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. 2022 Oct 2;317:114876. doi: 10.1016/j.psychres.2022.114876

Information seeking, mental health and loneliness: Longitudinal analyses of adults in the UK COVID-19 mental health and wellbeing study

Sarah Wilding a, Daryl B O'Connor a,⁎, Eamonn Ferguson b, Karen Wetherall c, Seonaid Cleare c, Ronan E O'Carroll d, Kathryn A Robb e, Rory C O'Connor e
PMCID: PMC9526871  PMID: 36215778

Abstract

Information seeking has generally been seen as an adaptive response to the COVID-19 pandemic. However, it may also result in negative outcomes on mental health. The present study tests whether reporting COVID-related information seeking throughout the pandemic is associated with subsequently poorer mental health outcomes. A quota-based, non-probability-sampling methodology was used to recruit a nationally representative sample. COVID-related information seeking was assessed at six waves along with symptoms of depression, anxiety, mental wellbeing and loneliness (N = 1945). Hierarchical linear modelling was used to assess the relationship between COVID-related information seeking and mental health outcomes. Information seeking was found to reduce over time. Overall, women, older and higher socioeconomic group individuals reported higher levels of information seeking. At waves 1-4 (March-June 2020) the majority of participants reported that they sought information on Covid 1-5 times per day, this decreased to less than once per day in waves 5 and 6 (July-November 2020). Higher levels of information seeking were associated with poorer mental health outcomes, particularly clinically significant levels of anxiety. Use of a non-probability sampling method may have been a study limitation, nevertheless, reducing or managing information seeking behaviour may be one method to reduce anxiety during pandemics and other public health crises.

Keywords: Coronavirus, Depression, Anxiety, Social media, Public health, Isolation

1. Introduction

The COVID-19 pandemic has involved a huge amount of information being shared by governments and public health agencies in order to keep people informed about health promoting behaviours to reduce the spread of COVID-19 (e.g., social and physical distancing, handwashing), to provide regular updates on government restrictions and lockdowns, and to allow populations to have access to clear and reliable COVID-related news (Liu, 2020; Naeem and Bhatti, 2020). This information has come from a variety of sources including through television and newspapers, online via search engines such as Google and via social media, as well as in person via friends and family (Statista, 2020; Tran et al., 2020). Google Trends data also demonstrated a particular spike in COVID-19 information seeking in March 2020 in the United States of America (Mangono et al., 2021). Moreover, one early study showed that many individuals consumed their COVID-19 information through the internet, online newspapers and social networks (e.g., Tran et al., 2020).

While information seeking has been adaptive in order to keep up to date with the latest information regarding the pandemic and initial data showed that specific up-to-date and accurate health information was related to lower levels of stress, anxiety and depression (Wang et al., 2020), it does not always result in positive outcomes. It can be difficult for people to evaluate accurate information when trying to deal with the deluge of information. Information seeking can also be a reassurance seeking behaviour aimed at reducing health-related fears and is particularly common where there is uncertainty about information presented in the media related to a specific topic (Guillaume and Bath, 2004). However, a high-level of information seeking can be associated with higher health anxiety (McMullan et al., 2019; Jagtap et al., 2021) and it is suggested that it can actually increase anxiety due to negative reinforcement (Starcevic and Berle, 2013). Relatedly, previous research has also shown that media messaging about infectious diseases can increase perceptions of public risk and anxiety (Sell et al., 2017). Moreover, extrapolating from work conducted during the Ebola and H1N1 outbreaks suggests that repeated exposure to the crisis (and associated information) during the COVID-19 pandemic may have led to increased anxiety and elevated stress responses that may lead to future negative health effects (Thompson et al., 2017; Garfin et al., 2020). Indeed, COVID-related information seeking has been found to be associated with obsessive-compulsive symptoms (Loosen et al., 2021) and other mental health problems (Gao et al., 2020). Additionally, greater COVID-related information seeking is related to an increase in pandemic-related preventative health behaviours such as hand washing (Liu, 2020). It is also related to a decline in other health-related searches (e.g., doctors’ appointments; Mangono et al., 2021) and associated with increased distress which is subsequently associated with reduced adherence to preventative measures (Siebenhaar et al., 2020).

In this paper, we report data from the UK COVID-19 Mental Health and Wellbeing study, a longitudinal, national survey that ran from March 2020 to July 2021. The present study focused on information seeking across the six waves of the study and whether it was related to symptoms of depression, anxiety, and wellbeing, as mental health outcomes, together with loneliness.

2. Methods

Participant recruitment was conducted by Taylor McKenzie, a social research company. A non-probability sample of adults (aged 18 years or older) was recruited from across the UK to the UK COVID-19 Mental Health and Wellbeing study (UK COVID-19-MH), with a longitudinal study design. UK COVID-19-MH has been detailed previously (O'Connor et al., 2021, O'Connor et al., 2022) and the main research questions were preregistered at AsPredicted.org (#41910).

Between 31st March and November 2020, members of an existing online UK panel (Panelbase.net) were invited by email to take part in an online survey on health and wellbeing. At wave 1, 7471 panel members were invited to take part and 3077 were included in the final sample (target sample was n=3,000). A quota sampling methodology was employed, with quotas based on age (18–24 years: 12%; 25–34: 17%; 35–44: 18%; 45–54: 18%; 55–64: 15%; ≥65: 20%), gender (women: 51%; men: 49%), socioeconomic grouping (SEG; assessed via The National Readership Survey social grade; AB:27%; C1: 28%; C2: 20%; DE: 25%, based on occupation, where A, B and C1 are higher and categories C2, D, E are lower) and region of the UK (12 regions). The panel has approximately 300,000 registered adult members and of those invited, 4394 did not take part in the survey. The majority were screened out as a particular quota was full (n=3527) and the remainder dropped out (n=867).

The first three waves occurred within the first 6 weeks of the UK lockdown, and the subsequent three waves were roughly every 2 to 3 months, with the interval between waves increasing over time. The survey included questions on a wide range of psychological and social measures along with questions about COVID-19. In this paper we test whether reporting COVID-related information seeking is associated with poorer mental health outcomes. As outlined above and in our previous work (O'Connor et al., 2022), a total of 3077 adults completed the survey at wave 1. Findings for waves 1 (31 March to 9 April 2020) to 6 (1st October to – 4th November 2020) are reported in the current paper. At wave 2, 89% of participants (n = 2742) completed the survey, 85% (n = 2604) completed wave 3, 77.5% (n = 2384) completed wave 4, 69.7% (n = 2144) completed wave 5 and 74.2% (n = 2283) completed wave 6. 1945 (63.2%) participants completed all six waves of the study.

2.1. Ethical approval

Participants provided written informed consent online. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human subjects/patients were approved by the University of Glasgow's Medical, Veterinary and Life Sciences Ethics Committee (approval number: 200190146) and participants consented for their data to be used in the research. Participants received £1.50 for the completion of each survey and were entered into prize draws.

3. Measures

Information seeking was assessed using a single item (“How often do you actively seek out information on COVID-19: Less than once a day; 1-5 times a day; 6-10 times a day; 11-20 times a day; 21-50 times a day; 50+times per day). Given the time urgency, this measure was devised by an expert panel of highly experienced researchers and considered to have good face validity. Moreover, similar measures have been used to assess frequency of information seeking in relation to a range of different health behaviours (e.g. Neumark et al., 2013; Jia et al., 2021).

Depressive symptoms were assessed via the nine-item Patient Health Questionnaire (PHQ-9; Löwe et al., 2004; Kroenke and Spitzer, 2002). The 7-item Generalized Anxiety Disorder (GAD-7; Spitzer, Kroenke, Williams and Lowe, 2006) tool was used to assess symptoms of generalised anxiety disorder. Both measures ask how often symptoms have been bothering the respondents in the past 2 weeks on a scale of 0 (not at all) to 3 (nearly every day). Cronbach's alpha for the scales in the current sample ranged from .90 to .93, and .92 to .94, respectively. Scores range from 0-27 on the PHQ-9 and 0-21 on the GAD-7, with higher scores indicating higher levels of depression and anxiety. Scores of≥10 on both measures are thought to indicate clinically significant cut-offs as indicators of at least moderate levels of depression and anxiety (Kroenke and Spitzer, 2002; Spitzer Kroenke, Williams and Lowe, 2006).

Mental wellbeing was assessed via the 7-item Short Warwick Edinburgh Mental Wellbeing Scale (SWEMWBS; Fat et al., 2017). Participants were asked to respond about their experiences over the last 2 weeks on a 1 (none of the time) to 5 (all the time) scale. Cronbach's alphas for the scale in the current sample ranged from .89 to .92. Scores range from 7-35 with a higher score indicating better mental wellbeing. A score of 19.3 is thought to indicate low levels of mental wellbeing and is used as a clinically significant cut-off (Fat et al., 2016).

Loneliness was measured using the UCLA 3-item loneliness scale (Hughes et al., 2004). Participants were asked to respond about their experiences over the last 2 weeks on a three-point scale (hardly ever, some of the time, often). Scores ranged from 3-9 where a higher score indicates greater loneliness. Cronbach's alphas for the scale in the current sample ranged from .88 to .90. To our knowledge, there are no published clinically significant cut-offs for this measure, we therefore conducted a median split on the data and treated any scores above the median as indicative of high loneliness.

To assess mental health status, participants were asked to indicate whether they had any mental health conditions, neuro-divergent disorders or alcohol/drug problems. To assess physical health status, participants were asked to indicate whether they had existing physical health conditions (e.g., diabetes, cancer, heart disease).

3.1. Analyses

Analyses were conducted in SPSS v.25 and multilevel models were conducted in HLM 8 (Raudenbush et al., 2011). We assessed how mental health outcomes and loneliness changed over the waves of the study along with whether information seeking differed by demographic characteristics (age, SEG, gender). Finally, we assessed whether information seeking was associated with mental health outcomes (depression, anxiety, wellbeing) and loneliness. Analyses were performed on the 1945 individuals who completed all six waves of the survey.

Hierarchical linear modelling was used to assess the relationship between COVID-related information seeking and mental health (depression, anxiety and wellbeing) and loneliness scores. The data were considered to have a two-level hierarchical structure. Level 1 variables (COVID-related information seeking, levels of depression, anxiety, mental wellbeing and loneliness at each wave) were group mean centred. Level 2 variables (gender (female, male), age (under and equal to 30, over 30), physical and mental health status (any mental health conditions reported: no/yes; any physical health conditions reported: no/yes), socioeconomic group (SEG): high (A + B + C1) vs. low (C2 + D + E)) were uncentred as they were dichotomous variables. The level 1 variables were modelled as random as we assumed that each of the within-person variables would vary from wave to wave. The level 2 variables were assumed to be fixed. For all the models tested here, an unrestricted level 1 variance-covariance structure was assumed. The main analyses were conducted in two blocks. First, we examined whether COVID-related information seeking was associated with each of the mental health and wellbeing outcomes (over the past 2 weeks) across the 6 waves. In these analyses, gender, age, SEG, whether participants had an existing mental health or physical health condition, and whether they lived alone or not were entered as covariates. Wave was also entered as a control variable in all analyses. Second, we explored whether the effects of COVID-related information seeking on mental health and loneliness were moderated by mental health status. Third, these analyses were repeated using the clinical cut-off to indicate high levels of depression, anxiety and loneliness and low levels of wellbeing using hierarchical Bernoulli models. Depression, anxiety and wellbeing scores were converted into a binary categorization based on previously published clinically meaningful cut-offs (scores of 10+ on the PHQ and GAD indicate moderate or severe levels of depression and anxiety; scores of 19.3 or less on the SWEMWBS indicate low wellbeing).

4. Results

4.1. Covid-related information seeking and mental health outcomes

4.1.1. Information seeking over time

From waves 1-4, the majority of participants reported that they sought information on Covid 1-5 times per day (see Fig. 1 & Table 1 ). During waves 5-6, the majority of participants reported that they sought information less than once per day, suggesting that information seeking reduced over time. The proportions of participants reporting information seeking more than 6 times per day also reduced over time, with the highest at wave 1 and lowest at wave 5.

Fig. 1.

Fig 1

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Information seeking over the six study waves (N=1945).

Table 1.

Information seeking and mental health scores across the six waves (N=1945).

Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6
31 Mar-9 Apr 10 Apr-27 Apr 28 Apr-11 May 27 May-15 Jun 17 Jul-7 Aug 1 Oct-4 Nov
Mean (SD)
Info seeking; N (%)
Less than 1x per day 449 (23.1) 501 (25.8) 610 (31.4) 805 (41.4) 1049 (53.9) 972 (50.0)
1-5 times a day 1240 (63.8) 1199 (61.6) 1139 (58.6) 1004 (51.6) 778 (40.0) 856 (44.0)
6-10 times a day 167 (8.6) 169 (8.7) 142 (7.3) 96 (4.3) 84 (4.3) 88 (4.5)
11-20 times a day 60 (3.1) 53 (2.7) 28 (1.4) 26 (1.3) 25 (1.3) 22 (1.1)
21-50 times a day 16 (0.8) 12 (0.6) 15 (0.8) 9 (0.5) 6 (0.3) 5 (0.3)
50+ times per day 13 (0.7) 11 (0.6) 11 (0.6) 5 (0.3) 3 (0.2) 2 (0.1)
Depression 5.6 (6.25) 5.49 (6.11) 5.54 (6.28) 5.25 (6.04) 5.24 (6.23) 5.38 (6.32)
Anxiety 4.9 (5.21) 4.71 (5.18) 4.55 (5.24) 4.37 (5.20) 4.28 (5.55) 4.42 (5.22)
Wellbeing 22.78 (6.13) 23.01 (6.23) 23.15 (6.23) 23.54 (6.38) 23.53 (6.45) 23.34 (6.41)
Loneliness 5.11 (1.92) 5.10 (1.92) 5.08 (1.92) 5.06 (1.94) 5.03 (1.93) 5.01 (1.95)
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4.1.2. Information seeking by participant characteristics

Chi squared analyses were conducted to assess the relationship between age, gender and SEG on information seeking over the six waves. There was a significant relationship between SEG and information seeking at waves 1, 2, 3, 5, and 6 (all p<.02) where people with high SEG occupations were more likely to report greater information seeking compared to people with low SEG occupations. There was a significant relationship between gender and information seeking at waves 3-6 (all p<.001), with women reporting greater information seeking than men. Finally, there was a significant relationship between age and information seeking at waves 2, 3, 4 and 6, with older individuals (aged over 30) reporting greater information seeking compared to participants under 30 (all p<.02).

4.1.3. Is Covid-related information seeking associated with mental health outcomes across the six waves?

The results of the hierarchical linear models found that there were significant positive associations between COVID-related information seeking and depression (Table 2 ; unadjusted, β = .25, p = .001; adjusted, β = 0.25, p = .001) and anxiety (unadjusted, β = .22, p<.001; adjusted, β = .22, p < .001) and a negative association between COVID-related information seeking and wellbeing (unadjusted, β = -.26, p<.001; adjusted, β = -.26, p < .001) and a positive association with loneliness (unadjusted, β = .05, p=.026; adjusted, β = .05, p = .026). The results showed that higher levels of COVID-related information seeking were associated with higher levels of depression, anxiety and loneliness and lower wellbeing across the 6 waves

Table 2.

Effects of COVID-related information seeking on depression, anxiety, wellbeing, and loneliness across the six waves.

Unadjusted
 Adjusted for covariates
Coeff SE d.f. P value Coeff SE d.f. P value
Depression
Intercept β00 4.51 0.13 1942 <0.001 β00 5.86 0.75 1936 <0.001
Physical health status β01 – – – – β01 -0.16 0.28 1936 0.555
Gender β02 – – – – β02 -0.04 0.26 1936 0.884
SEG β03 – – — — β03 -0.43 0.26 1936 0.098
Mental health status β04 – – – – β04 0.18 0.30 1936 0.547
Age β05 – – – – β05 0.15 0.33 1936 0.650
Living alone or not β06 – – — — β06 -0.34 0.34 1936 0.321
Level 1 slope
Info seeking – depression β10 0.25 0.08 1942 0.001 β10 0.25 0.08 1942 0.001
Level 1 slope
Wave - depression β20 -0.03 0.02 1942 0.079 β20 -0.04 0.02 1942 0.080
Anxiety
Intercept β00 4.53 0.11 1942 <0.001 β00 4.39 0.64 1936 <0.001
Physical health status β01 – – – – β01 -0.20 0.23 1936 0.399
Gender β02 – – – – β02 0.11 0.22 1936 0.612
SEG β03 – – — – β03 -0.24 0.22 1936 0.267
Mental health status β04 – – – – β04 0.22 0.25 1936 0.380
Age β05 – – – – β05 0.20 0.28 1936 0.488
Living alone or not β06 – – — — β06 -0.19 0.29 1936 0.505
Level 1 slope
Info seeking – anxiety β10 0.22 0.06 1942 <0.001 β10 0.22 0.06 1942 <0.001
Level 1 slope
Wave – anxiety β20 -0.09 0.02 1942 <0.001 β20 -0.09 0.02 1942 <0.001
Wellbeing
Intercept β00 23.23 0.13 1942 <0.001 β00 22.87 0.77 1936 <0.001
Physical health status β01 – – – – β01 0.09 0.28 1936 0.742
Gender β02 – – – – β02 -0.23 0.26 1936 0.378
SEG β03 – – — – β03 0.39 0.26 1936 0.135
Mental health status β04 – – – – β04 -0.49 0.30 1936 0.105
Age β05 – – – – β05 0.14 0.34 1936 0.135
Living alone or not β06 – – — — β06 -0.03 0.35 1936 0.930
Level 1 slope
Info seeking – wellbeing β10 -0.26 0.06 1942 <0.001 β10 -0.26 0.07 1942 <0.001
Level 1 slope
Wave – wellbeing β20 0.11 0.02 1942 <0.001 β 20 0.11 0.02 1942 <0.001
Loneliness
Intercept β00 5.06 0.04 1942 <0.001 β00 5.01 0.34 1936 <0.001
Physical health status β01 – – – – β01 -0.01 0.09 1936 0.928
Gender β02 – – – – β02 0.09 0.08 1936 0.296
SEG β03 – – — – β03 -0.06 0.08 1936 0.437
Mental health status β04 – – – – β04 0.15 0.09 1936 0.103
Age β05 – – – — β05 -0.01 0.11 1936 0.942
Living alone or not β06 – – — — β06 -0.09 0.11 1936 0.396
Level 1 slope
Info seeking – loneliness β10 0.05 0.02 1942 0.026 β10 0.05 0.02 1942 0.026
Level 1 slope
Wave – loneliness β20 -0.02 -0.01 1942 0.004 β 20 -0.02 0.01 1942 0.004
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4.1.4. Is Covid-related information seeking associated with clinically meaningful mental health outcomes?

The results of the hierarchical Bernoulli models found that there were significant positive associations between COVID-related information seeking and clinically meaningful levels of depression (unadjusted, Odds Ratio = 1.14, 95% CI 1.014,1.280; adjusted, Odds Ratio = 1.09, 95% CI 1.014,1.280), anxiety (unadjusted, Odds Ratio = 1.21, 95% CI 1.066,1.375; adjusted, Odds Ratio = 1.17, 95% CI 1.066,1.375) and wellbeing (unadjusted, Odds Ratio = 1.12, 95% CI 1.003,1.248; adjusted, Odds Ratio = 1.12, 95% CI 1.003,1.248). The association between COVID-related information seeking and clinically meaningful levels of loneliness were not statistically significant (Table 3 ).

Table 3.

Effects of COVID-related information seeking on clinically meaningful cut-offs for depression, anxiety, wellbeing and loneliness across the six waves.

Unadjusted
 Adjusted for covariates
OR 95% CI Coeff SE d.f. P value OR 95% CI Coeff SE d.f. P value
Depression
Intercept β00 0.15 (0.136,0.171) -1.88 0.06 1942 <0.001 β00 0.18 (0.087,0.353) -1.74 0.36 1936 <0.001
Physical health status β01 – – – – – – β01 0.91 (0.704,1.174) 0.01 0.11 1936 0.466
Gender β02 – – – – – – β02 0.96 (0.754,1.219) -0.04 0.12 1936 0.729
SEG β03 – – – – — — β03 0.78 (0.611,0.987) -0.25 0.12 1936 0.039
Mental health status (MH) β04 – – – – – – β04 1.11 (0.847,1.459) 0.11 0.14 1936 0.445
Age β05 – – – – – – β05 1.18 (0.864,1.606) 0.16 0.16 1936 0.299
Living alone or not β06 – – – – — — β06 0.91 (0.666,1.251) -0.09 0.16 1936 0.570
Level 1 slope
Info seeking – depression β10 1.14 (1.014,1.280) 0.13 0.06 1942 0.029 β10 1.09 (1.014,1.280) 0.13 0.06 1936 0.029
Level 1 slope
Wave – depression β20 0.97 (0.940,1.011) -0.02 0.02 1942 0.164 β20 0.98 (0.940,1.011) -0.03 0.02 1936 0.164
Anxiety
Intercept β00 0.09 (0.086,0.110) -2.33 0.06 1942 <0.001 β00 0.08 (0.037,0.159) -2.56 0.37 1936 <0.001
Physical health status β01 – – – – – – β01 0.86 (0.679,1.153) -0.12 0.13 1936 0.366
Gender β02 – – – – – – β02 1.04 (0.815,1.339) 0.04 0.13 1936 0.730
SEG β03 – – – – — — β03 0.93 (0.728,1.193) -0.07 0.13 1936 0.575
Mental health status (MH) β04 – – – – – – β04 1.13 (0.853,1.497) 0.12 0.14 1936 0.393
Age β05 – – – – – – β05 1.18 (0.856,1.627) 0.17 0.16 1936 0.312
Living alone or not β06 – – – – — — β06 0.87 (0.630,1.214) -0.13 0.17 1936 0.422
Level 1 slope
Info seeking – anxiety β10 1.21 (1.066,1.375) 0.19 0.07 1942 0.003 β10 1.21 (1.066,1.375) 0.19 0.07 1936 0.003
Level 1 slope
Wave – anxiety β20 0.96 (0.924,1.000) -0.04 0.02 1942 0.049 β20 0.96 (0.924,1.000) -0.04 0.02 1936 0.049
Wellbeing
Intercept β00 0.22 (0.199,0.248) -1.51 0.06 1942 <0.001 β00 0.27 (0.139,0.521) -1.31 0.34 1936 <0.001
Physical health status β01 – – – – – – β01 1.09 (0.858,1.392) 0.09 0.12 1936 0.470
Gender β02 – – – – – – β02 1.11 (0.882,1.392) 0.10 0.12 1936 0.379
SEG β03 – – – – — — β03 0.75 (0.600,0.945) -0.28 0.12 1936 0.014
Mental health status (MH) β04 – – – – – – β04 1.31 (1.009,1.691) 0.27 0.13 1936 0.042
Age β05 – – – – – – β05 0.97 (0.723,1.304) -0.03 0.15 1936 0.845
Living alone or not β06 – – – – — — β06 1.07 (0.794,1.436) 0.07 0.15 1936 0.664
Level 1 slope
Info seeking – wellbeing β10 1.12 (1.003,1.248) 0.11 0.06 1942 0.044 β10 1.12 (1.003,1.248) 0.11 0.06 1936 0.044
Level 1 slope
Wave – wellbeing β20 0.97 (0.940,1.005) -0.03 0.02 1942 0.044 β20 0.97 (0.940,1.005) -0.03 0.02 1936 0.097
Loneliness
Intercept β00 7.78 (6.909,8.763) 2.05 0.06 1942 <0.001 β00 10.49 (5.113,21.525) 2.35 0.37 1936 <0.001
Physical health status β01 – – – – – – β01 1.10 (0.843,1.427) 0.09 0.13 1936 0.490
Gender β02 – – – – – – β02 0.93 (0.729,1.194) -0.07 0.13 1936 0.580
SEG β03 – – – – — — β03 1.01 (0.791,1.293) 0.02 0.13 1936 0.931
Mental health status (MH) β04 – – – – – – β04 0.82 (0.624,1.090) -0.19 0.14 1936 0.175
Age β05 – – – – – – β05 0.88 (0.641,1.217) -0.12 0.16 1936 0.447
Living alone or not β06 – – – – — — β06 1.22 (0.879,1.689) 0.20 0.17 1936 0.235
Level 1 slope
Info seeking – loneliness β10 0.93 (0.818,1.054) -0.07 0.06 1942 0.251 β10 0.93 (0.818,1.054) -0.07 0.06 1936 0.251
Level 1 slope
Wave – loneliness β20 1.05 (1.013,1.093) 0.05 0.02 1942 0.008 β20 1.05 (1.013,1.093) 0.02 1936 0.008
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Note: OR = odds ratios.

4.1.5. Are the effects of COVID-related information seeking moderated by current mental health status?

Mental health status did not significantly moderate the relationship between COVID-related information seeking and any of the mental health outcomes or loneliness (See Supplementary Table 1).

5. Discussion

The general trend in the data was that information seeking declined over time, peaking at the earliest stages of the pandemic (waves 1 and 2; March- April 2020) with the majority of participants reporting they sought information between one and five times per day. By wave 5 (July-August 2020), the majority of participants reported that they sought information less than once a day. Nevertheless, information seeking was found to be related to all three mental health outcomes where higher levels of information seeking were associated with greater depression and anxiety scores and lower wellbeing.

Information seeking was also found to differ by participant demographics, with older participants, women and those from higher socioeconomic groups reporting greater information seeking. In addition, individuals reporting higher levels of information seeking were more likely to also report mental health outcomes that were at or above the clinically meaningful thresholds. In particular, high levels of information seeking were associated with the clinical threshold for moderate or severe anxiety on the GAD.

The current findings are consistent with other work that has explored the adverse effects of information seeking and exposure to media messaging during public health crises (Thompson et al., 2017; Garfin et al., 2020). Previously reported negative effects have included increased psychological distress which may have widespread impacts, including future mental health problems (e.g., Starcevic and Berle, 2013; Gao et al., 2020; Loosen et al., 2021) as well as potentially reducing compliance with preventative health behaviours (Siebenhaar et al., 2020). Moreover, these current findings are in line with other studies conducted during the COVID-19 pandemic and have important implications for future pandemics. For example, Loosen et al. (2021) found evidence that COVID-related information seeking was associated with increases in obsessive-compulsive symptoms and underlined the importance of closely monitoring the public's mental health during public health crises (Loosen et al., 2021). Similarly, Gao et al. (2020) found high prevalence of mental health problems, in particular, anxiety and depression, to be associated with high social media usage in individuals during the COVID-19 outbreak. Wang et al. (2021), in a large study of seven middle-income countries in Asia, also found that spending less time on health information was a protective factor for mental health. Taken together, there is a clear need for governments and health agencies to consider strategies to help mitigate the negative effects of the “infodemic” which often arises during public health emergencies.

How might high levels of information seeking about COVID-19 lead to adverse mental health outcomes? Evidence from other public health crises and traumatic events show that in cases of uncertainly, where information is unknown or badly communicated, exposure to this information can trigger heightened appraisals of threat, which lead to feelings of uncontrollability and increased levels of anxiety (Garfin et al., 2020; Taha et al., 2014). This is likely to be the case during the COVID-19 pandemic, particularly during the early stages before a vaccine had become viable and the future remained entirely uncertain (O'Connor et al., 2020, 2022). In another earlier example, during the Ebola outbreak in 2014, a study in the United States (where risk levels were low) found that higher levels of exposure to Ebola-related stories were associated with increased worry, distress and impaired functioning (Thompson et al., 2017).

It has also been shown that the amount and type of media exposure can negatively affect psychological distress following publicly traumatic events. For example, following the Boston Marathon bombings, individuals who had spent a large amount of time exposed to bombing-related media reported greater acute stress symptoms, with future exposure to these kind of events creating a cycle of distress (Garfin et al., 2020; Holman et al., 2014). Moreover, the same research team found that exposure to particular types of media also made a difference, such that exposure to graphic images (i.e., that included blood) was associated with elevated posttraumatic stress and fear 6 months after the bombings. Therefore, it is likely that the amount, type and often-graphic nature of media exposure will have contributed to the associations between COVID-19 information-seeking and psychological distress observed in the current study. Unfortunately, given the time urgent nature of setting up studies early in the pandemic together with the need to reduce participant burden, it was not possible to collect information on the different sources of information sought by participants. However, as outlined above, badly communicated, uncertain and graphic information may have had a particularly adverse effect on mental health outcomes during COVID-19 pandemic.

This study also found that COVID related information seeking was associated with higher levels of loneliness (while controlling for whether participants lived alone or not), although the association was small and did not translate into a meaningful clinical cut-off for loneliness. It is clear from recent meta-analytical findings that there were small, but robust increases in loneliness during the pandemic (Ernst et al., 2022). However, less is known about the psychological and behavioural factors that are associated with changes in loneliness levels. Therefore, the current finding is important as it suggests that information seeking may play a role in helping improving our understanding of individual differences in loneliness during times of uncertainty and risk.

Overall, the present research demonstrates that information seeking is associated with poorer mental health outcomes. Implications of this work suggest that management of information seeking behaviour may be one method to reduce anxiety and improve wellbeing during situations that involve large amounts of information sharing. Another method might be to target repetitive thinking such as worry and rumination likely triggered by information seeking. A recent meta-analysis has identified a number of useful interventions that can reliably reduce worry and rumination. For example, techniques that encourage individuals to challenge their thinking style, to disengage from the emotional responses brought on by worry or rumination as well as mindfulness-based approaches should be considered (McCarrick et al., 2021, see also Prudenzi et al., 2022). Additionally, due to the sheer amount of information from differing sources, of varying validity, that is available, it is difficult for people to know what sources of information to rely on or trust. It is also important that health authorities and governmental organisations provide clear information during these situations in order to reduce potential uncertainty that can stem from the varying quality of information available online. However, the current findings suggest that public health agencies should encourage members of the public to limit their information seeking and to have regular breaks from media and messaging. Future research might also usefully investigate the extent to which high levels of information seeking may contribute to ‘pandemic fatigue’ and emerging work on COVID-19 burnout and burnout more broadly (Al-Ghunaim et al., 2021; Wang et al., 2021).

Finally, we recognise that strengths and limitations of the present study include that the study is likely to have under-recruited individuals with particularly poor mental health outcomes and the use of a non-probability, quota-based sampling method is a shortcoming as not all members of the population had a chance to participate, and this may limit the generalisability of the findings. Nevertheless, it is important to note that utilisation of this sampling approach allowed us to ensure we recruited a nationally representative sample. The study also relies on self-reported outcomes including a single item measure of information seeking, though, it is worth noting that this measure was devised by a panel of experts, is similar to other measures and has good face validity. Additionally, we do not know the direction of causality and whether poorer mental health causes greater information seeking, or information seeking causes poorer mental health. Despite this, the study recruited a relatively large, quota-based sample and is able to report the relationship between the key variables over a substantial period of time.

In conclusion, the results of the current study show that information seeking during the COVID-19 pandemic was related to poorer mental health outcomes. The results were particularly pronounced for anxiety. Reducing or managing information seeking behaviour may be one method to reduce anxiety during situations involving large amounts of information sharing, such as pandemics and future public health crises.

Funding

This work was supported by the University of Glasgow, Samaritans, Scottish Association for Mental Health and the Mindstep Foundation.

Author contributions

S.W. and D.O'C. led the statistical analyses and led the drafting of the manuscript but all authors contributed to further drafting, revising and approved the final manuscript for submission.

Declaration of Competing Interest

None of the authors have any conflict of interest to declare.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psychres.2022.114876.

Appendix. Supplementary materials

mmc1.docx (29.5KB, docx)

References

  1. Al-Ghunaim T., Johnson J., Biyani C.S., O'Connor D.B. The psychological and occupational impact of the Coronavirus (COVID-19) pandemic on UK surgeons: a qualitative investigation. BMJ Open. 2021;11 doi: 10.1136/bmjopen-2020-045699. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ernst M., Niederer D., Werner A.M., Czaja S.J., Mikton C., Ong A.D., Rosen T., Brähler E., Beutel M.E. Loneliness before and during the COVID-19 pandemic: a systematic review with meta-analysis. Am. Psychol. 2022;77(5):660–677. doi: 10.1037/amp0001005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Fat L.N., Scholes S., Boniface S., Mindell J., Stewart-Brown S. Evaluating and establishing national norms for mental wellbeing using the short Warwick–Edinburgh Mental Well-being Scale (SWEMWBS): findings from the Health Survey for England. Qual. Life Res. 2017;26(5):1129–1144. doi: 10.1007/s11136-016-1454-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Holman E.A., Garfin D.R., Silver R.C. Media's role in broadcasting acute stress following the Boston Marathon bombings. Proc. Natl. Acad. Sci. 2014;111:93–98. doi: 10.1073/pnas.1316265110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Gao J., Zheng P., Jia Y., Chen H., Mao Y., Chen S.…Dai J. Mental health problems and social media exposure during COVID-19 outbreak. PLoS One. 2020;15(4) doi: 10.1371/journal.pone.0231924. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Garfin D.R., Silver R.C., Holman E.A. The novel coronavirus (COVID-2019) outbreak: amplification of public health consequences by media exposure. Health Psychol. 2020;39:355–357. doi: 10.1037/hea0000875. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Guillaume L., Bath P.A. The impact of health scares on parents’ information needs and preferred information sources: a case study of the MMR vaccine scare. Health Inform. J. 2004;10:522. [Google Scholar]
  8. Hughes M.E., Waite L.J., Hawkley L.C., Cacioppo J.T. A short scale for measuring loneliness in large surveys - Results from two population-based studies. Res. Aging. 2004;26:655–672. doi: 10.1177/0164027504268574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Jagtap S., Shamblaw A.L., Rumas R., Best M.W. Information seeking and health anxiety during the COVID-19 pandemic: the mediating role of catastrophic cognitions. Clin. Psychol. Psychother. 2021 doi: 10.1002/cpp.2684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Jia X., Pang Y., Liu L.S. Online health information seeking behavior: a systematic review. In Healthcare. 2021;9(12):1740. doi: 10.3390/healthcare9121740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity measure. Psychiatr. Ann. 2002;1:509–515. [Google Scholar]
  12. Löwe B., Kroenke K., Herzog W., Gräfe K. Measuring depression outcome with a brief self-report instrument: sensitivity to change of the Patient Health Questionnaire (PHQ-9) J. Affect. Disord. 2004;81(1):61–66. doi: 10.1016/S0165-0327(03)00198-8. [DOI] [PubMed] [Google Scholar]
  13. Liu P.L. COVID-19 information seeking on digital media and preventive behaviors: the mediation role of worry. Cyberpsychol. Behav. Soc. Netw. 2020;23(10):677–682. doi: 10.1089/cyber.2020.0250. [DOI] [PubMed] [Google Scholar]
  14. Loosen A.M., Skvortsova V., Hauser T.U. Obsessive–compulsive symptoms and information seeking during the Covid-19 pandemic. Transl. Psychiatry. 2021;11(1):1–10. doi: 10.1038/s41398-021-01410-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Mangono T., Smittenaar P., Caplan Y., Huang V.S., Sutermaster S., Kemp H., Sgaier S.K. Information-seeking patterns during the COVID-19 pandemic across the United States: longitudinal analysis of google trends data. J. Med. Internet Res. 2021;23(5):e22933. doi: 10.2196/22933. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. McCarrick D., Prestwich A., Prudenzi A., O’Connor D.B. Health effects of psychological interventions for worry and rumination: a meta-analysis. Health Psychol. 2021;40:617–630. doi: 10.1037/hea0000985. [DOI] [PubMed] [Google Scholar]
  17. McMullan R.D., Berle D., Arnáez S., Starcevic V. The relationships between health anxiety, online health information seeking, and cyberchondria: systematic review and meta-analysis. J. Affect. Disord. 2019;245:270–278. doi: 10.1016/j.jad.2018.11.037. [DOI] [PubMed] [Google Scholar]
  18. Naeem S.B., Bhatti R. The Covid-19 ‘infodemic’: a new front for information professionals. Health Info. Libr. J. 2020;37(3):233–239. doi: 10.1111/hir.12311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Neumark Y., Lopez-Quintero C., Feldman B.S., Hirsch Allen A.J., Shtarkshall R. Online health information seeking among Jewish and Arab adolescents in Israel: results from a national school survey. J. Health Commun. 2013;18(9):1097–1115. doi: 10.1080/10810730.2013.778360. [DOI] [PubMed] [Google Scholar]
  20. O'Connor D.B., Aggleton J.P., Chakarabati D., Cooper C.L., Creswell C., Dunsmuir S., Fiske S.T., Gathercole S., Gough B., Ireland J.L., Jones M.V., Jowett A., Kagan C., Karanika-Murray M., Kaye L.K., Kumari V., Lewandowsky S., Lightman S., Malpass D., Meins E., Morgan B.P., Morrison Coulthard L.J., Reicher S.D., Schacter D.L., Sherman S.M., Simms V., Williams A., Wykes T., Armitage C.J. Research priorities for the COVID-19 pandemic and beyond: a call to action for psychological science. Br. J. Psychol. 2020;111:603–629. doi: 10.1111/bjop.12468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. O'Connor R.C., Wetherall K., Cleare S., McClelland H., Melson A.J., Niedzwiedz C.L., O'Carroll R.E., O'Connor D.B., Platt S., Scowcroft E., Watson B., Zortea T., Ferguson E., Robb K.A. Mental health and well-being during the COVID- 19 pandemic: longitudinal analyses of adults in the UK COVID-19 mental health and wellbeing study. Br. J. Psychiatry. 2021;218:326–333. doi: 10.1192/bjp.2020.212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. O'Connor D.B., Wilding S., Ferguson E., Cleare S., Wetherall K., McClelland H., O'Connor R.C. Effects of COVID-19-related worry and rumination on mental health and loneliness during the pandemic: longitudinal analyses of adults in the UK COVID-19 Mental Health and Wellbeing study. J. Ment. Health. 2022 doi: 10.1080/09638237.2022.2069716. [DOI] [PubMed] [Google Scholar]
  23. Prudenzi A., Graham C.D., Rogerson O., O'Connor D.B. Mental health during the COVID-19 pandemic: exploring the role of psychological flexibility and stress-related variables. Psychol. Health. 2022 doi: 10.1080/08870446.2021.2020272. [DOI] [PubMed] [Google Scholar]
  24. Raudenbush S.W., Bryk A.S., Congdon R. 2011. HLM 7.00 for Windows. Skokie (Illinois): Scientific Software International. [Google Scholar]
  25. Sell T.K., Boddie C., McGinty E.E., Pollack K., Clegg Smith K., Burke T.A., Rutkow L. Media messages and perception of risk for Ebola virus infection, United States. Emergency Infect. Dis. 2017;23:108–111. doi: 10.3201/eid2301.160589. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Siebenhaar K.U., Köther A.K., Alpers G.W. Dealing with the COVID-19 infodemic: Distress by information, information avoidance, and compliance with preventive measures. Front Psychol. 2020:2981. doi: 10.3389/fpsyg.2020.567905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Starcevic V., Berle D. Cyberchondria: towards a better understanding of excessive health-related internet use. Expert. Rev. Neurother. 2013;13(2):205–213. doi: 10.1586/ern.12.162. [DOI] [PubMed] [Google Scholar]
  28. Statista (2020). What sources do you actively use to keep informed about the COVID-19 /coronavirus pandemic? https://www.statista.com/statistics/1108009/sources-of-information-about-the-covid-19-corona-pandemic/ Accessed April 2022.
  29. Taha S.A., Matheson K., Anisman H. H1N1 was not all that scary: uncertainty and stressor appraisals predict anxiety related to a coming viral threat. Stress Health. 2014;30:149–157. doi: 10.1002/smi.2505. [DOI] [PubMed] [Google Scholar]
  30. Thompson R.R., Garfin D.R., Holman E.A., Silver R.C. Distress, worry, and functioning following a global health crisis: a national study of Americans’ responses to Ebola. Clin. Psychol. Sci. 2017;5(3):513–521. [Google Scholar]
  31. Tran BX, Dang AK, Thai PK, Le HT, Le XTT, Do TTT, Nguyen TH, Pham HQ, Phan HT, Vu GT, Phung DT, Nghiem SH, Nguyen TH, Tran TD, Do KN, Truong DV, Vu GV, Latkin CA, Ho RCM, Ho CSH. Coverage of health information by different sources in communities: implication for COVID-19 epidemic response. Int. J. Environ. Res. Public Health. 2020;17:3577. doi: 10.3390/ijerph17103577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Wang C., Pan R., Wan X., Tan Y., Xu L., Ho C.S., Ho R.C. Immediate psychological responses and associated factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. Int. J. Environ. Res. Public Health. 2020;17(5):1729. doi: 10.3390/ijerph17051729. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Wang C, Tee M, Roy AE, Fardin MA, Srichokchatchawan W, Habib HA, et al. The impact of COVID-19 pandemic on physical and mental health of Asians: a study of seven middle-income countries in Asia. PLoS One. 2021;16(2) doi: 10.1371/journal.pone.0246824. [DOI] [PMC free article] [PubMed] [Google Scholar]

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