Agreeableness and Conscientiousness promote successful adaptation to the Covid-19 pandemic through effective internalization of public health guidelines

Social distancing (SD) was an effective way of reducing virus transmission during the deadly and highly infectious COVID-19 pandemic. Using a prospective longitudinal design, the present study explored how the Big 5 traits relate to variations in SD in a sample of university students (n = 285), and replicated these findings using informant reports. Self-determination theory's concepts of autonomous motivation and intrinsic community values were explored as potential mechanisms linking traits to SD. Individuals who were higher on trait agreeableness and conscientiousness engaged in more SD because they more effectively internalized the importance and value of the guidelines as a function of their concerns about the welfare of their communities. Informant reports confirmed trait agreeableness and conscientiousness to be associated with more SD. These results enhance our understanding of individual differences associated with better internalization and adherence to public health guidelines and can inform future interventions in similar crises.

Predicting disordered gambling across adolescence and young adulthood from polygenic contributions to Big 5 personality traits in a UK birth cohort

BACKGROUND AND AIMS

Previous research has demonstrated phenotypical associations between disordered gambling (DG) and Big 5 personality traits, and a twin study suggested that shared genetic influences accounted for a substantial portion of this relation. The present study examined associations between DG and polygenic scores (PSs) for Big 5 traits to measure the shared genetic underpinnings of Big 5 personality traits and DG.

DESIGN

Zero-inflated negative binomial regression models estimated associations between Big 5 PSs and past-year and life-time assessments of DG in a longitudinally assessed population-based birth cohort.

SETTING

United Kingdom.

PARTICIPANTS

A total of 4729 unrelated children of European ancestry from the Avon Longitudinal Study of Parents and Children (ALSPAC) with both phenotypical and genetic data.

MEASUREMENTS

Phenotypical outcomes included past-year assessment of DG using the problem gambling severity index (PGSI) and life-time assessment of DSM-IV pathological gambling symptoms (DPG) across the ages of 17, 20 and 24 years. Polygenic scores were derived for the Big 5 personality traits of agreeableness, extraversion, conscientiousness, openness and neuroticism using summary statistics from genome-wide association studies (GWAS).

FINDINGS

PSs for agreeableness [β= - 0.25, standard error (SE) = 0.054, P = 3.031e-6, ΔR²  = 0.008] and neuroticism (β=0.14, SE = 0.046, P = 0.0017, ΔR²  = 0.002) significantly predicted PGSI scores over and above included covariates (i.e. sex and first five ancestral principal components). PSs for agreeableness (β= - 0.20, SE = 0.056, P = 0.00036, ΔR²  = 0.003) and neuroticism, when interactions with age were taken into account (β = 0.29, SE = 0.090, P = 0.002, ΔR²  = 0.004), also predicted DPG scores.

CONCLUSIONS

Polygenic contributions to low agreeableness and high neuroticism appear to predict two measures of disordered gambling (problem gambling severity index and life-time assessment of DSM-IV pathological gambling symptoms). Polygenic scores for neuroticism interact with age to suggest that the positive association becomes stronger from adolescence through young adulthood.

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

Background

Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions.

Objective

The purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets.

Methods

In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets.

Results

Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found.

Conclusions

Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.