Facial Expressions Track Depressive Symptoms in Old Age.

depressive symptoms digital biomarker emotion facial expression facial muscle late-life depression

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
10 Aug 2023
Historique:
received: 14 03 2023
revised: 17 07 2023
accepted: 20 07 2023
medline: 28 8 2023
pubmed: 26 8 2023
entrez: 26 8 2023
Statut: epublish

Résumé

Facial expressions play a crucial role in the diagnosis of mental illnesses characterized by mood changes. The Facial Action Coding System (FACS) is a comprehensive framework that systematically categorizes and captures even subtle changes in facial appearance, enabling the examination of emotional expressions. In this study, we investigated the association between facial expressions and depressive symptoms in a sample of 59 older adults without cognitive impairment. Utilizing the FACS and the Korean version of the Beck Depression Inventory-II, we analyzed both "posed" and "spontaneous" facial expressions across six basic emotions: happiness, sadness, fear, anger, surprise, and disgust. Through principal component analysis, we summarized 17 action units across these emotion conditions. Subsequently, multiple regression analyses were performed to identify specific facial expression features that explain depressive symptoms. Our findings revealed several distinct features of posed and spontaneous facial expressions. Specifically, among older adults with higher depressive symptoms, a posed face exhibited a downward and inward pull at the corner of the mouth, indicative of sadness. In contrast, a spontaneous face displayed raised and narrowed inner brows, which was associated with more severe depressive symptoms in older adults. These findings suggest that facial expressions can provide valuable insights into assessing depressive symptoms in older adults.

Identifiants

pubmed: 37631616
pii: s23167080
doi: 10.3390/s23167080
pmc: PMC10459725
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Research Foundation of Korea
ID : NRF-2017R1D1A1A02018479.

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Auteurs

Hairin Kim (H)

Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul 07061, Republic of Korea.

Seyul Kwak (S)

Department of Psychology, Pusan National University, Busan 46241, Republic of Korea.

So Young Yoo (SY)

Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul 07061, Republic of Korea.

Eui Chul Lee (EC)

Department of Human-Centered Artificial Intelligence, Sangmyung University, Hongjimun 2-Gil 20, Jongno-Gu, Seoul 03016, Republic of Korea.

Soowon Park (S)

Division of Teacher Education, College of General Education for Truth, Sincerity and Love, Kyonggi University, Suwon 16227, Republic of Korea.

Hyunwoong Ko (H)

Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul 06355, Republic of Korea.
Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Republic of Korea.

Minju Bae (M)

Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Republic of Korea.

Myogyeong Seo (M)

Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul 07061, Republic of Korea.

Gieun Nam (G)

Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul 07061, Republic of Korea.

Jun-Young Lee (JY)

Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul 07061, Republic of Korea.

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