Dataset Information

The Role of Personality in Becoming Aware of Age-Related Changes.

ABSTRACT: Awareness of age-related change (AARC) refers to an individual's conscious knowledge about the gains and losses resulting from growing older. Personality traits reflect dispositional patterns of behavior, perception, and evaluation and should therefore influence the experience of AARC. The 4.5-year longitudinal study examines this association between personality traits and AARC in a sample of 423 individuals aged 40 to 98 years (M = 62.9 years) using latent change analyses. After controlling for sex, health, and education, a different pattern of associations for cross-sectional vs. longitudinal relations. Cross-sectionally, neuroticism was positively related to AARC losses, whereas openness, conscientiousness, and neuroticism were positively related to AARC gains. Longitudinally, the impact of personality traits on change in AARC was rather limited with only higher conscientiousness acting as a predictor of decreases in AARC losses over time. Overall, the findings add to the existing literature on associations between personality traits and subjective aging. Specifically, the results indicate that personality traits are differentially related to awareness of age-related gains in comparison to awareness of age-related losses.

SUBMITTER: Rupprecht FS

PROVIDER: S-EPMC7194201 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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The Role of Personality in Becoming Aware of Age-Related Changes.

Rupprecht Fiona S FS Dutt Anne J AJ Wahl Hans-Werner HW Diehl Manfred K MK

GeroPsych 20190527 2

Awareness of age-related change (AARC) refers to an individual's conscious knowledge about the gains and losses resulting from growing older. Personality traits reflect dispositional patterns of behavior, perception, and evaluation and should therefore influence the experience of AARC. The 4.5-year longitudinal study examines this association between personality traits and AARC in a sample of 423 individuals aged 40 to 98 years (<i>M</i> = 62.9 years) using latent change analyses. After controll ...[more]

PMID: 32362819

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Project description:Awareness of Age-Related Change (AARC) describes to what extent people become aware of changes which they attribute to getting older. So far little is known regarding how different AARC dimensions change over time, to what extent these changes in different domains of AARC gains and losses are interrelated, and which predictors account for inter-individual differences in within-person longitudinal trajectories. Specifically, the extent to which individuals perceive age-related gains and losses might be shaped by their chronological age, their personality as well as by their general views on aging (i.e., their age stereotypes). We investigated changes in global and domain-specific AARC gains and losses over about five years in a sample of originally N = 423 participants aged 40 to 98 years at baseline. We analyzed the role of personality traits and age stereotypes for levels and changes of AARC, taking into account participants' age at baseline and controlling for gender, education, and subjective health. Based on longitudinal multilevel regression models, we observed mean-level declines in most AARC gain domains. In contrast, perceived general AARC losses, as well as AARC losses in health and physical functioning, in cognitive functioning and in social-cognitive/socio-emotional functioning remained, on average, stable over time. Baseline scores on AARC gains (global scale) were higher among individuals with higher neuroticism, openness, conscientiousness and more positive age stereotypes. Additionally, the association of higher neuroticism with higher AARC gain scores was stronger among individuals with more positive age stereotypes. Higher neuroticism and more negative age stereotypes also predicted higher baseline scores on AARC losses (global scale). At the same time, higher neuroticism was associated with a steeper decrease in AARC loss perceptions over time. Most of the intercorrelations within the intercepts and within the intra-individual trajectories of the different AARC domains were positive, but small in size. Our findings show the importance of considering trajectories of age-related gains and losses in parallel and across multiple developmental domains when investigating the subjective perception of the aging process. They also suggest that personality traits and general age stereotypes are related with individual experiences of aging.

Project description:Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in developed countries. Identifying patients at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Recently, deep-learning-based models have been developed and achieved superior performance for late AMD prediction. However, most existing methods are limited to the color fundus photography (CFP) from the last ophthalmic visit and do not include the longitudinal CFP history and AMD progression during the previous years' visits. Patients in different AMD subphenotypes might have various speeds of progression in different stages of AMD disease. Capturing the progression information during the previous years' visits might be useful for the prediction of AMD progression. In this work, we propose a Contrastive-Attention-based Time-aware Long Short-Term Memory network (CAT-LSTM) to predict AMD progression. First, we adopt a convolutional neural network (CNN) model with a contrastive attention module (CA) to extract abnormal features from CFPs. Then we utilize a time-aware LSTM (T-LSTM) to model the patients' history and consider the AMD progression information. The combination of disease progression, genotype information, demographics, and CFP features are sent to T-LSTM. Moreover, we leverage an auto-encoder to represent temporal CFP sequences as fixed-size vectors and adopt k-means to cluster them into subphenotypes. We evaluate the proposed model based on real-world datasets, and the results show that the proposed model could achieve 0.925 on area under the receiver operating characteristic (AUROC) for 5-year late-AMD prediction and outperforms the state-of-the-art methods by more than 3%, which demonstrates the effectiveness of the proposed CAT-LSTM. After analyzing patient representation learned by an auto-encoder, we identify 3 novel subphenotypes of AMD patients with different characteristics and progression rates to late AMD, paving the way for improved personalization of AMD management. The code of CAT-LSTM can be found at GitHub.

Dataset Information

The Role of Personality in Becoming Aware of Age-Related Changes.

Publications

The Role of Personality in Becoming Aware of Age-Related Changes.

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