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Problematic mobile phone and social media use

among adolescents and its relationship with

cyberbullying, cybervictimisation and social

anxiety

Scientic Reports

Received: 11 September 2025

Accepted: 8 January 2026

Cite this article as: GuisotL., AparisiD.,

DelgadoB.etal. Problematic mobile

phone and social media use among

adolescents and its relationship with

cyberbullying, cybervictimisation and

social anxiety. Sci Rep (2026). https://doi.

org/10.1038/s41598-026-35842-6

Laura Guisot, David Aparisi, Beatriz Delgado & María Carmen Martínez-Monteagudo

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Problematic mobile phone and social media use among adolescents and

its relationship with cyberbullying, cybervictimisation and social anxiety

Laura Guisot1, David Aparisi1, Beatriz Delgado1 and María Carmen

Martínez-Monteagudo1

1 Department of Developmental Psychology and Didactics. Faculty of

Education. University of Alicante (Spain).

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Correspondence Author: David Aparisi (Department of Developmental

Psychology and Didactics. Faculty of Education. University of Alicante,

Alicante 03080, Spain. david.aparisi@ua.es)

Abstract

The use of mobile phone and social media has become a global and

unstoppable phenomenon, especially among adolescents, largely due to

the ease of access to numerous applications that facilitate

communication and social interaction via the Internet. This study

examines the relationship between problematic mobile phone and social

media use, cyberbullying, and social anxiety in a representative sample of

secondary school adolescents. A total of 1164 students with an age range

of 12 to 18 years (

= 14.56;

= 1.4) completed a battery of self-

report measures to assess problematic mobile phone and social media

use and social anxiety. The results indicate that students with high

problematic use of mobile phone and social media have significantly

higher levels of cyberbullying and social anxiety compared to those with

low and medium problematic use. Furthermore, logistic regression

analyses showed that cyberbullying, cybervictimisation and social

anxiety, specifically, fear of negative evaluation were significant

predictors of problematic mobile phone and social media use, indicating

a higher probability of dependence as levels of cybervictimisation and

social anxiety increase. Furthermore, women are more likely than men to

experience PMPU and PSMU. The results suggest the need to implement

interventions aimed at improving emotional management and reducing

problematic behaviours related to technology use.

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Keywords: Mobile phone; social media; cyberbullying; social anxiety;

adolescents.

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Introduction

The use of mobile phone and social media has become a global

phenomenon, largely due to the ease of access to numerous applications

that facilitate communication via the internet and increase interpersonal

connections [1]. Their benefits are numerous and valuable, although

these digital tools have addictive potential due to their design and

characteristics [2]. In this regard, a large number of studies have

identified a growing problematic use of these devices among adolescents,

as well as an increase in conflicts with parents, emotional problems and

digital hygiene issues [3, 4]. Adolescents spend an average of four hours

on their mobile phones, with video games and social media being the

most common activities [5]. Recent studies confirm that problematic use

of mobile phones and social media is linked to changes in impulse control

and greater difficulty in stopping their use, characteristics typical of

addiction [6]. To counteract the negative effects, it would be advisable to

work on self-control in young people [7, 8, 9], good usage habits [10], and

better emotional management [11].

This study seeks to explore the problematic mobile phone use

(PMPU) and social media (PSMU) among adolescent students and

analyse its relationship with cyberbullying and social anxiety. This is

because the use of technology found in mobile phones can impact the

mental health, development and well-being of minors [12]. Furthermore,

it has been observed that problematic use of social media is closely

linked to the search for social validation and emotional dissatisfaction,

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which can exacerbate problems such as isolation and emotional

dependence in adolescents [13].

PMPU and PSMU

The conceptualisation of PMPU and PSMU use responds to risky

behavioural patterns, not clinically validated disorders. It is true that

studies such as that by [14] have used the diagnostic criteria for Internet

gaming disorder (IGD) to assess patterns of problematic mobile phone

and Internet use. This trend is observed in other research focused on

IGD, while problematic mobile phone or social media use still lacks a

formal diagnostic definition [15, 16]. This approach reflects a growing

need to differentiate between various forms of problematic technology-

related behaviour, including mobile phone use, social media use, and

online gaming.

PMPU is characterised by difficulty controlling its use,

which negatively affects daily activities and emotional well-being [16].

Social anxiety plays an important role in this behaviour, as many

adolescents turn to their mobile phones to avoid social interactions,

which exacerbates their isolation [17]. In terms of PSMU, the constant

search for validation is linked to greater emotional dissatisfaction and

dependence on virtual interactions [13]. These patterns of use affect not

only mental health, but also interpersonal relationships and academic

performance [16]. Given the prevalence of these behaviours, further

research is needed to develop diagnostic criteria that address both

problematic mobile phone and social media use [17].

Currently, there is a high prevalence of PMPU among adolescents,

ranging from 10% to 38.5% [18, 19]. Specifically, in Spain, the

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prevalence of inappropriate or PMPU is 15.4% [20]. This study obtains

results similar to those discussed in the scientific articles by [21] and

[22] with 13.54% and 12.5%, respectively.

Focusing on the adolescent

population, studies show variations in the prevalence of problematic

mobile phone use. The study by [23] included participants from non-

European countries. They examined cross-cultural differences in 1709

young adults from Spain, the United States, and Colombia in terms of

mobile phone use and its relationship to anxiety and depression. They

concluded that 56.6% could be classified as having ‘occasional problems

with mobile phone use’ and 7.7% as having ‘frequent problems with

mobile phone use’. In Nepal, the prevalence among adolescents was

21.7% [24]. In India, PMPU among adolescents reached 21% [25]. In

China, the study reported a prevalence of 27.92% among adolescents

[26].

PMPU manifests differently according to gender, age, and country

[27]. In terms of gender, women tend to have more problems with mobile

phone use and more dependency-related behaviours [28]. Women report

more difficulties with mobile phone use, as they spend a significant

amount of time on their phones, which leads to higher phone bills, while

men are significantly more likely to use social media passively [29, 30].

However, these findings are not conclusive, as other studies conclude

that men are more likely to exhibit this problematic use [31]. Similarly,

research conducted by [32] revealed that in a sample of adolescents, the

age group with the highest percentage of problematic mobile phone use

was the youngest, with a prevalence of approximately 80%.

As for the

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negative repercussions of PMPU and PSMU, both prolonged and

excessive use of mobile phones can lead to symptoms such as anxiety,

behavioural and emotional problems, behavioural addiction, poorer sleep

quality, bad moods and low psychological well-being [33]. In relation to

this, research carried out by [34] found that one-third of those surveyed

had nomophobia and one-tenth had high levels of phubbing, a term that

describes the act of ignoring people present in a social interaction by

paying more attention to one's mobile phone. In fact, adolescents feel

compelled to check their mobile phones to keep up to date, and this in

turn is linked to anxiety and depression, as there is also a significant

relationship between fear of missing out (FoMO) and problematic use of

mobile phones and social media [35].

Cyberbullying, cybervictimisation and PMPU and PSMU

Cyberbullying is considered intentional and repetitive harassment

perpetrated by a person or group and carried out through digital devices

such as mobile phones [36]. This phenomenon is most prevalent among

adolescents and young adults, who tend to be the main users of mobile

devices and social media. In particular, people who use mobile phones

and social media problematically are more vulnerable, as both

phenomena share a digital platform. Thus, greater use of digital media

translates into more risks encountered and less participation in

constructive socialisation, which can lead to aggressive and

inappropriate behaviour [37]. Furthermore, PMPU is positively

associated with participation in cyberbullying, increasing the rates of

perpetration of this behaviour and causing negative emotional states

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[38].

In this regard, a study conducted with adolescents found that

emotional regulation problems and psychiatric symptoms were risk

factors for PMPU and cyberbullying [39]. These data are also

corroborated by the research of [40], who identify that cybervictimisation

can drive problematic mobile phone use as a strategy to reduce the

negative feelings resulting from cyberbullying. This relationship can be

explained by the fact that adolescents, when experiencing negative

emotions as a result of online bullying, tend to use mobile phones and

social media excessively as a form of escape or emotional regulation,

which in turn increases their dependence on these devices [16, 17]. Thus,

recent research also highlights the role of psychological insecurity and

lack of family support as key factors in perpetuating this cycle of

victimisation and problematic social media use [18].

Furthermore, social

anxiety has been identified as a determining factor in the relationship

between problematic social media use and cyberbullying. Studies have

shown that adolescents with higher levels of social anxiety are more

likely to develop a dependence on social media, using these platforms as

a refuge from face-to-face social interactions [16]. This behaviour can

make them more vulnerable to cyberbullying and, as a result, intensify

their problematic use of these platforms as a way of coping with the

negative emotions that arise from these experiences. For example,

research by [34] found that social anxiety and the need for constant

validation on social media were highly correlated with fear of missing out

(FoMO), which, in turn, increased mobile phone dependence and

exposure to cyberbullying.

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On the other hand, it has been observed that age and gender

differences also influence the dynamics of PMPU and cyberbullying.

Adolescent girls tend to have more problems related to excessive use of

social media, which makes them more susceptible to cybervictimisation.

This could be due, in part, to the fact that young women are more likely

to seek emotional validation and social acceptance on digital platforms,

which increases their exposure to cyberbullying [28, 30]. On the other

hand, men tend to be more involved in passive behaviours on social

media, which could explain the weaker relationship between their

problematic use and online victimisation [16, 29].

In conclusion, all the

articles discussed agree on a link between problematic mobile phone use

and cyberbullying. To reduce the inappropriate use of cell phones and

social media, as well as cyberbullying, various studies have opted for

psychoeducational strategies focused on raising awareness and

promoting responsible use of cell phones and social media, as well as

educating about cyberbullying. Other specialists suggest limiting the use

of cell phones in classrooms or incorporating them for educational

purposes with the aim of minimizing adolescents' exposure to these risks

during school hours and encouraging healthier use of devices outside the

school environment [36]. This measure could help reduce the time

adolescents spend on their cell phones and social media, which would

reduce their exposure to victimization and improve their overall

psychological well-being.

Social anxiety and PMPU and PSMU

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Social anxiety is defined as the fear of negative social scrutiny and

evaluation, characterised by tension and nervousness in social settings

[41]. According to the revised version of the Diagnostic and Statistical

Manual of Mental Disorders (DSM-5-TR) of the American Psychiatric

Association [42], social anxiety disorder is characterised by an intense

and persistent fear of situations in which the person may be evaluated by

others. People with this disorder fear acting in ways that will be

negatively judged, which can lead to significant avoidance of social

situations or facing them with great distress.

Numerous studies have identified a significant relationship between

problematic mobile phone and social media use and social anxiety in

adolescents. This relationship may be mediated by factors such as

cybervictimisation, psychological insecurity, and unregulated social

media use [27].

Thus, [43] found in a sample of 14- and 17-year-old

students, using the Questionnaire for Adolescent Problematic Mobile

phone Use [44] and the Social Anxiety Scale for Adolescents [45], that

social anxiety affects dependence on mobile phones and social networks,

as people with social anxiety feel more protected and in a relatively safe

environment when interacting via mobile phone. [46] also found a

positive relationship between social anxiety and problematic mobile

phone use in a sample of students aged 10 to 14 using The Mobile phone

Addiction Scale [47] and The Liebowitz Social Anxiety Scale for Children

and Adolescents (LSAS-CA-SR) [48]. It follows that communication via

mobile phones allows adolescents with social anxiety to compensate for

their lower social skills [49].

A recent meta-analysis of these variables

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supported a significant positive correlation between social anxiety and

mobile phone addiction, suggesting that social anxiety is a predictor of

the development of mobile phone addiction in adolescents and adults

[50].

In summary, empirical evidence shows that mobile phones and

social media allow socially anxious individuals to find a safe place

through which they can communicate and, therefore, a way to avoid

offline social situations through problematic use of the same [41, 51].

The present Study

There is not much research analysing the relationship between

problematic mobile phone and social media use and its relationship with

cyberbullying, cybervictimisation and social anxiety, and most studies

used different population samples. Therefore, taking into account the

limitations of previous studies, the research objectives of this study are

to analyse the differences in cyberbullying, cybervictimisation and social

anxiety between adolescents with a problematic mobile phone use and

problematic social media use, to determine its impact on adolescents.

Based on the objectives described above, the following hypotheses are

proposed: on the one hand, differences are expected to be found in the

variables of cyberbullying, cybervictimisation and social anxiety between

students with different degrees of problematic mobile phone and social

media use, with students with more problematic use obtaining

significantly higher scores in cyberbullying, cybervictimisation, and

social anxiety (hypothesis 1); on the other hand, cyberbullying,

cybervictimisation and social anxiety will be significant predictors of both

problematic mobile phone use and social media use (hypothesis 2).

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Several studies conducted in different countries have shown that

cyberbullying and cybervictimisation are related to problematic use of

social media [52] and mobile phones among adolescents [37]. The study

justifies the need to investigate these variables due to the negative

repercussions associated with problematic use of both social media and

mobile phones in adolescence. These problematic behaviours are linked

to symptoms such as anxiety, behavioural and emotional problems,

addiction, impaired sleep quality, general malaise and low psychological

well-being [53]. Furthermore, excessive use of these devices can intensify

the experience of cyberbullying and exacerbate social anxiety, creating a

harmful cycle that profoundly affects the emotional and social

development of adolescents [41, 54]. In this context, understanding how

cyberbullying and social anxiety influence problematic mobile phone and

social media use is crucial to addressing and mitigating these adverse

effects.

Method

Participants

The reference population included students of Secondary Education

from the province of Alicante (Spain). The initial sample consisted of

1210 students from 1st year of Secondary Education to 1st

Baccalaureate, randomly selected from six secondary schools, specifically

five public and one private, with around 200 students per school. Of this

total, 46 (3.8%) were excluded due to omissions or errors in their

responses. Thus, the final sample consisted of 1164 students (599

females (52%) and 565 males (48%) aged between 12 and 18 (

= 14.56;

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= 1.4), with 130 (11.2%) aged between 12 and 13; 301 (25.9%) aged

13 to 14; 333 (28.6%) aged 14 to 15; 250 (21.5%) aged 15 to 16, and 150

(12.9%) aged 16 to 18. The Chi-square test of homogeneity of frequency

distribution revealed that there were no statistically significant

differences between the ten groups of gender x year (

² = 9.7;

= .28).

Instruments

To assess problematic mobile phone use, the Problematic Mobile

phone Use Scale from the

Problematic Use of New Technologies

Questionnaire

[55] was used. The scale consists of 10 items that assess

the frequency and intensity of problematic mobile phone use and

measure associated symptoms, such as recurrent thoughts of being

connected (e.g., ‘Are you thinking about using your mobile phone hours

before you actually use it?’), feelings of irritability or withdrawal (e.g.,

‘Do you feel nervous if it has been a long time since you last used your

mobile phone?’ or ‘Do you feel bad (sad, anxious or irritable) when you

cannot use your mobile phone?’), inattention to educational, family or

social activities (e.g. ’Do you continue to use your mobile phone even

though this causes problems with others, in your studies, with your

family...?’ or ‘Do you lie to your family or friends about how much time

you spend on your mobile phone?’), social isolation, among others, using

a 5-point Likert scale: 1 (never) to 5 (always). The subscale obtained an

adequate reliability index (

= .87) for the sample analysed. Recent

research has concluded that rates of PMPU among adolescents are

between 20–30% [56, 57].

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Problematic social media use was assessed using the

Problematic

Social Media Use Questionnaire

[58], which consists of 13 items that

measure the intensity of social media use. Respondents answered using a

Likert scale with five response options (1=never; 5=always) (e.g. ‘Do you

think in advance about when you will be able to connect to social media?’

or ‘Has the time you spend using social media affected your performance

(grades) or your motivation to study?’). The questionnaire allows for the

identification of the frequency of the main behaviours associated with

problematic social media use, such as dependence, interference with

daily activities, discomfort, and lack of control. The reliability index of the

test in this study was adequate (

= .86). Several studies have concluded

that between 25-30% of adolescents who use social media in a

problematic way say they spend a lot of time thinking about social media

apps and use social media apps very often to forget about their problems

[59].

Social anxiety was assessed using the

Social Anxiety Scale for

Adolescents

(SAS-A) [60], a self-report measure that assesses social fears

and concerns and avoidance in social situations in adolescents. It consists

of 18 items that measure social anxiety and 4 filler items. The SAS-A

includes three subscales: Fear of Negative Evaluation (FNE) consists of 8

items that assess fears, concerns, or worries regarding peers' negative

evaluations (e.g., ‘I worry about what others say about me’); Social

Avoidance and Distress in New Situations (SAD-N) consists of 6 items

that assess social avoidance and distress in new social situations or with

unfamiliar peers (e.g., ‘I get nervous when I talk to peers I do not know

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very well’); and Social Avoidance and Distress-General (SAD-G) consists

of 4 items that assess general social inhibition, distress, and discomfort

(e.g., “I am quiet when I am with a group of people”). Items from each

subscale are summed such that higher scores reflect greater social

anxiety. Reliability indices (α) were adequate for the FNE (.93), SAD-N

(.88) and SAD-G (.81) subscales, and the overall SAS-A score (.93).

Finally, the

Peer Bullying Screening

(2016) was used to assess

cyberbullying [61]. This is a self-report that assesses both face-to-face

bullying (Bullying subscale) and electronic bullying (Cyberbullying

subscale). In the present study, we only used the Cyberbullying subscale:

15 items of cyberbullying and 15 items of cybervictimization. This

assesses 15 electronic bullying behaviors (e.g., sending offensive and

insulting messages, making offensive calls, disseminating photos or

videos on YouTube, making frightening anonymous calls, blackmailing or

threatening someone) to identify victims and bullies in the past year. The

questionnaire is answered using a Likert scale with four response options

(1=never; 4=always). The psychometric studies carried out by the

original authors confirm the adequate internal consistency of the test (α

=. 91). The reliability indices of the subscales of the cyberbullying

questionnaire in the study sample were good: cybervictimization (

.87), and cyberbullying (

= .89).

Procedure

After obtaining approval for collaboration from the management and

guidance departments of the educational centres, as well as the informed

consent of the families of the participating students, the questionnaires

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were answered collectively and anonymously by the students in the

computer room using an online form. The researchers were present

during the administration of the tests to clarify any doubts and verify that

the students completed the questionnaires independently and voluntarily.

The average response time for the tests was 20 minutes. The study,

including the consent methods used, received approval from the

Research Ethics Committee (UA-2023-02-07). In addition, all regulations

concerning research involving human subjects were observed, in

accordance with the ethical principles set forth in the Declaration of

Helsinki.

Statistical analysis

First, the sample was grouped according to problematic mobile

phone/social media use scores into: (1) low problematic use (scores equal

to or below the 25 percentile), (2) medium problematic use, and (3) high

problematic use (scores equal to or above the 75 percentile). Secondly, to

analyse the differences in cyberbullying, cybervictimisation and social

anxiety between the three groups, an analysis of variance (ANOVA), once

the normality of the scores and the homogeneity of the variances has been

calculated using the Shapiro Wilk test, and Bonferroni post hoc test were

performed to identify between which groups these differences existed. In

addition, the effect size was calculated using the eta-squared index and

Cohen's

[62]. Regarding the interpretation of the effect size, values less

than or equal to .20 indicate a very small or insignificant effect size, those

between .20 and .49 are considered small, those between .50 and .79 are

moderate, and those above .80 are considered large. In addition,

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Spearman's correlations were calculated to measure the statistical

relationship between the different variables in the study. Finally, to

evaluate the explanation of cyberbullying and social anxiety on

problematic social media and mobile phone use, a stepwise forward

logistic regression analysis based on the Wald method was performed. To

estimate the fit of each model, the percentage of correctly matched cases

was calculated, as well as Nagelkerke's

2. The probability of an event

occurring was quantified using the odds ratio (

). Thus,

values

greater than 1 establish that the probability of an event (e.g., problematic

use of mobile phones/social media) is greater than that of no event, and

values from 0 to 0.99 indicate that the possibility of an event is lower than

the probability of no event. SPSS 23.0 (IBM Corporation) was used for

ANOVA and logistic regression analysis.

Results

Differences in cyberbullying, cybervictimisation and social anxiety in

students with low, medium, and high PMPU

The results obtained indicate that there are statistically significant

differences in cyberbullying and social anxiety scores between the

different groups of PMPU (see Table 1). Post hoc tests indicate that

students with high PMPU obtain significantly higher scores in

cybervictimisation than the low (

= 2.39,

= .00) and medium PMPU

groups (

= 1.78,

= .00). On the other hand, students with high PMPU

score significantly higher on cyberbullying than students with medium (

= 1.34,

= .00) and low (

= 1.98,

= .00) problematic mobile phone

use. In fact, students with medium PMPU had significantly higher scores

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for cyberbullying (

=.64,

= .02) than those with low PMPU. Effect sizes

were moderate for differences in cybervictimisation (

> .50) and small

for differences in cyberbullying (

> .20).

(Insert Table 1 here)

In addition, students with high PMPU use score significantly higher

on the scale of fear of negative evaluation, social avoidance and

discomfort in new situations, and social avoidance and discomfort in

social situations in general than students with average PMPU (FNE:

1.58,

= .00; SAD-N:

= 1.21,

= .01; SAD-G:

= 1.63,

= .00) and low

(FNE:

= 3.75,

= .00; SAD-N:

= 2.70,

= .00; SAD-G:

= 2.04,

.00). Additionally, students with moderate PMPU have significantly

higher scores in social anxiety in all its manifestations (FNE:

= 2.16,

= .00; SAD-N:

= 1.50,

= .00; SAD-G:

= 3.35,

= .00) than those with

low PMPU (see Table 2). The effect sizes for differences in social anxiety

were moderate between the high and low PMPU groups (

> .50) and

small between the medium group and the rest of the groups analysed (

> .20).

Differences in cyberbullying, cybervictimisation and social anxiety

among students with low, medium, and high PSMU

The results of the variance analyses indicate that there are

statistically significant differences in cyberbullying and social anxiety

scores between groups (see Table 2). Specifically, post-hoc tests detected

that students with high scores in PSMU had significantly higher scores in

cyberbullying and cybervictimisation than groups with low scores (

1.53,

= .00, ;

= 3.35,

= .00) and average scores on PSMU (

= 1.37,

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= .00;

= 2.57,

= .00). Effect sizes were moderate in all cases (

.50).

In addition, students with high scores on PSMU obtained

significantly higher scores than students with average and low scores on

the fear of negative evaluation (

= 4.71,

= .00 ;

= 8.12,

= .00) and

social avoidance and discomfort in new situations (SAD-N) scales (

3.28,

= .00;

= 4.81,

= .00). In fact, students with moderate PSMU

scored significantly higher than those with low PSMU (

= 3.41,

= .00;

= 3.35,

= .00) on the SAD-N subscale. On the other hand, adolescents

with high PSMU use exhibit significantly more social avoidance and

discomfort in situations in general than students with medium (

= 1.78,

= .00) and low (

= 2.36,

= .00) PSMU. All effect sizes for differences

in social anxiety were moderate (

> .50).

Regarding correlations between variables (see Table 3), most were

moderate to high, highlighting the relationship between PMPU and

PSMU (

= .68), the relationship between cyberbullying and

cybervictimization (

= .46), cyberbullying and the FNE scale of social

anxiety (

= .70), PMPU and PSMU and social anxiety, especially on the

FNE scale (

= .31 and

= .39).

(Insert Table 2 here)

(Insert Table 3 here)

Association between PMPU, cyberbullying, cybervictimisation and

social anxiety

Logistic regression analyses yielded five explanatory models for

PMPU based on the scores of the predictor variables analysed (see Table

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4). All AUC values were in the range of 0.75 to 0.80, indicating that the

models were able to distinguish between classes well. Thus, one model

was created using cyberbullying scores and another using

cybervictimisation scores, with 62.3% (

²=51.34;

=.00) and 63%

(

²=54.87;

=.00) of cases correctly classified by the models. The

goodness of fit (Nalgerkerke's

²) was .09 and .10, respectively. The

indicate that adolescents are 19% and 16% more likely to exhibit high

PMPU as their cyberbullying and cybervictimisation scores increase by

one unit, respectively.

Social anxiety symptoms also significantly explain PMPU. The model

based on fear of negative evaluation (

²=81.07;

=.00) correctly

classified 64.8% of cases, the model based on social avoidance and

discomfort in new situations classified 60.7% (

²=45.87;

=.00), and the

model of social avoidance and discomfort in social situations in general

classified 65.9% (

²=95.15;

=.00) of cases correctly. The Nagelkerke

fit indices for the models were .14, .08, and .16, respectively. The

indicate that students are 13%, 10%, and 16% more likely to maintain

PMPU as the FNE, SAD-N, and SAD-G social anxiety subscales increase

by one unit, respectively.

Finally, logistic regression was calculated taking into account the

variables of gender and age. The data allowed us to create a model for

the age variable (

²=15.92;

=.00) with 63% of cases classified correctly,

and Nalgerkerke's

² indicators was .07. The

indicate that the

probability of high PSPU increases by 2.59 in the case of women.

(Insert Table 4 here)

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Association between PSMU, cyberbullying, cybervictimisation, and

social anxiety

Based on the logistic regression analyses, it was possible to create

five explanatory models of PSMU based on cyberbullying and social

anxiety (see Table 5). Likewise, all AUC values were in the range of 0.75

to 0.80, indicating that the models were able to distinguish between

classes well. Thus, a model was also obtained to predict the probability of

PSMU through cyberbullying, with 65.9% (

²=32.14;

=.00) of cases

correctly classified, and a Nalgerkerke's

² of .08. The

indicates that

the probability of high PSMU in adolescents is 1.27 times higher for each

unit increase in cyberbullying. Likewise, a predictive model of PSMU

through cybervictimisation was created, with 66.5% (

²=70.93;

=.00) of

cases correctly classified. The goodness of fit (Nalgerkerke's

²) was .16.

The

indicates that the probability of high PSMU is 1.25 times greater

as cybervictimisation increases by one unit.

With regard to social anxiety, three explanatory models were

created for PSMU based on scores for fear of negative evaluation

(

²=49.06;

=.00), social avoidance and discomfort in new situations

(

²=38.66;

=.00), and social avoidance and general discomfort

(

²=27.99;

=.00), with 70%, 70%, and 67.5% of cases classified

correctly, respectively. Nalgerkerke's

² indicators were adequate for the

models: .25, .20, and .15. The

indicate that the probability of high

PSMU increases by 1.12 as the fear of negative evaluation score

increases, by 1.16 as social avoidance and discomfort in new social

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situations increases by one unit, and by 1.25 as social avoidance and

discomfort in social situations in general increases by one unit.

Finally, logistic regression was calculated taking into account the

variables of sex and age. The data allowed us to create a model for the

gender and age variable (

²=11.50;

=.00) with 56.1% of cases classified

correctly, and Nalgerkerke's

² indicators was .02. The

indicate that

the probability of high PSMU increases by 1.56 in the case of women and

the probability of high PSMU decreases by 5% as age increases.

(Insert Table 5 here)

Discussion

The results of this study show a relationship between problematic

mobile phone and social media use and cyberbullying and social anxiety

in adolescents, confirming the proposed hypotheses. The first hypothesis

suggested that students with higher problematic use of social media and

mobile phones would score significantly higher on cyberbullying,

cybervictimisation, and social anxiety, which was verified through the

analyses. These results are consistent with previous research

highlighting the role of technology in the development of problematic

behaviours among young people. Studies such as that by [13] suggest

that excessive use of social media exacerbates the search for emotional

validation, which in turn contributes to a cycle of dependence and

unregulated use, which can lead to behaviours such as cyberbullying.

This coincides with the findings of this research, where students with

more problematic social media use scored higher on cyberaggression and

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cybervictimisation, which could be associated with lower self-control and

increased impulsivity [7].

On the other hand, in relation to social anxiety, it was found that

adolescents with high levels of problematic mobile phone and social

media use also scored higher on fear of negative evaluation and social

avoidance, which reinforces the findings of studies such as that by [17],

who stated that problematic mobile phone use is mediated by social

anxiety. These results underscore the idea that the use of technology

offers a space perceived as safe for adolescents who experience

difficulties in face-to-face interactions, serving as a way to avoid direct

social contact and exacerbating their technological dependence.

Furthermore, the relationship between problematic mobile phone

and social media use and cyberbullying and cybervictimisation, as

proposed in hypothesis 2, was supported by logistic regression analyses.

Adolescents who reported being victims of cyberbullying showed a

greater tendency towards problematic use of their devices, which

coincides with the findings of [39], who identified cybervictimisation as a

factor that drives the use of mobile phones and social media as a

mechanism for coping with the emotional distress caused by bullying.

This phenomenon is also addressed by [27], who argue that excessive use

of mobile phones and social media can contribute to perpetuating online

aggression, creating a cycle of dependency and cyberbullying.

The results also confirmed that social anxiety is a significant

predictor of problematic mobile phone and social media use. Previous

studies such as that by [50] support this claim, showing that social

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anxiety has a significant positive correlation with problematic mobile

phone use, suggesting that socially anxious adolescents turn to

technology to avoid face-to-face interactions, increasing their

dependence on these devices. This behaviour is particularly relevant in

adolescence, a critical stage for the development of social skills, where

problematic use of technologies can interfere with adolescents' social

and emotional development. Adolescents with higher levels of social

anxiety tend to use mobile phones to meet their psychological needs,

which ultimately increases their dependence [63]. In addition,

adolescents tend to have a fairly low level of self-control and are

susceptible to external distractions (such as mobile phones and social

media), making them a ‘vulnerable group’ for problematic use of these

devices.

Therefore, this study provides new evidence on the relationship

between problematic mobile phone and social media use, cyberbullying,

and social anxiety in adolescents, expanding current knowledge in these

areas. It is important to note that adolescents with more problematic use

of these technologies also reported greater difficulties in emotional

regulation, reinforcing the need to implement intervention programmes

that promote self-control and appropriate management of emotions in

this population, as suggested by [8].

Limitations and Practical Implications

Despite the significant contribution of these findings, the study has

some limitations. One of them is the lack of a longitudinal assessment

that would allow us to observe the development of these behaviours over

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time, which would be necessary to better understand the underlying

dynamics of problematic mobile phone and social media use. In this

regard, most studies analyse the issue in a cross-sectional manner, but

few studies, mostly involving university students [64, 65], investigate the

dynamic associations between changes in cyberbullying,

cybervictimisation and social anxiety and the PSPU and PSMU over time

using latent growth models. Latent growth curve models allow us to

estimate intra-individual fluctuations in the trajectory of change over

time and assess how changes in social anxiety symptoms and PSMU, for

example, may be interrelated.

Furthermore, although the study explored gender differences, it did

not go beyond pointing out general differences, so future research could

analyze in greater detail how these variables interact according to

gender and other sociodemographic characteristics. It would also be

valuable to extend the study to other populations beyond the

geographical and school environment of the sample, as results may vary

in different cultural or educational contexts.

Likewise, it would be useful to analyse in detail the mediating effect

of cyberbullying and cybervictimisation on problematic mobile phone and

social media use on social anxiety.

Finally, this study suggests that interventions should focus on

developing digital and emotional skills in adolescents, promoting healthy

use of technology and addressing the root causes of social anxiety and

cyberbullying. Educational policies that limit the use of mobile devices in

school settings could also contribute to reducing these problematic

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behaviours, as proposed by [36]. It would also be relevant to involve

families in these interventions, as emotional support at home has been

shown to be a key protective factor against problematic technology use

[18].

Conclusions

The results of this study show that students with high scores in

PMPU obtained significantly higher scores in cyberbullying and

cybervictimisation. In addition, students with high PMPU score

significantly higher on the scale of fear of negative evaluation, social

avoidance and discomfort in new situations, and social avoidance and

discomfort in social situations in general.

In the case of adolescents with high scores in PSMU had

significantly higher scores in cyberbullying and cybervictimisation than

groups with low scores. Also, students with high scores on PSMU

obtained significantly higher scores than students with average and low

scores on the fear of negative evaluation and social avoidance and

discomfort in new situations scales. On the other hand, adolescents with

high problematic social media use exhibit significantly more social

avoidance and discomfort in situations in general. Logistic regression

analyses showed that cyberbullying, cybervictimization, and social

anxiety are significant predictors of PMPU and PSMU. Furthermore,

women are more likely than men to experience PMPU and PSMU.

These results point to the need to take preventive measures against

cyberbullying and cybervictimization, which are closely related to the

problematic use of new technologies. In addition, intervening in

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psychological variables such as social anxiety may be key to preventing

potentially harmful situations for adolescents in the future, teaching

them psychoeducational strategies that promote the responsible use of

both mobile phones and social media.

Conflict of interest and other declarations: The authors declare no

conflict of interest.

Ethics approval and consent to participate: Standards regarding

research on humans were respected, in accordance with the ethical

principles of the Declaration of Helsinki and the Ethics Committee (UA-

2022-03-21).

Consent for publication: The authors consent to the publication of the

manuscript.

Availability of data and material: Data available if required

(beatriz.delgado@ua.es).

Competing interests: Not applicable.

Funding: This research was funded by the Ministry of Science and

Innovation, the Agency and the European Regional Development Fund

(Project PID123118NA-100 funded by MCIN /AEI

/10.13039/501100011033 / FEDER, EU).

Acknowledgements Not applicable.

Authors contributions section: DA and BD conceived of the study,

participated in its design and coordination, and drafted the manuscript;

DA and BD performed a critical review of the manuscript and assisted

with interpretation of the findings; BD assisted with the study conception

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and participated in the statistical analyses; LG and MCMM participated

in the design of the study, data interpretation, and assisted in drafting

the manuscript. All authors read and approved of the final manuscript.

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Table 1

Differences in cyberbullying, cybervictimisation and social anxiety

traits among students with low, medium, and high PMPU

Low PMPU

Medium PMPU

High PMPU

Statistical

significance

M (SD)

C.I.

(95%)

M (SD)

C.I.

(95%)

M (SD)

C.I.

(95%)

η²

Cyberbullying

12.67

(2.68)

12.39-

12.94

13.31

(2.88)

13.08-

13.53

14.65

(5.00)

14.13-

15.17

30.1

.06

Cybervictimisati

13.52

(3.67)

13.14-

13.90

14.12

(3.53)

13.85-

14.40

15.91

(5.22)

15.37-

16.45

34.6

.08

Social Anxiety

FNE

14.95

(5.50)

14.38-

15.52

17.11

(5.25)

16.71-

17.52

18.70

(5.30)

18.15-

19.25

45.0

.12

SAD-N

12.73

(5.16)

12.19-

13.25

14.22

(4.94)

13.83-

14.59

15.43

(5.37)

14.87-

15.99

25.3

.07

SAD-G

13.82

(4.87)

13.31-

14.32

15.87

(4.61)

15.51-

16.22

17.50

(4.88)

16.99-

18.01

54.5

.05

Note

. PMPU: Problematic Mobile Phone Use; FNE: Fear of Negative Evaluation;

SAD-N: Social Anxiety and Distress-New; SAD-G: Social Anxiety and Distress-

General; M: Mean; SD: Standard Deviation.

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Table 2

Differences in cyberbullying, cybervictimisation, and social anxiety

among students with low, medium, and high PSMU

Low PSMU

Medium PSMU

High PSMU

Statistical

significance

M (SD)

C.I.

(95%)

M (SD)

C.I.

(95%)

M (SD)

C.I. (95%)

η²

Cyberbullying

15.47

(3.22)

15.02-

15.91

15.63

(1.38)

15.52-

15.74

17.00

(4.32)

16.48-

17.52

25.7

.06

Cybervictimisati

16.26

(2.61)

15.90-

16.62

17.02

(3.26)

16.76-

17.28

19.60

(6.53)

18.81-

20.38

44.9

.08

Social Anxiety

FNE

14.27

(7.50)

12.75-

15.79

17.68

(8.04)

16.71-

18.65

22.39

(8.60)

20.80-

23.98

27.4

.09

SAD-N

11.59

(5.30)

10.52-

12.67

13.12

(5.15)

12.49-

13.74

16.40

(5.92)

15.31-

17.49

23.3

.08

SAD-G

6.32

(3.11)

5.69-6.95

6.90

(3.17)

6.52-7.29

8.69

(3.65)

8.01-9.36

16.3

.06

Note

. PSMU: Problematic Social Media Use; FNE: Fear of Negative Evaluation;

SAD-N: Social Anxiety and Distress-New; SAD-G: Social Anxiety and Distress-

General; M: Mean; SD: Standard Deviation.

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Table 3

Correlations between PMPU, PSMU, cyberbullying,

cybervictimisation and social anxiety

PMPU

PSMU

Cyberbullyi

Cyberv

SA-FNE

SA-SAD-N

SA-SAD-G

PMPU

.68**

.19**

.31**

.29**

.19**

PSMU

.68**

.28**

.33**

.39**

.32**

.31**

Cyberbullying

.19**

.28**

.46**

.10*

.07

.06

Cybervictimisation

.19**

.33**

.46**

.35**

.20**

.25**

Social Anxiety-

FNE

.31**

.39**

.10*

.35**

.70**

.69**

Social Anxiety

SAD-N

.29**

.32**

.70**

.20**

.70**

.71**

Social Anxiety

SAD-G

.19**

.31**

.07

.25**

.69**

.71**

Note

. PMPU: Problematic Mobile Phone Use; PSMU: Problematic Social Media

Use; FNE: Fear of Negative Evaluation; SAD-N: Social Anxiety and Distress-New;

SAD-G: Social Anxiety and Distress-General.

**Correlation is significant at the .01 level (bilateral).

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Table 4

Association between PMPU, cyberbullying, cybervictimisation, and

social anxiety

Predictor

variable

S.E.

Wald

C.I.

95%

Cyberbullying

.18

.03

33.67

.00

1.19

1.12-

1.27

Constant

-2.39

.41

34.35

.00

0.09

Cybervictimisat

ion

.14

.02

40.76

.00

1.16

1.11-

1.21

Constant

-2.11

.33

40.81

.00

0.21

FNE

.12

.01

71.27

.00

1.13

1.10-

1.17

Constant

-2.12

.26

65.97

.00

0.12

SAD-N

.10

.01

42.66

.00

1.10

1.07-

1.13

Constant

-1.37

.22

38.64

.00

0.25

SAD-G

.15

.02

81.53

.00

1.16

1.13-

1.20

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Constant

-2.38

.27

75.69

.00

0.09

Gender

.95

.24

15.59

.00

2.59

1.61-

4.16

Note

. FNE: Fear of Negative Evaluation; SAD-N: Social Anxiety and Distress-New;

SAD-G: Social Anxiety and Distress-General; CI: Confidence Interval; OR: Odds

Ratio.

Table 5

Association between PSMU, cyberbullying, cybervictimisation and

social anxiety

Predictor variable

S.E.

Wald

C.I. 95%

Cyberbullying

.24

.06

15.55

.00

1.27

1.13-1.44

Constant

-.22

.09

5.18

.02

.80

Cybervictimisation

.22

.03

40.25

.00

1.25

1.17-1.34

Constant

-.52

.11

22.02

.00

.59

FNE

.12

.02

37.76

.00

1.12

1.08-1.17

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Constant

-2.18

.36

36.74

.00

.11

SAD-N

.15

.03

31.22

.00

1.16

1.10-1.22

Constant

-2.17

.39

30.59

.00

.11

SAD-G

.22

.05

22.48

.00

1.25

1.14-1.37

Constant

-.1.71

.36

22.53

.00

.18

Gender

.44

.16

7.44

.00

1.59

1.13-2.14

Age

-.05

.03

3.86

.00

.95

.90-1.00

Note. FNE: Fear of Negative Evaluation; SAD-N: Social Anxiety and Distress-New;

SAD-G: Social Anxiety and Distress-General; CI: Confidence Interval; OR: Odds

Ratio.

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