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Published on: December 2, 2015
Clustering Analysis of Emotional Expression, Personality Traits, and Psychological Symptoms
Lingping Meng1,2, Mingzheng Li2,3, Xiao Sun1
1School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China.
Background:
This study examined age-related differences and interrelationships among psychological symptoms, personality traits, and emotional expression styles in a community sample of 151 participants aged 10-77 years, spanning four age groups: adolescents, young adults, middle-aged adults, and older adults.
Methods:
Psychological symptoms were assessed using the SCL-90, personality traits using the Big Five Inventory-2 (BFI-2), and emotional expression patterns were derived from facial expression recognition via a convolutional neural network (CNN) model. Kruskal-Wallis H tests were used to examine age-related differences. K-means cluster analysis was applied to identify emotional expression patterns, and logistic regression was used to construct a mental health risk screening model.
Results:
The young adult group (19-35 years) achieved the highest scores on the depression (M = 1.73) and anxiety (M = 1.61) dimensions, indicating a higher level of psychological distress during this life stage. Personality traits showed a significant developmental trajectory: neuroticism decreased with age (H(3) = 17.09, p < 0.001, η2 = 0.11), declining from 2.69 in the young adult group to 2.17 in the older adult group; conscientiousness increased with age (H(3) = 37.39, p < 0.001, η2 = 0.24), representing the most substantial age-related effect. K-means clustering identified three distinct emotional expression patterns: Cluster 1 was characterised by happiness, Cluster 2 by anger, disgust, and fear, and Cluster 3 by neutrality, sadness, and surprise. Cluster 2 exhibited the highest scores on neuroticism, anxiety, depression, and mood swings, and scored significantly higher than the other two clusters on interpersonal sensitivity, depression, anxiety, and hostility (p < 0.05). Mental health risk screening indicated that 26.5% of participants were classified as high-risk. Logistic regression analysis (AUC = 0.742) showed that neuroticism was the strongest predictor of elevated mental health risk (OR = 4.58), while extraversion (OR = 0.41) and conscientiousness (OR = 0.57) were significant protective factors.
Conclusions:
These findings provide exploratory evidence regarding age-related patterns of psychological symptoms and personality traits in a convenience sample and offer preliminary support for personality-based mental health risk screening. Notably, the SCL-90 was employed as a screening tool rather than for clinical diagnosis. Given the unequal age group sizes, particularly the small young adult subgroup, generalisability across the lifespan should not be assumed.
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