Facial Expressions Track Depressive Symptoms in Old Age.
Hairin KimSeyul KwakSo Young YooEui Chul LeeSoowon ParkHyunwoong KoMinju BaeMyogyeong SeoGieun NamJun-Young LeePublished in: Sensors (Basel, Switzerland) (2023)
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.
Keyphrases
- depressive symptoms
- social support
- soft tissue
- sleep quality
- cognitive impairment
- physical activity
- mental health
- magnetic resonance imaging
- computed tomography
- mass spectrometry
- magnetic resonance
- poor prognosis
- autism spectrum disorder
- bipolar disorder
- drug induced
- atomic force microscopy
- single molecule
- psychometric properties