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Abstract

The COVID-19 pandemic has claimed an extremely high number of lives worldwide, causing widespread panic and stress. The current research examined whether COVID-19 stress was associated with everyday cognitive failures, using data from a seven-day daily diary study of 253 young adults in Singapore. Multilevel modeling revealed that COVID-19 stress was significantly associated with cognitive failures even after adjusting for demographic factors, both at the within-person and between-persons levels. Specifically, individuals experienced more cognitive failures on days they experienced more COVID-19 stress (as compared to their own average levels of COVID-19 stress), and individuals who experienced more COVID-19 stress overall (as compared to individuals who experienced less COVID-19 stress overall) experienced more cognitive failures in general. While a large body of work has evidenced the detrimental effects of COVID-19 stress on individuals’ well-being, the current findings provide novel insights that these stressors may negatively impact individuals’ cognitive functioning as well.

Keywords

COVID-19 stress cognitive failures daily diary multilevel modeling

COVID-19 stress and cognitive failures in daily life

The COVID-19 pandemic has claimed an extremely high number of lives worldwide. As of early April 2022, there have been more than 505 million confirmed cases of COVID-19 infection and more than 6 million deaths (World Health Organization, 2022). Beyond the loss of life, the fear of potential infection and constant worry about the health of loved ones have resulted in significant stress and anxiety for many individuals (Xu et al., 2020). Moreover, the resultant abrupt changes from higher unemployment rates and social isolation have caused widespread financial insecurity and loneliness, compounding the experienced stress during the pandemic (Vatansever et al., 2021). Furthermore, unlike other daily stressors that occur due to various context-dependent factors specific to each person (e.g., relationship stressors, work-related stressors), COVID-19 related stressors can be experienced by almost everyone concurrently in the world today (Arıcan & Kafadar, 2022). Thus, COVID-19 related stressors are a more widespread phenomenon in the current background that is particularly important to study. While the economic and socioemotional consequences of COVID-19 stress have been well established, little is currently known about its cognitive consequences.

One potential cognitive consequence of COVID-19 stress is related to daily cognitive failures. Cognitive failures are broadly defined as failures to perform simple tasks that a person is normally capable of completing without errors (Lange & Süß, 2014), such as stopping at the traffic light when it turns red or remembering to attend a work meeting. Cognitive failures can be distinguished from other cognitive errors as being the result of lapses of attention or slips of memory rather than a result of a lack of knowledge, and individuals who experience cognitive failure more often report frequent lapses of action, perception, and memory (Bridger et al., 2013). Although cognitive slips and errors are common in daily living, they have been found to have broad and potentially major implications in real-world outcomes. For example, cognitive failures make individuals more vulnerable to the effects of stress (Broadbent et al., 1982; Mahoney et al., 1998), leading to outcomes such as job impairment, workplace-related mishaps, and injuries and hospitalization (Abbasi et al., 2021; Wallace & Vodanovich, 2003a, 2003b). Cognitive failures have also been found to lead to increased susceptibility to driving error and car accidents (Allahyari et al., 2019; Larson & Merritt, 1991). Several studies also indicate that self-reported cognitive failures predict laboratory measures assessing cognitive abilities such as working memory or attentional control (Unsworth et al., 2012a; Unsworth et al., 2012b).

Research examining the antecedents of cognitive failures has suggested that stress can have detrimental effects on one's cognitive functioning (Mahoney et al., 1998). In particular, it is thought that the negative effect of stress on cognitive functioning may be explained through the mediating role of mind wandering (Hartanto & Yang, 2020), which refers to thoughts about one's goals that are not related to the primary task at hand (Morin & Racy, 2021; Smallwood & Schooler, 2006). While mind-wandering is not necessarily maladaptive, it can negatively affect cognitive functioning if driven by anxiety or stress, and lead to cognitive failures (Burdett et al., 2016; McVay & Kane, 2010). For example, stressful life events, such as the COVID-19 pandemic, may increase the likelihood of engaging in task-unrelated thoughts (e.g., worrying about potential infection) and decrease the effectiveness of disengaging from such thoughts (Klein & Boals, 2001). These mental diversions compete for limited cognitive resources and the attempts to suppress undesired thoughts consume even more resources (Smeekens & Kane, 2016). Indeed, as illustrated by literature on working memory, these mental diversions may result in poorer reasoning (Kane et al., 2005), multitasking (Colom et al., 2010), and learning (Noël, 2009). Given the literature on the implications of cognitive failure, as well as the pervasiveness of stress during this pandemic, it behooves us to properly examine the relationship between COVID-19 stress and cognitive failure.

The present study thus seeks to investigate the effect of COVID-19 stress on everyday cognitive failures. In order to reduce the possibility that individual differences may confound any observed associations between COVID-19 stress and cognitive failures, we employed a multilevel approach by utilizing a diary study design. This study design allowed us to examine day-to-day fluctuations in COVID-19 stress and cognitive failures, while controlling for several individual differences in the form of demographic factors (i.e., age, sex, race, household income, and subjective socioeconomic status) previously linked to our variables of interest.

Method

Participants and design

Data for the current study were collected as part of the second wave of a larger project broadly examining daily experiences and well-being (see Goh et al., 2023; Majeed et al., 2023; Ng, Lua et al., 2022; Ng, Majeed et al., 2023), conducted with young adults in Singapore with the daily diary portions running from June 24, 2021 to August 12, 2021 (i.e., during the country's partially relaxed “Phase 3 Heightened Alert”¹ and the stricter semi-lockdown “Phase 2 Heightened Alert”² of the COVID-19 pandemic; Abu Baker, 2021; Ang, 2021).

Undergraduate students from the last author's university were recruited for the study through the local subject pool system.³ Participants completed a baseline questionnaire, followed by a seven-day self-administered diary survey taken nightly. Within one to two days of completing the baseline questionnaire, a link to the first diary survey was emailed to participants at 8.00 p.m., with the link to the consecutive diary surveys being sent each evening at 8.00 p.m. for the remaining six days. Each daily diary survey was left available for completion until 3.00 a.m. the next day. A total of 1,721 observations of daily data were obtained from 253 participants, where each participant provided an average of 6.80 days of observations (97% completion rate; see Table 1). The data collection procedure was approved by the Institutional Review Board at the last author's university (IRB-20-118-A075-M4[621]). All participants provided informed consent prior to data collection.

Table 1.

Summary of descriptive statistics

Variable	N	M	SD	Observed Range	Theoretical Range
Person level
Sex (% female)	253	77%
Race (% Chinese)	253	75%
Age (in years)	253	22.11	1.63	19–29
Monthly household income	253	3.00	1.43	1–6	1–6
Subjective socioeconomic status	253	6.11	1.25	2–10	1–10
Vaccination status (% vaccinated)	253	71%
Average stressor exposure	253	.37	.29	.00–1.00	.00–1.00
Average COVID-19 stress	253	0.32	0.46	0.00–2.63	0.00–4.00
Average cognitive failures	253	0.34	0.35	0.00–2.87	0.00–3.00
Day level
Daily stressor exposure (% stressor days)	1721	38%
Daily COVID-19 stress	1721	0.32	0.52	0.00–3.60	0.00–4.00
Daily cognitive failures	1721	0.33	0.44	0.00–3.00	0.00–3.00

Note: Ns for person level refer to number of participants, while Ns for day level refer to number of observations (days).

Measures

Baseline

Demographic covariates. Participants were asked to report their sex (male or female), race (Chinese, Malay, Indian, or other), age, and monthly household income (1 = less than $2,000, 2 = $2,000–$5,999, 3 = $6,000–$9,999, 4 = $10,000–$14,999, 5 = $15,000–$19,999, 6 = more than $20,000). Subjective socioeconomic status was measured using a 10-point ladder scale (Adler et al., 2000), with the bottom rung (1) representing the lowest status and the top rung (10) representing the highest status.

COVID-19 vaccination status. Participants were asked to report their COVID-19 vaccination status (currently vaccinated or unvaccinated).

Daily

Daily stressor exposure. Daily stressor exposure was measured each day for each participant through the Daily Inventory of Stressful Events (Almeida et al., 2002). The inventory is a checklist of seven types of stressors (arguments, avoided arguments, discrimination, work/education stressors, home stressors, network stressors, and “others”), and participants were asked to indicate if any of the stressors occurred to them “since yesterday.” In line with existing work on daily stressor exposure (e.g., Majeed et al., 2021; Ng et al., 2022; Rush et al., 2019), the construct was operationalized as a binary variable. Specifically, if at least one stressor was experienced on that day, the day was categorized as a stressor day, and otherwise, it was categorized as a non-stressor day. The intra-class correlation for daily stressor exposure was .64.

Daily COVID-19 stress. COVID-19 stress was measured using five items selected from the COVID Stress Scales (Taylor et al., 2020), administered daily. The COVID Stress Scales have been validated across a number of different samples (e.g., Asmundson et al., 2022; Carlander et al., 2022; Khosravani et al., 2021; Taylor et al., 2020), and correlate strongly with other COVID-19 fear-related measures (e.g., Khosravani et al., 2021; Pakpour et al., 2021) while showing significantly greater correlations with anxiety-related measures rather than depression-related measures (Khosravani et al., 2021; Taylor et al., 2020). The current study used five items adapted from the COVID Stress Scales to measure COVID-19-related stress and anxiety symptoms on four factors: (1) danger and contamination, (2) xenophobia, (3) compulsive checking, and (4) traumatic stress symptoms. Participants were asked to indicate “how you have felt or behaved this way today” in relation to three worries and two behaviors on a 5-point Likert scale (0 = Not at all, 4 = Extremely for worries; 0 = Never, 4 = Almost always for behaviors). Specifically, the items on worries were: “Today, I was worried about catching the virus” (danger and contamination), “Today, I was worried that our healthcare system will be unable to keep me and my loved ones safe from the virus” (danger and contamination), and “Today, I was worried that foreigners are spreading the virus in Singapore” (xenophobia). The items on behaviors were: “Today, I checked online and offline media (e.g., Facebook, Straits Times) for information concerning COVID-19” (compulsive checking) and “Today, I had trouble concentrating because I kept thinking about the virus” (traumatic stress symptoms). All items were averaged such that higher overall scores reflected greater levels of daily COVID-19 stress (α_within= .63, α_between = .86). The intra-class correlation for daily COVID-19 stress was .74. A plot of average COVID-19 stress levels (per participant) over time, in comparison to the number of COVID-19 cases and deaths in Singapore (data retrieved from Mathieu et al., 2020), is provided in Figure 1.

Figure 1.

Participant-average levels of COVID-19 stress and national numbers of COVID-19 cases and deaths over time.

Daily cognitive failures. Cognitive failure was measured using the 13-item Cognitive Failures in Everyday Life Scale (Lange & Süß, 2014), administered daily. Each item (e.g., “Did your mind unintentionally wander, at any point of time today?”) was rated on a 4-point Likert scale (0 = Never, 3 = Several times), with higher scores reflecting greater daily cognitive failure (α_within= .75, α_between = .95). The intra-class correlation for daily cognitive failures was .57.

Analytic plan

Due to the two-level nature of the diary data (i.e., repeated measures nested within individuals), multilevel modeling⁴ was conducted to examine the association between daily COVID-19 stress and daily cognitive failures across the seven days (Level 1) nested within participants (Level 2).

Main analyses

The Level 1 predictor, daily COVID-19 stress, was person-mean centered and entered as both a random and fixed predictor, and its person-level mean was reintroduced as a fixed predictor at Level 2 in order to fully decompose daily COVID-19 stress into its within- and between-persons components (Bolger et al., 2012). Time—in terms of number of days since the start of data collection for the whole sample—was included as a person-mean-centered Level 1 covariate with both random and fixed components with its person-level mean reintroduced as a Level 2-fixed covariate in order to account for changes over time due to the changing pandemic situation. The unadjusted model was thus as follows, with γ₁₀ and γ₀₁ representing the within-persons and between-persons parameters of interest respectively:

Level 1: (Daily cognitive failures) _di = B_0i + B_1i(daily COVID-19 stress) _di + B_2i(time) _di + ε _di

Level 2: B_0i = γ₀₀ + γ₀₁(average COVID-19 stress) _i + γ₀₂(average time) _i + μ_0i

B_1i = γ₁₀ + μ_1i

B_2i = γ₂₀ + μ_2i

An adjusted model was estimated with the inclusion of demographic factors, COVID-19 vaccination status, and daily stressor exposure. Age, sex, race, objective socioeconomic status (i.e., monthly household income), and subjective socioeconomic status were included as Level 2-fixed components in an adjusted model in order to rule out demographic confounds. Stressor exposure was included as random and fixed components at Level 1 in terms of stressor days versus non-stressor days, and as a fixed component at Level 2 in terms of average stressor exposure across the seven days. Categorical predictors (i.e., sex, race, and daily stressor exposure) were dummy-coded with male, majority race (i.e., Chinese), and non-stressor day serving as the reference categories respectively. The equations for the adjusted model are thus as follows:

Level 1: (Daily cognitive failures) _di = B_0i + B_1i(daily COVID-19 stress) _di + B_2i(time) _di + B_3i(daily stressor exposure)_di + ε_di

Level 2: B_0i = γ₀₀ + γ₀₁(average COVID-19 stress) _i + γ₀₂(average time) _i + γ₀₃(age) _i + γ₀₄(sex) _i + γ₀₅(race)_i + γ₀₆(objective SES)_i + γ₀₇(subjective SES)_i + γ₀₈(average stressor exposure)i+ μ_0i

B_1i = γ₁₀ + μ_1i

B_2i = γ₂₀ + μ_2i

B_3i = γ₃₀ + μ_3i

At Level 1, B_0i (the intercept coefficient for each participant i) indicates participant i's average level of cognitive failures. B_1i shows the change in daily cognitive failures in relation to daily COVID-19 stress. At Level 2, B_0i was modeled as a function of between-participant differences. This includes participant i's average COVID-19 stress over the duration of the seven days and other covariates. B_1i represents the slope coefficient for each participant i. μ_0i and μ_1i indicate the deviation of each participant's intercept and slope respectively from the model-implied values.

Additional analyses

Leave-one-out multiverse analysis. As the daily COVID-19 Stress measure was adapted and had not yet been established in its current form, we conducted a leave-one-out multiverse analysis to evaluate plausible analytic alternatives in parallel and interpreted collectively. The purpose for this analysis was to see if our results were robust across the different operationalizations of COVID-19 stress. Specifically, we repeated the unadjusted and adjusted analyses previously mentioned but with all 31 possible combinations of the five items used to create the COVID-19 Stress measure.

Exploratory lagged analysis. We repeated the main analyses with a one-day time lag in a preliminary examination of whether the association between COVID-19 stress and cognitive failures could spill over onto the next day. Specifically, we modeled next-day cognitive failures as a function of today's COVID-19 stress and covariates.

Exploratory mediation analysis. We repeated the main analyses with COVID-19 vaccination status as a predictor of daily COVID-19 stress, resulting in a mediation model (for conceptual representation, see Figure 2).

Figure 2.
Conceptual representation of mediation.

Transparency and openness

The current work's design and its analysis plan were not pre-registered. Relevant materials, data, analytic code, as well as a supplemental information (zero-order correlation matrix at Level 2 and residual QQ plot) have been made publicly available on Researchbox #466 (https://researchbox.org/466).

Unless otherwise stated, all analyses were conducted in R version 3.6.3 (R Core Team, 2020). Data cleaning and manipulation were performed using dplyr version 1.0.10 (Wickham et al., 2022). Descriptives were extracted using psych version 2.2.9 (Revelle, 2021). Multilevel scale reliabilities were calculated via semTools version 0.5-5 (Jorgensen et al., 2021). ICCs were calculated by merTools version 0.5.2 (Knowles et al., 2020). Multilevel modeling was conducted via lme4 version 1.1-28 (Bates et al., 2014) with significance testing by lmerTest version 3.1-3 (Kuznetsova et al., 2017), where maximum likelihood estimation was used to fit all models. Effect sizes in the form of standardized coefficients were calculated by effectsize version 0.6.0.1 (Ben-Shachar et al., 2020) using the “pseudo” method. Multilevel mediation was conducted via mlma version 6.2-1 (Yu & Li, 2022). Sensitivity power analyses were performed using Mplus version 8 (Muthén & Muthén, 2012).

Results

Main analyses

We found significant positive associations between daily COVID-19 stress and daily cognitive failures both at the within-persons and between-persons levels (Table 2).⁵ Specifically, to a small extent, participants reported more cognitive failures on days they experienced more COVID-19 stress compared to their own average (unadjusted: β = .07, 95% CI = [.02, .12], γ₁₀= 0.08, SE = 0.03, p = .006; adjusted: β = .06, 95% CI = [.01, .11], γ₁₀= 0.07, SE = 0.03, p = .020). Additionally, to a moderate extent, participants who experienced higher levels of COVID-19 stress in general also reported more cognitive failures than participants who experienced lower levels of COVID-19 stress (unadjusted: β = .17, 95% CI = [.04, .30], γ₀₁= 0.12, SE = 0.05, p = .009; adjusted: β = .18, 95% CI = [.09, .27], γ₀₁= 0.13, SE = 0.03, p < .001).

Table 2.
Full results of main multilevel analyses

Unadjusted Adjusted

Predictor Std. Est. Est. SE p Std. Est. Est. SE p

Fixed effects

Intercept .00 0.39 0.07 <.001 .00 0.18 0.27 .509

Within-persons

Time −.12 −0.02 0.00 <.001 −.10 −0.01 0.00 .001

COVID-19 stress .07 0.08 0.03 .006 .06 0.07 0.03 .019

Stressor exposure .20 0.11 0.02 <.001

Between-persons

Time −.10 0.00 0.00 .108 −.05 0.00 0.00 .281

COVID-19 stress .17 0.12 0.05 .009 .18 0.13 0.03 <.001

Stressor exposure .41 0.46 0.06 <.001

Age −.01 0.00 0.01 .817

Sex (0 = male, 1 = female) .09 0.07 0.04 .094

Race (0 = majority, 1 = minority) .01 0.00 0.04 .892

Objective SES −.02 −0.01 0.01 .641

Subjective SES −.02 −0.01 0.01 .626

Random effects

Intercept 0.11 0.04

Time 0.00 0.00

COVID-19 stress 0.01 0.01

Stressor exposure 0.03

Residual 0.07 0.07

Note: N_participants= 253, N_observations= 1721. Estimates for random effects indicate variances. Bolded rows indicate statistical significance at p < .050.

Leave-one-out multiverse analyses

Leave-one-out multiverse analyses revealed that our main findings were generally robust across the various operationalizations of daily COVID-19 stress (Figure 3). All point estimates were consistent with the main findings (i.e., positive in sign), although some combinations of items (e.g., the combination of items 1, 2, and 4 at the within-persons level, and the combination of items 1 and 4 at the between-persons level) produced nonsignificant associations (ps ≥ .050) with daily cognitive failures.

Figure 3.
Summary of leave-one-out multiverse analyses.

Exploratory lagged analyses

We found that daily COVID-19 stress was not predictive of next-day cognitive failures (unadjusted: β = −.02, 95% CI = [−.07, .04], γ₁₀ = −0.02, SE = 0.03, p = .525; adjusted: β = −.02, 95% CI = [−.07, .03], γ₁₀ = −0.02, SE = 0.03, p = .455). This suggests that the association between COVID-19 stress and cognitive failures is short-lived, and does not spill over to the next day.

Exploratory mediation analyses

The mediation analysis revealed a significant indirect mediation pathway from COVID-19 vaccination status through COVID-19 stress in predicting cognitive failures (B = −0.01, 95% CI = [−.007, −.017]). This suggests that being vaccinated for COVID-19 is associated with decreased levels of COVID-19 stress, which in turn is associated with lower levels of cognitive failures.

Discussion

We analyzed data from a daily diary study of 253 young adults in Singapore across seven days during the COVID-19 pandemic. We found that COVID-19 stress was consistently associated with poorer cognitive functioning both at the within-persons and between-persons levels, such that individuals experienced more cognitive failures on days they experienced more COVID-19 stress (as compared to their own average levels of COVID-19 stress), and also that individuals who experienced more COVID-19 stress overall (as compared to individuals who experienced less COVID-19 stress overall) experienced more cognitive failures in general. Adjusting for demographic covariates did not change the observed patterns. Of note, leave-one-out multiverse analyses of the five items forming the COVID-19 stress measure preliminarily suggest that the associations observed in the current work are mostly driven by stress from two specific items (i.e., “Today, I was worried that foreigners are spreading the virus in Singapore” and “Today, I had trouble concentrating because I kept thinking about the virus”).

The current work is the first to empirically examine the associations between COVID-19 stress and cognitive failures. The use of a daily diary methodology allowed us to examine this relationship in an ecologically valid setting, and also allowed us to rule out stable individual differences that may account for the positive within-persons associations between COVID-19 stress and cognitive failure. Taken together, the findings of the current work suggest that the stress arising from the COVID-19 situation may indeed be associated with increased levels of cognitive failures among individuals both at the day-to-day level and in general. The positive associations observed in the current study are in line with much of the literature broadly examining stress on cognitive failures (Mahoney et al., 1998). Considering past works suggesting that mind-wandering mediates the positive relationship between stress and cognitive failures (Hartanto & Yang, 2020), we posit that mind-wandering may also be a possible explanation for the observed relationship between COVID-19 stress and cognitive failure. Further research into this potential mechanism is warranted, given the lack of direct examination of this mechanism in the current work. Nonetheless, our findings suggest that beyond detriments to affective and socioeconomic outcomes, COVID-19 stress may be associated with poorer cognitive outcomes as well.

In addition to our main results, we uncovered two notable exploratory findings. First, we did not observe any lagged associations or spillover of daily COVID-19 stress onto cognitive failures on the next day. This contrasts with existing works on other types of stress and cognitive failure—for example, daily work-related stress has been found to be associated with higher levels of next-day cognitive failure (Kottwitz et al., 2022). Situating our current exploratory findings within the existing literature suggests that the relationship between COVID-19 stress and cognitive failure may be more transient in nature, perhaps due to differences in the nature of the stressor experience itself. Second, vaccination against COVID-19 appeared to play a protective role, with our current data indicating that vaccinated individuals reported reduced levels of COVID-19 stress in general. Consequently, this reduction in stress might indirectly protect against cognitive failures. These exploratory findings, while brief, provide valuable insights that could guide future research and interventions not just within the context of COVID-19, but within the broader stress literature.

The current study is not without limitations. First, while the original version of the COVID-19 Stress Scale has been validated with two large North American samples (Taylor et al., 2020) and a large South American sample (Pulido & Jimenez, 2020), it has not been validated with any Asian samples, nor has the shortened daily version adapted for the current daily diary study been validated in any context. As such, future studies should be conducted to assess the psychometric properties of the five-item version employed in the current study. Second, while the daily diary method is high in ecological validity, minimizes issues of recall bias, and allows for the disentangling of within- and between-persons associations, its nonexperimental nature does not allow for causal conclusions to be drawn. Thus, it is possible—though not theoretically likely—that higher levels of cognitive failures drive higher levels of COVID-19 stress,⁶ in opposition to our hypothesized direction.

In sum, the present work provides novel insights into the associations between daily COVID-19 stress and daily cognitive failures. While a large body of work has evidenced the detrimental effects of COVID-19 stress on individuals’ sense of well-being, the current findings suggest that these stressors may negatively impact individuals’ cognitive functioning as well. In turn, this may have unfavorable consequences on individuals’ day-to-day functioning (e.g., in logical reasoning and decision-making), as previously alluded to. Considering the pervasiveness of COVID-19 stress today and the high likelihood that the pandemic will continue affecting individuals’ lives in the near future (Chia & Hartanto, 2021), it is undoubtedly important to consider the downstream effects of the pandemic on individuals in the society. Beyond ensuring adequate medical support and infrastructure in the current pandemic, it is crucial for community leaders and policymakers to ensure that individuals have adequate resources to mitigate the stresses arising from the pandemic situation.

Footnotes

Acknowledgements

We are grateful to Frosch Quek, Gloria Lai, Harkiran Kaur, Harleen Kaur, Joax Wong, Manmeet Kaur, Wei Ming Ooi, and Xin Yi Poh for their assistance in data collection.

Authors contributions

NMM and AH developed the study concept and analytic plan. NMM and KTASK performed the data analysis and interpretation. NMM, KTASK, MYL, JLC, and VYQL drafted the paper, and all authors provided critical revisions. All authors approved the final version of the paper for submission.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article: This research was supported by a grant awarded to AH by Singapore Management University through research grants from the Ministry of Education Academy Research Fund Tier 1 (21-SOSS-SMU-023) and Lee Kong Chian Fund for Research Excellence. The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Data availability statement

The materials and dataset analyzed during the current study have been made publicly available in the Researchbox repository (#466; https://researchbox.org/466).

ORCID iDs

Nadyanna M. Majeed

K. T. A. Sandeeshwara Kasturiratna

Jonathan L. Chia

Notes

References

1.
Abbasi M. Falahati M. Kaydani M. Fallah Madvari R. Mehri A. Ghaljahi M. Yazdanirad S. (2021). The effects of psychological risk factors at work on cognitive failures through the accident proneness. BMC Psychology, 9(1), 1–162. https://doi.org/10.1186/s40359-021-00669-5

2.
Abu Baker J. (2021, June 10). Up to 5 in a group allowed from Jun 14; dining-in may resume on Jun 21 in phased easing of COVID-19 curbs. Channel News Asia . https://www.channelnewsasia.com/singapore/cap-of-5-people-social-gatherings-dining-in-phase-3-covid-19-1846791

3.
Adler N. E. Epel E. S. Castellazzo G. Ickovics J. R. (2000). Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy white women. Health Psychology, 19(6), 586–592. https://doi.org/10.1037//0278-6133.19.6.586

4.
Allahyari T. Saraji G. N. Adi J. Hosseini M. Iravani M. Younesian M. Kass S. J. (2019). Cognitive failures, driving errors, and driving accidents. International Journal of Occupational Safety and Ergonomics, 14(2), 149–158. https://doi.org/10.1080/10803548.2008.11076759

5.
Almeida D. M. Wethington E. Kessler R. C. (2002). The daily inventory of stressful events: An interview-based approach for measuring daily stressors. Assessment (Odessa, Fla.), 9(1), 41–55. https://doi.org/10.1177/1073191102009001006

6.
Ang H. M. (2021, July 20). Return to Phase 2 (Heightened Alert): Dining-in to be suspended, group sizes back down to 2. Channel News Asia . https://www.channelnewsasia.com/singapore/phase-2-heightened-alert-dining-in-gathering-group-size-covid-19-2047211

7.
Arıcan H. Kafadar H. (2022). Examining the relationship between coronavirus anxiety and cognitive failures. Neuropsychiatric Investigation, 60(4), 1–7. https://doi.org/10.5152/NeuropsychiatricInvest.2022.22012

8.
Asmundson G. S. G. Rachor G. Drakes D. H. Boehme B. A. E. Paluszek M. M. Taylor S. (2022). How does COVID stress vary across the anxiety-related disorders? Assessing factorial invariance and changes in COVID Stress Scale scores during the pandemic. Journal of Anxiety Disorders, 87, 102554. https://doi.org/10.1016/j.janxdis.2022.102554

9.
Bates D. Mächler M. Bolker B. Walker S. (2014). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01

10.
Ben-Shachar M. Lüdecke D. Makowski D. (2020). Effect size: Estimation of effect size indices and standardized parameters. Journal of Open Source Software, 5(56), 2815. https://doi.org/10.21105/joss.02815

11.
Bolger N. Stadler G. Laurenceau J.-P. (2012). Power analysis for intensive longitudinal studies. In Handbook of research methods for studying daily life (pp. 285–301). The Guilford Press.

12.
Bridger R. S. Johsen SÅK Brasher K. (2013). Psychometric properties of the Cognitive Failures Questionnaire. Ergonomics, 56(10), 1515–11524. https://doi.org/10.1080/00140139.2013.821172

13.
Broadbent D. E. Cooper P. F. FitzGerald P. Parkes K. R. (1982). The Cognitive Failures Questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21(1), 1–16. https://doi.org/10.1111/j.2044-8260.1982.tb01421.x

14.
Burdett B. R. D. Charlton S. G. Starkey N. J. (2016). Not all minds wander equally: The influence of traits, states and road environment factors on self-reported mind wandering during everyday driving. Accident Analysis & Prevention, 95(Pt A), 1–7. https://doi.org/10.1016/j.aap.2016.06.012

15.
Carlander A. Lekander M. Asmundson G. J. G. Taylor S. Bagge R. O. Bagge A.-S. L. (2022). COVID-19 related distress in the Swedish population: Validation of the Swedish version of the COVID Stress Scales (CSS). PloS One, 17(2), e0263888. https://doi.org/10.1371/journal.pone.0263888

16.
Chia J. L. Hartanto A. (2021). Cognitive barriers to COVID-19 vaccine uptake among older adults. Frontiers in Medicine, 8, 756275. https://doi.org/10.3389/fmed.2021.756275

17.
Colom R. Martínez-Molina A. Shih P. C. Santacreu J. (2010). Intelligence, working memory, and multitasking performance. Intelligence, 38(6), 543–551. https://doi.org/10.1016/j.intell.2010.08.002

18.
Goh A. Y. H. Chia S. M. Majeed N. M. Chen N. R. Y. Hartanto A. (2023). Untangling the additive and multiplicative relations between natural scenery exposure and human–animal interaction on affective well-being: Evidence from daily diary studies. Sustainability, 15(4), 2910. https://doi.org/10.3390/su15042910

19.
Hartanto A. Yang H. (2020). Testing theoretical assumptions underlying the relation between anxiety, mind wandering, and task-switching: A diffusion model analysis. Emotion, 22(3), 493–510. https://doi.org/10.1037/emo0000935

20.
Jorgensen T. D. Pornprasertmanit S. Schoemann A. M. Rosseel Y. (2021). semTools: Useful tools for structural equation modeling (0.5-5) [R]. https://CRAN.R-project.org/package=semTools

21.
Kane M. J. Hambrick D. Z. Conway A. R. A. (2005). Working memory capacity and fluid intelligence are strongly related constructs: Comment on Ackerman, Beier, and Boyle (2005). Psychological Bulletin, 131(1), 66–71. https://doi.org/10.1037/0033-2909.131.1.66

22.
Khosravani V. Asmundson G. J. G. Taylor S. Sharifi Bastan F. Samimi Ardestani S. M. (2021). The Persian COVID Stress Scales (Persian-CSS) and COVID-19-related stress reactions in patients with obsessive-compulsive and anxiety disorders. Journal of Obsessive-Compulsive and Related Disorders, 28, 100615–100615. https://doi.org/10.1016/j.jocrd.2020.100615

23.
Klein K. Boals A. (2001). The relationship of life event stress and working memory capacity. Applied Cognitive Psychology, 15(5), 565–579. https://doi.org/10.1002/acp.727

24.
Knowles J. E. Frederick C. Whitworth A. (2020). merTools: Tools for Analyzing Mixed Effect Regression Models (Version 0.5.2). https://cran.r-project.org/package=merTools

25.
Kottwitz M. U. Wehrt W. Gerhardt C. Augusto Coelho D. Schmutz D. Elfering A. (2022). Yesterday’s work–home conflict and actigraphically recorded sleep-onset latency as predictors of today’s cognitive failure. Journal of Business and Psychology, 37(3), 509–524. https://doi.org/10.1007/s10869-021-09766-z

26.
Kuznetsova A. Brockhoff P. B. Christensen R. H. B. (2017). lmerTest Package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13). https://doi.org/10.18637/jss.v082.i13

27.
Lange S. Süß H. M. (2014). Measuring slips and lapses when they occur—Ambulatory assessment in application to cognitive failures. Consciousness and Cognition, 24, 1–11. https://doi.org/10.1016/j.concog.2013.12.008

28.
Larson G. E. Merritt C. R. (1991). Can accidents be predicted? An empirical test of the Cognitive Failures Questionnaire. Applied Psychology: An International Review, 40(1), 37–45. https://doi.org/10.1111/j.1464-0597.1991.tb01356.x

29.
Maas C. J. M. Hox J. J. (2004). The influence of violations of assumptions on multilevel parameter estimates and their standard errors. Computational Statistics & Data Analysis, 46(3), 427–440. https://doi.org/10.1016/j.csda.2003.08.006

30.
Mahoney A. M. Dalby J. T. King M. C. (1998). Cognitive failures and stress. Psychological Reports, 82(3_suppl), 1432–1434. https://doi.org/10.2466/pr0.1998.82.3c.1432

31.
Majeed N. M. Kasturiratna K. T. A. S. Lua V. Y. Q. Li M. Y. Hartanto A. (2023). Discrimination and cognitive failures in Singapore and the US: An investigation of between- and within-persons associations through multilevel modelling. International Journal of Intercultural Relations, 94, 101805. https://doi.org/10.1016/j.ijintrel.2023.101805

32.
Majeed N. M. Tan J. J. X. Tov W. Hartanto A. (2021). Dispositional optimism as a buffer against emotional reactivity to daily stressors: A daily diary approach. Journal of Research in Personality, 93, 104105. https://doi.org/10.1016/j.jrp.2021.104105

33.
Mathieu E. Ritchie H. Rodés-Guirao L. Appel C. Gavrilow D. Giattino C. Hasell J. Macdonald B. Dattani S. Beltekian D. Ortiz-Ospina E. Roser M. (2020). Coronavirus Pandemic (COVID-19) . Our World In Data. http://OurWorldInData.org

34.
McVay J. C. Kane M. J. (2010). Does mind wandering reflect executive function or executive failure? Comment on Smallwood and Schooler (2006) and Watkins (2008). Psychological Bulletin, 136(2), 188–197. https://doi.org/10.1037/a0018298

35.
Morin A. Racy F. (2021). Dynamic self-processes. In Rauthmann J. F. (Ed.), The handbook of personality dynamics and processes (pp. 365–386). https://doi.org/10.1016/B978-0-12-813995-0.00015-7

36.
Muthén B. O. Muthén L. K. (2012). Mplus version 7: User’s guide. Author.

37.
Ng M. H. S. Lua V. Y. Q. Majeed N. M. Hartanto A. (2022). Does trait self-esteem serve as a protective factor in maintaining daily affective well-being? Multilevel analyses of daily diary studies in the US and Singapore. Personality and Individual Differences, 198, 111804. https://doi.org/10.1016/j.paid.2022.111804

38.
Ng T. L. Y. Majeed N. M. Lua V. Y. Q. Hartanto A. (2023). Do executive functions buffer against COVID-19 stress? A latent variable approach. Current Psychology. https://doi.org/10.1007/s12144-023-04652-8

39.
Noël M.-P. (2009). Counting on working memory when learning to count and to add: A preschool study. Developmental Psychology, 45(6), 1630–1643. https://doi.org/10.1037/a0016224

40.
Pakpour A. H. Griffiths M. D. Lin C.-Y. (2021). Assessing psychological response to the COVID-19: The fear of COVID-19 Scale and the COVID Stress Scales. International Journal of Mental Health and Addiction, 19(6), 2407–2410. https://doi.org/10.1007/s11469-020-00334-9

41.
Pulido E. G. Jimenez L. (2020). Validation to Spanish version of the COVID-19 Stress Scale [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/qgc3h

42.
R Core Team. (2020). R: A language and environment for statistical computing (3.6.3) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/

43.
Revelle W. (2021). psych: Procedures for psychological, psychometric, and personality research (2.1.6) [R]. Northwestern University.

44.
Rush J. Rast P. Almeida D. M. Hofer S. M. (2019). Modeling long-term changes in daily within-person associations: An application of multilevel SEM. Psychology and Aging, 34(2), 163–176. https://doi.org/10.1037/pag0000331

45.
Smallwood J. Schooler J. W. (2006). The restless mind. Psychological Bulletin, 132(6), 946–958. https://doi.org/10.1037/0033-2909.132.6.946

46.
Smeekens B. A. Kane M. J. (2016). Working memory capacity, mind wandering, and creative cognition: An individual-differences investigation into the benefits of controlled versus spontaneous thought. Psychology of Aesthetics, Creativity, and the Arts, 10(4), 389–415. https://doi.org/10.1037/aca0000046

47.
Taylor S. Landry C. A. Paluszek M. M. Fergus T. A. McKay D. Asmundson G. J. G. (2020). Development and initial validation of the COVID Stress Scales. Journal of Anxiety Disorders, 72, 102232. https://doi.org/10.1016/j.janxdis.2020.102232

48.
Unsworth N. Brewer G. A. Spillers G. J. (2012a). Variation in cognitive failures: An individual differences investigation of everyday attention and memory failures. Journal of Memory and Language, 67(1), 1–16. https://doi.org/10.1016/j.jml.2011.12.005

49.
Unsworth N. McMillan B. D. Brewer G. A. Spillers G. J. (2012b). Everyday attention failures: An individual differences investigation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(6), 1765–1772. https://doi.org/10.1037/a0028075

50.
Vatansever D. Wang S. Sahakian B. J. (2021). COVID-19 and promising solutions to combat symptoms of stress, anxiety and depression. Neuropsychopharmacology, 46(1), 217–218. https://doi.org/10.1038/s41386-020-00791-9

51.
Wallace J. C. Vodanovich S. J. (2003a). Can accidents and industrial mishaps be predicted? Further investigation into the relationship between cognitive failure and reports of accidents. Journal of Business and Psychology, 17(4), 503–514. https://doi.org/10.1023/A:1023452218225

52.
Wallace J. C. Vodanovich S. J. (2003b). Workplace safety performance: Conscientiousness, cognitive failure, and their interaction. Journal of Occupational Health Psychology, 8(4), 316–327. https://doi.org/10.1037/1076-8998.8.4.316

53.
Wickham H. François R. Henry L. Müller K. Vaughan D. (2022). dplyr: A Grammar of data manipulation (1.1.0) [R]. https://CRAN.R-project.org/package=dplyr

54.
World Health Organization. (2022). WHO Coronavirus (COVID-19) Dashboard . World Health Organization. https://covid19.who.int

55.
Xu C. Xu Y. Xu S. Zhang Q. Liu X. Shao Y. Xu X. Peng L. Li M. (2020). Cognitive reappraisal and the association between perceived stress and anxiety symptoms in COVID-19 isolated people. Frontiers in Psychiatry, 11, 858. https://doi.org/10.3389/fpsyt.2020.00858

56.
Yu Q. Li B. (2022) mlma: Multilevel mediation analysis (6.2-1) [R]. https://CRAN.R-project.org/package=mlma

			Unadjusted				Adjusted
Predictor			Std. Est.	Est.	SE	p	Std. Est.	Est.	SE	p
Fixed effects
		Intercept	.00	0.39	0.07	<.001	.00	0.18	0.27	.509
	Within-persons
		Time	−.12	−0.02	0.00	<.001	−.10	−0.01	0.00	.001
		COVID-19 stress	.07	0.08	0.03	.006	.06	0.07	0.03	.019
		Stressor exposure					.20	0.11	0.02	<.001
	Between-persons
		Time	−.10	0.00	0.00	.108	−.05	0.00	0.00	.281
		COVID-19 stress	.17	0.12	0.05	.009	.18	0.13	0.03	<.001
		Stressor exposure					.41	0.46	0.06	<.001
		Age					−.01	0.00	0.01	.817
		Sex (0 = male, 1 = female)					.09	0.07	0.04	.094
		Race (0 = majority, 1 = minority)					.01	0.00	0.04	.892
		Objective SES					−.02	−0.01	0.01	.641
		Subjective SES					−.02	−0.01	0.01	.626
Random effects
		Intercept		0.11				0.04
		Time		0.00				0.00
		COVID-19 stress		0.01				0.01
		Stressor exposure						0.03
		Residual		0.07				0.07

COVID-19 stress and cognitive failures in daily life: A multilevel examination of within- and between-persons patterns

Abstract

Keywords

COVID-19 stress and cognitive failures in daily life

Method

Participants and design

Measures

Baseline

Daily

Analytic plan

Main analyses

Additional analyses

Transparency and openness

Results

Main analyses

Leave-one-out multiverse analyses

Exploratory lagged analyses

Exploratory mediation analyses

Discussion

Footnotes

Acknowledgements

Authors contributions

Declaration of conflicting interests

Funding

Data availability statement

ORCID iDs

Notes

References