Changes in depressive symptoms during the COVID-19 pandemic differ by personality type: Findings from The Irish Longitudinal Study on Ageing

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Methods Data from 5 waves of The Irish Longitudinal Study on Ageing (TILDA), including a COVID-19 specific sub-study were included.. Linear mixed effects models fit by maximum likelihood examined personality traits as potential sources of heterogeneity in changes in depressive symptoms associated with the COVID-19 pandemic occurring over time. Results Participants (n=3,404, 56.7% female) were aged 50 years and older. In the COVID-19 Wave, depressive symptoms were 0.29 points higher (b=0.29, 95%CI: 0.16–0.42; p<0.001) per 1-SD increase in neuroticism, 0.12 points higher (b=0.12. 95%CI: 0.00–0.24; p=0.045) per 1-SD increase in extraversion, and 0.14 points lower (b=-0.14, 95%CI: -0.25–-0.03; p=0.014) per 1-SD increase in openness than would have been expected from the trends observed before the pandemic. Conclusions Depressive symptoms were significantly higher during COVID-19 compared to what would have been expected from the trends observed prior to the pandemic. People who scored higher in neuroticism and extraversion, and lower on openness, reported the greatest increases in depressive symptoms. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://hrbopenresearch.org/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://hrbopenresearch.org/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://hrbopenresearch.org/articles/8-42", "name": "Changes in depressive symptoms during the COVID-19 pandemic differ..." } } ] } Home Browse Changes in depressive symptoms during the COVID-19 pandemic differ... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article O'Maoileidigh B, McDowell C, McCrory C et al. Changes in depressive symptoms during the COVID-19 pandemic differ by personality type: Findings from The Irish Longitudinal Study on Ageing [version 1; peer review: 1 approved with reservations] . HRB Open Res 2025, 8 :42 ( https://doi.org/10.12688/hrbopenres.14031.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Changes in depressive symptoms during the COVID-19 pandemic differ by personality type: Findings from The Irish Longitudinal Study on Ageing [version 1; peer review: 1 approved with reservations] Brendan O'Maoileidigh https://orcid.org/0000-0002-7816-2152 1 , Cillian McDowell 1 , Cathal McCrory 1 , Rose Anne Kenny https://orcid.org/0000-0002-9336-8124 1 , Celine DeLooze https://orcid.org/0000-0003-4654-8357 1 , Mark Ward https://orcid.org/0000-0001-6309-4866 1 Brendan O'Maoileidigh https://orcid.org/0000-0002-7816-2152 1 , Cillian McDowell 1 , [...] Cathal McCrory 1 , Rose Anne Kenny https://orcid.org/0000-0002-9336-8124 1 , Celine DeLooze https://orcid.org/0000-0003-4654-8357 1 , Mark Ward https://orcid.org/0000-0001-6309-4866 1 PUBLISHED 20 Mar 2025 Author details Author details 1 Medical Gerontology (The Irish Longitudinal Study on Ageing), Trinity College Dublin School of Medicine, Dublin, Please select, Dublin 2, Ireland Brendan O'Maoileidigh Roles: Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Cillian McDowell Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Cathal McCrory Roles: Data Curation, Funding Acquisition, Resources, Supervision Rose Anne Kenny Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Resources, Supervision Celine DeLooze Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Software, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Mark Ward Roles: Conceptualization, Investigation, Methodology, Resources, Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the TILDA gateway. This article is included in the Coronavirus (COVID-19) collection. This article is included in the Ageing Populations collection. Abstract Background This study examined the Big 5 personality traits as potential sources of heterogeneity in changes in depressive symptoms while accounting for pre-pandemic trends in depressive symptoms. Methods Data from 5 waves of The Irish Longitudinal Study on Ageing (TILDA), including a COVID-19 specific sub-study were included.. Linear mixed effects models fit by maximum likelihood examined personality traits as potential sources of heterogeneity in changes in depressive symptoms associated with the COVID-19 pandemic occurring over time. Results Participants (n=3,404, 56.7% female) were aged 50 years and older. In the COVID-19 Wave, depressive symptoms were 0.29 points higher (b=0.29, 95%CI: 0.16–0.42; p <0.001) per 1-SD increase in neuroticism, 0.12 points higher (b=0.12. 95%CI: 0.00–0.24; p =0.045) per 1-SD increase in extraversion, and 0.14 points lower (b=-0.14, 95%CI: -0.25–-0.03; p =0.014) per 1-SD increase in openness than would have been expected from the trends observed before the pandemic. Conclusions Depressive symptoms were significantly higher during COVID-19 compared to what would have been expected from the trends observed prior to the pandemic. People who scored higher in neuroticism and extraversion, and lower on openness, reported the greatest increases in depressive symptoms. READ ALL READ LESS Keywords Depression, Mental Health, COVID-19, Personality Corresponding Author(s) Brendan O'Maoileidigh ( [email protected] ) Close Corresponding author: Brendan O'Maoileidigh Competing interests: No competing interests were disclosed. Grant information: Health Research Board [TILDA-2023-001]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 O'Maoileidigh B et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: O'Maoileidigh B, McDowell C, McCrory C et al. Changes in depressive symptoms during the COVID-19 pandemic differ by personality type: Findings from The Irish Longitudinal Study on Ageing [version 1; peer review: 1 approved with reservations] . HRB Open Res 2025, 8 :42 ( https://doi.org/10.12688/hrbopenres.14031.1 ) First published: 20 Mar 2025, 8 :42 ( https://doi.org/10.12688/hrbopenres.14031.1 ) Latest published: 20 Mar 2025, 8 :42 ( https://doi.org/10.12688/hrbopenres.14031.1 ) Background There is an established body of research showing that many health outomes vary by personality type ( Friedman & Kern, 2014 ; Fry & Debats, 2009 ; Luchetti et al. , 2016 ). Much of this research on personality and health operationalises personality using the the ‘Big 5’ model that proposes five disctinct personality types: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In terms of the health outcomes studied, research on depression among older adults specifically, found that both the presence of a depression diagnosis and severity of depressive symptomology were associated with higher neuroticism, and lower extraversion and conscientiousness ( Koorevaar et al. , 2013 ). A review by Klein et al. (2011) showed that depression was associated with neuroticism/negative emotionality, extraversion/positive emotionality, and conscientiousness. Importantly, their review of different models that have been proposed to explain the association between personality and depression also suggests that personality type contributes to both the onset and course of depression. The COVID-19 pandemic provides a unique context within which to re-examine the association between personality and depression. The Irish Longitudinal Study on Ageing has shown that the prevalence of clinically-significant depressive symptoms increased threefold among older adults during the pandemic meaning there was a stark increase in a very short period of time that cannot be explained solely by typical risk factors such as the loss of a spouse or functional decline ( Briggs et al. , 2018 ; Ward et al. , 2023 ). Much of the increase observed during the pandemic was due to loneliness brought on by public health strategies employed to limit the spread of the SARS-CoV-2 virus that profoundly impacted many aspects of daily life and at times severely restricted social contacts ( Ward et al. , 2023 ). These restrictions were keenly felt by older adults whom, along with other at-risk groups, were required to stay at home for long periods of time to control case numbers and relieve pressure on health services. This sudden and dramatic impact of the pandemic on people's everyday lives constituted a stressful event that led to increased psychological distress, poorer self‐reported mental health, and increased depression and loneliness. ( Armitage & Nellums, 2020 ; Bailey et al. , 2021 ; Ettman et al. , 2020 ; Green et al. , 2021 ; Groarke et al. , 2021 ; Hwang et al. , 2020 ; Krendl & Perry, 2021 ; Pierce et al. , 2020 ; Ward et al. , 2021 ; Wu, 2020 ). In this context, the pandemic provides a unique context within which to re-examine the role of personality type in accounting for variation in the progression of depressive symptomology. In response to the dramatic changes experienced during the pandemic, a number of recent studies have examined associations between personality and mental health during the pandemic. These found that higher neuroticism was associated with worse mental health and more negative appraisals of the pandemic ( Kroencke et al. , 2020 ; Li et al. , 2022 ; Liu et al. , 2021 ; Modersitzki et al. , 2021 ; Mourelatos, 2023 ; Shokrkon & Nicoladis, 2021 ; Wang et al. , 2023 ). However, findings regarding the relationship between extraversion and depressive symptoms have been mixed with some studies providing evidence of a positive relationship, ( Liu et al. , 2021 ; Modersitzki et al. , 2021 ) and some a negative relationship between extraversion and depressive symptoms ( Shokrkon & Nicoladis, 2021 ). Finally, higher openness has tended to be associated with less negative appraisals of the pandemic. ( Modersitzki et al. , 2021 ). Specific to an older population, findings from a study examining the relationships between personality types and depression among adults aged 60 and older living in China showed concienciousness, extraversion, and agreeableness were negatively related to depression, while neuroticism was found to be positively related to depression. Numerous other factors have also been identifed that explain differences between invdividuals and groups accumulation of mental health difficulties during this period. Among these factors were changes to physical activity behaviours ( Cindrich et al. , 2021 ; Meyer et al. , 2020 ), employment ( McDowell et al. , 2021a ), and loneliness ( McDowell et al. , 2021b ), each of which were associated with an increase in depressive symptoms during the pandemic. While many of these factors can focus on physical health and behaviours, and sociality, intrinsic individual-level characteristics or traits, including personality, may also add to our understanding of the variation observed in older adults’ psychological responses to the pandemic. Many of the above studies conducted during the pandemic share a number of important limitations. For instance, participants were mostly first recruited during the pandemic and it was therefore not possible to account for levels of depressive sympromology prior to the pandemic ( Krendl & Perry, 2021 ). Many of these studies also included convenience samples which substantially increases risk of bias and limit the generalisability of the findings ( Pierce et al. , 2020 ). To address these limitations, we use data from a large cohort of older adults first recruited in 2009 with depressive symptomology measured on multiple occasions before and once during the pandemic. Our hypothesis is that changes in depressive symptomology during the COVID-19 pandemic varied by personality type. This study aim was to investigate the Big 5 personality traits as potential sources of heterogeneity in changes in depressive symptoms associated with the COVID-19 pandemic among older adults while controlling for prior depressive symptoms and other covariates. Methods This study was done in adherence with STROBE guidelines ( Elm et al. , 2007 ). Ethical approval for the wider TILDA study was granted by the Faculty of Health Sciences Research Ethics Committee in Trinity College Dublin. TILDA adheres to the 1964 Helsinki declaration and its later amendments. Ethical approval specific to the COVID-19 study was obtained from the Irish National Research Ethics Committee COVID-19 on 17th June 2020, Application number: 20-NREC-COV-030-2. Consent to participate: Explicit informed consent was obtained from all participants in each Wave. In all Waves, participants were provided with detailed information regarding the purpose of the study, their rights as participants, and the measures taken to ensure confidentiality and data protection in advance of participation. Participation was voluntary, and participants were free to withdraw from the study at any point. TILDA adheres to the 1964 Helsinki declaration and its later amendments. In Waves 2–5, written consent was obtained. In Wave 6, consent was obtained verbally via telephone due to COVID-19 restrictions and recorded by the interviewer. TILDA adheres to the 1964 Helsinki declaration and its later amendments. Study design Data were from The Irish Longitudinal Study on Ageing (TILDA), a longitudinal study that collects information on the health, economic, and social situation of a nationally representative sample of community-dwelling adults aged 50+ in Ireland. Details of TILDA’s methodology are fully described elsewhere ( Donoghue et al. , 2018 ; Kearney et al. , 2011 ; Kenny et al. , 2010 ). The first wave of data collection was conducted between October 2009 and July 2011, and subsequent waves occurred every two years. TILDA’s COVID-19 sub-study was carried out from July to November 2020. A detailed study protocol describing all aspects of this sub-study are also available ( Ward et al. , 2021 ). Sample Participants were included in our analyses if they: participated in and were aged 50 years or older during the COVID sub-study; completed the 60-item NEO-Five Factor Inventory at Wave; and completed the eight item Centre for Epidemiological Studies Depression Scale (CESD-8) at Waves 2, 3, 4, 5, and the COVID Wave. Participants with missing covariate data (n=38; 1.1%) were subsequently excluded, resulting in an analytic sample of 3,370 ( Figure 1 ). Figure 1. Flowchart showing how the final analytic sample was arrived at. Missing data Missing data was addressed using full information maximum likelihood (FIML) estimation, so that parameter estimates were calculated using all available information, including cases with missing data on covariates ( Asparouhov & Muthen, 2010 ) Personality At Wave 2, the 60-item NEO-Five Factor Inventory (NEOFFI) questionnaire assessed neuroticism – the degree to which a person experiences the world as threatening and beyond his/her control; extraversion – which reflects positive mood, optimism, and the degree to which a person needs attention and social interaction; openness – the degree to which a person needs intellectual stimulation, change, and variety; agreeableness – the degree to which a person needs pleasant and harmonious relationships with others; and conscientiousness – which reflects planning behaviour and future orientation, and the degree to which a person is willing to comply with conventional rules, norms, and standards ( McCrae & Costa, 2004 ). Responses to each item were scored on a five-point Likert scale (‘strongly disagree’ to ‘strongly agree’), resulting in scores ranging from zero to 48 for each trait. The five-factor structure of the NEOFFI has been confirmed in numberous studies and populations ( McCrae, 2002 ). Internal consistency (α) for each of the five factors among TILDA partiicpants was: 0.84 (Neuroticism), 0.73 (Extraversion), 0.72 (Openness), 0.70 (Agreeableness), and 0.78 (Conscientiousness) ( Nolan et al. , 2019 ). To aid interpretation of regression results, scores for each trait are expressed as z-scores, meaning each trait has a mean of 0 and a standard deviation of 1. Ethical approval for the wider TILDA study was granted by the Faculty of Health Sciences Research Ethics Committee in Trinity College Dublin. TILDA adheres to the 1964 Helsinki declaration and its later amendments. Ethical approval specific to the COVID-19 study was obtained from the Irish National Research Ethics Committee COVID-19 on 17th June 2020, Application number: 20-NREC-COV-030-2. Depressive symptoms Depressive symptoms were measured at Wave 2, Wave 3, Wave 4, Wave 5, and in the COVID questionnaire, using the 8-item Center for Epidemiological Studies Depression scale (CESD-8). Each item was scored on a four-point Likert scale from none or almost none of the time (score 0) to all or almost or all of the time (score 3). CESD-8 scores ranged from zero to 24, with higher scores indicating higher depressive symptomology. The CESD-8 has been shown to be consistent, reliable, and valid for use within the TILDA cohort ( O’Halloran et al. , 2014 ). A score ≥9 was used to define cases of clinically-meaningful depressive symptoms, which has been shown to have good sensitivity and specificity ( Briggs et al. , 2018 ). Covariates Covariates measured at the same wave as personality (Wave 2) were selected based on theoretical, and/or prior empirical association with personality and/or depression. The sociodemographic charateristics included were: age (years), sex (male or female), and education level (none/primary, secondary, or tertiary education). The health behaviour variables were: smoking (never, past, or current); problem drinking, assessed using the CAGE scale (yes, no, or not reported) ( Mayfield et al. , 1974 ); and physical activity, assessed using the short-form International Physical Activity Questionnaire that measures whether individuals meet the minimum recommended levels of 150+ minutes of moderate and/or rigorous activity per week) ( Craig et al. , 2003 ; Hagströmer et al. , 2006 ). Antidepressant (ATC code N06A) use was also controlled for. Physical health covariates were number of physical limitations (continuous), cardiometabolic conditions (0, 1, or ≥2), and other chronic conditions (0, 1, ≥2). Number of physical limitations was determined by asking about, and subsequently summing, the number of difficulties with: walking, running, sitting, sit-to-stand, stair climbing, reaching overhead, stooping, kneeling crouching, lifting heavy weights, pushing or pulling large objects and picking small coins from table ( Bull et al. , 2020 ; Lee et al. , 2011 ). Cardiometabolic conditions were self-reported doctor diagnosis of angina, atrial fibrillation, diabetes, heart attack, heart failure, heart murmur, high blood pressure, high cholesterol, stroke, and transient ischemic attack. Chronic conditions were doctor diagnosis of self-reported arthritis, asthma, cancer, cirrhosis/serious liver damage, lung disease, Parkinson’s disease, osteoporosis, and thyroid problems. Analyses All analyses were conducted in Stata 17.0 ( StataCorp, 2021 ). The baseline characteristics of participants at Wave 2 are presented in Table 1 . In multivariate analyses, linear mixed effect models with maximum likelihood estimation were used to examine personality traits as potential sources of heterogeneity in changes in depressive symptoms associated with the COVID-19 pandemic while accounting for existing trends in depressive symptoms that were already occurring over time. A continuous time variable, parameterised as the number of years since data collection was included. In Model 1 (baseline model), interactions between the COVID-19 period indicator and the Big Five personality factors and their main effects were fitted along with the time variable and its squared term. In Model 2 (fully adjusted model), interactions between the COVID-19 period indicator and all covariates (age, sex, education, health behaviours and physical health measures) and their main effects were added to Model 1. Table 1. Characteristics of the study sample at Wave 2 in comparison with the excluded cohort. Study sample (n=3,404) Excluded participants (n=4316) Neurotiscism 18.0±7.5 Extraversion 28.6±5.8 Openness 28.3±6.0 Agreeableness 34.0±5.0 Consciensiousness 33.6±5.3 Age (years) 62.9±7.9 67.2±10.5 Sex Male 1,460 (43.3) 2077 (42.5) Female 1,910 (56.7) 1833(48.1) Education Primary/none 595 (17.7) 1465(37.6) Secondary 1,383 (41.1) 1487(34.5) Third/higher 1,389 (41.3) 949(24.3) Smoker Never 1,617 (48.0) 1616(41.4) Past 1,361 (40.4) 1554(39.8) Current 392 (11.6) 738(18.9) Physically active No 1,804 (53.0) 1907(48.8) Yes 1,428 (42.0) 1837(42.6) NR 172 (5.1) 166(4.3) Problem Drinking No 2791 ( 82.8) 2226(51.6) Yes 456 (13.5) 322(7.5) NR 123 (3.65) 1768(41.0) Using antidepressants 222 (6.6) 425(10.9) Chronic conditions 0 1775 (52.7) 1907(48.8) 1 1498 (44.5) 1837(42.6) ≥2 96 (2.8) 166(4.3) Cardiovascular conditions 0 2968 (88.1 3183(81.4) 1 387 (11.5) 691(16.0) ≥2 15 (0.5) 36(0.9) Physical Limitations 1.8 ± 1.9 2.6 ± 2.5 Numbers are N(%) or mean±standard deviation NR=not reported Results Participant characteristics The flowchart presented in Figure 1 shows how the final analytic sample was arrived. Participants who did not take part in the COVID-19 sub-study, did not complete the NEO Five Factor Inventory at Wave 2, did not have at least one CES-D8 measurement, and who were aged under 50 at the time of the COVID-19 sub-study were not included. Participant characteristics at baseline are presented in Table 1 . Analyses included 14,135 observations from 3,404 participants. The average age was 62.9 years and 56.7% were female. The mean personality scores for Neuroticism were 18.0±7.5; Extraversion: 28.6±5.8; Openness: 28.3±6.0; Agreeableness: 34.0±5.0; and Conscientiousness: 33.6±5.3. ( Table 1 ) Mean CESD-8 depression scores at each wave are presented in Figure 2 and were significantly higher in the COVID Wave compared to the previous three waves ( d =0.65, 95%CI: 0.61–0.69). Internal consistency (α) calculated for the current sample for the each of the five factors was 0.81 (Neuroticism), 0.72 (Extraversion), 0.78 (Openness), 0.80 (Agreeableness), and 0.77 (Conscientiousness). ( Figure 2 ) Figure 2. Mean depressive symptoms and 95% confidence intervals measured using the eight item Center of Epidemiological Studies Depression Scale (CESD-8; range: 0–24) at Wave 3 (n=3,386), Wave 4 (n=3,310), Wave 5 (n=3,199), and the COVID Wave (n=3,076). Results from the baseline model presented in Table 2 show that, in the COVID Wave, depressive symptoms were 0.32 points higher per 1-SD increase in neuroticism (b=0.32, 95%CI: 0.20–0.44; p <0.001) and 0.08 points lower per 1-SD increase in openness (b=-0.08, 95%CI: -0.27–-0.05; p =0.004) than would have been expected from the trends observed before the pandemic. Depressive symptoms did not differ by extraversion ( p =0.199), agreeableness ( p =0.368) or conscientiousness ( p =0.168) in the COVID Wave from the trends observed before the pandemic ( Table 2 , Panel A). There was no three-way interaction between the COVID-19 period indicator, neuroticism and openness ( p =0.128). ( Table 2 ) Table 2. Association between personality traits at Wave 2 and depression symptoms (Wave 2 to COVID Wave). In Model 1 (Panel A), association between depressive symptoms during COVID-19 and the Big Five personality factors (presented below) were fitted along with the time variable and its squared term. In Model 2 (Panel B),Associations between depressive symptoms during COVID-19 and all covariates at Wave 2 (age, sex, education, smoking, alcohol consumption, physical activity, physical limitations, cardiometabolic conditions and other chronic conditions) were added to Model 1. N=3,404 Model 1 Model 2 B (95% CI) P value B (95% CI) P value Neuroticism 0.32 (0.20, 0.44) <0.001 0.29 (0.16, 0.42) <0.001 Extraversion 0.08 (-0.04, 0.20) 0.199 0.12 (0.00, 0.24) 0.045 Openness -0.08 (-0.27, -0.05) 0.004 -0.14 (-0.25, -0.03) 0.014 Agreeableness -0.05 (-0.16, 0.06) 0.368 -0.10 (-0.22, 0.02) 0.102 Conscientiousness -0.09 (-0.21, 0.04) 0.168 -0.08 (-0.21, 0.02) 0.187 Age 0.04 (0.03, 0.06) <0.001 Sex Female 0.17 (-0.07, 0.41) 0.164 Education Secondary 0.09 (-0.23, 0.40) 0.584 Tertiary 0.01 (-0.32, 0.34) 0.957 Smoking Past -0.04 (-0.26, 0.19) 0.754 Current 0.61 (0.26, 0.97) 0.001 Problem alcohol -0.16 (-0.47, 0.16) 0.322 Antidepressant use 0.29 (-0.16, 0.73) 0.207 Physically active -0.01 (-0.23, 0.21) 0.944 Physical limitations -0.00 (-0.07, 0.06) 0.948 Cardiometabolic conditions 1–2 -0.13 (-0.47, 0.22) 0.467 ≥3 2.67 (0.93, 4.42) 0.003 Other chronic conditions 1 0.15 (-0.09, 0.38) 0.219 ≥2 0.86 (0.17, 1.54) 0.015 T 2 -0.02 (-0.04, -0.01) <0.001 -0.02 (-0.04, -0.01) <0.001 Constant 2.87 (2.60, 3.13) <0.001 2.79 (2.01, 3.56) <0.001 Figure 3 shows the unstandardised regression coefficients for the change in CESD-8 depression scores symptoms in the COVID Wave, per 1-SD increase in each personality trait. The full results for Model 2 which included all of the covariates are presented in Table 2 (Panel B). In the COVID Wave, depressive symptoms were 0.29 points higher (b=0.29. 95%CI: 0.29–0.42; p <0.001) per 1-SD increase in neuroticism, 0.12 points higher (b=0.12. 95%CI: 0.00–0.24; p =0.045) per 1-SD increase in extraversion, and 0.14 points lower (b=-0.14, 95%CI: -0.25–-0.03; p =0.014) per 1-SD increase in openness than would have been expected from the trends observed before the pandemic. Depressive symptoms did not differ by agreeableness ( p =0.145) or conscientiousness ( p =0.752) in the COVID Wave from the trends observed before the pandemic. There were no three- or four-way interactions between the COVID-19 period indicator, neuroticism, extraversion, and openness (all p =0.213). ( Figure 3 ) Figure 3. Results from linear mixed models (unstandardised regression coefficients and associated 95% confidence intervals) illustrating change in depressive symptoms in the COVID Wave, per 1-SD increase in each personality trait, compared to what would have been expected from the trends observed before the pandemic. * p =0.047; ** p =0.011; *** p <0.001. Discussion Taking advantage of the unique context of the COVID-19 pandemic, this study provides further prospective evidence of the association between personality and both the onset and course of depressive symptoms among older adults. In this cohort of 3,404 community-dwelling older adults in Ireland, depressive symptoms were significantly higher during COVID-19 compared to what would have been expected from the trends observed among these same individuals in the ten years prior to the pandemic. Importantly, the magnitude of this COVID-19-related increase in depressive symptoms varied according to personality type. Higher levels of neuroticism and extraversion and lower levels of openness were associated with greater increases in depressive symptoms. These findings further our understanding of the association of personality with in mental health and have important implications for identifying at-risk individuals and subsequently tailoring prevention and treatment programs. Neuroticism, the degree to which a person experiences the world as threatening and beyond his/her control, is the personality trait that has shown strongest associations with mental health outcomes in prior research. In the current study, it was also associated with the greatest increase in depressive symptoms, supporting previous evidence of its association with greater stress in response to both the threat of disease and also social restrictions ( Liu et al. , 2021 ) This is likely explained in part by the association of neuroticism with greater concerns about finances and relationships which were heightened even more than usual during the COVID‐19 pandemic ( Aschwanden et al. , 2021 ) Additional, it has also been proposed that people who score high in neuroticism may have focused more on COVID-19-related information and consequences than people who score lower in this trait ( Khosravi, 2020 ). Extraversion reflects positive mood, optimism, and the degree to which a person needs attention and social interaction. Those who score higher on extraversion typically report higher subjective well-being, as well as several of its facets (for example, happiness, life satisfaction, and quality of life) ( Steel et al. , 2008 ), although recent cross-sectional evidence showed that it was associated with greater perceived stress during COVID-19 ( Liu et al. , 2021 ). Similarly, in the current study extraversion was associated with a greater increase in depressive symptoms during COVID-19 compared to prior trends. The COVID-19 containment strategies employed in Ireland to protect the health of older adults were specifically designed to minimise in-person contact between individuals, which likely had a greater impact on people who scored higher on extraversion. Compounding this, 71% of adults aged ≥50 years had internet access throughout the pandemic and, among those who did, less than half used it for social activities like audio/video calls (43%) or social media (40%). It is therefore plausible that the COVID-19 containment strategies had a greater impact on loneliness and social isolation, two well-established risk factors for depressive symptoms ( Domènech-Abella et al. , 2019 ) among those with higher extraversion by inhibiting their need for social engagement. Openness, the degree to which a person needs intellectual stimulation, change, and variety, has previously been shown to be among the personality traits associated with better resilience in coping with the COVID-19 pandemic ( Fernández et al. , 2020 ) and associated with lower COVID-19 fear and better sleep quality ( Ahmed et al. , 2021 ). Cross-sectional studies have also shown associations between openness and its facets with lower perceived stress and better psychological outcomes during COVID-19 ( Ahmed et al. , 2021 ; Modersitzki et al. , 2021 ). Supporting these previous findings, in the current study, individuals who scored higher on openness reported smaller increases in depressive symptoms during the pandemic. One possible pathway by which personality traits influenced pandemic-related changes in depressive symptoms is through their relationships with physical activity. Meta-analytic evidence supports positive associations between physical activity and extraversion, conscientiousness, and openness, and negative associations between physical activity and neuroticism ( Sutin et al. , 2016 ; Wilson et al. , 2015 ) while physical activity has also been negatively impacted by the pandemic, and these negative changes associated with worse mental health ( Meyer et al. , 2020 ). Prior to the pandemic, a growing body of research had sought to elucidate the complex relationships between personality, physical activity, and mental health ( McDowell et al. , 2020 ; Moor & Geus, 2018 ; Wilson et al. , 2016 ). Future research should consider the interrelations of personality and activity behaviours (e.g., sedentary behaviour and physical activity) and their association with COVID-19-related changes in mental health. This study has a several key strengths. Firstly, the longitudinal study design allowed the examination of changes in depressive symptoms associated with the COVID-19 pandemic, expanding on findings from cross-sectional studies that could not account for mental health prior to the pandemic. Secondly, the sample was randomly selected from the national population, building on recent evidence that primarily relies on convenience samples. Nonetheless, there are several limitations to the current study. Although the CESD-8 has been well-validated in the TILDA cohort, the gold standard to establish a clinical diagnosis of depression is a diagnostic structural interview. Secondly, personality traits would be better examined using multiple data sources (for example peer reports, experience sampling measurements) rather than a single questionnaire; however, these additional data sources are not assessed in TILDA. Finally, this study is observational and, although we controlled for key potential confounders of the relationships of interest, it remains plausible that confounding still exists. Conclusions Depressive symptoms increased significantly among older adults during the COVID-19 pandemic compared to what would have been expected from the trends observed prior to the pandemic. People who score higher on the personality traits neuroticism and extraversion, and lower on openness, had reported the greatest increases in depressive symptoms. While we have demonstrated differences according to personalitgy type in the accumulation of deprsssive symptoms during the COVID-19 pandemic specifically, our findings support previous studies that examined this association prior to the pandemic. Taken together, these findings can inform our understanding of the association of personality with both the onset and course of depression and may therefore contribute to the early identification of at-risk individuals and the tailoring of prevention and treatment programs. Data availability Underlying data The first five waves and COVID-Sub study of TILDA data are available from the Irish Social Science Data Archive (ISSDA) at www.ucd.ie/issda/data/tilda/ . To access the TLDA survey data, please complete an ISSDA Data Request Form for Research Purposes , sign it, and send it to ISSDA by email ( [email protected] ). 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 20 Mar 2025 ADD YOUR COMMENT Comment Author details Author details 1 Medical Gerontology (The Irish Longitudinal Study on Ageing), Trinity College Dublin School of Medicine, Dublin, Please select, Dublin 2, Ireland Brendan O'Maoileidigh Roles: Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Cillian McDowell Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Cathal McCrory Roles: Data Curation, Funding Acquisition, Resources, Supervision Rose Anne Kenny Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Resources, Supervision Celine DeLooze Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Software, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Mark Ward Roles: Conceptualization, Investigation, Methodology, Resources, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information Health Research Board [TILDA-2023-001]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 20 Mar 2025, 8:42 https://doi.org/10.12688/hrbopenres.14031.1 Copyright © 2025 O'Maoileidigh B et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics VIEWS $counts.viewCount downloads Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article O'Maoileidigh B, McDowell C, McCrory C et al. Changes in depressive symptoms during the COVID-19 pandemic differ by personality type: Findings from The Irish Longitudinal Study on Ageing [version 1; peer review: 1 approved with reservations] . HRB Open Res 2025, 8 :42 ( https://doi.org/10.12688/hrbopenres.14031.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 20 Mar 2025 Views 0 Cite How to cite this report: Sharpe BM. Reviewer Report For: Changes in depressive symptoms during the COVID-19 pandemic differ by personality type: Findings from The Irish Longitudinal Study on Ageing [version 1; peer review: 1 approved with reservations] . HRB Open Res 2025, 8 :42 ( https://doi.org/10.21956/hrbopenres.15402.r46956 ) The direct URL for this report is: https://hrbopenresearch.org/articles/8-42/v1#referee-response-46956 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 27 May 2025 Brinkley M Sharpe , University of Georgia, Athens, GA, USA Approved with Reservations VIEWS 0 https://doi.org/10.21956/hrbopenres.15402.r46956 This manuscript examines the Big 5 personality traits as predictors of COVID-19 pandemic-associated changes in depressive symptoms in older adults. A major strength of this manuscript is the use of a large, pre-existing longitudinal sample (i.e., TILDA) with several pre-pandemic ... Continue reading READ ALL This manuscript examines the Big 5 personality traits as predictors of COVID-19 pandemic-associated changes in depressive symptoms in older adults. A major strength of this manuscript is the use of a large, pre-existing longitudinal sample (i.e., TILDA) with several pre-pandemic data collection events. The sample’s demographic makeup (i.e., age) is also a strength as COVID-19’s medical and social impact is thought to have been particularly strong for older individuals. The authors could strengthen their manuscript considerably by clarifying and justifying their analytic approach and providing greater scaffolding for the reader in the presentation of results. Major Comments Further information about the specification of the linear mixed effects models employed would be helpful. Without more details or analytic code, I am not able to adequately consider whether the approach taken was appropriate. For example, in the description of the time variable (i.e., “number of years since data collection”), what time point does “data collection” refer to? Why were interaction effects probed? (Would there be sufficient power to detect them?) Revision should seek to clarify what analyses were performed, to provide justification for the analytic approach, and include information about any alternative approaches considered/attempted. Regarding the inclusion of a large number of covariates in Model 2, please consider literature on the “perils of partialling” (e.g, refer to 1 ). Please provide a table of zero-order correlations between study variables. Are there any differences in personality, demographics, or depression for participants who did not participate in the COVID wave versus those who did? Minor Comments No citations present for Big 5/FFM in the introduction. Consider citing Goldberg (1993) for a review of the literature. The term “five factor model” (FFM) is typically used when the Big 5 traits are measured using the NEO Personality Inventory and related instruments (including the NEO-FFI). Figure 1 and corresponding information provided in the manuscript text is unclear. Were participants excluded for being under age 50 at Wave 2 or at the COVID wave? Were cases with missing data included or not? Information provided is contradictory. Repetitiveness of information is present throughout (e.g., ethics statement, NEO-FFI alpha values). In the service of transparency and reproducibility, I would encourage the authors to make code/syntax available as supplemental materials. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Lynam DR, Hoyle RH, Newman JP: The perils of partialling: cautionary tales from aggression and psychopathy. Assessment . 2006; 13 (3): 328-41 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Five-Factor Model of Personality I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Sharpe BM. Reviewer Report For: Changes in depressive symptoms during the COVID-19 pandemic differ by personality type: Findings from The Irish Longitudinal Study on Ageing [version 1; peer review: 1 approved with reservations] . HRB Open Res 2025, 8 :42 ( https://doi.org/10.21956/hrbopenres.15402.r46956 ) The direct URL for this report is: https://hrbopenresearch.org/articles/8-42/v1#referee-response-46956 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 20 Mar 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 20 Mar 25 read Brinkley M Sharpe , University of Georgia, Athens, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Sharpe B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 27 May 2025 | for Version 1 Brinkley M Sharpe , University of Georgia, Athens, GA, USA 0 Views copyright © 2025 Sharpe B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript examines the Big 5 personality traits as predictors of COVID-19 pandemic-associated changes in depressive symptoms in older adults. A major strength of this manuscript is the use of a large, pre-existing longitudinal sample (i.e., TILDA) with several pre-pandemic data collection events. The sample’s demographic makeup (i.e., age) is also a strength as COVID-19’s medical and social impact is thought to have been particularly strong for older individuals. The authors could strengthen their manuscript considerably by clarifying and justifying their analytic approach and providing greater scaffolding for the reader in the presentation of results. Major Comments Further information about the specification of the linear mixed effects models employed would be helpful. Without more details or analytic code, I am not able to adequately consider whether the approach taken was appropriate. For example, in the description of the time variable (i.e., “number of years since data collection”), what time point does “data collection” refer to? Why were interaction effects probed? (Would there be sufficient power to detect them?) Revision should seek to clarify what analyses were performed, to provide justification for the analytic approach, and include information about any alternative approaches considered/attempted. Regarding the inclusion of a large number of covariates in Model 2, please consider literature on the “perils of partialling” (e.g, refer to 1 ). Please provide a table of zero-order correlations between study variables. Are there any differences in personality, demographics, or depression for participants who did not participate in the COVID wave versus those who did? Minor Comments No citations present for Big 5/FFM in the introduction. Consider citing Goldberg (1993) for a review of the literature. The term “five factor model” (FFM) is typically used when the Big 5 traits are measured using the NEO Personality Inventory and related instruments (including the NEO-FFI). Figure 1 and corresponding information provided in the manuscript text is unclear. Were participants excluded for being under age 50 at Wave 2 or at the COVID wave? Were cases with missing data included or not? Information provided is contradictory. Repetitiveness of information is present throughout (e.g., ethics statement, NEO-FFI alpha values). In the service of transparency and reproducibility, I would encourage the authors to make code/syntax available as supplemental materials. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Lynam DR, Hoyle RH, Newman JP: The perils of partialling: cautionary tales from aggression and psychopathy. Assessment . 2006; 13 (3): 328-41 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Five-Factor Model of Personality I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Sharpe BM. Peer Review Report For: Changes in depressive symptoms during the COVID-19 pandemic differ by personality type: Findings from The Irish Longitudinal Study on Ageing [version 1; peer review: 1 approved with reservations] . HRB Open Res 2025, 8 :42 ( https://doi.org/10.21956/hrbopenres.15402.r46956) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://hrbopenresearch.org/articles/8-42/v1#referee-response-46956 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); }); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe7918f3f4fdf94',t:'MTc3OTI0MDU0Nw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();