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Factores predictores de la salud estudiantil: Tecnoestrés, estrés académico y
apoyo social
Predictive Factors of Student Health: Technostress, Academic Stress, and
Social Support
Ángela Asensio-Martínez1,2,3 , Arancha Morales-Villuendas1, Alejandra Aguilar-
Latorre1,2,3 , Barbara-Masluk1,2 , Santiago Gascón-Santos1,3 & María Antonia
Sánchez-Calavera2,3,4
1Department of Psychology and Sociology, University of Zaragoza. Zaragoza, Spain.
2Aragonese Primary Care Research Group (GAIAP), Institute for Health Research Aragón (IIS
Aragón). Zaragoza, Spain.
3Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS,
RD21/0016/0005), Carlos III Health Institute. Madrid, Spain.
4Department of Medicine, Psychiatry and Dermatology, University of Zaragoza. Zaragoza,
Spain.
*Corresponding author: bmasluk@unizar.es
Recibido 2024-04-12. Aceptado 2024-07-09
Resumen
En los últimos años el tecnoestrés ha sido definido como un estado psicológico negativo que se
relaciona con el uso de las Tecnologías de la Información y la Comunicación (TIC). El objetivo de este
estudio es analizar los niveles de tecnoestrés y su relación con la salud en estudiantes universitarios.
Se ha realizado un estudio descriptivo, cuantitativo y transversal, mediante encuesta autoinformada.
Este análisis se realizó mediante un cuestionario online cumplimentado de forma anónima por 389
estudiantes de pregrado, máster y doctorado, pertenecientes en su mayoría a universidades de la
Comunidad de Aragón. Además del tecnoestrés, se estudiaron los niveles de estrés académico, el
apoyo social y el uso de nuevas tecnologías para explorar su relación con la salud. Los resultados de
este estudio demostraron que, aunque la mayoría de los participantes no experimenta altos niveles
de tecnoestrés, aquellos que sí lo experimentan muestran que el estrés académico, las conductas y
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emociones generadas por el uso de las TIC, el tecnoestrés, el género y la satisfacción con el apoyo
social predicen la salud de los estudiantes.
Palabras clave: tecnoestrés; estrés académico; apoyo social; TIC; salud general.
Abstract
In recent years, technostress has been defined as a negative psychological state that is related to the
use of Information and Communication Technologies (ICT). The objective of this study is to analyze
the levels of technostress and its relationship with health in university students. A descriptive,
quantitative cross-sectional study has been carried out through a self-reported survey. This analysis
was carried out using an online questionnaire filled out anonymously by 389 undergraduate, master's
and doctoral students belonging mostly to universities in the Community of Aragon. In addition to
technostress, levels of academic stress, social support and the use of new technologies were studied
to explore their relationship with health. The results of this study demonstrated that, although most
participants do not experience high levels of technostress, those who do show that academic stress,
behaviors and emotions generated by the use of ICT, technostress, gender, and satisfaction with social
support predict students' health.
Keywords: technostress; academic stress; social support; ICT; general health.
INTRODUCTION
The situation experienced during the COVID-19 pandemic, along with other phenomena, has made
mental health a recurring theme. As a result of the confinement decreed by the Government in Spain
(March 14, 2020), the necessity of adopting to online teaching methods and the increase in
autonomous work by students became clear, since attendance at universities was cancelled. This
period saw a surge in the utilization of Information and Communication Technologies (ICT), both in
the work and academic spheres, as well as in the personal sphere, bringing to light the debate on the
right. to disconnect digitally and escalating stress levels.
The World Health Organization (WHO) defines stress as “the set of physiological reactions that
prepares the body for action”. On the one hand, “distress” occurs when the demands exceed the
capacities of the individual to face or control them, with harmful consequences; and on the other
hand, "eustress" occurs when activation helps us finish tasks on time, increasing performance (Caldera
Montes & Pulido Castro, 2007; Minaya Lozano, 2008).
Stress reactions can arise in various domains such as work, academics, etc., and are influenced by
multiple variables including environmental demands, perceived control (or lack thereof), individual
coping resources, and social support (Johnson & Hall, 1988; Karasek & Theorell, 1990). It is estimated
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that between 15 and 25% of Spanish students suffer from stress, nervousness and anxiety during
student life (del Toro Añel et al., 2014).
It would be the mismatch between the demands and the available resources when dealing with ICTs,
which leads to a high level of unpleasant psychophysiological activation and the development of
negative attitudes towards them, generating a type of stress called technostress (José et al., 2017;
Salanova, 2003).
Technostress is defined as a negative psychological state that is related to the use of ICTs, exposure
to them or an anticipatory fear or threat of their use in the future (Salanova et al., 2003). Along the
same lines as stress, one could differentiate between "techno-distress", when there is a reluctance to
accept and use ICT, fear of interacting with ICT and an attitude of rejection, and fatigue, mental and
cognitive exhaustion; and “techno-eustress”, when the process in which an individual experiment with
ICT is interpreted as challenging or exciting, generating positive results (Salanova, 2003; Salanova et
al., 2006; Tarafdar et al., 2019).
Technostress affects both workers and students with limited technological resources who tend to be
rejected by the use of ICT and perceive it as something negative, as well as people accustomed to the
use of new technologies, who due to continuous training, recycling and acquisition of new knowledge
without time to assimilate it they feel frustrated (Minaya Lozano, 2008). The digital migration forced
by the pandemic caused by COVID-19 produced consequences such as rejection, denial, fear,
uncertainty and a series of other problems that affect the mental state and an acceleration in the
processes of stress and exhaustion (burnout) (Carvalho et al., 2021; Guerra et al., 2022).
The interaction model of demands, control, and social support by Johnson & Hall, (1988) y Karasek &
Theorell (1990), applies to the stress experienced by students. In this model, social support acts as a
stress modulator if the individual has the necessary quantity and quality of social support. Conversely,
if social support is lacking or if the individual faces discrimination or intimidation, social support can
become a new stressor. Furthermore, social support can promote adaptive health behaviors, provide
well-being, or inhibit the negative effects of stress (Barra Almagi, 2004).
Technostress presents consequences such as cognitive symptoms of anxiety, irritability, concentration
and memory difficulties, and alterations in time perception, especially in artificially lit environments.
Physiological symptoms of technostress include muscle pain, headaches, insomnia, and eye fatigue.
Additionally, behavioral symptoms may manifest, such as nervous movements, frequent blinking, or
talking to the computer (Minaya Lozano, 2008; Salanova et al., 2007). Previous studies confirm that
exam taking, academic overload, maintaining or obtaining scholarships, presenting work, lack of time,
and completing studies within stipulated deadlines generate academic stress (González, 2017).
Thus, in the world of work, teleworking has been related to certain psychosocial risks such as less work
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commitment, less social support, absenteeism, lower performance and burnout (Hung et al., 2015;
Kasemy et al., 2022; Sardeshmukh et al., 2012; Tarafdar et al., 2015; Van Steenbergen et al., 2018).
Burnout is the result of prolonged exposure to chronic stress factors and can occur, among others, in
situations of lack of development, neglect, lack of recognition, lack of control and overload (Montero-
Marín et al., 2013). However, it also has advantages, it has been proven that teleworking promotes
autonomy, flexibility, speed of work, control, time management, increases productivity and quality of
life (Ayyagari et al., 2011; Barrera-Algarín et al., 2013; Tavares, 2017; Van Steenbergen et al., 2018).
Despite their familiarity with ICT (Prensky, 2001), the university population may be vulnerable to the
effects of ICTs due to frequent and extensive use, both in academic and personal context. The constant
digital connection implies spending most of the time using ICT, which causes lower class attendance,
a decrease in the quality of study and concentration, and produces a negative impact on the academic
performance of the student body (Upadhyaya & Vrinda, 2021). It also has implications for physical
health, such as eyestrain, headaches, backaches, digestive problems, and for mental health, such as
irritability, frustration, demotivation, anxiety, memory and concentration problems, addiction,
burnout and reduced satisfaction (Mahapatra & Pati, 2018; Rodríguez-Vásquez et al., 2021; Samaha
& Hawi, 2016; Sánchez-Macías et al., 2021; Tams et al., 2014; Wang et al., 2021).
For all these reasons, it is necessary to deepen the knowledge of the levels of stress produced by ICTs
in university students, one of the groups that use ICTs the most during learning, and their relationship
with health.
The main objective was to analyse the levels of technostress in university students and their
relationship with health. The secondary objectives were to analyse the levels of academic stress and
social support in university students and how they are related to health, and to analyse the association
of the use of new technologies with health in university students.
METHODS
Desing
A descriptive, quantitative, cross-sectional study was carried out through a self-reported online
survey.
Participants
The study has been carried out with a sample of the population of university students, over 18 years
of age. According to Government of Spain and Ministry of Universities (Ministerio de Universidades.
Gobierno de España, 2021), the total number of students enrolled in the Spanish University System
(SUE) in the 2020-2021 academic year is 1,679,518, including Bachelor, Master and Doctorate
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students. With a margin of error of 5% and a probability of success of 95%, with a confidence level of
95% and an accuracy of 3%, a sample of at least 213 individuals was needed. After the survey
administration period ended (Ministerio de Universidades. Gobierno de España, 2021) a final sample
of 389 participants was obtained, predominantly from universities in the Community of Aragon. The
inclusion criteria were signing the informed consent and being enrolled in the 2021/2022 academic
year in a Bachelor's, Master's or Doctorate at a Spanish University.
Instruments
The dependent variable of the study was health, defined by the WHO as "a state of complete physical,
mental and social well-being, and not merely the absence of disease or infirmity" (Organización
Mundial de la Salud, 1946). It was analysed through the Goldberg General Health Questionnaire (GHQ-
28). Which is subdivided into 4 subscales, with 7 questions each, referring to somatic symptoms,
anguish/anxiety, social dysfunction and depression (Lobo et al., 1986). Following Godoy-Izquierdo et
al. (2002) the questionnaire has been taken as a positive indicator of the current level of health or
well-being, and higher scores have better states of general physical and psychological health.
Therefore, obtaining a high score in the subscale of "physical state" or somatic symptoms indicates a
good level of physical health, in the subscales of "anxiety" and "depression" indicates the absence of
anxious and depressive symptoms and in the subscale of social dysfunction or "everyday well-being"
indicates that one has the personal capacity to develop a healthy and functional daily life.
The answers are presented in Likert format with 4 possibilities (from 0 to 3). The individual scores for
each item, which were added together from the scores for each subscale (from 0 to 21 points), as well
as the total for general health obtained from the sum of the latter, are those that were introduced in
the statistical analysis. The original questionnaire in its validation in Spanish presents good
psychometric qualities with a Cronbach's Alpha score of 0.97 for the full scale, 0.93 for somatic
symptoms, 0.92 for anguish/anxiety, 0.91 for social dysfunction and 0.97 for depression (Godoy-
Izquierdo et al., 2002). Similarly, the internal consistency of the GHQ-28 scale in the participating
sample was excellent (α = 0.92).
The independent variables of the present study are described below.
The sociodemographic variables of sex, age, nationality, marital status, place of residence, work
environment, current course enrolled and university of origin, were collected through an 8-item
questionnaire developed ad hoc for the study.
Technostress is defined as "a negative psychological state related to the use of ICT or threat of its use
in the future, which leads to a high level of unpleasant psychophysiological activation and the
development of negative attitudes towards ICT" (Salanova, 2003). It was evaluated through the
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Technostress Questionnaire (Techno-anxiety and techno-fatigue) developed by (Salanova et al., 2006)
(WoNT Prevenció Psicosocial ©). This questionnaire consists of 26 items, which evaluate technostress
as psychosocial damage with three types of dimensions: 1) Affective (anxiety vs. fatigue), 2) Attitudinal
(skeptical attitude towards technology) and 3) Cognitive (beliefs of inefficiency in the use of
technology). The items are answered through a Likert-type frequency scale that ranges between "0"
(nothing/never) and "6" (always/every day).
Obtaining high scores in these three dimensions is an indicator of technostress in its two
manifestations: techno-anxiety and techno-fatigue; to present techno-anxiety high scores must be
obtained in anxiety, skepticism and inefficiency and for techno-fatigue high scores in fatigue,
skepticism and inefficiency.
Having high scores in any of the dimensions does not indicate technostress, but it means it could
develop or appear in the future if the appropriate measures are not taken. The original questionnaire
has adequate internal consistency, exceeding in all cases the minimum Cronbach's Alpha score of 0.70,
which ensures the validity and reliability of the measures (Salanova et al., 2006); internal consistency
increased in the present study, being excellent (α = 0.93). Academic stress, defined by the WHO as the
“physiological, emotional, behavioral and cognitive activation reaction to academic stimuli and
events”. Analysed through the Academic Stress Questionnaire at the University (CEAU) (García-Ros et
al., 2012), which consists of 21 items grouped into 4 stress-generating factors during the university
period: academic obligations (completion of compulsory tasks and assignments, academic overload,
activities related to study and completion of evaluation tests), academic record and perspectives of
future (future academic situations or problems, getting good grades, keeping or getting a scholarship
or choosing subjects during the degree), interpersonal difficulties (conflicts with faculty and students,
and competitiveness) and expression and communication of ideas (presentations of work,
participation in class activities and discussions, and tutorials). The items are answered through a
Likert-type scale with 1 being no stress and 5 being very stressed; thus, the higher the score, the
greater the presence of academic stress. The original questionnaire, in its version validated in Spanish,
has adequate internal consistency for the four dimensions, with a Cronbach's Alpha value of 0.70 to
0.80 (García-Ros et al., 2012); equating the internal consistency of the CEAU scale in the present study
with a Cronbach's Alpha value of 0.70 to 0.86. The Use of ICT, defined as those technologies used for
the management and transformation of information, which allow creating, modifying, storing,
protecting and recovering that information (Cristóbal & Romaní, 2009), was analysed with the CUTIC-
28 Questionnaire, designed to measure the frequency and use in the educational field of ICTs with an
Internet connection, evaluating their usefulness and the emotion generated by their use among
university students (Jiménez Rodríguez et al., 2017). The questionnaire collects data on digital
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behaviors and opinions on the usefulness of ICT in computer support (computer or laptop) or Tablet,
and mobile phone (Smartphone-cell phone). It consists of 28 items distributed in two groups of 14
items (the items of both supports are identical) and in three dimensions frequency of use of ICT,
usefulness of ICT and behaviour/emotion generated by ICT. The responses are recorded in time
frequency intervals (hours per day) and with a Likert scale from 1 to 5 points (from never to always).
Following the recommendation of Jiménez Rodríguez et al. (2017), values above 2 in the frequency
and behavior dimensions exceed the mean value, in the usefulness dimension the mean is above 3.5.
The original questionnaire has good internal consistency with an alpha coefficient of 0.86. Similarly,
the internal consistency of the questionnaire in the sample of this study was good (α = 0.82).
Finally, perceived social support, understood as the cognitive assessment that there is a relationship
of trust with others, who can be counted on in case of need (Martínez-López et al., 2014), was analysed
through the Spanish version of the Social Support Questionnaire-Short Form.
This questionnaire is made up of 6 items, which represent moments of tension or need in different
situations. For each item, the number of people that everyone perceives as willing to help and support
them in a certain situation, and the degree of satisfaction with said support, is evaluated. The items
related to the degree of satisfaction are answered through a Likert scale of 1 to 6 points (very
dissatisfied to very satisfied) and the number of people with a 9-point scale (from 1 to 9 people). This
questionnaire measures two different aspects of perceived social support, availability and the index
of satisfaction with perceived availability. To calculate them, the average of the scores obtained is
made, with a maximum of 36 for the satisfaction score and 54 for availability. The original
questionnaire validated in Spanish has good psychometric properties with Cronbach's Alpha figures of
0.89 and 0.94 (Martínez-López et al., 2014); in addition, the internal consistency of the questionnaire
in the sample of this study was very good (α = 0.89).
Procedure
Snowball sampling was carried out (Johnson, 2005) through social networks, university bulletin boards
and distribution lists. To request participation in the study, the link to the anonymous online survey
carried out in Google Forms, was sent. The dissemination period was carried out during March and
April 2022. Once this period had passed, an anonymized database was created from all the responses
obtained.
Ethical aspects
This project has been approved by the Research Ethics Committee of Aragon (No. PI22-114) and by
the Data Protection Office of the University of Zaragoza (No. RAT 2022-49). The participants gave their
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informed consent and accepted the privacy policy of Google and the form for the exercise of the right
to data protection of the University of Zaragoza. The current regulations regarding data protection
were followed (Organic Law 3/2018, of December 5, on the Protection of Personal Data and Guarantee
of Digital Rights). All the data was anonymous, and no personal data was recorded that would make
it possible to identify who responded.
Data analysis
To examine the composition of the sample, a descriptive analysis was performed based on frequencies
(categorical variables) and means and standard deviation (continuous variables). The normality or
non-normality of the data was verified using the Kolmogorov-Smirnov test with the Lillierfors
modification since the sample was greater than 30 cases. Next, to address the main objective and
study the relationship between levels of technostress in university students and health, a correlation
analysis of the independent variables with the dependent variable of general health (GHQ-28) was
performed.
In addition, to analyse which variables predict better levels of general health, a multiple linear
regression analysis was performed. The significance level adopted was p<0.05. Statistical analyzes
were performed with SPSS software (version 25.0) (IBM Corp., 2017) and the study had no missing
data.
RESULTS
A total of 389 subjects participated in the study, all of them signed the informed consent and filled
out the survey in its entirety. Of the total participating sample, 116 were men and 273 women. As can
be seen in Table 1, the age of the sample ranges between 18 and 60 years, with a mean of 24.93 years.
More than half of the sample is “single” (69.4%) and “lives with relatives” (52.4%). Most of the sample
resides in the Autonomous Community of Aragon (86.6%) and are enrolled at the University of
Zaragoza (91%). 71% of the participating student body is enrolled in a "university degree" and 54.5%
of the sample does not work.
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Table 1
Descriptive results and correlation of sociodemographic variables and general health
Variables
Values
Pearson´s correlation with
general health
Genderb, Women
273 (70.2)
-.28**
Agea
24.93 (7.86)
.09
Place of residenceb
Aragón
Other autonomous communities
Outside of Spain (Erasmus programme)
337 (86.6)
44 (11.3)
8 (2.1)
.05
Marital statusb
Married or not single
Single
Divorced
114 (29.3)
270 (69.4)
5 (1.3)
.01
Cohabitationb
Single
With couple
With couple and children
With family
With friends or flatmate
Student residence
26 (6.7)
43 (11.1)
24 (6.2)
204 (52.4)
74 (19.0)
18 (4.6)
-.08
Educational levelb
University Degree
Master´s degree
PhD
Postgraduate studies
276 (71.0)
28 (7.2)
83 (21.3)
2 (0.5)
.12*
Universityb
Zaragoza University
Other universities
354 (91.0)
35 (9.0)
.02
Workb
Do not work
Work between 1 and 10 hours
Works between 10 and 20 hours
Works between 20 and 30 hours
Works between 30 and 40 hours
Works more than 40 hours
212 (54.5)
43 (11.1)
19 (4.9)
23 (5.9)
49 (12.6)
43 (11.1)
.08
Note. N=389. aMean (Standard Deviation). bFrequency (percentage). *p<0.05; **p<0.01.
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Regarding the remaining study variables, the results presented in Table 2 show that 16.5% of the
sample experience techno-fatigue (high scores in fatigue, skepticism and inefficiency) and 17.5%
experience techno-anxiety (high scores on anxiety, skepticism, and inefficacy). Thus, a high score in
the dimensions measured indicates that 18.3% experience technostress, while 81.7% do not. The
factor that causes the most academic stress in the participating university students is "academic
obligations" (M=3.31).
Regarding the general health of the sample, taken in a positive sense, the participants present the
lowest score in "social dysfunction" (M=9.37), and on the contrary, the highest average is found in the
"depression" dimension” (M=17.91). Concerning the frequency of ICT use, the participants spend
between an hour and a half and three hours a day using ICT for messaging, games, and social networks
(M=1.78). Regarding the behaviour or emotion that ICT generates in the participants, they feel
irritable, anxious, or agitated sometimes because they do not use the Internet, sometimes they have
stopped doing some activity because they are connected and/or sometimes surfing the Internet gives
them relief and peace of mind (M=1.81). The participants consider the use of the Internet with a
mobile phone or computer to work in the classroom, in groups, to search for information and to
investigate frequently or frequently useful (M=3.5). Finally, 45% of the sample is "very satisfied" with
the social support they perceive and on average the sample had the support of 5 people.
Table 2
Descriptive results and correlation of the variables technostress, academic stress, use of ICT and general health.
Variables
Value
Pearson´s correlation with
general health
Technostressb
No
Yes
318 (81.7)
71 (18.3)
-.29**
Techno-fatigue
No
Yes
325 (83.5)
64 (16.5)
Techno-anxiety
No
Yes
321 (82.5)
68 (17.5)
Academic stressa
Academic obligations (OA)
Student record and future prospects (EP)
Interpersonal difficulties (DI)
Expression and communication of own ideas (EC)
3.31 (.87)
2.79 (.95)
2.18 (.97)
2.91 (1.01)
-.47**
-.41**
-.29**
-.33**
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General healtha
Somatic symptoms (physical status)
Ansxiety-Insomnia
Social dysfunction (everyday well-being)
Depression
13.35 (4.32)
12.67 (4.94)
9.37 (3.67)
17.91 (3.95)
Use of ITCa
Frequency
Utility
Behaviour/Emotion
1.78 (.68)
3.50 (.74)
1.81 (.53)
-.05
.00
-.28**
Social support satisfactionb
Very unsatisfied
Unsatisfied
Somewhat unsatisfied
Somewhat satisfied
Satisfied
Very satisfied
10 (2.6)
9 (2.3)
16 (4.1)
40 (10.3)
139 (35.7)
175 (45.0)
.23**
Availability of social supporta
5.18 (2.02)
.22**
Note. N=389. aMean (Standard deviation). bFrequency (percentage). *p<0.05. **p<0.01.
Regarding the correlation between the study variables and general health, Tables 1 and 2 show how
general health, with its grouped dimensions, presents a significant correlation with the male gender
(-.28; p<0 .01), which is related to better general health. In addition, both a higher level of education
(.12; p<0.05) and satisfaction (.23; p<0.01) and availability of social support (.22; p<0.01) present a
statistically significant relationship significant and positive with general health. On the other hand,
general health presents a statistically significant and negative relationship with technostress (-.29;
p<0.01), with all dimensions of academic stress [(OA -.47; p<0.01) (EP -.41; p<0.01) (DI -.29; p<0.01)
(EC -.33; p<0.01)] and with the behaviour and emotion generated by the use of ICTs (-, 28, p<0.01);
therefore, a lower score in these variables is related to better general health.
Finally, Table 3 presents the results of the multiple linear regression, between the variables that
obtained a statistically significant result in the correlation and general health, intending to analyse its
predictive capacity on it. In the sample, being male (β = -3.370; p = 0.012), presenting a lower score in
total technostress (β = -0.233; p = 0.003), lower score in academic obligations (OA) (β = -4.074; p <
0.001), a higher score in social support satisfaction (β = 1.221; p = 0.028) and a higher score in
behaviour and emotion generated by the use of ICTs (β = -3.804; p = 0.001) are predictors of better
health. This model explains 31.9% of the global variance [adjusted R2 = 0.319, F(10.378) = 19.182, p
< 0.001].
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Table 3
Multiple linear regression between general health and sex, educational level, technostress, academic stress, social support
and behaviour in the use of ICT
Model
Non-standardized
coefficients
Standardized
coefficients
t
p
95% CI for B
Collinearity statistics
B
Dev.
Error
Beta
Lower
Limit
Upper
Limit
Tolerance
VIF
(Constant)
82.75**
4.85
17.05
.00
73.21
92.29
Gender
-3.37**
1.33
-.11
-2.53
.01
-5.98
-.75
.84
1.19
Studies
-.27
.69
-.01
-.39
.69
-1.64
1.09
.90
1.10
Technostress
-.23**
.07
-.13
-2.94
.00
-.38
-.07
.87
1.13
Academic stress
OA
-4.07**
.91
-.26
-4.47
.00
-5.86
-2.28
.49
2.02
EP
-1.19
.78
-.08
-1.52
.12
-2.74
.34
.54
1.82
DI
-.31
.68
-.02
-.46
.64
-1.65
1.02
.71
1.40
EC
-.61
.65
-.04
-.92
.35
-1.90
.68
.69
1.43
Social support
Availability
.40
.31
.06
1.27
.20
-.22
1.03
.74
1.34
Satisfaction
1.22*
.55
.10
2.20
.02
.13
2.31
.76
1.31
Use of ICT
Behaviour/Emotion
-3.80**
1.10
-.15
-3.43
.00
-5.97
-1.62
.87
1.13
Note. CI: confidence interval. Dependent variable: General Health. *p<0.05. **p<0.01.
DISCUSSION
The main objective of this study was to analyse the levels of technostress in university students and
their relationship with health, for which a sample of 389 students was obtained, exceeding the
necessary sample size of 203 individuals. Regarding the representativeness of the sample, it presents
an adequate number. In addition, the sample is made up of a higher percentage of women than men.
This aspect coincides with the greater presence of women within the university student body, which,
according to the Government of Spain and the Ministry of Universities (2021), the percentage of
university women is higher than that of men.
In terms of general health, the participants exhibited poorer social functioning and lower daily well-
being, although they reported adequate levels of depression. These findings align with the 2020
European Health Survey in Spain, where a significant proportion of young people rated their health as
very good (54%) or good (48%) (Gobierno de España & Ministerio de Sanidad, 2020). Based on the
results, the majority of participants (81.7%) do not experience technostress, including its two
manifestations, techno-anxiety and techno-fatigue, and exhibit moderate levels of academic stress.
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However, in the few previous studies, in general, a presence of a moderate to high level is observed
in Spanish-speaking university students, both of technostress (Arredondo-Hidalgo & Caldera-
González, 2022; Guerra et al., 2022) as well as of academic stress (Estrada et al., 2021; Rivas et al.,
2014). In addition, the participants present adequate values in the frequency, usefulness behavior
and emotion generated by the use of ICTs and are satisfied with the perceived social support.
Regarding the relationship of the study variables with general health, technostress, academic stress
in all its dimensions (academic obligations, records and prospects, interpersonal difficulties, and
expression and communication of one's ideas), and behavior or emotions produced using ICTs present
a statistically significant and negative correlation with general health. This indicates that students who
experience more technostress, more academic stress, and behaviors such as irritability or anxiety
regarding the use of ICTs will have poorer general health. Additionally, being male, the level of
education, and the availability and satisfaction with social support are statistically significantly and
positively related to the health of the students. Following Borrel et al. (2004), social class, the territory
in which one lives, and the level of education influence mortality and perceived health.
Regarding the predictive model of general health, it is observed how the "academic obligations"
dimension of academic stress is the one with the greatest predictive power on health, followed by
behavior and emotion generated by the use of ICT, satisfaction with social support and technostress.
Academic obligations include carrying out mandatory tasks and assignments, academic overload,
activities related to study, and taking assessment tests such as exams, which, as pointed out by Martín
Monzón (2007), they are “one of the fundamental academic stressors in student life, with sensitive
effects at the behavioral, cognitive and physiological-emotional levels”. Likewise, various studies
conclude that exams, time distribution, meeting stipulated deadlines and academic overload are the
situations that cause the greatest academic stress (Feldman et al., 2008; Fin, Para, & Al, 2019;
González, 2017). All of this, together with techno-stress, generated by the use of ICT, can lead to health
consequences similar to those of psychosocial stress, such as cognitive symptoms (difficulties with
concentration and memory, mental fatigue, cognitive overload), emotional symptoms (anxiety,
irritability, nervousness, burnout), and physiological (muscle pain, headaches, insomnia, eye fatigue)
(Galvin et al., 2021; Kasemy et al., 2022; Martín Rodriguez, 2020; Tams et al., 2014).
Likewise, to explain the consequences that the use of ICTs has on a person, it is necessary to take into
account the training they have, how often they use them and for how long, since behavior and
emotion (irritability, relaxation and addiction) generated by the use of ICT will be determined by the
demands and the lack of resources generated after its implementation (Salanova, 2003).
In a university context, in which the use of ICT has been consolidated and increased, in the vast
majority of students use a laptop, tablet or mobile phone (Smartphone) in the classroom (González
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Elices, 2021), It is important to keep in mind the explanatory capacity of the health of ICT use reflected
in this study, since there is a significant impact on academic performance, motivation, fatigue and life
satisfaction of students (Guerra et al., 2022; Samaha & Hawi, 2016).
According to the results, being a man is related to having better health, but this data should be taken
with caution, since the sample is mostly made up of women (70.2%). There are numerous studies on
the relationship between gender and health, however studying this relationship is complex since it
can come from behavioral, attitudinal and structural factors related to gender (Matud, 2017). There
is evidence that women are more likely to be affected by the stress of the people around them and
more academic stress (García, 2019; García-Ros et al., 2012; Matud, 2017).
Finally, point out how satisfaction with social support is what provides predictive capacity for health,
being more relevant than its availability (quality vs. quantity). Due to the new forms of social
interaction based on social networks, social ties are weaker and more immediate, easy to create but
with low commitment, generating a high availability of social support but with little satisfaction
(Rogero García & Durán Heras, 2009). Furthermore, according to (Barra Almagi, 2004) social support
can promote adaptive health behaviors, provide well-being, or inhibit the negative effects of stress.
Regarding the limitations of the study, most of the respondents belong to one of the four university
campuses of the University of Zaragoza, distributed throughout the three provinces of the
Autonomous Community of Aragon. It is recommended to carry out a study with a sample that
includes other universities with a greater offer of online degrees, since the University of Zaragoza,
except the period of confinement due to the pandemic, is a face-to-face university. Likewise, the
average age is 25 years old, since it is relatively high, the age at which the studies started should be
analysed. In addition, as it is an online questionnaire whose sampling has been carried out by the
snowball method and not randomly among the university population, subjects interested in obtaining
a specific result or more interested in the study could have completed the survey; and being online
automatically excludes people without internet access (Arroyo Menéndez & Finkel, 2019).
CONCLUSIONS
This study has identified factors that could be targeted for interventions to help students maintain or
improve their health. Academic obligations, behaviors or emotions caused by ICTs, gender,
technostress, and satisfaction with social support all predict students' overall health.
Although the study participants do not present high levels of technostress, they do experience
moderate levels of academic stress. Therefore, it would be advisable to establish achievable deadlines
for academic work and activities, regulate students' workload, and implement time optimization
programs, as stress caused by academic obligations is a strong predictor of their health. Additionally,
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establishing programs related to the learning and correct use of ICTs and improving the digital skills
environment is recommended, especially given its association with irritability, addiction to
technologies, or technostress among students. These programs could also apply to other areas,
generating strategies to improve technological skills and coping mechanisms that could aid students
in integrating into the highly digitized labor market.
Finally, it is recommended to replicate this study among working individuals to identify which variables
are related to technostress and its consequences on workers' health, thus allowing the
implementation of prevention and intervention strategies in the field of occupational health hazard
prevention.
AVAILABILITY OF DATA AND MATERIALS
Data generated or analyzed during this study are available from the corresponding author upon
reasonable request.
CONFLICT OF INTERESTS
The authors declare that the research was conducted in the absence of any commercial or financial
relationships that could be construed as a potential conflict of interest.
FUNDING
None.
AUTHORS' CONTRIBUTIONS
AAM, and AMV conceived and planned the experiments. AMV carried out the experiments. AAM, and
AMV, and AAL contributed to the interpretation of the results. AAM, and AMV, and AAL took the lead
in writing the manuscript. All authors provided critical feedback and helped shape the research,
analysis and manuscript.
ACKNOWLEDGMENTS
We wish to thank the University of Zaragoza, the Aragonese Primary Care Research Group (GAIAP,
B21_23R) that is part of the Department of Innovation, Research and University at the Government of
Aragón (Spain); the IIS Aragón; the RICAPPS that received a research grant from the Carlos III Institute
of Health, Ministry of Science and Innovation (Spain), awarded on the call for the creation of Health
Outcomes-Oriented Cooperative Research Networks (RICORS), with reference RD21/0016/0005, co-
funded with European Union – NextGenerationEU funds, which finance the actions of The Recovery
and Resilience Facility (RRF); and Feder Funds “Another way to make Europe.
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