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RESEARCH ARTICLE
Loneliness in the UK during the COVID-19
pandemic: Cross-sectional results from the
COVID-19 Psychological Wellbeing Study
Jenny M. GroarkeID
1
*, Emma Berry
1
, Lisa Graham-Wisener
1
, Phoebe E. McKenna-
Plumley
1
, Emily McGlinchey
2
, Cherie Armour
1,2
1Centre for Improving Health-Related Quality of Life (CIHRQoL), School of Psychology, Queen’s University
Belfast, Belfast, United Kingdom, 2Stress Trauma and Related Conditions (STARC) Research Lab, School
of Psychology, Queen’s University Belfast, Belfast, United Kingdom
*j.groarke@qub.ac.uk
Abstract
Objectives
Loneliness is a significant public health issue. The COVID-19 pandemic has resulted in lock-
down measures limiting social contact. The UK public are worried about the impact of these
measures on mental health outcomes. Understanding the prevalence and predictors of
loneliness at this time is a priority issue for research.
Method
The study employed a cross-sectional online survey design. Baseline data collected
between March 23rd and April 24th 2020 from UK adults in the COVID-19 Psychological
Wellbeing Study were analysed (N = 1964, 18–87 years, M = 37.11, SD = 12.86, 70%
female). Logistic regression analysis examined the influence of sociodemographic, social,
health and COVID-19 specific factors on loneliness.
Results
The prevalence of loneliness was 27% (530/1964). Risk factors for loneliness were younger
age group (OR: 4.67–5.31), being separated or divorced (OR: 2.29), scores meeting clinical
criteria for depression (OR: 1.74), greater emotion regulation difficulties (OR: 1.04), and
poor quality sleep due to the COVID-19 crisis (OR: 1.30). Higher levels of social support
(OR: 0.92), being married/co-habiting (OR: 0.35) and living with a greater number of adults
(OR: 0.87) were protective factors.
Conclusions
Rates of loneliness during the initial phase of lockdown were high. Risk factors were not spe-
cific to the COVID-19 crisis. Findings suggest that supportive interventions to reduce loneli-
ness should prioritise younger people and those with mental health symptoms. Improving
emotion regulation and sleep quality, and increasing social support may be optimal initial tar-
gets to reduce the impact of COVID-19 regulations on mental health outcomes.
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OPEN ACCESS
Citation: Groarke JM, Berry E, Graham-Wisener L,
McKenna-Plumley PE, McGlinchey E, Armour C
(2020) Loneliness in the UK during the COVID-19
pandemic: Cross-sectional results from the COVID-
19 Psychological Wellbeing Study. PLoS ONE
15(9): e0239698. https://doi.org/10.1371/journal.
pone.0239698
Editor: Michio Murakami, Fukushima Medical
University School of Medicine, JAPAN
Received: July 14, 2020
Accepted: September 11, 2020
Published: September 24, 2020
Copyright: ©2020 Groarke 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
author and source are credited.
Data Availability Statement: Data cannot be
shared publicly because of privacy and ethical
restrictions. Data are available from the faculty of
Engineering and Physical Sciences at Queen’s
University Belfast Research Ethics Committee
(email: facultyreceps@qub.ac.uk), the
corresponding author, and/or the PI (Armour: c.
armour@qub.ac.uk) for researchers who meet the
criteria for access to confidential data, in
conjunction with an appropriate data sharing
agreement.
Introduction
On January 31
st
, 2020 the first case of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) which causes COVID-19, was confirmed in the UK. On March 23
rd
a state of
lockdown was announced by UK governments across the four devolved nations. Since this
time, the UK population has experienced a considerable reduction (and in some cases a com-
plete absence) of in-person social contact. While this acute phase of lockdown will be loosened
with decreasing cases of COVID-19, periods of physical distancing are likely to be enforced
with new waves of transmission.
With UK mental health services straining to allocate resources to support the growing num-
ber of people with mental health problems pre-pandemic; it is predicted that there will be an
upsurge of service demand as a result of the psychological sequela of COVID-19 [1]. This is a
concern echoed worldwide [2]. In fact, among the UK public, fears surrounding the psycho-
logical harms of COVID-19 are ranked above that of physical wellbeing [3]. Prior to the pan-
demic the UK government had identified loneliness as a significant public health issue, and it
has been described as an epidemic [4]. Loneliness is a priority focus if we are to fully under-
stand the psychosocial impact of the COVID-19 pandemic [1].
Loneliness and COVID-19
As physical distancing rules have resulted in a decline of in-person social contact, it is suggested
that rates of loneliness will rise, which may increase prevalence of mood disorders, self-harm, and
suicide, and exacerbate pre-existing mental health conditions [1]. Loneliness is associated with
worse physical and mental health [5–8] and increases mortality risk [9,10]. While situational lone-
liness is associated with mortality risk, it is more pronounced in individuals experiencing chronic
loneliness [11]. This suggests that, without intervention, prolonged loneliness can have a profound
negative impact on health and wellbeing. Systematic review findings recommend that interven-
tions addressing loneliness should focus on individuals who are socially isolated and should target
determinants of loneliness which are amenable to change [12].
While existing evidence provides a framework to understand factors which inflate vulnerability
to loneliness, we lack a comprehensive understanding of how this might differ in the context of a
pandemic. In particular, how psychosocial factors or factors specific to disease-containment poli-
cies might elevate or mitigate risk. Moreover, in non-pandemic contexts, evidence suggests that
the prevalence of loneliness ranges from 6–76%; with variations across demographic groups
[5,13–17] and countries [18,19]. Considering the drastic changes in the current social context, it is
conceivable that the prevalence of situational loneliness will be high; which is substantiated by the
publics’ concerns regarding the impact of social isolation on mental health [1,3].
Risk factors for loneliness
Much of what we know in regard to risk factors for loneliness emerges from research with
older adults, with a smaller body of research with adolescents and younger adults. Associations
between age and loneliness have been positive [20], negative [21], and u-shaped with peaks in
younger and older adulthood [19,22]. Findings on gender differences have also been mixed,
with some studies reporting higher loneliness in females [23] and others finding no effect of
gender [7,8,10]. Risk of loneliness is greater among individuals with mental [24] and chronic
physical health conditions [25], however the direction of the effect is unclear.
The COVID-19 crisis presents many challenges for managing feelings of loneliness. Studies
of quarantine have found that individuals struggle to adapt to a way of life incongruent with
humans’ social nature [26], and report a range of negative psychological reactions to quaran-
tine, including loneliness [27,28]. In non-pandemic contexts, geographical isolation, living
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Loneliness in the UK during the COVID-19 pandemic
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Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
alone, and lack of social engagement predicts loneliness in adult and older adult populations
[5,13,15,20,29–31]. Limited social interaction is a particularly important risk factor for loneli-
ness among younger people [19,32]. On the other hand, close relationships and social capital
have been associated with lower odds of being lonely [7,24]. Redundancy or unemployment
due to the pandemic may challenge people’s economic security, and socioeconomic status,
lower income, and unemployment have been associated with increased loneliness [7,33]. Peo-
ple working in key roles during the pandemic, especially those in healthcare professions, are
under increased pressure, and there is concern that their mental health may be at risk [1]. It is
not clear how this might extend to experiences of loneliness. However, one study conducted
during the SARS outbreak did find that loneliness was reported by both healthcare workers
and non-healthcare workers [34].
Recent evidence in the context of COVID-19 reports high levels of distress and loneliness
in US regions with quarantine or shelter-in-place guidelines [35–38]. In the UK, 36% of
respondents reported feeling sometimes or often lonely during COVID-19 [39], and Bu et al.,
[40] found that prevalence of severe loneliness was 14% and remained relatively stable over 6
weeks of lockdown. Being younger, female [39–41], having lower socioeconomic status, a pre-
existing mental health condition, and living alone increased the odds of being lonely [40,41].
During physical distancing in Spain, younger people, females, those with less social contact
and lower sleep quality reported higher loneliness [21]. Financial concerns and worries about
the prolonged impact of quarantine are associated with loneliness; as are feelings of fear, bore-
dom, and uncertainty [1,26,42]. Studies have found that more frequent in-person contact miti-
gates the impact of the pandemic on loneliness [37,38], and that living with others, larger
social network size, and greater social support are protective factors [40]. Furthermore loneli-
ness in the current pandemic context is associated with increased depression, anxiety and sui-
cidal ideation in the US [36,43], and with greater depression, anxiety, and stress in the UK
[44]. In Poland, loneliness had a negative impact on mental health symptoms and increased
participants’ affective response to aspects of the COVID-19 crisis [42].
Aims and objectives
Existing evidence surrounding loneliness in the context of COVID-19 has revealed several key
determinants of loneliness and the negative impact on mental health outcomes if experiences
persist without intervention. There is a need to build on this small body of research. The aim
of the current study is to explore the prevalence of loneliness, as well as, risk and protective fac-
tors in a UK context. In doing so, this study helps to address key research priorities for the
COVID-19 pandemic identified by researchers and the general public [1,3]. This is essential in
order to guide an evidence-based public health approach to prevent psychological morbidity
as a result of the current and future waves of COVID-19, and may also be an important factor
in the public’s ability to adhere to physical distancing regulations over time.
Our primary objectives were to 1) determine the rate of loneliness among adults in the UK
during the early stages of the COVID-19 pandemic reported via the COVID-19 Psychological
Wellbeing Study and 2) identify differences in sociodemographic, social, health, and COVID-
19-specific factors between people with and without loneliness to determine the risk and pro-
tective factors for loneliness.
Methods
Study design
This study uses data from the COVID-19 Psychological Wellbeing study, an online study of
mental health in the UK during the COVID-19 pandemic. The study began on the day the UK
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lockdown was announced (March 23
rd
) and closed for new entries on April 24
th
2020. In the
current study, we examine the cross-sectional baseline data of the COVID-19 Psychological
Wellbeing study. Full methodological details of the study are reported elsewhere [45]. We used
the STROBE cross-sectional checklist when writing this report [46].
Procedure
The study was approved by the ethical review panel in the faculty of Engineering and Physical
Sciences at Queen’s University Belfast (Reference: EPS 20_96) and also Glasgow Caledonian
University Health and Life Sciences Ethics Committee, (Reference: HLS/PSWAHS/19/157).
Participants were recruited via social media platforms (e.g., Twitter, Facebook). Additional
data was collected using a panel of UK residents hosted by Prolific. After providing informed
consent participants completed the online survey, which was administered through Qualtrics.
Participants
There were 2511 responses to the baseline survey. Following screening for inclusion criteria
(i.e., UK residents over 18 years of age, informed consent provided) and data quality (i.e., not
completing any measures, or having a completion time less than half the median completion
time), 522 respondents were removed from the dataset. This resulted in 1989 eligible
participants.
Of these eligible participants, 1402 (70.5%) were recruited via Prolific and received com-
pensation for their time (£1–2). Those who were recruited via a social media campaign
(29.5%) were included into a prize draw for one of six £150 vouchers. There were some signifi-
cant, albeit slight, differences in sociodemographic, COVID-19, social and health factors across
these two recruitment strategies (S1 Table). Relative to participants recruited through social
media, the sample recruited via Prolific had a higher proportion of respondents from England,
more males, were younger, had lower self-rated income and education. More of the respon-
dents recruited through Prolific were self-isolating and fewer were in keyworker roles. How-
ever, recruitment strategy had no association with level or prevalence of loneliness.
Measures
Loneliness. Loneliness was measured using the Three-Item Loneliness Scale [47]. The
scale measures three different aspects of loneliness, (social connectedness, relational connect-
edness and self-perceived connectedness), with higher scores indicating higher levels of loneli-
ness. Scores above 6 have been used as a cut-off point for loneliness in past research [7,48].
The psychometric properties of the scale are well documented [47,49]. Reliability (i.e., internal
consistency) of the measure was high in the current sample (Cronbach’s alpha: α= .83).
Sociodemographic variables. Participants provided information on their country of resi-
dence, gender, age, self-rated income level (below average, average, above average), employ-
ment status (full-time, part-time, unemployed, self-employed [full or part-time], not able to
work, retired, student), and highest level of educational attainment (no qualifications, com-
pleted secondary school to o-level, GCSE or similar, completed Secondary school to A-level or
similar, certificate of Higher Education or similar, Diploma of Higher Education or similar,
Undergraduate degree, Postgraduate Degree, Doctoral Degree).
COVID-19 variables. Participants were asked to indicate their current living status in
relation to COVID-19 at the time of completing the baseline survey (‘I am living as normal’, ‘I
am not self-isolating but have cut down my usual activities as a precaution’, ‘I am not self-iso-
lating but have been told to work from home’, ‘I am self-isolating as I do not want to get ill, but
I am not high risk, ‘I am self-isolating as I do not want to get ill, but I am regarded as high
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risk’, ‘I am self-isolating as I do not want others to get ill’, ‘I have been told to self-isolate due to
possible symptoms of COVID-19’, ‘I have been told to self-isolate due to a diagnosis of
COVID-19’, or ‘I have been ordered by the government or local authority to self-isolate/stay
home’). Participants were also asked whether they themselves (at the time of survey comple-
tion) are currently in quarantine or have been in the past. Participants were asked if they were
caring for someone with COVID-19. Participants were asked if they were working as part of
the government assigned key worker roles (including health and social care, education and
childcare, transport, public services, government, food, public safety, utilities).
Social variables. Levels of social support was measured using the Perceived Social Support
Questionnaire-Brief Form [50] The measure contains 6 items which are rated on a 5-point
Likert scale, with the response categories ranging from 1 (‘not true at all’) to 5 (‘very true’).
Higher scores reflect higher perceived social support. Previous research supports the reliability
and validity of the scale [50,51], and reliability was very high (α= .87) in this sample.
Participants were asked about their relationship status (single/never married, married/liv-
ing with partner, separated or divorced, widowed). Participants were also asked about the type
of area they lived in (city, town, rural), and to specify the number of adults over 18 and chil-
dren under 18 living in their place of residence.
Health variables. Pre-existing physical or mental health conditions. Participants were
asked whether they have ever suffered from several physical or mental health conditions.
These included, asthma, heart disease, cancer, diabetes, shortness of breath, several mental
health disorders or another kind of chronic condition not specified.
Post-Traumatic Stress Disorder (PTSD). The PTSD Checklist for DSM-5 was used to mea-
sure symptoms of PTSD (PCL-5) [52]. The measure contains 20 items, rated on a five-point
Likert scale (‘0 = Not at all’ to ‘4 = Extremely’) that mirror the DSM-5 criteria for PTSD. In
keeping with previous research [53] a cut off score of 34 was used to indicate ‘probable PTSD’.
The excellent psychometric properties of the PCL-5 are well-established [52,54]. Internal con-
sistency of the measure was very high in the current sample (α= .96). To capture post-
COVID-19 trauma responses the wording of the PCL-5 was slightly modified (i.e., “Keeping
your coronavirus (COVID-19) experiences in mind, please read each problem carefully and
indicate how much you have been bothered by that problem IN THE PAST MONTH").
Generalised anxiety disorder & major depression. Symptoms of generalised anxiety disorder
and major depressive disorder were measured using the seven item Generalised Anxiety Dis-
order scale (GAD-7) [55] and the nine item Patient Health Questionnaire (PHQ-9) [56]. Previ-
ous research has demonstrated the excellent psychometric properties of the GAD-7 [55,57–59]
and the PHQ-9 [60–62] across a range of clinical and non-clinical populations. In the current
sample reliability was very high for the GAD-7 (α= .94) and for the PHQ-9 (α= .91). Both
scales measure symptomatology based on the past two weeks, with item responses ranging
from 0 (not at all) to 3 (nearly every day). Across both measures higher scores yield higher
degrees of symptom severity, with scores of 10 or more indicating clinical concern [56,63].
This threshold was therefore used in the current study.
Emotional dysregulation.The Difficulties in Emotion Regulation Scale—Short Form
(DERS-SF) [64] was used to measure emotional dysregulation. The DERS-SF contains 18 items
rated on a 5-point likert scale, ranging from 0 to 5. The response categories were, ‘almost never’
(1), ‘sometimes’ (2), ‘about half of the time’ (3), ‘most of the time’ (4), and ‘almost always’ (5). In
comparison to the original long form, the psychometric properties of the DERS-SF are excellent
[64]. Internal consistency of the measure in the current sample was very high (α= .90).
Sleep quality. Sleep quality in general, as well as, sleep quality over the past month in rela-
tion to COVID-19 was assessed. Participants were asked to rate their sleep quality in reference
to both of these aspects as either ‘very good’, ‘fairly good’, ‘fairly bad’ or ‘very bad’.
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Defining variables
Age was recoded into 6 age range categories (18–24, 25–34, 35–44, 45–54, 55–64, 65+).
Employment status was collapsed into those in employment (full-time, part-time, self-
employed) versus those who were not currently employed. Educational attainment and self-
rated income were treated as continuous variables with higher scores indicating higher attain-
ment and higher income. A new variable was created to identify those participants living alone
(versus not living alone). New variables were created for presence of a physical health condi-
tion (inclusive of asthma, heart disease, cancer, diabetes and shortness of breath) and mental
health condition (inclusive of PTSD, MDD, phobia, social phobia, Obsessive Compulsive Dis-
order, GAD, psychotic disorder, eating disorder and health anxiety). Research has shown that
co-morbidity and multi-morbidity are associated with worse mental health and quality of life
[65–67] and greater loneliness [68], as such, new variables were created for the number of
physical health and mental health conditions that participants reported. New categorical vari-
ables were also created for scores meeting the clinical threshold for depression (scores of 10 or
higher on the PHQ-9), anxiety (scores of 10 or higher on the GAD-7) and probable PTSD
(scores of 34 or higher on the PCL-5).
Statistical analysis
Of the 1989 eligible respondents, 1964 completed the measure of loneliness and are the focus
of the analyses. As less than 5% of the data were missing pairwise and listwise deletion were
implemented [69,70]. In the current study statistical significance was determined as p<.05.
For interpreting results a more conservative alpha level of .01 may be preferred, given multiple
comparisons and in light of the Bonferroni approach. Exact p-values are reported for all tests.
When interpreting the findings, the reader should balance the reported significance level with
the magnitude of effect, the quality of the study, and with findings of other studies. Absolute
numbers, percentages, or means with standard deviations are reported. Unadjusted associa-
tions between potential risk factors and loneliness were assessed by the independent t-test for
continuous variables and the chi-square test for categorical variables. For continuous variables
and categorical variables with more than 2 levels, unadjusted odds ratios (ORs) were obtained
by separately fitting each variable against the binary loneliness classification (univariate analy-
ses). Factors that were found to be related to loneliness (using a less conservative threshold of
p0.10) were then entered into a multivariable logistic regression model using stepwise back-
ward selection. Multivariable logistic regression examines the contribution of each variable in
distinguishing between groups (with or without loneliness), while controlling for the other
variables in the model and was used to assess the relative predictive ability of sociodemo-
graphic factors, COVID-19 specific factors, social factors, and health factors in explaining
prevalence of loneliness.
Results
Sample characteristics
Participants were aged 18 to 87 years; average age was 37.11 (SD = 12.86). Participants were
mostly white (92.7%) females (70.4%), and not religious (57.5%). All participants were resident
in the UK (38.1% were living in England, 36.2% in Scotland, 23.4% in Northern Ireland, and
only 2.3% lived in Wales). The majority of respondents were employed (71.9%), however,
37.9% of participants rated their income level as below average. More than half the sample had
a university degree (58.5%). Remaining sample characteristics are presented in the first col-
umn of Table 1.
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Table 1. Sample characteristics, and prevalence of loneliness across sociodemographic, COVID-19, social and health factors.
Sample total Low/no Loneliness Loneliness Unadjusted OR (CI) ß p
N (%) 1964 (100) 1434 (73.4) 530 (26.6)
SOCIODEMOGRAPHIC FACTORS
UK nation .180
Northern Ireland 23.4 76.7 23.3 Ref (1.00)
England 38.1 71.2 28.7 1.33 (1.02, 1.74) 0.29 .035
Scotland 36.2 72.3 27.7 1.27 (0.97, 1.66) 0.24 .088
Wales 2.3 76.1 23.9 1.04 (0.51, 2.11) 0.04 .921
Gender .911
Male 29.6 73.1 26.9 Ref (1.00)
Female 70.4 73.4 26.6 1.02 (0.82, 1.26) 0.16 .889
Age Group <.001
18–24 16.7 59.0 41.0 20.48 (4.92, 85.27) 3.02 <.001
25–34 33.4 71.8 28.2 11.61 (2.81, 48.01) 2.45 .001
35–44 23.9 78.0 22.0 8.30 (1.99, 34.55) 2.12 .004
45–54 14.6 74.8 25.2 9.92 (2.36, 41.65) 2.29 .002
55–64 8.4 79.4 20.6 7.66 (1.78, 32.93) 2.04 .006
65+ 3.1 96.7 3.3 Ref (1.00)
Employed 71.9 75.1 24.9 Ref (1.00) .001
Not 28.1 67.5 32.5 1.45 (1.17, 1.80) 0.37 .001
Income 0.79±0.71 0.86±.72 0.59±.65 0.57 (0.49, 0.66) -0.56 <.001^
Educational attainment 5.22±1.86 5.32±1.84 4.84±1.77 0.87 (0.83, 0.92) -0.14 <.001^
COVID-19 FACTORS
Quarantined 3.7 82.2 17.8 1.73 (0.94, 3.17) 0.55 .081
Not 96.3 72.8 27.2 Ref (1.00)
Self-isolating 58.9 71.0 29.0 1.28 (1.04, 1.57) 0.25 .020
Not 41.1 75.8 24.2 Ref (1.00)
Self-isolating [High Risk] .017
Yes 9.2 66.1 33.9 1.61 (1.14, 2.28) 0.48 .007
Other reasons 49.7 71.9 28.1 1.22 (0.99, 1.52) 0.20 .062
Not self-isolating 41.1 75.8 24.2Ref (1.00)
Self-isolating [by order] .049
Yes 15.4 69.5 30.5 1.37 (1.02, 1.84) 0.32 .033
Other reasons 43.5 71.5 28.5 1.25 (1.00, 1.55) 0.22 .048
Not self-isolating 41.1 75.8 24.2Ref (1.00)
Caring [COVID-19] 5.5 71.3 28.7 1.09 (0.71, 1.68) 0.90 .682
Not 94.5 73.1 26.9 Ref (1.00)
Key worker 37.4 74.6 25.4 0.88 (0.72, 1.08) -0.13 .229
Not 62.6 72.1 27.9 Ref (1.00)
SOCIAL FACTORS
Social support 21.64±5.79 22.75±5.22 18.65±6.19 0.88 (0.87, 0.90) -0.13 <.001^
Relationship Status <.001
Single/never married 36.8 59.9 40.1 Ref (1.00)
Married/co-habiting 56.8 84.1 15.9 0.28 (0.23, 0.35) -1.27 <.001
Separated/divorced 5.2 53.1 46.9 1.32 (0.87, 2.02) 0.28 .195
Widowed 1.2 65.2 34.8 0.80 (0.33, 1.91) -0.23 .612
Household size
Number of adults in the home 2.22±0.96 2.25±.92 2.14±1.07 0.89 (0.80, 0.98) -0.12 .026^
(Continued)
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Loneliness prevalence
Loneliness was defined as having a score on the 3-item loneliness scale in the top quartile (i.e.,
a score of 7 or higher). The overall prevalence of loneliness was 27% (530/1964). The mean
score was 5.36 (SD = 1.92). In the past week 49% to 70% of respondents reported feeling iso-
lated, left out, or lacking companionship some of the time or often (Fig 1).
Table 1. (Continued)
Sample total Low/no Loneliness Loneliness Unadjusted OR (CI) ß p
Number of children in the home 1.63±0.95 1.66±.98 1.54±0.85 0.87 (0.78, 0.97) -0.14 .011^
Living alone 14.8 57.9 42.1 2.25 (1.74, 2.92) 0.81 <.001
Not 85.2 75.6 24.4 Ref (1.00)
Urbanicity .375
Rural 21.9 71.1 28.9 Ref (1.00)
Town 44.0 74.5 25.5 0.84 (0.65, 1.09) -0.17 .192
City 34.2 72.3 27.8 0.94 (0.72, 1.23) -0.06 .670
HEALTH FACTORS
Physical Health Condition 24.8 69.3 30.7 1.27 (1.01, 1.59) 0.24 .036
None 75.2 74.2 25.8 Ref (1.00)
Number of health conditions 0.29±0.55 0.28±0.55 0.33±.058 1.18 (1.00, 1.41) 0.17 .057^
Mental Health condition 30.6 59.0 41.0 2.64 (2.14, 3.25) 0.97 <.001
None 69.4 79.2 20.8 Ref (1.00)
Number of mental health conditions 0.55±1.05 0.42±0.91 0.92±1.31 1.50 (1.37, 1.64) 0.41 <.001^
Depression—clinical threshold 34.0 49.2 50.8 5.98 (4.82, 7.42) 1.79 <.001
Not 66.0 85.3 14.7 Ref (1.00)
Anxiety–clinical threshold 30.3 52.2 47.8 4.18 (3.38, 5.17) 1.43 <.001
Not 69.7 82.0 18.0 Ref (1.00)
Probable PTSD 19.4 43.8 56.2 5.13 (4.05, 6.51) 1.63 <.001
Not 80.6 80.0 20.0 Ref (1.00)
Emotion regulation difficulties 42.43±13.22 39.17±11.67 51.20±13.14 1.08 (1.07, 1.09) 0.74 <.001^
Sleep quality [general] 2.22±0.79 2.10±.75 2.54±.81 2.03 (1.78, 2.32) 0.71 <.001^
Sleep quality [COVID-19] 2.47±0.84 2.34±.80 2.84±.81 2.13 (1.87, 2.43) 0.76 <.001^
Notes
= X
2
test
^ = independent samples t-test. Numerical values with standard deviation are mean scores, values without standard deviation are percentages. OR = unadjusted odds
ratio. CI = 95% confidence intervals. ß = regression coefficient.
https://doi.org/10.1371/journal.pone.0239698.t001
Fig 1. Percentage of responses to each item of the 3-item UCLA loneliness scale.
https://doi.org/10.1371/journal.pone.0239698.g001
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Risk factors for loneliness
Univariate analyses. Table 1 compares participants with (26.6%) and without loneliness
(73.4%) on sociodemographic factors, factors specific to COVID-19, social factors and health
factors. Overall country of residence had no impact on loneliness, however, relative to living in
Northern Ireland living in England increased the odds of being lonely (OR: 1.33, CI: 1.02,
1.74). The prevalence of loneliness decreased with age (Fig 2), with 18-24-year old’s having the
highest frequency of loneliness (41%), whereas only 3% of people over 65 were classified as
lonely. There was no association between gender and loneliness. Loneliness was associated
with lower income, lower educational attainment and was more prevalent in people out of
employment. In relation to COVID-19, prevalence of loneliness was higher for those self-iso-
lating, and for those self-isolating because they are considered high risk or have been ordered
to self-isolate. However, loneliness was not related to being in or having been in quarantine.
Further, odds of loneliness were not higher for key workers, or for people caring for someone
with COVID-19. Loneliness was less frequent in people who are married or living with a part-
ner. There was an inverse relationship between household size and loneliness. Living alone
more than doubled the odds of being lonely. People who were lonely also had lower perceived
social support. Odds of loneliness were higher for those with pre-existing physical and mental
health conditions. Rates of loneliness were twice as high among people whose scores met clini-
cal criteria for depression, anxiety and probable PTSD. Higher emotion regulation difficulties
and lower sleep quality were also associated with loneliness.
Multivariable analysis. The 23 factors that were significant at the alpha level .10 in the
univariate analyses were entered into multivariable logistic regression. The final model with
adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for the various predictors is
shown in Table 2.
Relative to those over 65 years of age, younger adults were 4–5 times more likely to be
lonely (18–24 [OR]: 5.31, CI: 1.13–24.96], 25–34 [OR: 4.67, CI: 1.02–21.33], or 45–54 [OR:
4.75, CI: 1.03–21.92]). Compared to being single, being separated or divorced more than dou-
bled the odds of being lonely (OR: 2.29, CI: 1.31–4.00), whereas being married or cohabiting
was associated with lower odds of being lonely (OR: 0.35, CI: 0.26–0.46). Odds of loneliness
decreased with greater number of adults living in the same home (OR: 0.87, CI: 0.76–1.00) and
with higher levels of perceived social support (OR: 0.92, CI: 0.90–0.94). People meeting clinical
criteria for diagnosis of major depressive disorder were almost twice as likely to be lonely (OR:
1.74, CI: 1.24–2.44). Greater difficulties with emotion regulation (OR: 1.04 CI:1.03–1.05), and
Fig 2. Prevalence of loneliness (%) by age group.
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worse quality sleep due to the COVID-19 situation (OR: 1.30 CI: 1.09–1.55) also increased the
odds of being lonely.
Discussion
The overall prevalence of loneliness in this sample was 27%. In univariate analyses, younger
age, lower income, unemployment, less education, relationship status, smaller household size,
living alone, lower social support, having a physical or mental health condition, meeting clini-
cal criteria for depression, anxiety and PTSD, emotion regulation difficulties and poor sleep
quality were all associated with loneliness. Self-isolating for any reason, including being high-
risk or having been advised to shield was also associated with loneliness. In the adjusted analy-
ses while controlling for all other factors, younger age group, being separated/divorced, scores
indicative of depression, poor sleep quality due to COVID-19 and difficulties in emotion regu-
lation were significant risk factors for loneliness. Whereas, being married or co-habiting, living
with a greater number of adults, and having higher levels of perceived social support were pro-
tective factors.
Research prior to the pandemic estimated the prevalence of loneliness to be between 6 and
76%. The rate of loneliness reported here is the same as that found by Victor and Yang [19] in
their pre-pandemic research with 2393 adults in the UK (aged 15–97 years). Using a single-
item measure of loneliness they found that prevalence of loneliness was 27%. Considering
research during the pandemic, prevalence in the current study falls between rates in the US
Table 2. Multivariable logistic regression analysis of factors associated with loneliness.
ß aOR (CI) p
Constant -2.73 0.06 .002
Age Group
18–24 1.67 5.31 (1.13, 24.96).034
25–34 1.54 4.67 (1.02, 21.33).047
35–44 1.31 3.71 (0.81, 17.00) .091
45–54 1.56 4.75 (1.03, 21.92).046
55–64 1.13 3.10 (0.65, 14.81) .156
65+ Ref (1.00)
Social support -0.08 0.92 (0.90, 0.94) <.001
Relationship Status
Single/never married Ref (1.00)
Married/co-habiting -1.06 0.35 (0.26, 0.46) <.001
Separated/divorced 0.83 2.29 (1.31, 4.00).004
Widowed 0.69 1.99 (0.69, 5.72) .200
Household size
Number of adults living in the home -1.38 0.87 (0.76, 1.00) .046
High depression 0.55 1.74 (1.24, 2.44).001
High anxiety 0.31 1.34 (0.96, 1.92) .085
Emotion regulation difficulties 0.04 1.04 (1.03, 1.05)<.001
Sleep quality [COVID] 0.26 1.30 (1.09, 1.55).003
Probable PTSD 0.33 1.39 (0.97, 1.98) .071
Notes; aOR = adjusted odds ratio; CI = confidence intervals; ß = regression coefficient
= risk factor and
= protective factor.
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(43%) and the UK (14%-36%) [36,39,40]. There is emerging evidence from the US suggesting
that levels of loneliness are high but stable during the COVID-19 pandemic [71].
Sociodemographic factors
Studies of loneliness during COVID-19 report higher prevalence among females [21,39,40].
Outside of the current crisis, findings on gender differences in loneliness are mixed, and find-
ings of this study conform with those showing no association [7,8,48]. As expected, lower
income and less education were associated with loneliness. Adjusting for other significant pre-
dictors, these socioeconomic factors were not significant risk factors for loneliness. A recent
study by Shovestul et al. [72] found that relative to other demographic and socioeconomic fac-
tors, age is the most important risk factor for loneliness. Similarly, in the multivariable analy-
ses, regression coefficients show that age group was the strongest predictor of loneliness in this
study. Despite research showing that the relationship between age and loneliness is u-shaped
[19,22], we found an inverse relationship between age and loneliness, with very high preva-
lence of loneliness in the younger age groups and very low rates of loneliness among the over-
65s. This is in keeping with studies of COVID-19, showing higher loneliness in younger people
[21,40,41]. Younger adults may be disproportionately affected by disease-containment policies
(e.g., school/university closures) that increase social isolation placing them at higher risk of
loneliness [32]. However, as older adults and males were underrepresented in this study sam-
ple, we cannot offer definitive conclusions as to age or gender differences in the impact of lock-
down [45].
Social factors
All the protective factors for loneliness were social variables, supporting a link between social
isolation and loneliness. This is in keeping with recent research on loneliness in the UK during
the pandemic, that also found social factors were protective [39,40]. The particular variables
that were significant predictors in this study (i.e., being married/co-habiting, number of adults
living in the household, availability of social support) indicate that the closeness and quality of
relationships may be important. It may also indicate that face-to-face interactions are key. In a
study of the impact of COVID-19 restrictions in the US, frequent in-person interactions were
associated with lower loneliness, but not remote or virtual interactions [37]. It will be difficult
to develop interventions to reduce loneliness targeting these social factors, at least until physi-
cal distancing regulations are relaxed.
Health factors
There are numerous cross-sectional studies showing that loneliness is more prevalent among
people with mental health conditions [73,74]. There is also compelling evidence that loneliness
precedes depression [6]. Many explanations are put forward for this link, for example, the
stigma of loneliness may cause those who are already marginalised due to their mental illness
to withdraw further, or perhaps behavioural symptoms of depression make social participation
more burdensome [75]. During the COVID-19 pandemic loneliness is a significant risk factor
for depression, anxiety, stress, mental health symptoms and suicidal ideation [36,42–44]. In
the current study of adults in the UK during the COVID-19 crisis, meeting the clinical thresh-
old for major depressive disorder was a significant risk factor for loneliness. Having difficulty
regulating emotions also significantly increased the odds of being lonely, and is worthy of fur-
ther investigation as a potential mechanism of the relationship between isolation, loneliness,
and mental health. Longitudinal studies will be necessary to disentangle the temporal
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dynamics linking loneliness and mental health outcomes throughout different phases of the
government lockdown.
COVID-19 specific factors
Being a keyworker during the pandemic has been associated with worse mental health out-
comes [45,76,77]. However, in spite of additional stressors, key workers were at no greater risk
for loneliness in this study. There is a link between social isolation and loneliness [32], and
quarantine has been associated with negative psychological effects [26–28]. Therefore, it was
unexpected that in this study quarantine was not associated with loneliness, and in the
adjusted analyses self-isolating was no longer a significant risk factor for loneliness. It is possi-
ble that the negative impact of quarantine and isolation on loneliness and mental health may
be more pronounced in children and adolescents [78]. Another possible explanation is that the
COVID-19 pandemic is unique in the sense that unlike previous outbreaks the disease-con-
tainment policies were applied at a population level, irrespective of disease status. The univer-
sal nature of the UK lockdown may have mitigated its impact on loneliness. Poor quality sleep
due to COVID-19, however, remained a significant risk for loneliness. This is in line with stud-
ies in Greece and France that have reported sleep problems are common during this pandemic,
and that loneliness is a major contributor to insomnia [79,80]. Indeed, sleep problems have
recently been proposed as a mechanism of the relationship between loneliness and health [81].
Overall, factors specific to COVID-19 were not significant predictors of loneliness. This is
consistent with a recent study showing that the same risk factors predicted loneliness before
and during the pandemic [41]. Together this suggests that interventions to reduce the negative
impact of the lockdown should target those who are most at-risk of loneliness outside of the
current crisis–that being the young, unemployed, people with low income or education, and
people with mental health conditions (i.e., depression). These findings also suggest that exist-
ing interventions to reduce loneliness may be effective in this context also [12,82]. Of the risk
factors identified in the current study, difficulties in emotion regulation and sleep quality may
be the most appropriate to target as they are amenable to change, for example, through cogni-
tive behavioural interventions [83,84].
Strengths
This study is timely and contributes to a small body of emerging research evidencing preva-
lence and determinants of loneliness during the pandemic in the UK [39,40], thereby address-
ing key research priorities for understanding the mental health impact of the pandemic
identified by the UK public and the academic community [1,3]. The study identifies a number
of significant risk and protective factors for targeted intervention and provides support for
existing research in this area through use of a large sample and confirmatory analysis using a
well-validated measure of loneliness [47].
Limitations
1. The sample was not randomly selected. This is typical of existing research in the area (e.g.,
[40,41], and reflects a rapid emergency data collection exercise [45]. When comparisons
were made to the UK census data, older adults and males were found to be under-repre-
sented in the sample, and this may be of particular importance in the context of loneliness
research.
2. This study focused on experiences of loneliness ‘in the past week’. Due to the COVID-19
restrictions on social contact some people will be experiencing severe and sustained
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loneliness for the first time. Future studies using alternative designs will be needed to distin-
guish people experiencing loneliness because of COVID-19 from those who are chronically
lonely.
3. Due to the survey being conducted online there was a reliance on self-reported measures of
diagnoses and mental health symptoms. That being said, the measures selected have well-
established psychometric properties. Related to this limitation is the issue that with much
research moving online in response to physical distancing regulations, prospective partici-
pants who do not have access to computers and the internet are excluded. Without oppor-
tunities for digital alternatives for social contact these people may be particularly isolated
and at risk for loneliness during the COVID-19 lockdown. It is important to consider the
impact of the digital divide on study findings on the impact of the lockdown.
4. The cross-sectional design of the study means we cannot determine causality. This is partic-
ularly pertinent with regard to the ongoing debate as to whether loneliness causes mental
health conditions, is a mental health condition in itself, or results from mental health symp-
toms [82]. It will be important to understand this within the context of COVID-19 also.
Conclusion
The UK public are concerned about the impact of the lockdown on their mental health [1,3].
More than one quarter of the respondents in the COVID-19 Psychological Wellbeing Study
were classified as lonely, suggesting that UK lockdown policies have had a negative impact. In
the absence of longitudinal studies examining the same cohort before and after the lockdown
this interpretation remains speculative. Being younger, separated or divorced, meeting the
clinical threshold for major depressive disorder, having poor quality sleep and difficulties regu-
lating emotions were significant risk factors for loneliness during the initial stage of the lock-
down. However, being married, living with a partner or other adults, and having greater social
support were protective. Our findings suggest that supports aimed at improving emotion regu-
lation, sleep quality, and increasing social support may be the most impactful for mitigating
the mental health impact of the lockdown, and that interventions should focus on those people
most at-risk for loneliness prior to the lockdown.
Supporting information
S1 Table. Prevalence of loneliness and sample characteristics across recruitment strategy.
(DOCX)
Author Contributions
Conceptualization: Jenny M. Groarke, Emma Berry, Lisa Graham-Wisener, Phoebe E.
McKenna-Plumley.
Data curation: Emily McGlinchey.
Formal analysis: Jenny M. Groarke.
Investigation: Emily McGlinchey, Cherie Armour.
Project administration: Jenny M. Groarke, Cherie Armour.
Resources: Cherie Armour.
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Supervision: Cherie Armour.
Visualization: Jenny M. Groarke.
Writing – original draft: Jenny M. Groarke, Emma Berry, Lisa Graham-Wisener.
Writing – review & editing: Jenny M. Groarke, Emma Berry, Lisa Graham-Wisener, Phoebe
E. McKenna-Plumley, Emily McGlinchey, Cherie Armour.
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