Mental Distress and Its Contributing Factors Among Young People During the First Wave of COVID-19: A Belgian Survey Study

Abstract

Background: The outbreak of the COVID-19 pandemic in 2020 and its associated measures led to high levels of mental distress in the general population. Previous research indicated that young people are especially vulnerable for a wide range of mental health problems during the pandemic, but little is known about the mechanisms. This study examined mental distress and its contributing factors among young Belgian people.
Methods: An online survey was widely distributed in Belgium during the country’s lockdown and 16 to 25-year-olds were selected as a subsample. Mental distress was assessed using the 12-item General Health Questionnaire (GHQ-12), and a threshold of ≥ 4 was used to discriminate mental distress cases from non-cases. Bivariate and multivariate logistic regression analyses were performed to evaluate possible predictors of mental distress, including demographics, chronic condition, history of mental health problems, social support, exposure to COVID-19, and several changes in everyday activities.
Results: A total of 2008 respondents were included, of which the majority was female (78.09%) and student (66.82%). The results indicate that about two thirds (65.49%) experienced mental distress. In the multivariate regression model, significant (p < .01) predictors of mental distress were female gender (OR = 1.78), low social support (OR = 2.17), loneliness (OR = 5.17), a small (OR = 1.63) or large (OR = 3.08) increase in social media use, a small (OR = 1.63) or large (OR = 2.17) decrease in going out for drinks or food, and a decrease in doing home activities (OR = 2.72).
Conclusion: Young people experience high levels of mental distress during the COVID-19 pandemic. Our findings indicate that the mental distress was highest among women, those experiencing loneliness or low social support and those whose usual everyday life is most affected. The psychological needs of young people, such as the need for peer interaction, should be more recognized and supported.

Full text

ORIGINAL RESEARCH
published: 28 January 2021
doi: 10.3389/fpsyt.2021.575553
Frontiers in Psychiatry | www.frontiersin.org 1January 2021 | Volume 12 | Article 575553
Edited by:
Jutta Lindert,
University of Applied Sciences Emden
Leer, Germany
Reviewed by:
Johan Bilsen,
Vrije University Brussel, Belgium
Brecht Devleesschauwer,
Sciensano, Belgium
Johan Van Der Heyden,
Sciensano, Belgium
*Correspondence:
Eva Rens
eva.rens@uantwerpen.be
Specialty section:
This article was submitted to
Public Mental Health,
a section of the journal
Frontiers in Psychiatry
Received: 23 June 2020
Accepted: 06 January 2021
Published: 28 January 2021
Citation:
Rens E, Smith P, Nicaise P, Lorant V
and Van den Broeck K (2021) Mental
Distress and Its Contributing Factors
Among Young People During the First
Wave of COVID-19: A Belgian Survey
Study. Front. Psychiatry 12:575553.
doi: 10.3389/fpsyt.2021.575553
Mental Distress and Its Contributing
Factors Among Young People During
the First Wave of COVID-19: A
Belgian Survey Study
Eva Rens 1
*, Pierre Smith 2, Pablo Nicaise 2, Vincent Lorant 2and Kris Van den Broeck 1
1Research Group Family and Population Health (FAMPOP), Faculty of Medicine and Health Sciences, Chair Public Mental
Health, Collaborative Antwerp Psychiatry Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium, 2Institute of
Health and Society, Institut de Recherche Santé & Société (IRSS), Université Catholique de Louvain, Brussels, Belgium
Background: The outbreak of the COVID-19 pandemic in 2020 and its associated
measures led to high levels of mental distress in the general population. Previous research
indicated that young people are especially vulnerable for a wide range of mental health
problems during the pandemic, but little is known about the mechanisms. This study
examined mental distress and its contributing factors among young Belgian people.
Methods: An online survey was widely distributed in Belgium during the first wave
of COVID-19 in March, and 16–25-year-olds were selected as a subsample. Mental
distress was assessed using the 12-item General Health Questionnaire (GHQ-12), and
a threshold of ≥4 was used to discriminate mental distress cases from non-cases.
Bivariate and multivariable logistic regression analyses were performed to evaluate
possible predictors of mental distress, including demographics, chronic condition, history
of mental health problems, social support, exposure to COVID-19, and several changes
in everyday activities.
Results: A total of 2,008 respondents were included, of which the majority was female
(78.09%) and student (66.82%). The results indicate that about two thirds (65.49%)
experienced mental distress. In the multivariable regression model, significant (p<0.01)
predictors of mental distress were female gender (OR =1.78), low social support (OR =
2.17), loneliness (OR =5.17), a small (OR =1.63), or large (OR =3.08) increase in social
media use, a small (OR =1.63) or large (OR =2.17) decrease in going out for drinks or
food, and a decrease in doing home activities (OR =2.72).
Conclusion: Young people experience high levels of mental distress during the
COVID-19 pandemic. Our findings indicate that mental distress was highest among
women, those experiencing loneliness or low social support and those whose usual
everyday life is most affected. The psychological needs of young people, such as the
need for peer interaction, should be more recognized and supported.
Keywords: COVID-19, pandemic, mental distress, mental health, adolescence, social isolation, young people,
coronavirus

Rens et al. Young People and COVID-19
INTRODUCTION
The outbreak of COVID-19 impacted the whole world in 2020, as
it was the first time that a new strain of coronavirus was declared a
pandemic. Coronaviruses usually cause mild to moderate upper-
respiratory tract symptoms, but COVID-19 is one of the three
coronaviruses that can cause severe and life-threatening infection
in humans (1). Globally, measures were taken to “flatten the
curve,” that is, to avoid an overburdened healthcare and further
spreading of the virus (2). Social distancing strategies led to stay-
at-home orders, closing of non-essential businesses and schools,
and canceling of events. By the first week of April, half of the
world’s population was in some form of quarantine (3).
The COVID-19 pandemic and its associated measures
inevitably affected the mental health of the general population.
Several studies assessed the psychological impact on the general
public, and reviews confirm that well-being is lower with higher
scores of depression, anxiety, and stress compared to baseline
measures (4,5). Researchers suggested that children and young
people could be disproportionally affected during the pandemic
because of several reasons, such as increased pressure on families,
decreased peer contact, decreased social activities, and closure of
schools, universities and support services (6,7). Several studies
indicate that young age is indeed a risk factor for a wide range of
mild to severe mental health problems during disease outbreaks,
such as depressive disorders and anxiety-related disorders (8–
19). While the level of mental distress is even under normal
circumstances generally high among young people, a longitudinal
study demonstrated that young people even experienced the
steepest increase of mental distress during the COVID-19
pandemic (15). One explanation is that adolescents are highly
affected by social deprivation because of a heightened need for
peer interaction and an increased risk of perceived social isolation
(20,21). While the use of digital technologies might mitigate
some of the negative effects of social distancing, young people’s
affinity with social media might also pose a threat to their well-
being when they are confronted with information overload and
“fake news,” which is especially detrimental during global crises
(22,23).
Besides young age, some other risk factors seem related to
mental health problems during the COVID-19 pandemic. Most
of these are factors are pre-existing factors, such as female gender,
lower socio-economic status and low social support (8,15,16,
18,19,24–28). Other risk factors are specifically linked to the
COVID-19 pandemic, such as having an infected relative (8,19,
25,26).
This study aims to contribute to a better understanding of
the associated factors of mental distress among 16–25-year-
olds during the beginning of the first wave of the COVID-19
pandemic in Belgium. Specially, we were interested in the impact
of lockdown-related changes in various life domains which are
relevant for youth, such as changes in social media use, time spent
at home and the frequency of several social and leisure activities.
We hypothesized that those reporting the highest impact on their
everyday life would also be the ones experiencing the most mental
distress. Moreover, we expected young people from vulnerable
groups to be highly affected, such as those with a chronic disease
or who consulted a professional for mental health problems in
the past. Finally, we also expected loneliness and a lack of social
support to contribute to mental distress.
METHODS
Study Design
An online web survey was distributed in Belgium through social
media and national news outlets during the beginning of the
first wave of the COVID-19 pandemic in 2020. The Belgian
government took the first restriction measures on March 13th,
as schools, bars and restaurants were closed. Five days later, a
lockdown was declared and non-essential journeys and social
gatherings were prohibited. The survey was opened 2 days after
the start of the lockdown, on March 20th. The survey was named
“Covid and I,” was aimed at the general population and was
available in English, French, and Dutch. After 3 weeks, 21,734
respondents filled in the survey. For our research question,
all 2,085 respondents aged between 16 and 25 years old were
selected. All 77 cases with missing data were filtered out, resulting
in a total of 2,008 respondents.
Informed consent was obtained from all participants. The
Belgian Law does not require an approval from an Ethical Board
for an online survey with the general population.
Measures
Mental Distress
The 12-item General Health Questionnaire (GHQ-12) was used
for the assessment of mental distress. The GHQ-12 is a short,
validated scale for detecting non-specific mental disturbance in
the general population and is suitable for young people (29–
31). We used the GHQ-scoring method (i.e., 0 0 1 1) which
yields an overall score ranging from 0 to 12, with higher scores
indicating higher mental distress. We used a threshold of ≥4 to
discriminate mental distress cases from non-cases, based on prior
research indicating discriminant validity is optimal at this cut-off
point (32).
Predictor Variables
Demographic characteristics included age, gender, student status,
and living alone or not. Respondents were asked if they have
a chronic condition and whether or not they consulted a
professional for mental health reasons in the last 12 months.
Social support was measured using the 3-item Oslo Social
Support Scale (OSSS-3) and sum scores were operationalized
into three categories: poor support (3–8), moderate support (9–
11), and strong support (12–14) (33). Loneliness was measured
using an adapted version of the UCLA 3-item Loneliness scale
with a four-point Likert scale (“never,” “once in a while,” “fairly
often,” and “very often”), yielding a score from 0 to 9 (34,35).
Respondents with a score ≥6 were categorized as experiencing a
high level of loneliness.
Exposure to COVID-19 was considered present when
someone reported having a current or past COVID-19 infection,
or when one has a family member who has a current or past
COVID-19 infection. As for the change in time spent at home,
respondents indicated their usual (i.e., before the outbreak of
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Rens et al. Young People and COVID-19
COVID-19) and current time at home, dichotomized as “part
of the day” vs. “whole day” and a variable was constructed
expressing whether there was a change in time at home or not.
Respondents indicated their usual and current daily social
media use, categorized as “<3 h,” “between 3 and 6 h,” and “more
than 6 h.” Then, three categories were distinguished based on
the difference between usual and current use: no increase, small
increase (change of one category), and large increase (change of
two categories) in social media use.
Finally, change in everyday life was measured by assessing
the impact on the following activity types: visiting relatives
and friends, going out for drinks or food (to a pub, party,
...), practicing sports or hobbies, and doing home activities
(reading a book, watching a movie, ...). Respondents indicated
their usual and current activity level for the separate activities
(“never/0 times a week,” “once a week,” “2–3 times a week,”
and “more than 4 times a week”), and responses were scored
from 1 to 4, respectively. Per activity type, the current level
score was subtracted from the usual level score, resulting in
variables representing the difference between the pre-COVID-
19 and current activity level. For “visiting friends and relatives”
and “going out for drinks and food,” three categories were
distinguished: no decrease, small decrease (i.e., one category
change) and large decrease (i.e., more than one category change),
as an increase of these activities doesn’t make sense in light of the
social distancing measures. For the variables “practicing sports or
hobbies” and “doing home activities,” both directions of change
were included: no change, a decrease or an increase of the activity.
Statistical Analyses
Sample characteristics and the prevalence of mental distress
are described using percentages and means. Cross tabulations
were used to explore associations between GHQ caseness and
demographics. No weights were applied to the data set. This
decision was made because the weights for male respondents
would be large (>2) when striving for a balanced data set,
resulting in highly reduced accuracy and possibly even more bias.
Logistic regressions were used to predict the odds of
experiencing significant mental distress, i.e., to discriminate
between GHQ-cases and non-cases. First, bivariate associations
were assessed between each potential predictor and mental
distress, expressed as crude odds ratios (COR) and corresponding
99% confidence intervals (99% CI). Second, variables with
a Wald test p<0.100 in bivariate analyses were included
in a multivariable logistic regression model and adjusted
odds ratios (AOR) with a 99% CI were estimated. No
interactions were included because we wanted to focus on
the main effects and the model already included a substantial
amount of predictors. Collinearity diagnostics were assessed
using VIFs in a linear regression model with the total
GHQ-score as the dependent variable, and revealed no
collinearity. In all analyses, independent variables with a
Wald test p<0.010 were taken as significant predictors of
mental distress.
RESULTS
Sample Characteristics
Table 1 shows the demographic sample characteristics and
descriptive data of all predictors and the outcome. A total of 2,008
respondents aged 16–25 years old completed the survey, with a
mean age of 22.27 years old (SD =2.29). Most of the respondents
filled in the questionnaire in French (84.76%), which suggests
that the majority of the participants are from the Walloon part of
Belgium or from Brussels. 12.35% of the respondents filled in the
questionnaire in Dutch and 2.9% in English. The majority of the
sample is female (78.09%), student (66.83%), and lives together
with others (92.78%). Only 4 respondents (0.20%) were infected
with COVID-19, but about one in 10 (10.71%) reported having
an infected relative. 23.16% consulted a professional in the last
12 months for mental health reasons, and 12.70% reports having
a chronic condition. The mean GHQ-12 score was 5.38 (SD =
3.45) and approximately two thirds (65.49%) of the respondents
scored 4 or higher, indicating that the level of mental distress is
generally high in the sample.
Before the lockdown, only 3.49% of the respondents report
being at home the whole day, whereas this is the case for 87.00%
of the respondents during the lockdown. There was an increase
in time at home for 83.52% of the sample. Before the lockdown,
53.19% of respondents used social media <3 h a day, 42.48%
between 3 and 6 h a day, and only 4.33% for more than 6 h a day.
During the lockdown, only 13.99% of respondents used social
media <3 h a day, 47.61% between 3 and 6 h a day, and 38.40%
for more than 6 h a day. Overall, there was no increase in social
media use for 34.96% of the sample (only 1.59% reported a small
decrease), a small increase for 54.23% of the sample, and a large
increase for 9.81% of the sample. Frequency distributions of the
four activity types before and during the lockdown are presented
in Table 2.
Predictors of Mental Distress
Prevalence estimates of mental distress within each predictor
category together with the results of the logistic regression
analyses are presented in Table 3. Risk factors found to be
significantly associated with being a GHQ-case in bivariate
analyses included the female gender (OR =1.61, 99% CI 1.21–
2.14), having had a mental health consultation in the last 12
months (OR =1.63, 99% CI 1.20–2.21), experiencing moderate
(OR =1.55, 99% CI 1.12–2.13) or low social support (OR =2.97,
99% CI 2.05–4.33), experiencing loneliness (OR =6.41, 99% CI
4.54–9.06), experiencing a change in one’s daily time at home
(OR =1.58, 99% CI 1.15–2.17), experiencing a small (OR =1.94,
99%CI 1.50–2.51) or large (OR =4.97, 99% 2.86–8.64) increase in
one’s social media use, a large decrease in the frequency of going
out for drinks or food (OR =1.82, 99% 1.23–2.71), a decrease in
practicing sports or hobbies (OR =1.50, 99% CI 1.14–1.98), and a
decrease in doing home activities (OR =2.89, 99% CI 1.51–5.88).
Variables with p<0.100 in the crude analyses were fitted in the
multivariable logistic regression model, resulting in a model with
12 predictors: gender, chronic condition, prior mental health
consultation, social support, loneliness, exposure to COVID-19,
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Rens et al. Young People and COVID-19
TABLE 1 | Demographics and descriptive characteristics of the predictors (N=
2,008).
Variable %
Mental distress (GHQ score ≥4) 65.49
Age
16–21 33.12
22–25 66.88
Female 78.09
Student 66.82
Living alone 7.22
Chronic condition 12.70
Prior mental health consultation 23.16
Social support
High 17.18
Moderate 53.34
Low 29.48
Experiencing loneliness 32.42
Exposure to COVID-19 11.30
Increase in time at home 83.52
Social media use
No increase 34.96
Small increase 55.23
Large increase 9.81
Visiting friends and relatives
No decrease 13.94
Small decrease 20.27
Large decrease 65.79
Going out
No decrease 11.35
Small decrease 48.16
Large decrease 40.49
Sports or hobbies
Decrease 44.67
No change 35.86
Increase 19.47
Home activities
Decrease 5.58
No change 60.91
Increase 33.52
change in time at home, change in social media use, change
in going out for drinks or food, and change in frequency of
practicing sports or hobbies and home activities. The results
indicated the adjusted odds of experiencing mental distress were
higher among women (OR =1.78, 99% CI 1.29–2.46), among
those experiencing loneliness (OR =5.17, 99% CI 3.60–7.44) or
low social support (OR =2.17, 99% CI 1.42–3.29), and among
those with a small (OR =1.63, 99% CI 1.22–2.17) or large (OR =
3.08, 99% CI 1.70–5.61) increase in social media use, a small (OR
=1.63, 99% CI 1.04–2.52) or large (OR =2.17, 99% CI 1.35–3.48)
decrease in going out for drinks or food and a decrease in doing
home activities (OR =2.72, 99% CI 1.30–5.67).
TABLE 2 | Percentual distribution of frequency levels for different activity types
before and during the COVID-19 pandemic (N=2,008).
Visits Going out Sports or hobbies Home activities
Before lockdown
0 times/week 1.69 10.31 10.31 1.29
1 time/week 9.51 48.66 31.62 12.10
2–3 times/week 32.82 34.91 41.33 27.09
≥4 times/week 55.98 6.13 16.73 59.51
During lockdown
0 times/week 59.76 98.46 37.05 1.49
1 time/week 25.20 1.15 19.02 1.84
2–3 times/week 7.52 0.35 25.45 10.91
≥4 times/week 7.52 0.05 18.48 85.76
DISCUSSION
This study aimed to describe mental distress and its associated
factors among 16–25-year-olds during the beginning of the
COVID-19 pandemic in Belgium. An internet survey was
widely distributed from mid-March to early April 2020, while
the country was in lockdown. A first observation is that the
prevalence of mental distress in the sample was very high, as
approximately two thirds (65.49%) had a GHQ-12 score of 4
or higher.
As a comparison, only about one in three (36.7%) of young
people aged 16–24 experienced significant mental distress (GHQ
≥4) during the first wave of the COVID-19 pandemic in a
probability sample of the UK general population (15). So, either
Belgian youth is remarkably more distressed than UK youth, or
the use of a non-probability sample caused a large bias. Another
explanation for the extreme level of mental distress in Belgian
youth is that the survey was completed at the early beginning of
the pandemic which may have caused an emotional “spike,” while
the UK survey was taken from mid- to late April.
As a pre-COVID-19 reference, 17% of Belgian 15–24-year-
olds experienced significant mental distress in 2018, and a meta-
analysis estimated that one in four adolescents (defined as 10–19
years old) had a score of 4 or higher on the GHQ-12 worldwide
(36,37). This suggests that ∼3-fold increase of mental distress
in Belgian transition age youth occurred during the outbreak
of COVID-19. A similar conclusion was reached in another
study on mental health during the beginning of the COVID-
19 pandemic in Belgium, in which an online survey sample was
used and weighted to match the Belgian population: the authors
found that the prevalence of anxiety disorders has doubled
among 16–24-year-old boys and tripled among girls, and that the
prevalence of depressive disorders has tripled among girls and
even quadrupled among boys in that age group (38). These are
very alarming findings, but caution is needed in interpreting the
increase in mental health problems, as non-probability samples
were used during the COVID-19 pandemic.
Pierce et al. (39) warned against the use of non-probability
sampling in mental health surveys during the COVID-19
pandemic, as it inevitably introduces bias that cannot be fully
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Rens et al. Young People and COVID-19
TABLE 3 | Prevalences of mental distress in each predictor category and the odds ratios and confidence intervals of mental distress in logistic regression analyses (N=
2,008).
Predictor % distress COR 99% CI pAOR 99% CI p
Age
16–21 66.62 1
22–25 64.93 0.93 0.72–1.20 0.454
Gender
Male 56.82 1 1
Female 67.92 1.61 1.21–2.14 <0.001 1.78 1.29–2.46 <0.001
Student status
No student 64.11 1
Student 66.17 1.09 0.85–1.41 0.362
Living alone
No 65.32 1
Yes 67.59 1.11 0.69–1.78 0.581
Chronic condition
No 64.8 1 1
Yes 70.2 1.28 0.88–1.86 0.091 1.16 0.76–1.76 0.379
Past mental health care consultation
No 63.06 1 1
Yes 73.55 1.63 1.20–2.21 <0.001 1.34 0.96–1.89 0.026
Social support
High 52.75 1 1
Moderate 63.31 1.55 1.12–2.13 0.001 1.40 0.99–1.99 0.014
Low 76.86 2.97 2.05–4.33 <0.001 2.17 1.42–3.29 <0.001
Loneliness
No 54.46 1 1
Yes 88.46 6.41 4.54–9.06 <0.001 5.17 3.60–7.44 <0.001
Exposure to COVID-19
No 64.74 1 1
Yes 71.37 1.36 0.91–2.02 0.049 1.28 0.82–1.99 0.148
Increase in time at home
No 56.50 1 1
Yes 67.27 1.58 1.15–2.17 <0.001 1.20 0.84–1.71 0.197
Social media use
No increase 53.85 1 1
Small increase 69.34 1.94 1.50–2.51 <0.001 1.63 1.22–2.17 <0.001
Large increase 85.28 4.97 2.86–8.64 <0.001 3.08 1.70–5.61 <0.001
Visiting friends and relatives
No decrease 61.07 1 1
Small decrease 61.18 1.00 0.67–1.51 0.977 0.94 0.59–1.50 0.738
Large decrease 67.75 1.34 0.94–1.90 0.032 1.09 0.73–1.64 0.579
Going out
No decrease 55.70 1 1
Small decrease 64.32 1.43 0.98–2.11 0.016 1.63 1.04–2.52 0.005
Large decrease 69.62 1.82 1.23–2.71 <0.001 2.17 1.35–3.48 <0.001
Sports or hobbies
No change 62.22 1
Decrease 71.24 1.50 1.14–1.98 <0.001 1.32 0.97–1.80 0.019
Increase 58.31 0.85 0.61–1.18 0.202 0.89 0.62–1.28 0.394
Home activities
No change 63.70
Decrease 83.93 2.98 1.51–5.88 <0.001 2.72 1.30–5.67 <0.001
Increase 65.68 1.09 0.84–1.41 0.389 0.84 0.62–1.12 0.116
COR, Crude Odds Ratio in bivariate analysis; AOR, Adjusted Odds Ratio in multivariable analysis; 99% CI, 99% Confidence Interval.
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Rens et al. Young People and COVID-19
controlled. Several of the following limitations can explain the
extreme level of mental distress in the study. First, it is possible
that the level of mental distress was already higher in the sample,
even before the outbreak of COVID-19. Indeed, almost one
in four participants reported past mental health problems and
the majority of the participants were female. Importantly, late
adolescent and young adult girls were found to experience more
mental health problems in Belgium compared to boys (40). The
overrepresentation of girls in the sample may therefore give an
overestimation of the true prevalence of mental distress among
young people. We acknowledge that an internet-based sample
is in general not representative, for example because of self-
selection bias and because the most vulnerable may not be
reached. Moreover, we have no reliable information about the
place of residence, the ethnic group and the socio-economic
status of the participants. Finally, we emphasize that a screening
is not equal to a diagnosis, and that mental distress describes
a wide range of troubling symptoms but is not equal to a
clinical mental disorder. Notwithstanding these limitations, the
level of mental distress in Belgian youth as reported in this
study is striking and the study provides valuable insights about
the contributing factors of mental distress among youth in
transition age.
Ten percent more women compared to men were found to
experience significant mental distress, and female gender was
predictive for mental distress in both crude and multivariable
analyses. This is in line with previous studies reporting the female
gender as a risk factor for low psychological well-being during
the COVID-19 pandemic (5,24,27,28). Notably, women report
lower mental health compared to men even in normal conditions,
but the pandemic is contributing to an even growing gender
inequality (15).
Previous research suggested that mental wellbeing is low
among students and that the mental health effects of the
pandemic are high in this group, but our findings indicate that
student status is not predictive for mental distress (19,26). The
high levels of mental health problems in student samples can
therefore possibly be better explained by young age, rather than
student status in itself. Moreover, little differences were found
between 16–21-year-olds and 22–25-year-olds, and no effect of
living alone was found.
We expected that vulnerable young people with a chronic
condition or people who consulted a professional for mental
health problems in the last 12 months would be highly affected
by the pandemic, as previously demonstrated in a Turkish and
Italian sample of the general population (8,27). However, both
variables were no significant predictors at p<0.01 in the
multivariable model. One possible explanation for the lack of
association between the presence of a chronic condition and
mental distress is that the young people with a chronic condition
in our sample may not be heavily impaired, or are not at increased
risk for developing complications when infected with COVID-19.
A history of mental health problems was significant in
bivariate analysis, but was only marginally significant (p=0.026)
when covariates were included. This is surprising, given that the
majority of a sample of UK youth with mental health needs
reported that the pandemic made their condition worse, and
many mental health support services were unavailable during the
pandemic (6). A longitudinal case-control study found that the
mental health of patients with a psychiatric disorder remained
worse than those without a psychiatric disorder, but that the
COVID-19 pandemic did not increase the symptom-severity
(41). A possible explanation for the lack of a strong association
in our study is that the mental health problems for which they
consulted a professional in the last 12 months are rather mild,
or that these problems are already treated adequately. Moreover,
there may be a high proportion of young people with pre-existing
mental health problems who did not consult a professional for
mental health reasons in the last 12 months.
As expected, young people who feel lonely or with low
social support experience high mental distress. Social distancing
rules should therefore go together with increased attention for
social support and connectedness, especially as this can be an
important buffer in times of adversity (42). It is assumed that
loneliness and a lack of social support cause mental distress, but
the direction of the association is however unclear due to the
cross-sectional design.
Some factors, such as having an infected relative or
experiencing lockdown-related changes in one’s daily life, are
factors that are uniquely linked to the COVID-19 situation.
Inconsistent findings have been reported as regards to the link
between having an infected relative and mental distress (8,19,
26,27). In this study, being infected with the virus or having an
infected family member is not a predictor of mental distress. Few
studies have explored the role of changes in one’s daily life, but a
mobile app study suggested that it is not the quarantine in itself,
but rather the impact it has on one’s regular daily life that predicts
mental health problems (43).
To our knowledge, this is the first study that examines the
impact of changes in several everyday activities on youth mental
health. The results indicate that some lifestyle changes contribute
to mental distress in young people, while other changes are less
relevant. Specifically, we found that an increase in the frequency
of social media use, a decrease in going out for drinks or food and
a decrease in doing home activities significantly predicted mental
distress, while changes in time spent at home, visiting friends and
relatives, and practicing sports or hobbies did not.
While previous research identified high social media use as a
risk factor for anxiety and depression, the relationship may be
more complex than initially thought (19,44,45). It is possible that
excessive use of social media only affects those who, under regular
circumstances, do not use social media that often. This idea is
supported by the finding that the odds of mental distress were 3-
fold greater among those with a large (i.e., more than 3 h) increase
in social media use compared to before the pandemic. Further
research is needed to confirm this hypothesis and to examine
whether the type and content of the social media exposure
matters. For example, social media use can even be beneficial in
some cases, as previous findings indicated that social media can
also provide informational, emotional, and peer support (45).
The significant effect of a large decrease in going out for
food or drinks can be explained by the importance of this
activity for some young people. Transition age is often a period
of freedom and leisure before taking up adult responsibilities.
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Rens et al. Young People and COVID-19
For example, nightlife is important for some young people. The
findings indicate that mental distress is highest among those
who usually go out to meet peers more than once a week. It
can be assumed that this subgroup of outgoing young people
is characterized by high sensation-seeking, and that the lack of
sensation causes distressing boredom. This idea is supported
by research which found that the COVID-19 measures feel
more unnatural for extravert people, leading to higher decreases
in mental wellbeing and social connectedness as compared to
introvert people (46,47).
Finally, a decrease in home activities was present for only a
small minority of the sample and significantly predicted mental
distress. The mechanisms remain unclear, but the fear and stress
may be paralyzing for some people, causing them to stop doing
relaxing activities. A decrease in practicing sports or hobbies
was only marginally significantly contributing to mental distress,
while other studies found an association between an increase
of sedentary behavior and poorer mental health (48,49). It is
possible that the association was less pronounced in this study
because not all hobbies entail physical activity, and not all
physical activity is categorized as sport (e.g., going for a walk).
Nevertheless, governments should allow and facilitate physical
activity in times of adversity, as sedentary behavior threatens both
physical and mental health.
In conclusion, Belgian youth is clearly troubled by the
COVID-19 pandemic and the associated social distancing
measures. Mental distress among Belgian 16–25-year-olds was
very high during the first wave of the pandemic. Female gender,
low social support, high loneliness and changes in social media
use, going out for drinks or food and doing home activities
were found to be contributing to mental distress. Fortunately,
the mental health consequences of the pandemic are widely
recognized and studied. Young people were however an often
forgotten group in the COVID-19 pandemic, because most
attention went in the first place to the elderly at risk. While young
people are at low risk for the physical effects of COVID-19, young
age is an important correlate of low mental health during the
pandemic. We call for increased attention to the psychological
needs of young people, for whom the effects of social deprivation
and the disruption of everyday life are particularly detrimental.
Authorities should therefore aim to reduce the impact on public
and personal life as much as is safely possible. Young people are
a broad group and should be treated accordingly, for example by
not only focusing on students. While it might be unethical to give
young people certain privileges in complying with the measures,
governments must allow self-development and peer contact in
safe conditions. Some concrete decisions than can be made are
to leave schools and universities open and to provide safe spaces
for young people in unstable home environments. Those in the
greatest need, such as women and people with no supportive
network, should be targeted for counseling and social support.
Studies investigating the aftermath of the COVID-19 pandemic
should further contribute to the understanding of the long-term
psychological effects of such a global disaster.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
Ethical review and approval was not required as it is a
population-based, online survey without the collection of
personal data, which is in accordance with the local legislation
and institutional requirements. Participants were provided with
the legal information relating to consent, and online informed
consent was obtained from all participants.
AUTHOR CONTRIBUTIONS
PS, PN, VL, and KV designed the questionnaire and the online
survey. ER organized the database, analyzed the data, and wrote
the draft of manuscript. All authors contributed to and approved
the submitted manuscript.
FUNDING
This research was supported by a grant from the King Baudouin
Foundation, Grant No.: 2020-J1812640-216406.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2021 Rens, Smith, Nicaise, Lorant and Van den Broeck.
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