReproducibility of ResultsSurveys and QuestionnairesTaiwan

Identifiers

PMID41433269
PMCPMC12725749

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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.