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WHAT MAKES ONE FEEL EUSTRESS OR DISTRESS IN QUARANTINE? AN
ANALYSIS FROM CONSERVATION OF RESOURCES (COR) THEORY
Short title: RESOURCES, EUSTRESS AND DISTRESS IN COVID-19
QUARANTINE
M. Dolores Merino*1, M. Dolores Vallellano1, Coral Oliver1 and Inmaculada
Mateo1,2,3
1 Universidad Complutense de Madrid, Madrid, Spain
2 CIBER de Epidemiología y Salud Pública (CIBERESP), Spain
3 Escuela Andaluza de Salud Pública, Granada, Spain
*Corresponding author information: M. Dolores Merino. Universidad Complutense de
Madrid. Facultad de Psicología. Departamento de Psicología Social, del Trabajo y
Diferencial. Campus de Somosaguas, 28223 – Pozuelo de Alarcón, Madrid, Spain. e-
mail: lolamerino@psi.ucm.es
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Abstract
Objective. Health quarantines produce serious deterioration in psychological
health, which becomes more affected the longer the quarantine lasts. According to the
Conservation of Resources theory from Hobfoll (1989), those people who have a good
supply of resources will be able to cope better with the adversities and will show less
distress. The objective of this research is to identify what are the resources that, in a
situation of confinement under the threat of COVID-19, predict eustress or well-being,
and the loss or lack of which resources predict distress or discomfort.
Design and Method. 839 people (70.3% women) complete an online
questionnaire during the first week of COVID-19 confinement in Spain. The sample is
weighted to obtain a distribution that is similar to the Spanish population. Using multiple
linear regression analysis, factors are identified that are associated with eustress and
distress based on the Conservation of Resources theory.
Results. A model is identified that explains 55% of the variance of eustress
consisting mostly of personal resources, with vitality as the recourse having the most
weight. The factors that explain distress (18.9% of the variance) are those related to work
(employment situation, work satisfaction and time devoted to work) and conditions in the
home (space).
Conclusions. The models that predict eustress and distress are completely
different. Based on these results, a series of recommendations are proposed aimed at
increasing eustress and reducing distress in a situation of confinement. Additionally,
proposals are offered for future research.
Keywords: Distress, eustress, resources, COVID-19, quarantine.
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Due to the global pandemic caused by COVID-19, many countries (including
Spain, Real Decreto 463/2020) have instituted a quarantine in an attempt to control its
serious consequences. Although science and history demonstrate that quarantines are
effective in controlling epidemics (Newman, 2012), they are often unpleasant experiences
for those who experience them as they involve separation from the social circle, loss of
freedom of movement, uncertainty about the state of the disease, boredom, etc. (Brooks
et al., 2020), compounded by work uncertainty, the need to reorganize time and habits,
being with the family 24 hours a day, etc. The review that Brooks et al. (2020) have done
on the impact of quarantines from COVID-19 shows that these produce a serious
deterioration of individuals’ psychological health, which becomes more affected the
longer the quarantine lasts. There are numerous studies reporting adverse psychological
effects associated with the quarantine (Cava, Fay, Beanlands, McCay, & Wignall, 2005;
Desclaux, Badji, Ndione, & Sow, 2017; Pan, Chang, & Yu, 2005; Reynolds et al., 2008;
Wang et al., 2009). So understanding the main factors that trigger discomfort is essential
to design interventions that will help alleviate the negative effects of confinement.
One of the theories that can help in understanding the situation and which has
taken on greater significance in recent years in the study of stress is the Conservation of
Resources theory from Hobfoll (1989, 2002, 2011). For this author, resources are aspects
that people value in their own right and/or serve as a means to an end. The fundamental
principle of this theory claims that people strive to maintain, protect and build resources
because eustress depends on gaining these things and distress on their loss. Distress,
commonly known as stress, refers to the negative response to stressors that results in
negative affect and in harm to mental health. On the other hand, eustress is defined as the
positive response to adversity and is reflected in the presence of positive affect and well-
being (McGowan, Gardner, & Fletcher, 2006; Watson, Clark, & Tellegen, 1988). When
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there is the threat of a loss of resources, an actual loss of them, or lack of gain following
resource investment, the result is a lack of adaptation to the environment, which leads to
distress. On the contrary, gaining and conserving resources are linked with eustress.
Hobfoll (1989) classifies resources in four types: 1) Objects: are resources valued
for their physical nature, for example, having a home. Contributions from Environmental
Psychology have made clear the relevance of characteristics of the home, of having
sufficient space and the number of people who live there in regard to well-being, stress
and even family violence (Corral et al., 2011; Macintyre et al., 2003; Solari & Mare,
2012). All these aspects could potentially be relevant, as the home is the space people
living there are obligated to share for the duration of the quarantine.
2) A second category of resources is what Hobfoll (1989) calls conditions:
conditions are resources to the extent that they are valued and sought after. Within this
category are included, for example, having a job or being married. We think these two
conditions might be relevant in experiencing distress or eustress in a situation of
confinement due to COVID-19. As to the first, Jahoda (1982) notes that employment
provides clear benefits (income, social relations, etc.) and latent ones (psychological),
while unemployment causes well-being to deteriorate, increases distress and affects
mental health. Literature on the subject clearly establishes a link between psychological
deterioration and unemployment (Merino, Privado, & Arnáiz, 2019). It should be
emphasised that we live in a time of great work uncertainty. In fact, it is expected that the
pandemic’s impact on the world economy will have unprecedented severity (McKibbin
& Fernando, 2020). Considering what has been said, analysis of the employment situation
of the worker during confinement is a relevant matter in understanding and explaining
the distress or eustress experienced during same.
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In regard to the second condition, being married or not, results of research done
on well-being consistently support the connection between being married and well-being
(Waite, Luo, & Lewin, 2009). In a situation of confinement such as the current one,
having a stable partner (whether married or not) can be a crucial element of psychological
support that helps in understanding the factors influencing the eustress or distress
experienced during confinement.
3) The third resource group is what Hobfoll (1989) refers to as personal
characteristics, referring mainly to positive personality attributes or psychological
resources that are characterized by being valued in their own right as they are associated
with favourable results for the individual such as, for example: well-being, physical
health, mental health, etc., and because they make it possible to adapt better to the
environment, to changes, and encourage the individual to progress in achieving their
personal goals and in satisfying their needs (Hobfoll, 2002; Merino & Privado, 2015).
Examples of psychological resources are: optimism, resilience, self-esteem, etc. Having
these types of resources is clearly associated with eustress, while their lack or loss are
linked to distress (Hobfoll, 2002). In a situation as threatening as COVID-19,
understanding which psychological resources explain eustress and the loss or lack of
which ones lead to distress seems crucial to us in order to develop strategies to help with
confinement.
4) The fourth group of resources is called energies, and with this Hobfoll (1989)
refers to resources that are not valued in and of themselves but because they can be used
to obtain other resources; for example, time, money and knowledge are energy resources.
The structuring and organization of our time is of vital importance for well-being
(Csikszentmihalyi, 2013; Jahoda, 1982). In a situation of confinement, knowing which
activities people invest more time in and, of these, which are associated with eustress and
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which with distress seems of great importance to us and will be a matter to be studied.
Likewise, social class, as an expression of the combination between money and level of
studies, could be a factor that will help to understand the differences in eustress and
distress in people who are confined. There is research evidence on the relationship
between these variables (O’Halloran & Gordon, 2014).
Finally, it is important to consider that although Hobfoll (1989) does not include
social resources in the original formulation of his theory, he later speaks (Hobfoll, 2002)
of social support as a resource with value as a means or vehicle to achieve other resources.
The literature shows that social support is an important element in the well-being of
individuals and that the lack or loss of this can cause deterioration in the person’s
psychological health (Chen, Westman, & Hobfoll, 2015; Hobfoll, 2002). In a situation of
confinement like the current one, social support in its different aspects: affective,
emotional, instrumental or material, and in relation to leisure and distraction (Sherbourne
& Stewart, 1991) could be extremely relevant.
In conclusion, we wish to point out that the COR theory (Hobfoll, 1989) has been
applied in numerous contexts, among others disasters such as the earthquake in Turkey
in 1999 (Sumer, Karanci, Berument, & Gunes, 2005), the attack on the Twin Towers in
2001 (Hobfoll, Tracy, & Galea, 2006) and Hurricane Katrina (Zwiebach, Rhodes, &
Roemer, 2010). However, as far as we know, it has never been applied to a health crisis
such as the one coming from COVID-19.
This theory predicts that people who have a good supply of resources will be able
to cope better with adversities (in our case the coronavirus and its consequences:
confinement, the threat of a loss of resources, etc.) and will show less discomfort than
those who have a worse or more fragile supply of resources (Hobfoll & Lilly, 1993;
Hobfoll, Tracy, & Galea, 2006). In accordance with the above, the objective of this
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research is to identify what are the resources that, in a situation of confinement under the
threat of COVID-19, predict eustress or well-being, and the loss or lack of which ones
predict distress or discomfort. Knowledge on this will allow us to propose action plans
oriented toward maximising the well-being of the citizenry.
Method
Sample
Using a non-probability sampling procedure called snowball or chain sampling
(Martínez-Arias, Castellanos, & Chacón, 2014), the participation of 839 people in the
study was achieved (70.08% identified themselves as women, 28.12% as men and 0.35%
marked the option "I prefer not to say"). With an average age of 47.14 (SD = 17.42). Of
these, 14 (2.6%) had a primary education or no studies; 265 (31.7%) secondary education
and 559 (66.7%) higher education.
Post-survey weight adjustments were used, applying multiplicative factors to the
base weights to compensate for under-coverage and over-coverage of certain population
groups in relation to population distribution (Instituto Nacional de Estadística, INE,
2019). Multiplicative factors were derived through calibration based on gender, age and
educational attainment (Deville & Sarndall, 1992).
The study was approved by the Ethics Committee in Spain. All the participants
were informed in writing about the study and accepted the informed consent.
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Measures
Distress and Eustress
The Positive and Negative Affect Schedule (PANAS) scale adapted to Spanish and
updated (López-Gómez, Hervás, & Vázquez, 2015) from the original scale of Watson et
al. (1988) was used. We consider it suitable to use affective state to measure eustress
and distress, because according to Watson et al (1988):
Positive Affect (PA) reflects the extent to which a person feels enthusiastic, active,
and alert. High PA is a state of high energy, full concentration, and pleasurable
engagement, whereas low PA is characterized by sadness and lethargy. In contrast,
Negative Affect (NA) is a general dimension of subjective distress and
unpleasurable engagement that subsumes a variety of aversive mood states,
including anger, contempt, disgust, guilt, fear, and nervousness (….) (p.1063).
In other studies positive and negative affect have also been considered as indicators of
distress and eustress (Merino, Privado & Arnaiz, 2019; Nelson & Simon, 2003). PANAS
has 20 items with five points of response and two dimensions: positive affect (Cronbach's
alpha=0.91) and negative affect (Cronbach's alpha=0.85). The scores on each dimension
of the scale are presented from 1 to 5.
Resources measurement
Objects
Four questions were adapted from the June 2019 barometer from the Centre for
Sociological Research (Centro de Investigaciones Sociológicas, CIS, 2019) on
characteristics having to do with housing and coexistence during the period of
confinement: perception of having enough space in the dwelling (continuous variable: 1-
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5); living with people who interfere with work (yes/no); type of dwelling (apartment
building/single-family or semi-detached home); number of people living together
(continuous variable: 1-5).
Conditions
Three variables were included: (a) a question adapted from the January 2019 CIS
on employment status during confinement (self-employed or as a paid employee) whose
work conditions have not changed during confinement, with a job whose conditions have
been modified during confinement (a temporary layoff, reduction of working hours, on
leave), non-active (retired, student, homemaker); (b) job satisfaction: a question from the
National Health Survey [Encuesta Nacional de Salud, ENS, 2017); (c) partnership status
(having a partner or not).
Personal characteristics
This was evaluated using the Positive Psychological Functioning (PPF) scale
(Merino & Privado, 2015) having 33 items with five points of response which are grouped
in 11 dimensions: autonomy, resilience, self-esteem, purpose in life, enjoyment,
optimism, curiosity, creativity, sense of humour, environmental mastery, and vitality. For
each dimension in this study, the following Cronbach's alpha values were obtained: .85,
.82, .82, .77, .75, .82, .85, .84, .87, .77, .82.
Energies
A measure of social class based on occupation and having six levels was included
(Domingo-Salvany et al., 2013). Additionally, a question was developed on the time in
hours dedicated to 10 daily activities. Finally, a question was asked on sufficient income
in the home: with current household income, making ends meet can be done: very easily
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(5) with much difficulty (1), from the EAS-2015-2016 (Sánchez, García, & Mayoral,
2017). Education: Continuous (1- 6), 1 without studies and 6 post-graduate, adapted from
the January 2018 CIS.
Social support
This consists of five items adapted from the Abbreviated Duke Social Support
Index (2006). In this sample, the Cronbach's alpha coefficient was .78. Additionally, a
prepared question was added on the extent to which there are people to share leisure
activities with (on a scale of 1 to 5). The analyses were carried out item by item.
Control factors: Age and sex were included as control variables in the final multiple
regression model.
Procedure
An online questionnaire was designed using the Surveys application from Google,
which people accessed after reading information on the research and accepting the
informed consent. The link to the questionnaire was launched through announcements on
social media and e-mail lists, and disseminated by following a chain sampling procedure,
mentioned above. On average, the questionnaire took 13 minutes to complete and
participants did not receive economic compensation.
Responses to the questionnaire were received between March 25 to 30, 2020. It is
important to highlight that during this period, the epidemic was in a major outbreak with
more than 8.000 positive cases a day and over 800 deaths a day, with a collapse of the
Health System. Previously, the Royal Decree 463/2020 established the state of alarm on
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March 14: limiting the unrestricted movement of people, as a necessary measure to
contain the spread of the disease.
Data Analysis
First, descriptive statistics were calculated for all the variables used. Continuous
variables were described as mean and standard deviation (±SD). The categorical variables
were analysed through frequencies and percentages.
Linear regression models were used to analyse the factors associated with positive affect
(eustress) and negative affect (distress). Through the Kolmogorov-Smirnov test, the
continuous variables that did not follow normal distribution were identified. Those that
did not meet this condition were transformed using Napierian logarithm to bring them
closer to normal distribution.
To reach the final regression model, initially univariate models were done with all
predictor variables based on the COR theory and dependent variables of eustress and
distress. Subsequently, multivariate regressions were done for eustress and distress by
resource type (objects, conditions, personal characteristics, energies and social support),
including those variables from each resource group that were associated with each
dependent variable in the univariate regression analysis.
Finally, total multivariate regression analyses were done for eustress and distress
using the variables corresponding to all types of "resources" that in the previous
regression analyses (by type of resources) showed p<.05. Finally, age and sex were added
to the analysis as predictors to observe their possible influence on the results of the final
model.
The results are presented as regression coefficients (β) with their corresponding
confidence intervals at 95% and adjusted R2 values. For the analyses, the SPSS
statistical package version 26 was used.
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Results
Descriptive
The mean scores and deviations for the main quantitative variables are presented
in Table 1, and scores for the study’s qualitative variables are in Table 2. The average
score on eustress was 3.20 (SD = 0.776) and of distress was 2.35 (SD = 0.778).
Compared with others’ research the mean of eustress (PA) is almost equal, but the
average of distress (NA) is higher in this study (López-Gómez, Hervás y Vázquez,
2015;Watson, Clark, Tellegen, 1988).
The variables are classified, according to the COR theory, on the basis of the
resource groups they are integrated in. The mean scores in the different personal resources
are similar, with sense of humour having the highest score (mean = 3.95; SD = 0.859) and
control of the environment having the lowest (mean = 3.317; SD = 0.704). Table 2 shows
the frequency statistics for qualitative variables.
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Multiple regression analysis
With the aim of finding out about the most important predictors of eustress and
distress for each resource group, separate regression analyses were done on the variables
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included in: object resources, conditions, energies, personal characteristics and social
support. Results can be seen for eustress in Appendix S1, and results for distress are in
Appendix S2.
To find out which factors are associated with distress and eustress considering all
the possible types of resources, multiple linear regression analyses were done including
all variables that had proved significant in the regression analysis from the previous phase
for each dependent variable. Finally, an analysis was done including age and sex as
predictors in order to analyse their possible influence on the final model. The stepwise
method was used.
In Table 2 we can see the resulting model, composed of seven predictors that
explain 55% variance of eustress, adjusted R2 = .550, F(7.620) = 109.171, p < .001. The
variable that explains most of the variance is vitality, the contribution from the rest of the
model is very weak even though it shows significant coefficients (p < .001). All the
model’s variables are positively associated with eustress except the variable that analyses
the number of people living in the home at the time of confinement (β = -0.036; p =
0.022).
After adjusting for the variables of sex and age, the model practically does not
vary, and all the model’s variables maintain statistically significant regression
coefficients (see Table 2 models adjusted by sex and age).
In regard to distress, the model explains 18.9% with five predictor variables, R2=
0.189, F5.387 = 18.990, p < 0.001. The variable having the greatest weight in the model is
the labour situation, measured as being active or not (R2 = 0.138). The active population
has higher levels of distress (β = -0.378; p = 0.000) with respect to the non-active. All
variables are associated negatively with distress except for the time devoted to paid work.
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Those who devote more time to this activity during confinement have greater distress (β
= 0.104; p = 0.019).
In the case of distress, as sex and age variables are included as predictors to
analyse their possible influence on the model, this model explains 3% more variance than
the previous one. With space in the home and time spent working losing their
significance, and in addition to the sex and age variable, adding a new predictor for
distress, which is not having help in the case of becoming ill (β = -0.085; p = 0.024). In
any case, the greatest amount of variance in distress continues to be explained by the
variable of being active or not (adjusted R2 = 0.138).
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Discussion
The results indicate that overall models for eustress and distress are completely different.
In the case of eustress, the model explains 55% of variance and the psychological resources, with
vitality the main predictor of eustress, taking on special relevance. People who score high in
vitality are more likely to experience positive affect in a situation of confinement than those who
score low. And vitality explains 49% of the variance of eustress. This result is very consistent
with the literature on the subject, which predicts that vitality is a powerful indicator of personal
well-being (Ryan & Frederick, 1997). Vitality, as it has been measured (Merino & Privado 2015),
refers to the amount of internal energy the person perceives they have as well as the enthusiasm
and spirit with which they take on their chores. Therefore, it is not merely to feel alive or activated
but also, and very relevantly, implies feeling involved, connected, excited about what one does
(Ryan & Frederick, 1997). Vitality enables us to be more active and more productive (Deniz &
Satici, 2017). It is the basic resource that nourishes the intrinsic motivation to act and which, in
turn, is related to other resources such as curiosity, autonomy or self-esteem (Ryan & Frederick,
1997). Additionally, it is closely related to physical and mental health. In fact, the lack of vitality
is clearly an indicator of illness, whether physical or psychological (Deniz & Satici, 2017; Ryan
& Frederick, 1997). Thus, fatigue and physical illness often coincide with a lack of vitality, as
happens with depression, anxiety and distress (Deniz & Satici, 2017). This relationship with
physical and mental health is something that is extremely important in the situation of
confinement due to COVID-19, as they are two of the most significant threats facing the
population: the fear of contracting the illness and the deterioration of psychological health that
situations of quarantine bring about (Brooks et al., 2020). The rest of the psychological resources
that explain eustress are: curiosity, self-esteem and environmental mastery. However, the model
shows that their potential explanatory value is much lower than that of vitality, as between all
three little more than 4% of variance of eustress is explained. Curiosity refers to the interest in
doing new things, in learning, in discovering (Merino & Privado, 2015). It is therefore a source
of entertainment, distraction and also of information, all of which is very relevant in a situation
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of confinement (Brooks et al., 2020). Curiosity keeps each person active in what interests them
most. As to self-esteem, it is one of the main predictors of well-being (Fetvadjiev & He, 2019;
Lyubomirsky, Tkach, & DiMatteo, 2006). This refers to the overall feeling of acceptance of the
self; that one is worthwhile (Merino & Privado, 2015) and as such produces security in who one
is and what one does. It is logical that this feeling of security, and as a consequence of feeling
safe, acts as a protector of eustress in a situation of quarantine given the threat from COVID-19.
For its part, environmental mastery, although it only explains 0.9% of variance, is relevant for
eustress as it refers to the ability to select, create or manage spaces so they adapt to needs, values
and personal goals, thus providing a feeling of control over them and thereby facilitating better
coping with environmental stressors (Stafford, Deeg, & Kuh, 2016). Within the context of the
quarantine due to COVID-19, there is reason to believe it is a very valuable resource, as it
potentially would facilitate optimization of daily demands, adapting them to necessities. For
example: knowing what you have to do and what you do not, having accurate and useful
information, etc. All this enhances the feeling of being in control of the quarantine situation, and
of the virus itself, since control of the environment will make it possible to take effective measures
against it. Nevertheless, future research should put this hypothesis to the test.
In another sequence of resources, time devoted to physical activity emerges as a predictor.
People who devote more time to this show greater eustress than those who devote little time.
Although this variable has a very moderate explanatory value, as it accounts for 1.2% of variance
in eustress, it seems quite relevant to us in the context we analysed, where the majority of the
population has very limited mobility. This result is consistent with literature on the subject.
Various scientific studies have shown that regular physical activity provides important benefits
in people’s physical as well as psychological health (De Miguel et al., 2011; Gómez, Grimaldi,
Bernal, & Fernández, 2016).
The last two variables that, very modestly, predict eustress are: having someone to help
you if you become ill and the number of people who live in the home. The first forms part of
social support (Sherbourne & Stewart, 1991) and given that the possibility of falling ill due to
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COVID-19 is a looming reality, it makes a lot of sense that this comes up as a predictor of eustress.
The second predicts that the more people there are in the home, the less positive affect. The
literature shows that the number of people who live together in a house is a risk factor for distress
and even for family violence (Corral et al., 2011; Solari & Mare, 2012).
The model that predicts distress does not include personal resources as predictors of same.
In this case, the types of resources that take on importance are: conditions (employment situation
and job satisfaction), object resources (amount of space available in the home and, in the case of
working from home, interference from dependents or minors) and energies (devoting a lot of time
to work, and social class), all of which predict 18.8% of distress. Of these six resources, four are
related to work and, of these, the one that has the most explanatory weight is the labour situation.
In particular, the model notes that, in the situation arising from COVID-19, belonging to the group
of people who are actively working is a risk factor for distress, which explains the 13.7% of
variance. If we consider that the COR theory predicts that when resources are threatened it is
more likely that distress will be triggered (Hobfoll, 1989) and that, according to data from the CIS
(2020), 80.3% of Spaniards consider that the labour and economic consequences arising from
COVID-19 will be very serious, it is logical to think that the majority of Spaniards fear for their
jobs and, obviously, this constitutes a source of distress. The literature on the subject confirms
this connection (Merino et al., 2019). Another resource that predicts distress is job dissatisfaction.
This result is very consistent with the research, which notes that work satisfaction is a predictor
of overall level of satisfaction (Jarosova et al., 2016) and the opposite is also true, and highlights
that for the purposes of distress it is not so much having or not having work but whether the work
is satisfying or not. In fact, research on burnout shows that work can also be a very significant
source of distress (Acker, 2012; Caravaca-Sánchez, Carrión-Tudela, & Pastor-Seller, 2018;
Hombrados & Cosano, 2013). Another issue to investigate was to know the situation of people
who, having to work from home, are responsible for dependents or minors. This is a very unique
situation associated with COVID-19 confinement and, as such, no research has been found in
regard to it. The results, although modest, are in line with expectations, as the greater the
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interference, the higher distress perceived. Finally, the time dedicated to work is also a risk factor,
although quite moderate; the model predicts that the greater the amount of time, the higher the
distress (one reference on this).
The model also points out that the available space in the home can be a risk factor.
Specifically, perceiving that there is insufficient space predicts 2.2% of the variance of distress.
This result is in line with what was expected and is consistent with the literature on the subject
(Aliu & Adebayo, 2010; Corral et al., 2011; Solari & Mare, 2012). Finally, and quite discreetly,
the model shows that the lower the social class, the greater the risk of exhibiting distress. It is
reasonable to presume that this is because people belonging to this group have fewer resources at
their disposal than higher classes (money, work, education, etc.). The literature on the subject
notes that there is a clear relationship between psychological health and social class (Manstead,
2018). The model for distress is also more unstable because when you add sex and age as
predictors, space in the home as well as time spent working no longer account for variations in
distress. While a variable is added that also predicts eustress, having someone to take care of you
should that be necessary. It has already been mentioned earlier that these types of resources may
be of importance in the context of a pandemic like the one being experienced.
Recommendations
After analysis of the two models derived for eustress and distress, some
recommendations can be inferred:
1) Promote vitality and curiosity by remaining active, looking for tasks that are
intrinsically rewarding to do and taking advantage to devote time to them.
2) Encourage control of the environment: planning the available time properly by
organising routines that meet the needs of each person and achieve personal goals.
Search for truthful information on this pandemic that allows effective actions to be
taken to control the virus.
3) Include physical exercise in the routine, adjusting it to each age and physical typology.
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4) For the active population that senses their work is at risk, define and clarify social
protection measures for vulnerable groups.
5) Streamline the time devoted to work, alternating this with activities that are intrinsically
satisfying.
6) In the case of having many people in the home and/or the perception of a lack of space,
plan the distribution of shared spaces well based on the number of people and needs of
each one. Agree on this with all household members and respect it.
7) In the case of people who work at home and have dependents or minors, establish and
respect schedules to avoid interference between work and giving attention to these
people.
8) Seek support from family or friends in case of falling ill and if this is not possible, look
for this support through social services in the municipalities and at NGOs that have
volunteers.
Future research of the quasi-experimental kind should test to see if these recommendations
are effective along the lines laid out and thus promote well-being and reduce discomfort.
Limitations
We do not have similar studies in the bibliography that have assessed such a general
situation of confinement, nationally and even globally, to be able to compare our results.
Likewise, we do not have pre-test measures that would allow us to compare the results obtained.
Despite having an acceptable sample size, it is possible that there is bias from being
people who are close to patterns of the researchers in regard to location, social class and
educational level. Although in order to perform all the analyses, statistical adjustments were
used in order to take into account the population groups that had lower representation in the
sample in respect to distribution of the Spanish population in terms of sex, age, and educational
level (Deville & Sarndall, 1992).
20
It would be necessary to conduct longitudinal studies and with a longer confinement
period in order to analyse the evolution of the results.
The explanatory capability of the model of distress is somewhat limited, which can lead
us to think that there are relevant variables for it that have not been considered in this study.
Future research should address this issue.
21
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28
Table 1
Descriptive Statistics (quantitative variables)
N
Meana
Deviation (SD)a
Measures of eustress/distress
Eustress
805
3.28
.776
Distress
805
2.35
.778
Object resources
People living together
806
2.76
1.171
Perception of space
806
4.24
.972
Condition resources
Job satisfaction
621
3.05
.747
Personal resources
Self-esteem
806
3.674
.944
Resilience
806
3.938
.841
Optimism
806
3.681
.893
Creativity
806
3.663
.775
Autonomy
806
3.756
.856
Mastery
806
3.317
.704
Vitality
806
3.571
.904
Purpose
806
3.751
.859
S humour
806
3.950
.859
Enjoyment
806
3.780
.823
Curiosity
806
3.841
.831
Energy resources
Time devoted to work
494
3.21
1.915
Time devoted to intellectual tasks
806
2.91
1.111
Time devoted to physical activity
806
2.33
.776
Time devoted to passive leisure activities
806
2.08
.985
Time devoted to active leisure activities
806
3.11
.949
Time devoted to personal hygiene and house
cleaning
806
2.76
.867
Time devoted to social relationships
806
3.01
.930
Time devoted to helping others
806
1.78
.900
Time devoted to spiritual activities
806
1.20
.498
Time devoted to activities to know yourself
better
806
1.30
.588
Ease in making ends meet (Income)
806
2.64
1.19
Social support resources
Social support: have someone to care for you
in case of illness
806
3.97
1.235
Social support: have someone to help you out
economically
806
3.80
1.302
Social support: have someone to share your
feelings with
806
4.32
1.026
Social support: help in finding work
806
3.12
1.386
Social support: someone to help you take care
of minors if necessary
806
3.83
1.386
29
Social support: share moments of leisure
activity
806
4.14
1.109
Others
Age (in years)
800
46.6
17.131
(a) Statistics using data from the adjusted sample based on national population statistics
(Instituto Nacional de Estadística (INE), 2019)
30
Table 2
Descriptive Statistics (qualitative variables)
N
Percentageb
Object resources
Type of dwelling
Apartment building
585
72.8%
Single-family or semi-detached
home
219
27.2%
Living with someone interferes
with work
Yes
118
14.6%
No
688
85.4%
Condition resources
Work situation
Unemployed
77
11.0%
Working but with changes due to
COVID-19
135
19.3%
Working without changes due to
COVID-19
299
42.8%
Non-active population (student.
retired. homemaker)
188
26.8%
Relationship status
749
With partner
457
61.1%
Without partner
291
38.9%
Educationa
No studies
19
2.3%
Primary
64
7.9%
Secondary
304
37.8%
Vocational training
130
16.1%
Bachelor’s/degree
203
25.2%
Post-graduate degree
86
10.6%
Social classa
CNO Class I: Top management
and professionals with advanced
university degrees
171
28.1%
CNO Class II: Professions with
diplomas
50
8.1%
CNO Class III: Mid-level
occupations and self-employed
185
30.3%
CNO Class IV: Supervisors and
workers in technical jobs
17
2.7%
CNO Class V: Qualified workers
in the primary sector
34
5.5%
CNO Class VI: Unqualified
workers
154
25.3%
Others
Sex
806
31
Man
402
49.9%
Woman
403
50.1%
Note: CNO = National Classification of Occupations.
(a) The educational and social class variables are incorporated into subsequent
regression analyses as quantitative variables
(b) Descriptive statistics using data from the adjusted sample based on national
population statistics (Instituto Nacional de Estadística (INE), 2019).
32
Table 3
Results of final Linear Regression Models for eustress. Crude model and model adjusted for sex and age
Model 1a
Model adjusted for sex and agea
Beta
p
IC
IC
R2
Beta
p
IC
IC
R2
Vitality (1 to 5; 5=high)
.278
.000
.187
.369
.49
Vitality (1 to 5; 5=high)
.259
.000
.166
.351
.491
Curiosity (1 to 5; 5=high) .157 .000 .077 .238 .019
Self-esteem (1 to 5;
5=high)
.092 .035 .006 .177 .019
Self-esteem (1 to 5;
5=high)
.175 .000 .104 .247 .015 Curiosity (1 to 5; 5=high .141 .001 .059 .224 .011
Time for physical activity
(1 to 5; 5=more than four
hours) .076 .000 .035 .118 .012 Time for physical
activity (1 to 5; 5=more
than four hours) .077 .000 .036 .119 .011
E. Mastery (1 to 5; 5=high) .118 .001 .046 .191 .009
E. Mastery (1 to 5;
5=high)
.115 .002 .042 .188 .007
Support: have someone to
care for you in case of
illness (1 to 5; 5=strongly
agree)
.045 .013 .01 .08 .002 Self-esteem (1 to 5;
5=high) .140 .001 .06 .22 .007
Number of people living
together (1 to 5; 1=alone;
5=more than four)
-.036 .022 -.068 -.005 .003 Sex (1=woman; 0=man) -.036 .018 -.065 -.006 .004
Support: have someone
to care for you in case of
illness (1 to 5;
5=strongly agree)
.038 .034 .003 .073 .003
(a) Models constructed with scores from variables in Napierian logarithms.
33
Table 4
Results of final Linear Regression Models for distress. Crude model and model adjusted for sex and age
Crude model
a
Model adjusted for sex and age
a
Bet
a
IC (inferior)
IC
(superior)
p
R
2
Beta
IC
(inferior)
IC (superior)
p
R
2
Active
population
(1=non-active
population;
0=active
population)
-.378
-.488
-.267
.000
.137
Active
population (1=
non-active
population;
0=active
population)
-.259
-.38
-.137
.000
.138
Enough space (1
a 5; 5=enough)
-.181
-.309
-.054
.005
.022
Age (in years)
-.271
-.39
-.153
.000
.039
Work
satisfaction (1 to
5; 5=very
satisfied)
-.116
-.224
-.009
.034
.013
Work satisfaction
(1 to 5; 5=very
satisfied)
-.144
-.248
-.04
.007
.018
Time dedicated
to work (1 to 5;
5=more than
four hours)
.104
.017
.19
.019
.007
Sex (1=woman;
0= man)
.083
.014
.151
.018
.013
Social class (1
to 6; 6: very
high)
-.061
-.113
-.009
.021
.009
Support: help in
case of illness
-.085
-.159
-.011
.024
.008
(a) Models constructed with scores from variables in Naperian logarithms.
