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RESEARCH ARTICLE
Psychological well-being during the COVID-19
pandemic: Combining a web survey with
experience sampling methodology
Yury ShevchenkoID*, Noemi Huber ID, Ulf-Dietrich Reips
Research Methods, Assessment, and iScience, Department of Psychology, University of Konstanz,
Konstanz, Germany
*yury.shevchenko@uni.kn
Abstract
COVID-19-related regulations have impacted the economy and people’s well-being,
highlighting the long-standing problem of inequality. This research explored how COVID-19-
related restrictive policies, such as a lockdown or social distancing, affected people’s well-
being. In Study 1, a cross-sectional online survey (N= 685), we examined the associations
between socio-economic characteristics, the number of resources, their relative change,
people’s stress levels, and their support of restrictive policies. We found that financial loss
due to COVID-19, the number of children at home, and the intensity of restrictive measures
were associated with higher stress by restrictive measures. The lower support for restrictive
measures was observed among those who experienced financial loss due to COVID-19,
had more children at home, less frequently accessed COVID-19-related information in the
media, and did not perform self-isolation. Men were generally less supportive of restrictions
than women, and the number of new COVID-19 cases was negatively related to the support.
Lower stress and higher support for restrictive measures were positively associated with life
satisfaction. In Study 2, an experience-sampling survey (N
participants
= 46, N
responses
= 1112),
the participants rated their well-being and level of available resources daily for one month.
We observed that daily increases in well-being, characterized by higher life satisfaction and
lower levels of stress and boredom, were positively associated with more social communica-
tion and being outdoors. In summary, the findings support the resource and demand frame-
work, which states that people with access to resources can better cope with the demands
of restrictive policies. Implications for policies and interventions to improve well-being are
discussed.
Introduction
The novel coronavirus disease SARS-CoV-2 (COVID-19) outbreak began in December 2019.
Humanity responded by developing medication, vaccination, and attempting to stop the
spread of the virus and the overburdening of health services through a series of restrictive mea-
sures (e.g., "flatten the curve" action). Governmental restrictions included closing public
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OPEN ACCESS
Citation: Shevchenko Y, Huber N, Reips U-D
(2023) Psychological well-being during the COVID-
19 pandemic: Combining a web survey with
experience sampling methodology. PLoS ONE
18(3): e0282649. https://doi.org/10.1371/journal.
pone.0282649
Editor: Shihe Fu, Xiamen University, CHINA
Received: April 28, 2022
Accepted: February 19, 2023
Published: March 24, 2023
Copyright: ©2023 Shevchenko 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: The data can be
found on the project page in the Open Science
Framework, https://osf.io/xc7w3/.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
facilities, educational institutions, and borders, enforcing physical distancing, and private
quarantine.
These measures were often changing; they differed across countries and sometimes within
the same country for different regions. The COVID-19 Stringency Index developed in Oxford
provides a composite measure of governmental responses (e.g., school closure, travel bans)
and demonstrates the difference in policies across countries and time [1]. Combined with
other datasets [e.g., 2], such as the number of confirmed cases or deaths, the efficiency of gov-
ernment responses can be analyzed.
It’s generally agreed that the restrictive measures, such as a lockdown, negatively impacted
the economy, e.g., service businesses such as traveling, recreational activities, and restaurants
and cultural businesses such as concerts and theater performances. Economic consequences
can be measured and tracked. For example, the GDP of Germany decreased by five percent in
2020 [3].
At the same time, behavioral scientists have tried to understand how restrictive measures
affected people’s well-being. Wang et al. [4] showed the COVID-19 pandemic and its conse-
quences have increased negative emotions and decreased positive emotions globally. Previous
research documented the negative psychological effect of quarantine that may elicit post-trau-
matic stress symptoms, confusion, and anger [5]. Reappraisal interventions on such emotions
worked better during the COVID-19 pandemic when framed positive rather than controlling
[4]. Human behavior might change during the lockdown, as there was evidence that unhealthy
eating, smoking, and alcohol consumption increased during the lockdown time [6]. Social iso-
lation and loneliness might lead to mental health problems: anxiety, depression, self-harm,
and suicide attempts [7]. Comparing measures before and after the beginning of COVID-19,
researchers showed an increased level of generalized anxiety in the German population,
together with depression symptoms, psychological distress, and COVID-19-related fear [8].
The level of depression and anxiety was also elevated in women during pregnancy at the time
of the pandemic [9].
People with preexisting physical or mental health problems were more affected by lock-
down and social isolation [7]. For example, individuals with bulimia experienced more nega-
tive and less positive emotions after the introduction of lockdown measures. The change in
emotions was moderated by the amount of binge eating [10]. Also, individuals with a high
level of neuroticism consumed more information related to COVID-19, worried more about
the consequences of the crisis, and experienced more negative emotions than the control
group [11]. COVID-19 strained the capacity of health services, so people with general disabili-
ties received less rehabilitation than before [12].
Mentally and physically healthy people can also be negatively affected by COVID-19 in
terms of their well-being and mental health. In an interview study, 25% of parents stated that
their mental health has decreased, and 14% noticed a decrease in their children’s behavioral
health since the beginning of the pandemic [13]. Ghosh and colleagues [14] suggested that this
negative impact can be explained by the closing of schools, lack of outdoor activity, irregular
eating, and changes in sleeping behavior.
Different degrees of impairment during the COVID-19 pandemic were observed between
men and women, with women appearing to be more severely affected. Zamarro and colleagues
[15] theorized that policies such as school closures and social distancing led women to take
more parental duties in the absence of schools and relatives. One indicator for this theory is
that in academia, the number of published articles written by women decreased during
COVID-19, while the number of publications among men stayed at the same level [16].
The focus of this study is twofold. First, we want to understand how people perceive and get
influenced by COVID-19-related restrictive measures. Second, we aim to analyze what
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influences their well-being daily during the presence of restrictive measures. Findings on fac-
tors influencing adaptive capacity and welfare should eventually inform policy to design more
effective measures to prevent the spread of the pandemic and ensure people’s well-being.
The response to the COVID-19 pandemic is characterized by an equilibrium between
demands, such as wearing masks or limiting the number of contacts, and the resources avail-
able to manage them. This equilibrium has been unstable as the regulations have frequently
been changing since the pandemic’s beginning.
Cognitive psychology explains how people reflect on their environment; the source of stress
is seen as the perceived inability to cope with high demands [17]. Lazarus and Folkman [18]
described three main types of stress: challenge, threat, and harm. This typology can be used to
distinguish how people perceive the restrictions depending on the amount of coping resources.
Whereas some people feel challenged by the need to remain in quarantine, others perceive a
threat (e.g., losing a job), and a few undergo harmful consequences (e.g., depression, physical
illness). The loss or depletion of resources should cause stress, as described by the conservation
of resources theory [19]. Gaining resources, on the other hand, should increase coping abilities
and well-being.
For this study, we consider mainly three types of resources: economic (access to financial
and material assets, such as physical space), social (stable and meaningful relationships with
others), and informational (access to information). Based on viewing changes in COVID-
related social regulations (e.g., lockdown) as stress-inducing constraints, we formulated a
“resource and demand hypothesis”: We expect that people who have more economic, social,
and informational resources will be better adapted to restrictions, experience less stress and
have a higher level of well-being.
We present two empirical studies. In the first study, we evaluated in a cross-sectional online
survey how the socio-economic characteristics, the resources, and their relative change during
COVID-19 were associated with stress elicited by restrictions and with the support of restric-
tive policies. We found that financial losses due to COVID-19, children in the household, and
restrictive measures intensity were associated with higher stress. Lower support for restrictive
measures was observed among those who experienced financial losses due to COVID-19, had
more children in the household, accessed COVID-19-related information in the media less fre-
quently, and did not self-isolate. In the second study, we analyzed the dynamics of everyday
experience to investigate whether the change in the subjective sufficiency of resources was
related to well-being. We found that daily increases in well-being, indicated by higher life satis-
faction and lower levels of stress and boredom, were positively associated with more social
contacts and spending time outdoors.
Study 1
Hypotheses
Study 1 focused on how access to different types of resources (economical, socio-emotional,
and informational) is associated with 1) how much people are stressed by restrictive measures
and 2) how much people support these measures.
Previous research has shown how different resources help people cope with stressful situa-
tions. For example, it has been found that social support helps maintain resilience in the face
of adversity [20], and a vast network of social ties and personal support helps people cope with
high levels of stress [21]. Another study found that people who felt better informed about
COVID-19 felt less psychological distress [8]. Therefore, we hypothesized that people with
fewer resources should feel more stressed by COVID-19 restrictions.
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The amount of stress from the restrictions may be related to attitudes toward the authorities
that imposed the restrictions. Previous research has shown that trust in government is tied to
people’s satisfaction with public services. High levels of trust in an institution tend to extend to
other institutions, while dissatisfaction with a policy may lower support for other policies [22].
Based on these findings, we expected that people who feel more stressed by restrictions would
support institutions that imposed restrictions less than people who feel less stressed.
If both hypotheses are valid, i.e., people with fewer resources are more stressed, and more
stress is associated with less support, we should also observe that people with fewer resources
are less supportive of restrictive policies.
Method
Participants. Six hundred eighty-five participants completed the survey between October
2020 and December 2021 (see Table 1 and Table A1 in Appendix A of S1 File). Mean reported
age of participants was 45.7 years (range 18–70, SD = 10.9).
Design. Using the Internet to conduct scientific research worldwide has become a suc-
cessful route to more generalizable results [e.g., 23]. Study 1 was thus conducted as a one-time
online survey. The survey was programmed in lab.js [24] and hosted on the online data collec-
tion platform Open Lab [25]. The invitation link to the “CoroNOW” survey was placed at the
end of the German version of the WageIndicator Survey of Living and Working in Coronavi-
rus Times 2020–2021 [26]. By distributing the link to our survey inside of the WageIndicator
Survey, we connected the datasets from both surveys. The WageIndicator survey was used to
collect data on the effect of COVID-19 on jobs (e.g., change of workload) and lives of people
(e.g., home situation). Our study focused on the number of resources, level of stress, and sup-
port of different restrictive measures.
Measures. WageIndicator survey. In the WageIndicator survey, participants were asked
various questions about their living and working conditions [26]. We used the following vari-
ables in our analysis: socio-demographic (gender, age) and employment status, education
Table 1. Reported demographic information in Study 1.
N %
Gender
Female 435 63
Male 244 36
Not known 6 1
Having children
Yes 272 40
No 407 59
Not known 6 1
Employed
Yes 574 84
No 102 15
Not known 9 1
Education level
Low 72 10
Middle 353 52
High 254 37
Not known 6 1
Total 685 100
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level, number of rooms and children in the household. Education level was measured with the
question “What is the highest level of education you have attained?” and response options
according to the International Standard Classification of Education 1997 (ISCED-97), which
ranges from 0 (No education), 30 (Upper secondary education), 50 (First stage of tertiary edu-
cation) to 61 (Second stage of tertiary education, leading to an advanced research qualifica-
tion). For the analysis, we used the recoded variable, which was provided by the
WageIndicator survey, that divided the ISCED levels into three groups (0–29 = low, 30–
49 = medium, 50–61 = high).
CoroNow survey. The survey asked about income level, financial loss, pets in the household,
media consumption, isolation, and frustration level (see Table A1 in S1 File for questions).
The income level was assessed with a question about the annual income. The financial loss was
measured by asking the participants to indicate whether they lost money relative to their
monthly income due to COVID-19. The participants were also asked if they had a pet, and if
yes, what kind. Media consumption was measured by asking about the frequency of media
consumption related to COVID-19. The survey also asked the participants how frustrated they
felt with their current situation and whether they had ever self-isolated for at least seven days.
According to the National Health Service of the UK, self-isolation was defined as not leaving
home if one has symptoms of coronavirus (COVID-19) or lives with someone who does. Self-
isolation means not leaving home for any reason other than to exercise once a day, not going
out to buy food or collect medicine (but ordering online or asking neighbors and friends to do
grocery shopping and pharmacy visits), and not having visitors at home.
To assess their stress level, the participants were asked to what extent various policies in
their region constrained them personally. Answers were given on a visual analog scale [27]
between “not at all” and “very much”, resulting in values between 0 and 100 inclusive. The sup-
port for governmental restrictions was measured with the statement “I agree with the following
measures to contain the spread of coronavirus” and the visual analog scale between
“Completely disagree” and “Strongly agree”.
The eight policies were similar for both stress and support questions. The order of the items
was randomized for each participant.
1. a shutdown of shops and malls
2. closed borders
3. complete lockdown (only shopping and work allowed)
4. missing childcare
5. duty to wear masks
6. social isolation (no meet-ups with more than a fixed number of people)
7. a shutdown of cultural institutions
8. social distancing (the preset number of meters apart)
Analysis. The data pre-processing and analysis were performed using R [28]. Descriptive
statistics for measured variables are displayed in Table A1 in Appendix A of S1 File. We cre-
ated the stress and support scores by averaging answers to eight restrictive policies. Test reli-
ability of both scales with Cronbach’s alpha was used as an indication of internal consistency.
We used multiple regression models to test whether socioeconomic and personal variables pre-
dicted the stress and support scores in the whole sample and its subgroups. Pearson correla-
tions were computed for correlation analysis with α= 0.05 (two-sided tests) as the critical
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significance level. All reported effects are correlative, so they do not imply causality; rather, we
interpret the models in terms of the independent variables that explain the stress and support
score variance.
Ethics statement. Electronic informed consent was obtained before starting the survey
and fully complied with IRB regulations at the University of Konstanz. Participation was vol-
untary and anonymous. Participants could interrupt the survey at any time at no cost. The sur-
vey consisted of socio-demographic questions and self-report measures, such as attitudes
toward COVID-19 restrictive measures.
Results
Stress and support scales. The eight restrictive measures elicited different stress levels,
ranging from a less demanding policy of closing shops and malls to more demanding measures
to control social distance (Fig 1). To analyze whether the attitudes toward different restrictive
measures are homogeneous at the individual level and can be summarized into an average
score, we calculated the intercorrelation between the different policies for the stress and sup-
port questions. For the stress score, intercorrelation was high, as indicated by Cronbach’s
alpha = 0.81, 95% CI [0.79, 0.83]. For the support score, intercorrelation was very high, Cron-
bach’s alpha = 0.90, 95% CI = [0.88, 0.91]. In further analyses, the mean scores, indicating the
overall stress and support level for each participant, were used.
To assess the external validity of the stress measure, we computed the correlations of the
stress score with frustration and life satisfaction measures. The stress score was positively cor-
related with frustration, r= 0.45, p<.001; and negatively correlated with life satisfaction, r=
-0.26, p<.001.
Hypothesis 1: People with fewer resources are expected to feel more constrained by the
restrictions. The results of the multiple regression model indicate that the predictors explain
10% of the variance in the stress score, R
2
= 0.12, adj.R
2
= 0.10, F(14, 615) = 5.75, p<.001
(see Table 2). The stress was lower for the participants who did not experience financial loss
due to COVID-19, M= 46.11, SD = 26.46, in comparison to the participants who lost less than
one month’s income, M= 58.27, SD = 23.92, b= 11.31, SE = 2.96, p<.001, and the participants
who lost more than one month’s income, M= 60.53, SD = 27.16, b= 13.70, SE = 2.58, p<.001.
Fig 1. The amount of stress and support of different COVID-19-related policies. Error bars indicate 95% CIs. The
answers to the questions about the level of stress and support for each policy were recorded on a scale between 0 and
100. A higher score indicates higher stress levels (in red) or stronger support for restrictive policies (in blue). The
policies are ordered by the level of stress.
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The number of children was related to higher stress by restrictive measures, M
no children
=
48.34, SD
no children
= 25.95, M
one child
= 54.24, SD
one child
= 29.80, M
two children
= 57.52, SD
two chil-
dren
= 27.10, b= 3.31, SE = 1.21, p= .006. The severity of COVID-19 restrictions, as measured
by the stringency index, was positively associated with stress, i.e., the higher the stringency
index, the more stress participants experienced, b= 0.27, SE = 0.13, p= .043. The average stress
score when the stringency index was at the median (Med = 60.65) was M= 52.52, SD = 27.79.
The marginal effects of the predictors on the stress score are shown in Fig 2.
To investigate how predictors were related to stress by a specific restrictive policy, we have
repeated the analysis for each policy. The significance level of coefficients was set to α= .006 to
correct for multiple comparisons. Losing more than one month’s income compared with no
loss showed a higher stress score for each of the policies except complete lockdown. The num-
ber of children and the stringency index did not have significant associations with the stress
caused by a specific policy.
Table 2. Regression coefficients for Model 1 and Model 2.
Model 1—Stress Model 2—Support
Predictors Estimates CI p Estimates CI p
(Intercept) 32.97 12.05 – 53.89 0.002 29.08 6.81 – 51.35 0.011
Male (vs. Female) 3.00 -1.50 – 7.51 0.19 -8.12 -12.92 – -3.32 0.001
Age -0.13 -0.33 – 0.06 0.18 -0.08 -0.29 – 0.12 0.42
Education level 1.19 -2.20 – 4.58 0.49 1.73 -1.88 – 5.34 0.35
Employment 3.38 -2.54 – 9.31 0.26 -5.90 -12.21 – 0.41 0.07
Income level 0.77 -0.57 – 2.12 0.26 -0.82 -2.25 – 0.61 0.26
Rooms per person -1.39 -4.02 – 1.25 0.30 2.21 -0.59 – 5.02 0.12
Financial loss (less than one month income) 11.31 5.50 – 17.11 <0.001 -11.05 -17.23 – -4.87 <0.001
Financial loss (more than one month income) 13.70 8.62 – 18.77 <0.001 -18.18 -23.58 – -12.78 <0.001
Number of children 3.31 0.93 – 5.69 0.006 -4.21 -6.74 – -1.67 0.001
Home pet 0.29 -3.93 – 4.50 0.89 -2.43 -6.92 – 2.05 0.29
Information access -1.67 -3.66 – 0.32 0.10 3.06 0.93 – 5.18 0.005
Self-isolation -5.48 -11.18 – 0.22 0.06 9.12 3.06 – 15.19 0.003
Stringency index 0.27 0.01 – 0.53 0.043 0.19 -0.09 – 0.47 0.19
New COVID-19 cases (in thousands) 0.19 -0.10 – 0.48 0.20 -0.39 -0.69 – -0.08 0.014
Observations 630 630
R
2
/ R
2
adjusted 0.12 / 0.10 0.17 / 0.15
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Fig 2. Predicted values of the stress score for different values of financial loss, number of children, and stringency
index. 95% confidence intervals are shown for predicted values where all other predictors are held constant, i.e., their
mean values are used. Grey dots display the predicted data with the effects of all other predictors accounted for.
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Hypothesis 2: People with fewer resources should show a lower level of
support
The results of the multiple regression model indicate that the predictors explain 15% of the
variance in the support score, R
2
= 0.17, adj.R
2
= 0.15, F(14, 615) = 9.20, p<.001. The support
was higher for the participants who did not experience financial loss due to COVID-19,
M= 40.28, SD = 31.09, in comparison to the participants who lost less than one month’s
income, M= 28.17, SD = 27.55, b= -11.05, SE = 3.15, p<.001, and the participants who lost
more than one month’s income, M= 21.13, SD = 23.11, b= -18.18, SE = 2.75, p<.001. The
support for restrictive policies was higher for those participants who accessed COVID-
19-related information more frequently, M
less than once a week
= 17.24, SD
less than once a week
=
22.44, M
several times a day
= 33.40, SD
several times a day
= 30.30, b= 3.06, SE = 1.08, p= .005. Indi-
viduals who self-isolated supported the restrictions more than individuals who did not self-iso-
late, M= 43.53, SD = 33.63 vs. M= 31.92, SD = 28.92, b= 9.12, SE = 3.09, p= .003. Individuals
with children were less supportive of restrictions, M
no children
= 37.92, SD
no children
= 30.51, M
one
child
= 28.67, SD
one child
= 29.07, M
two children
= 27.37, SD
two children
= 28.07, b= -4.21, SE = 1.29,
p= .001. Men were less supportive of restrictions than women, M= 26.98, SD = 28.21 vs.
M= 37.40, SD = 30.23, b= -8.12, SE = 2.44, p= .001. An increase in the number of new
COVID-19 cases was associated with less support, b= -0.39, SE = 0.16, p= .014. The average
support score when the number of new COVID-19 cases was at the median (in thousands,
Med = 11.52) was M= 40.44, SD = 30.32. The marginal effects of the predictors on the stress
score are shown in Fig 3.
To investigate how predictors were related to the support for a specific restrictive policy, we
have repeated the analysis for each policy. The significance level of coefficients was set to α=
.006 to correct for multiple comparisons. Losing more than one month’s income compared to
no losing showed less support for each policy. The significant negative association of the num-
ber of children with support was present only for the shutdown of shops and malls, complete
lockdown, social isolation, and missing childcare policies. More frequent COVID-19-related
media consumption was positively associated with higher support for the shutdown of shops
and malls and complete lockdown policies. Self-isolation was positively related to higher
Fig 3. Predicted values of the support score for different values of financial loss, numberof children, media
consumption, self-isolation, new COVID-19 cases, and gender. 95% confidence intervals are shown for predicted
values where all other predictors are held constant, i.e., their mean values are used. Grey dots display the predicted data
with the effects of all other predictors accounted for.
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support for the closed-borders policy. Men supported the duty to wear masks, social isolation,
and social distancing policies less than women. The number of new COVID-19 cases was posi-
tively associated with reduced support for the social distancing policy.
Hypothesis 3: People who feel more constrained by the restrictions are expected to sup-
port them less than people who do not feel constrained. Participants who felt more stressed
by the restrictions supported them less than participants who felt less stressed (Fig 4), as indi-
cated by the negative correlation between the stress and support scores, r= - 0.62, p<.001.
Exploratory analysis. In the following section, we report an analysis of interactions
between different factors in order to better understand how individuals in different groups
(e.g., individuals who experienced financial losses due to COVID-19 and individuals who did
not) perceived restrictive measures.
Financial loss. We have found that the participants with higher financial loss reported
higher stress levels and lower support levels for restrictive measures. We explored the differ-
ences between the groups with different levels of financial loss with respect to other variables.
The groups did not differ significantly in age, gender, income, education, number of rooms
per person in the household, number of children, ownership of a pet, and frequency of con-
sumption of COVID-19-related information (ps>.05, see Table A2 in Appendix A of S1 File).
In the group that lost more than one month’s income, there were more unemployed
Fig 4. Stress and support scores.
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individuals, N
no loss
= 42 (10%) vs. N
less
= 13 (13%) vs. N
more
= 45 (28%), X
2
(2, n= 668) = 28.36,
p<.001. Experiencing financial loss was associated with a higher level of frustration with the
current situation, M
no loss
= 61.89, SD
no_loss
= 36.53 vs. M
less
= 68.12, SD
less
= 34.26 vs. M
more
=
85.25, SD
more
= 25.73, F(2, 674) = 27.67, p<.001, and a lower level of life satisfaction, M
no loss
= 5.55, SD
no_loss
= 2.61 vs. M
less
= 5.36, SD
less
= 2.43 vs. M
more
= 4.16, SD
more
= 2.66, F(2, 667) =
16.84, p<.001.
Stress and support levels differed between the financial loss and no loss groups depending
on whether they were employed or not, as confirmed by a significant interaction between
financial loss of more than one month’s income and employment for stress, b= -15.26,
SE = 6.26, p= 0.015, and support scores, b= 16.76, SE = 6.82, p= 0.014 (see Table A3 in
Appendix A of S1 File for regression coefficients). While in the no-loss group, employed indi-
viduals experienced more stress than non-employed individuals, M= 47.21, SD = 26.93, vs.
M= 36.90, SD = 20.48, in the group that lost more than one month’s income, employed indi-
viduals reported less stress than non-employed individuals, M= 59.31, SD = 26.52, vs.
M= 64.26, SD = 28.47. A similar pattern was observed for the support score. In the no-loss
group, employed individuals were less supportive of the restrictive policies than non-employed
individuals, M= 38.64, SD = 30.91 vs. M= 52.08, SD = 29.87. In the group that lost more than
one month’s income, employed individuals were more supportive of the restrictions than non-
employed individuals, M= 21.73, SD = 22.46 vs. M= 18.41, SD = 22.49 (Fig 5).
Stringency index. Because the intensity of COVID-19 restrictive measures (as measured by
the stringency index) was positively related to the level of stress they induced, we further exam-
ined the interaction between the stringency index and other variables, i.e., how various factors
were associated with stress and support for restrictive measures during periods when the strin-
gency index was low versus when it was high. We tested extended versions of Model 1 and
Model 2 where the interaction between the COVID-19 stringency index and the financial loss,
the number of children, media consumption, self-isolation, gender, or the number of new
COVID-19 cases were included. None of the interactions between the stringency index and
the other variables were statistically significant (ps>.05).
To further investigate the possible non-linear interactions between the stringency index
and other variables, we repeated the regression analysis for the two groups: the group in which
Fig 5. Stress and support scores by employment and amount of financial loss due to COVID-19.
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the stringency index was below or equal to the 25
th
percentile (“low stringency index”,
n= 232) and the group in which the stringency was above or equal to the 75
th
percentile (“high
stringency index”, n= 334). The groups did not differ significantly in age, gender, employ-
ment, income, financial loss, number of rooms per person in the household, ownership of a
pet, and number of children (ps>.05). The high stringency index group had a higher level of
media consumption than the low stringency index group, M
low
= 4.22, SD
low
= 1.20 vs. M
high
=
4.55, SD
high
= 0.96, t(560) = -3.56, p<.001.
With regard to the stress elicited by restrictive measures (Fig 6), loss of more than one
month’s income was associated with higher stress in both low and high stringency index
groups, b= 11.42, SE = 4.88, p= .02, and b= 14.13, SE = 3.54, p<.001. The media consump-
tion and the stringency index were negatively associated with stress only in the high stringency
index group, b= -3.51, SE = 1.59, p= .028, and b= -1.43, SE = 0.56, p= .011. In contrast, the
positive association between the number of children and stress was present only in the low
stringency index group, b= 5.35, SE = 2.14, p= 0.013.
Regarding the support for restrictive measures (Fig 7), losing more than one month’s
income was associated with lower support in both low and high stringency index groups, b=
-14.04, SE = 5.16, p= .007, and b= -21.58, SE = 3.73, p<.001. The effects of other variables dif-
fered for low and high levels of restrictive measures. When the stringency index was low, being
employed was associated with less support, b= -12.05, SE = 5.87, p= .041, while more rooms
per person and more frequent media consumption increased support, b= 5.05, SE = 2.43, p=
.039, and b= 4.82, SE = 1.67, p= .004. When the stringency index was high, on the other hand,
Fig 6. Standardized regression coefficients for stress in groups with different level of stringency index.
Standardized regression coefficients with 95% CIs are shown. The confidence interval of the effect estimate should not
overlap with zero for the effect to be considered statistically significant.
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losing less than one month’s income reduced support, b= -17.20, SE = 4.55, p<.001. Also,
male gender and pet ownership were negatively related to support, b= -10.83, SE = 3.41, p=
.002, and b= -7.12, SE = 3.21, p= .027. However, the increase in stringency index was associ-
ated with the increased support for restrictive measures, b= 1.55, SE = 0.59, p= .009.
We further explored which kind of pet was associated with less support for restrictive poli-
cies during the period when the stringency index was high. To analyze that, we entered
dummy variables in the multiple linear regression model for owning a cat (31% of participants
had at least one cat), a dog (20%), fish (4%), a small pet such as a hamster, guinea pig, or
mouse (5%), or another type of pet (5%). Only owning a cat was negatively related to the sup-
port level, b= -7.04, SE = 3.48, p= 0.044. The effects of other types of pets were not significant,
ps>0.05.
Regarding the specific type of restrictive policy, owning a cat did not have significant associ-
ations given that the significance level of coefficients was set up to α= .006 to correct for multi-
ple comparisons. Cat owners did not differ significantly from others in age, gender, education,
employment, income, financial loss, number of rooms per person in the household, and fre-
quency of consumption of COVID-19-related information. However, cat owners had more
children than the others, M
cat
= 0.88, SD
cat
= 1.08 vs. M
no cat
= 0.52, SD
no cat
= 0.83, t(330) =
-3.27, p= .001.
Self-isolation. We used a multiple logistic regression analysis to examine which factors were
associated with self-isolation. The decision to self-isolate was positively related to losing more
Fig 7. Standardized regression coefficients for support of restrictive measures in groups with different level of
stringency index. Standardized regression coefficients with 95% CIs are shown. The confidence interval of the effect
estimate should not overlap with zero for the effect to be considered statistically significant.
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than one month’s income, b= 2.04, SE = 0.55, p= 0.008, media consumption, b= 1.40,
SE = 0.19, p= 0.014, and new COVID-19 cases, b= 0.96, SE = 0.02, p= 0.037.
Life satisfaction. To answer the question of how restrictive measures did affect well-being,
we conducted a multiple regression model analysis. The model’s predictors explain 15% of the
variance in life satisfaction, R
2
= 0.17, adj.R
2
= 0.15, F(16, 613) = 7.65, p<.001 (see Table 3).
Higher life satisfaction was associated with male gender, age, education level, employment,
and number of children. People who lost more than one month’s income were less satisfied
with their life. The feeling of stress by restrictive measures was negatively associated with life
satisfaction, and support for restrictive measures, on the other hand, was positively related to
life satisfaction.
Discussion
In Study 1, we examined factors related to stress and support for restrictive measures, such as a
lockdown or wearing masks policy. We found that financial loss, more children in the house-
hold, and more intense restrictive measures were associated with higher stress levels. Concern-
ing the support for the restrictive policies, individuals who felt more stressed by the
restrictions showed less support. Financial losses and the number of children also played a role
here–individuals with losses and more children demonstrated less support for restrictions. In
addition, several other variables were related to the level of support–men (compared with
women), individuals who did not self-isolate, and individuals who accessed less COVID-
19-related information were less supportive of restrictive measures. The number of new
COVID-19 cases was also negatively related to the level of support. We discuss each of these
factors in more detail below.
Financial loss and employment. Individuals who experienced financial losses generally
were more stressed and less supportive of most restrictive measures, which was also consistent
Table 3. Regression coefficients for life satisfaction.
Life satisfaction
Predictors Estimates CI p
(Intercept) 3.06 1.01 – 5.10 0.003
Male (vs. Female) 0.58 0.15 – 1.02 0.008
Age 0.02 0.00 – 0.04 0.033
Education level 0.45 0.13 – 0.78 0.006
Employment 0.95 0.38 – 1.51 0.001
Income level -0.05 -0.18 – 0.08 0.47
Rooms per person 0.14 -0.11 – 0.39 0.27
Financial loss (less than one month income) 0.26 -0.30 – 0.81 0.70
Financial loss (more than one month income) -0.90 -1.40 – -0.40 <0.001
Number of children 0.24 0.01 – 0.47 0.041
Home pet -0.15 -0.55 – 0.25 0.46
Information access -0.05 -0.24 – 0.15 0.64
Self-isolation 0.02 -0.52 – 0.57 0.94
Stress by restrictive measures -0.02 -0.03 – -0.01 <0.001
Support for restrictive measures 0.01 0.00 – 0.02 0.013
Stringency index 0.00 -0.02 – 0.03 0.89
New COVID-19 cases (in thousands) -0.02 -0.05 – 0.00 0.08
Observations 630
R
2
/ R
2
adjusted 0.17 / 0.15
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with higher levels of frustration and lower life satisfaction. Loss of resources is a cause of stress,
which is predicted by the conservation of resources theory [19] and supported by previous
research [20]. Importantly, our results indicate that financial loss can be more relevant than
income level in predicting stress and support for restrictive measures. Income level did not
appear to be a significant factor in our study. This is in line with the research by Beland and
colleagues [29]. They found that employment status and work arrangements during COVID-
19 (such as remote working) were not related to self-reported levels of family stress, but the
inability to meet financial obligations increased reported family stress and domestic violence.
The income level, however, might play a role in other COVID-19-related decisions, such as the
vaccination decision. For instance, Peretti-Watel and colleagues [30] found that people with a
low income were more reluctant to get vaccinated.
In our study, individuals who had lost more than one month’s income were more often
unemployed than others. At the same time, combination of unemployment and financial loss
of more than one month’s income was associated with the highest level of stress and the lowest
level of support for restrictive measures. It is possible that unemployment was related to
COVID-19, so the loss of a job exacerbated the loss of income. About 1.09% of the population
in Germany is estimated to have lost their job due to COVID-19 [31]. Thus, loss of job and
income could be indicators of vulnerability. As discussed in previous research, vulnerable
groups are not only defined by age, illness, and homelessness, but may also represent various
socioeconomic groups that have difficulty coping financially with the crisis [32].
Employment itself did not guarantee a lower stress from restrictive measures, as shown by
the interaction of employment and financial loss in our dataset. In the group without financial
loss, employed individuals were more stressed by restrictive measures than were non-
employed individuals, which could be related to the extent to which the restrictions affected
their work and challenged them to adapt to a new situation (e.g., working remotely, wearing a
mask at work).
Children. Having children was related to higher well-being, as individuals with children
showed higher life satisfaction. At the same time, having children became associated with
more stress by restrictive measures and less support for restrictions during COVID-19. These
results coincide with other research showing that parental stress increased during the pan-
demic [33]. In particular, Calvano and colleagues [33] reported that more than 50% of parents
were stressed by social distancing, closure of schools and childcare facilities. We found similar
patterns, as people with children in our study were less supportive of social isolation and child-
care policies. Surprisingly, the association between stress and the number of children was not
present when the stringency index was high. Perhaps, when the restrictive measures were
intense, such as during the complete lockdown, other factors mitigated the association
between children and stress.
Stringency index and the number of new COVID-19 cases. More stringent restrictive
measures were associated with higher stress levels but were not related to policy support. In
contrast, the increasing number of new COVID-19 cases were not related to the stress caused
by restrictive measures but reduced support for them. It can be that when the number of new
COVID-19 cases was large, the restrictive measures were considered less effective, which was
reflected in a lower level of endorsement.
The different intensity levels of restrictions might represent different challenges for the pop-
ulation, so the stringency index’s effect is not linear. Exploring the differences between the
high and low stringency index times, we found that owning a home pet, particularly a cat, was
associated with less support for restrictive policies during the high stringency index times. It is
generally accepted that pet ownership can bring positive emotions of caring, companionship,
and the human-animal bond. During COVID-19, many pet owners reported that their pets
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gave them emotional comfort and positively impacted their lives [34]. In our study, we could
not find support for the benevolent effects of a home pet, neither with respect to the stress by
restrictive measures nor life satisfaction. On the contrary, we found that cat owners were less
supportive of restrictive regulations than others when the restrictive regulations were relatively
intense. Having a cat at home during the complete lockdown could mean difficulties in caring
for the animal, such as visiting a veterinarian [35]. Cat owners could feel more restricted by
the complete lockdown, which was not the case for dog owners. While owning a dog during
the lockdown might have some benefits, such as an excuse to go outside, keeping a cat could
not provide this advantage.
Another factor that was relevant only to the specific level of the severity index was house-
hold crowding, measured as the number of rooms per person. It was not associated with life
satisfaction or stress from restrictive measures. However, when restrictive measures were low,
people with more living space were more likely to endorse restrictions. Because people have
been spending much more time at home during the lockdown, the size of their living space
became more essential for their well-being. This is in line with previous research that has
shown that living in an overcrowded household can be associated with higher levels of distress
and that stress levels have even increased since the beginning of lockdowns; e.g., in April 2020,
39% of people in overcrowded households in the UK reported feeling distressed compared to
29% in non-overcrowded households [36]. Overcrowding might also be associated with the
financial pressure of housing payments, as overcrowded households might seek to share
accommodation costs by sharing them with more people.
Gender. Men were generally less supportive of COVID-19 restrictive measures than
women. In particular, this difference was present across several policies: duty to wear masks,
social isolation, and social distancing. Other studies confirm this finding, indicating that
women are more likely to perceive COVID-19 as a severe health problem and comply with
restrictive measures [37]. As Galasso and colleagues [37] argue, these differences can explain
the lower COVID-19 mortality among women and the more efficient response of women-led
countries to the pandemic [see also 38]. Women are also less likely than men to endorse
COVID-19 conspiracy theories [39].
Self-isolation. Individuals who had done self-isolation were more supportive of COVID-
19-related restrictions. The individuals who had isolated themselves might be exposed to
COVID-19 either by being sick themselves or living with someone who was sick. Additionally,
it could be that people who followed COVID-19-related recommendations were more likely to
impose self-isolation on themselves. The positive association between media consumption and
the decision to self-isolate can indicate that more informed people were more willing to self-
isolate but also that people during self-isolation might consume more COVID-19-related
information. It has been argued that the ability to isolate depends on people having the space
and resources to do so, and lost wages can be the primary reason for not following guidelines
[40]. In contrast, we found that losing one month’s income was positively associated with self-
isolation, suggesting that losing wages might also be a consequence of self-isolation.
Media consumption. The media consumption about COVID-19 can increase trust in
governmental actions [41] and compliance with prevention measures [42], but on the other
hand, can induce state anxiety when it is excessive [43,44]. Indeed, in our study, we found that
more frequent access to COVID-19-related information was positively associated with support
for restrictive policies. Additionally, this effect was more pronounced when the stringency
index was high. The reason for the positive correlation might be a causal influence of one vari-
able on the other (higher support increases media consumption or more media consumption
increases support) or a third variable that influences both support and the amount of media
consumption. One possible mediator between media consumption and support of restrictions
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could be COVID-19-related fear. Previous research has shown that increased media exposure
was associated with depression and COVID-19-specific anxiety in Germany [45]. Media expo-
sure can increase the level of fear, and fear can justify the use of restrictive policies to curb the
spread of the virus.
Life satisfaction. Socio-demographic and socio-economic characteristics such as male
gender, older age, education level, and employment were positively related to life satisfaction.
Many previous studies before COVID-19 found that life satisfaction did not depend on gender
[46,47]. Given that COVID-19 affected men and women unequally and often disadvantaged
women [15], this may explain why women reported lower levels of life satisfaction in our
study. The effects of age, education, and employment on life satisfaction are similar to results
from previous studies [46]. More importantly, stress from restrictive measures, but not the
stringency index itself, was negatively related to life satisfaction, confirming that psychological
well-being is not determined by stressful events but by how individuals perceive and adapt to
them.
To summarize the results of Study 1, we found that people’s psychological well-being dur-
ing COVID-19 was affected by the restrictive measures. Using a cross-sectional survey, we
examined the role of demographic and socioeconomic individual differences in response to
restrictive measures. Stress and the level of support for restrictive measures were related to
socioeconomic factors associated with people’s level of resources, such as financial loss. To
complement these findings with people’s perception of their own resources, we conducted a
second study that focused on how people perceive the amount of resources available to them.
In addition, we wanted to assess the dynamic relationship between resources and well-being:
How do changes in the amount of available resources affect well-being? To answer this ques-
tion, Study 2 was conducted as an experience sampling study with repeated measures of per-
ceived amount of available resources and well-being.
Study 2
Hypotheses
Study 1 confirmed that the loss of material resources was related to stress from restrictive mea-
sures. To examine how daily resource changes are related to well-being, we conducted Study 2.
We used the experience sampling method to collect data over one month. According to the
resource and demand hypothesis, positive resource change should be associated with better
well-being. Specifically, we expect that an increase in material resources (e.g., food), social con-
tacts, and information should reduce stress and negative emotions and improve life
satisfaction.
Method
Participants and COVID-19 situation. Forty-six students of the University of Konstanz
(38 women, five men, and three unknown) completed the study, which lasted four weeks (see
Table A4 in Appendix A of S1 File for summary statistics). The mean age of participants was
21.3 years (range 19–26, SD = 1.98). The study period was in March to May, 2021 (not all par-
ticipants began on the same date). During this period, the stringency index in Germany
decreased from 81.48 before the 1st of March to 77.78 from the 1
st
to the 20
th
of March to 75
after the 20
th
of March. The decrease in the stringency index indicates that COVID-19 restric-
tions were loosening during this period. The number of confirmed COVID-19 cases per day
increased from 6,248 on the 1
st
of March to 11,783 on the 21
st
of April and then decreased to
1,971 on the 31
st
of May [48].
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Design. Participation in the study began with an online onboarding survey similar to the
survey from Study 1. Because the participants had not taken the WageIndicator survey, the
onboarding survey included questions about country and city of residence, household size
(rooms, people), and a positive COVID-19 test in the past. The participants were then asked to
complete a daily survey over four weeks. Each day, at a randomly selected time between 9 am
and 9 pm, they received a notification via the "Samply research" mobile application with a link
to the survey. The order of questions in the daily survey was randomized for each participant
and each day.
Measures. Onboarding survey. The content of the onboarding survey was similar to the
"CoroNOW survey" used in Study 1. Because the purpose of Study 2 was to examine daily
changes in resource levels and well-being, our analysis focuses on the daily survey results.
Daily survey. The daily survey measured life satisfaction, negative emotions, and sufficiency
of material, social, and informational resources (see Table 4). All responses were given on a
visual analog scale and saved as values from 0 to 100. Life satisfaction was measured with the
question "How satisfied are you with your life at the current moment?”, and the end labels
were “Not at all” and “Very much satisfied”. Negative emotions included questions about anxi-
ety, worry about significant others, financial concerns, boredom, and stress, and the end labels
were “Not at all” and “Very much”. Finally, participants were asked to what extent the amount
of material resources, social contacts, and information about COVID-19 is currently sufficient
for them. End labels ranged from “Completely insufficient” to “Completely sufficient”. The
order of the questions was randomized for each participant and each measurement. We also
asked participants whether they were currently outdoors and whether they had been outdoors
the previous day.
Debriefing. At the end of the study participants received a debriefing, in which we thanked
them for participation and provided instructions how to receive participation credits.
Procedure. The study participants were recruited via the online recruitment platform
Sona at the University of Konstanz. Participation was rewarded with course credit points. The
study advertisement indicated that participants needed to have a smartphone with Internet
access to participate. Interested participants were invited to take the onboarding survey, which
could be completed on desktop computers or laptops. At the end of the onboarding survey,
participants were instructed to install the “Samply research” mobile application [available at
https://samply.uni-konstanz.de/,49] and participate in the daily survey there. Participants
were asked to join the study immediately after the onboarding survey, and the sampling
Table 4. Daily survey items.
Item Question (translated from German)
Life satisfaction How satisfied are you with your life at the current moment?
Fear How much do you fear to be infected at the current moment?
Worries about significant
others
How worried are you at this moment that your loved ones might be infected?
Economic concern How concerned are you about your potential economic loss at this moment?
Boredom How much are you bored at the current moment?
Stress How stressed do you feel at the current moment?
Material resources To what extent is your today’s basic supply (e.g. food, water, household goods)
sufficient?
Social communication
resources
How far is your today’s social communication sufficient for your normal level?
Information resources To what extent is the level of information about the current situation related to
COVID-19 (e.g., guidance from the authorities, news) you receive sufficient?
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schedule began the next day and lasted for four weeks. After completing the daily survey, par-
ticipants received a debriefing the next day, thanking them for their participation and giving
them instructions on obtaining their credit points.
Analysis. Data preprocessing and analysis were done using R [28]. Descriptive statistics
were calculated for measured variables (see Table A4 in Appendix A of S1 File). We applied
linear mixed-effects models using the nlme package [50] to analyze the effects of resources on
well-being. Following recommendations for the longitudinal data analysis [51], we separated
between- and within-subjects effects. The model was corrected for autocorrelation errors, and
the measurement day was included in the analysis to control for the influence of time. The
independent variables were centered on the mean to facilitate the interpretation of the model
results. We used random intercepts for each participant and random slopes (days within par-
ticipants) as random effects. The model estimated the fixed effects of the sufficiency of infor-
mation, communication, and material resources (both at the between- and within-subject
levels) on the well-being score. Other fixed effects were the measurement day, being outdoors
at the moment and yesterday, the stringency index, and the number of new COVID-19 cases.
Ethics statement. Electronic informed consent was obtained before starting the survey
and fully complied with IRB regulations at the University of Konstanz. Participation was vol-
untary, and participants could interrupt the study at any time. The survey consisted of socio-
demographic items and self-report measures.
Results
Well-being score. The well-being score was calculated as the average of the life satisfac-
tion and negative emotion scores (stress, fear, boredom, worries, and financial concern),
which were reversed by subtracting from 100. The intercorrelation of items in the well-being
score was moderate, as indicated by Cronbach’s alpha = 0.60, 95%CI [0.57, 0.64]. The mean
well-being score across participants was 66.20 (range 34.90–89.20, Med = 66.90, SD = 11.27).
The effect of resources on well-being. The results of the linear mixed-effects model indi-
cate that the predictors explain 24% of the variance in the well-being score; R
2marginal
= 0.24,
R
2conditional
= 0.72 (see Table 5). During the study, well-being improved over time, b= 0.20,
SE = 0.07, p= .002. At the between-subjects level, participants who scored higher in perceived
sufficiency of social contacts reported higher well-being on average, b= 0.36, SE = 0.10, p=
.001. On a daily basis, more sufficient social contacts were associated with higher well-being,
b= 0.12, SE = 0.01, p<.001. The participants who were outdoors at the time of survey response
reported higher well-being than those who were not outdoors, M
yes
= 71.76, SD
yes
= 14.46, vs.
M
no
= 64.93, SD
no
= 14.29, b= 3.29, SE = 0.68, p<.001. Being outdoors the day before the sur-
vey had no effect on well-being, M
yes
= 66.23, SD
yes
= 14.70, vs. M
no
= 64.56, SD
no
= 12.52,
b= 1.26, SE = 0.89, p= .16. Neither the stringency index nor the number of new COVID-19
cases were associated with well-being, b= -0.38, SE = 0.34, p= .26 and b= -0.04, SE = 0.04, p=
.39. The marginal effects of the predictors on the well-being score are shown in Fig 8.
Additionally, we have tested an extended version of the model where the interactions
between the stringency index, the number of new COVID-19 cases and the amount of com-
munication (WS deviation) were included. The interactions were not statistically significant
(ps>.05). We have also tested a regression model with fixed effects of each participant and
each measurement week. The main findings remained the same. More sufficient social con-
tacts were associated with higher well-being, b= 0.12, SE = 0.01, p<.001. The participants
who were outdoors at the time of survey had higher well-being than the participants who were
not outdoors, b= 3.12, SE = 0.72, p<.001, and well-being improved with each measurement
week, b= 1.59, SE = 0.24, p<.001.
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Table 5. Regression coefficients for well-being score.
Well-being
Predictors Estimates CI p
(Intercept) 90.93 40.38 – 141.49 <0.001
Being outdoors at the moment 3.29 1.96 – 4.61 <0.001
Being outdoors yesterday 1.26 -0.49 – 3.02 0.16
Day 0.20 0.07 – 0.33 0.002
Stringency index -0.38 -1.05 – 0.29 0.26
New COVID-19 cases (in thousands) -0.04 -0.12 – 0.05 0.39
Communication (BS mean) 0.36 0.15 – 0.57 0.001
Communication (WS deviation) 0.12 0.10 – 0.14 <0.001
Information (BS mean) -0.08 -0.22 – 0.06 0.28
Information (WS deviation) 0.01 -0.03 – 0.05 0.53
Material resources (BS mean) 0.15 -0.08 – 0.38 0.20
Material resources (WS deviation) 0.03 -0.01 – 0.07 0.15
Random Effects
σ
2
61.20
τ
00 participant
118.81
τ
11 participant x day
0.14
ρ
01 participant
-0.47
ICC 0.63
N
participant
46
Observations 1112
Marginal R
2
/ Conditional R
2
0.24 / 0.72
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Fig 8. Predicted values of the well-being score for different values of day, being outdoors at the moment, and
communication (between- and within-subject deviations). 95% confidence intervals are shown for predicted values
(marginal effects) conditioned on fixed and random effects. Grey dots display the raw data points.
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To understand which aspects of well-being were associated with the sufficiency of
resources, we repeated the analysis for each item of the well-being score: life satisfaction, stress
level, fear of getting infected, boredom, worries about significant others, and financial con-
cerns (see Appendix B in S1 File for regression tables).
The perceived sufficiency of social contacts had a positive association with life satisfaction
and a negative association with stress and boredom. At the between-subjects level, participants
higher in the perceived sufficiency of social contacts were more satisfied with their lives,
b= 0.51, SE = 0.15, p= .001, and were less stressed, b= -0.39, SE = 0.16, p= .019. On a daily
basis, more sufficient social contacts were associated with a higher level of life satisfaction,
b= 0.24, SE = 0.02, p<.001, and with a lower level of stress, b= -0.22, SE = 0.03, p<.001. The
boredom was negatively related to the perceived sufficiency of social contacts both daily and
between subjects. More sufficient social contacts on a particular day reduced boredom, b=
-0.28, SE = 0.03, p<.001, and participants with a higher perceived sufficiency of social con-
tacts were less bored on average, b= -0.69, SE = 0.11, p<.001.
The positive change in the perceived sufficiency of available daily material resources was
associated with fewer financial concerns, b= -0.07, SE = 0.03, p= .007. At the between-subjects
level, a higher level of perceived sufficiency of COVID-19 related information was associated
with higher fear of getting infected by COVID-19, b= 0.31, SE = 0.15, p= .039. On the other
hand, the fear declined each day, b= -0.54, SE = 0.15, p<.001. Worries about significant oth-
ers getting infected also decreased during the study, b= -0.71, SE = 0.14, p<.001. Being out-
doors at the moment of the survey was positively associated with life satisfaction, b= 4.33,
SE = 1.24, p<.001, and lower level of boredom and stress, b= -5.20, SE = 1.58, p= .001, and b
= -6.42, SE = 1.69, p<.001. Being outdoors one day before the measurement was positively
associated with life satisfaction, b= 3.68, SE = 1.64, p= .025.
Discussion
Study 2 aimed to explore how the changes in perception of the sufficiency of material, social,
and informational resources are related to well-being during the COVID-19 pandemic. The
data collected over four weeks allowed us to differentiate the relationships on two levels: 1)
between-subjects differences (how is the average level of the sufficiency of resources related to
the average level of well-being) and 2) within-subjects fluctuations (how is the change in the
sufficiency of resources related to the changes in well-being).
We found that well-being improved with more sufficient social contacts. In particular, a
higher subjective sufficiency of social contacts was associated with reduced stress and boredom
and increased life satisfaction. These results are in line with previous research. Alfawaz and
colleagues [52] found that people with a more intense family bond suffered less from anxiety
and depression during the lockdown. Family bonding included spending more time with fam-
ily members, e.g., cooking or exercising together at home. Having children at home during
quarantine decreases the risk of depression [53].
Access to material resources such as household goods is generally associated with financial
well-being. In our study, people worried more about potential financial losses when material
resources were insufficient. Perhaps the sufficiency of household goods might serve as a proxy
that indicates potentially more detrimental economic problems, e.g., loss of employment or
salary reduction.
Being outdoors significantly improved the reported well-being in terms of higher life satis-
faction and lower stress and boredom. This effect is in line with many other studies before
COVID-19 that demonstrated the positive effect of being in natural environments on well-
being [54]. Studies done during COVID-19 found that time spent outdoors was associated
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with increased positive emotions [55] and happiness [56]. Interestingly, being outdoors the
day before the survey was positively related to life satisfaction but did not affect stress or bore-
dom. These results suggest that the relationships between being outdoors and stress or bore-
dom are correlative, but not causal.
As in any experience-sampling study, the well-being of participants could change during
participation in the study. We observed two consistent trends over the measurement days: the
fear to get infected and the worries about significant others getting infected were both decreas-
ing over time. This coincides with the decrease of the stringency index in Germany during the
study period, which means that the restrictions were loosening. This positive trend, however,
partially contradicts the number of registered COVID-19 cases, which was increasing until the
middle of April 2021, and only after that started to go down. However, we found no consistent
effects of the stringency index or the number of new COVID-19 cases on fear or worries.
Thus, the decline in fear and worries could be an effect of repeated measurements, such as
some participants when asked repeatedly reported less fear and worries over time.
General discussion
COVID-19 has changed people’s lives. Aside from the primary effects of the pandemic on pop-
ulation health, many of these changes were secondary—consequences of alterations in social
regulations designed to prevent the spread of the virus. In this study, we wanted to understand
how the restrictive measures affected people’s well-being. We approached this question from
two different directions: One was a cross-sectional study that provided a snapshot of people’s
lives during the pandemic. The second was an experience-sampling study that captured the
dynamics of everyday life.
The results of both studies are consistent with a general “resource and demand” framework
that we established in the introduction. This framework, rooted in common sense, is sup-
ported in psychology, in the theory of resource conservation [19]. Within this framework, all
new social policies (e.g., wearing masks in public) are understood as demands. The response
that these demands elicit in people depends on their ability to cope. Access to material
resources, as well as non-material resources such as social contacts and information, is likely
to increase adaptive capacity and well-being, and lead to a generally positive evaluation of the
policy. On the other hand, lack or perceived loss of resources reduces adaptive capacity and
well-being and is associated with a more negative policy evaluation.
Policy implications
Support for restrictive policies is directly related to compliance—individuals who support restric-
tions (e.g., social distancing and mask-wearing) are more likely to comply. Previous research indi-
cated that individual differences such as liberal political ideology, high levels of risk aversion, self-
control, and need for cognition are associated with higher compliance [57]. Although these find-
ings are potentially informative, we believe that they are only a part of the picture, and compliance
should also be considered in the context of an individual’s ability to adapt to new policies. Some
of these, such as wearing masks, are easier than others, such as quarantine. This individualized
approach will also focus more on helping people build coping resources rather than individual dif-
ferences, such as political opinion. As we found in Study 2, potential stress factors that can make
people vulnerable to restrictive measures include financial loss, non-employment, and children in
the household, so individuals who lost their jobs and income or have children at home may need
additional support from the government to cope with the challenges of the pandemic.
One of the effective coping strategies in stressful times has been seeking social support cop-
ing [58]. In line with this, Study 2 demonstrated that well-being during COVID-19 was
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positively associated with the subjective sufficiency of social contacts. However, reducing social
contacts was one of the main restrictive measures to stop the spread of the pandemic, e.g., can-
celing public gatherings and restricting movement. How can social contacts be supported
without jeopardizing health? One way is to promote a “soft” version of social distancing, such
as wearing masks with other people or meeting outdoors (which also has a positive effect on
well-being, according to Study 2), without forcing strict self-isolation and avoiding any social
contact at all. Previous studies have shown that the way the restrictions or regulations are con-
veyed to the public might play a role, as a more autonomous type of message (“You are in
charge”) can produce less defiance than a controlling type of message based on shame and
blame (“Do you want to kill another person?”) [59]. Martela et al. [60] proposed communica-
tion guidelines, which may help to promote compliance. For example, they recommended to
emphasize a “shared identity and common fate” and “build trust through transparent and
open communication”.
Study limitations
There are two limitations of Study 2 related to the sample and measures. Study 2 was con-
ducted with a sample of college students that may not be representative of the general popula-
tion. Compared with Study 1, the sample of students was more homogenous in terms of their
socio-demographic and economic characteristics, such as age, income, or living conditions.
Although we focused on within-subject variation across multiple measurements, the effects
observed from Study 2 might be different in the general population.
The other limitation concerns the operationalization of the dependent variable. In Study 2,
we asked participants about the sufficiency of their resources. It could be that their responses
reflected not only the objective amount of resources but other subjective evaluations of their
current situations, such as general optimism. A more rigorous test of the idea that social com-
munication relates to well-being will be to ask participants to report about social contacts they
have engaged in. Another potential problem is that some other variable can account for the
positive correlation between the sufficiency of social contacts and well-being. For example, a
physical condition (being healthy vs. sick) might be related both to the sufficiency of social
contacts and well-being. We did not ask participants to report their physical condition daily.
However, we controlled for the variables related to being outdoors at the moment and the day
before the survey, which can serve as a proxy for their physical health (based on the expecta-
tion that physically ill people would mainly remain at home without going outside).
Future research
Future studies could explore the relationship between the content of consumed COVID-
19-related information and well-being. We assume that there are two types of informational
content that have different effects on people’s well-being. One is positive news and updates
about new developments, technologies, and measures that may relieve stress and improve
well-being. The other is distressing news that reports shocking events, dramatic half-truths,
and horrid future scenarios and induces fear. This idea originates from another study by our
research group, which compared COVID-19-related attitudes in Germany and Switzerland
and found systematic differences in news sources and COVID-19-related fear between the two
countries [61].
The theory of stress postulates the perceived inability to cope with high demands as the
main cause of stress [17]. Future studies may measure perceived ability to cope with restrictive
measures as a potential mediator between the measures’ intensity and stress level.
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An experience sampling study offers insight into everyday life that can reveal its dynamics.
Future studies could combine it with interventions that further test causal relationships. In
particular, the intervention related to social contacts and access to information could be inter-
esting to study. The potential question would be how the quality of social contacts or informa-
tion consumed affects well-being during the lockdown time. Another perspective is to explore
the moderating effect of personality, e.g., introversion vs. extroversion.
Longitudinal studies with the experience sampling method are sensitive to social context.
Future studies could incorporate the survey of perceptions of changing regulations to find out
how people adjust their attitudes and behavior in response to changes in policy.
Supporting information
S1 File.
(DOCX)
Author Contributions
Conceptualization: Yury Shevchenko, Ulf-Dietrich Reips.
Data curation: Yury Shevchenko.
Formal analysis: Yury Shevchenko.
Methodology: Yury Shevchenko.
Project administration: Yury Shevchenko.
Software: Yury Shevchenko.
Supervision: Ulf-Dietrich Reips.
Validation: Yury Shevchenko.
Visualization: Yury Shevchenko.
Writing – original draft: Yury Shevchenko.
Writing – review & editing: Yury Shevchenko, Noemi Huber, Ulf-Dietrich Reips.
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