From smartphone use to psychological distress: the mediating role of nomophobia among university students in Oman

Abstract

The swift incorporation of smartphones into everyday academic and social activities has elicited worries regarding their psychological effects among university students. This study investigated the correlations among smartphone usage, nomophobia, anxiety, depression, and perceived social support within a cohort of Omani undergraduate students. A cross-sectional survey approach was utilised, and data were analysed by correlational and mediation-moderation statistical models. The findings indicated that heightened smartphone usage strongly forecasted elevated levels of nomophobia, which, in turn, was identified as a robust predictor of both anxiety and depression. The use of smartphones did not directly predict depression, indicating that emotional dependency, rather than frequency of use, influences depressed symptoms. Nomophobia was identified as a mediator in the association between smartphone usage and psychological discomfort, indicating that reliance on smartphones exacerbates mental health risks via the emergence of dependency-related anxiety. Perceived social support did not mitigate the relationship between nomophobia and anxiety; rather, it significantly reduced the association between nomophobia and depression, suggesting a protective effect of supportive networks on long-term emotional well-being. These results underscore the necessity of treating emotional attachment to digital gadgets instead of concentrating exclusively on measures for reducing screen time. The study offers culturally pertinent insights on digital mental health within the Omani context and underscores the importance of enhancing social support systems to alleviate the adverse effects of nomophobia.

Introduction

Smartphones have become part of everyday living and have transformed communication, learning, entertainment, and social relations. Smartphone usage is especially widespread among students at universities, as these gadgets promote academic functions, access to online learning platforms, and maintain peer social networks [1]. Nevertheless, the enhanced reliance on smartphones has brought up the issue of psychological well-being, especially when the use is excessive or a matter of emotion [2]. Nomophobia, the fear or anxiety associated with the inability to access or use a mobile phone, is one of the psychological consequences of problematic smartphone attachment [3, 4]. Prior researchers have concluded it as a contemporary behavioural issue with mental health, academic, and everyday functioning implications for student cohorts [5].

Nomophobia has become a unique construct that characterizes smartphone-based anxiety caused by a sense of disconnection, the loss of the opportunity to receive online identity, and communication interruption [6]. Nomophobia, in contrast to the general smartphone addiction, is specifically related to anxiety caused by the lack of the product or connectivity. People with high nomophobia might check their phone all the time, not go to areas where phone usage is discouraged, and feel anxious when having to deal with battery depletion or being in trouble with a weak phone signal [7]. This trend of emotional attachment implies that smartphones are not only communication devices but also safety and reassurance mechanisms for several young adults.

The accumulating body of empirical data points to the fact that the high rate of smartphone dependence and nomophobia correlates with adverse psychological effects, such as anxiety and depression. As an example, a systematic review by Göktaş and Üstündağ [8] provided strong correlations between problematic smartphone usage and anxiety symptomology in university students. However, in a similar vein, recent cross-cultural research documented that nomophobia foretells increased depressive symptoms because of an increased sense of alienation, sleeping disorders, and decreased real-life interpersonal communication [9]. These results indicate that nomophobia can serve as an important mediator, which is why general smartphone use is converted to emotional distress. In other words, the use of smartphones per se might not be directly triggering anxiety and depression, but the psychological addiction and cross-linked anxiety of nomophobia can be the cause of such symptoms [10]. However, even though more studies are investigating how smartphone use affects mental health, most of them have only investigated the direct links between smartphone use and psychological distress. There hasn't been much research on how smartphone use leads to emotional outcomes, especially the role of nomophobia as a mediator.

Besides the direct impact on the emotional state, nomophobia can be influenced by the contextual and psychological resources, especially social support [11]. The perceived social support, or the extent to which individuals perceive themselves to be emotionally supported and appreciated by other people, is a long-acquired protective factor against mental health issues. Social support may also prevent stress, increase coping efforts, and decrease emotional susceptibility. It has also been recently reported that those with high-level social support are less inclined to adopt maladaptive smartphone behaviour as a coping strategy and less susceptible to nomophobic behaviour [12]. Moreover, the connection between nomophobia and psychological distress might be moderated by social support, whereby individuals with high social support might not be adversely affected by nomophobic anxiety. Nonetheless, there is a paucity of empirical data regarding this moderating influence, making it inconclusive which should be investigated further [13].

These relationships can be examined within the context of Oman, which is a valuable setting. Penetration of smartphones in Oman has been growing fast in the last decade, with recent statistics showing that almost all young adults and university students share the use of mobile phones [14]. Colleges and universities are adopting digital platforms into curriculum delivery and administration, which further popularizes permanent connectivity. Although this virtual space has evident educational advantages, it can additionally increase the risk of being psychologically addicted to cell phones. Current literature in Gulf nations has observed a growing trend in the levels of nomophobia, yet limited and piecemeal research has been conducted on nomophobia among Omani students in colleges [15]. In addition, few studies in Oman have covered the mediating and moderating factors between the use of smartphones and mental health.

Given the cultural context of Oman, which emphasizes the importance of family cohesion and social connectedness in promoting emotional well-being, the analysis of social support as a moderating factor is particularly relevant. It is also unclear whether strong social networks can alleviate the potential adverse psychological impacts of nomophobia among Omani college students or whether digital communication can replace, but not supplement, face-to-face support systems. Therefore, addressing these gaps is essential to better understand how smartphone use may contribute to psychological distress through nomophobia and whether social support can serve as a protective factor within the Omani cultural context. The gap is one of the aspects that have been addressed to facilitate culturally based knowledge on mental health risks of digital dependence in the area.

Theoretical support and hypotheses development

Theoretical support

The theoretical framework of this study is mainly based on the Attachment Theory [16]. This theory states that people forge emotional attachments with objects, people, or symbols that provide psychological safety. While originally developed to explain interpersonal bonds, contemporary extensions of attachment theory suggest that individuals can also form affective attachments to non-human objects, including digital technologies, particularly when these objects fulfil emotional regulation and security needs [17]. For instance, the prior research explained that the digital artefacts, especially smartphones, can serve as anxiety objects, as they have the capability of supplying constant connectivity, reassurance, and emotional control [18], [19]. This study argues that for adolescents and young adults, smartphones become a stable platform, where they have unlimited access to social networks, information, and emotional support. This repeated reliance facilitates the internalization of smartphones as psychological safety signals, thereby increasing emotional dependency.

Distraction or danger to this access causes distress, anxiety, and discomfort, which are conceptualized as nomophobia, the fear of being without his smartphone [20, 21]. Importantly, this study conceptualizes nomophobia as a mediating psychological mechanism rather than a direct outcome. That is, smartphone use alone does not inherently produce anxiety or depression,instead, it fosters emotional dependency, which manifests as nomophobia and subsequently translates into psychological distress [22]. As a result, increased use of smartphones enhances emotional reliance on the gadget, hence increasing the likelihood of displays of nomophobia. This conceptual correlation supports Hypothesis 1, as well as explaining the empirical relationship between nomophobia and elevated generalised anxiety and depressive symptomatology (Hypotheses 2 and 3). Thus, the transition from behavioural exposure (smartphone use) to emotional outcomes (anxiety and depression) occurs through an intermediate attachment-based process, consistent with contemporary digital attachment frameworks [23].

The ensuing dysfunction in affect can be explained by being detached from the smartphone, like separation anxiety, which is a feature of interpersonal attachment situations. The model also explains the fact that the negative effects of attachment insecurity, which are anxiety and depressive affect, can be mitigated by psychosocial resources, most notably perceived social support [24]. The Stress-Buffering Model of Social Support [25] assumes that social support alleviates the effect of the psychological stressors on emotional well-being. Within this framework, social support does not function as a causal mechanism (mediator) but rather as a contextual buffering factor (moderator) that influences the strength of the relationship between stressors and psychological outcomes.

As people see strong emotional, informational, or companionship support that they get in their family, peers, or social groups, they do not turn to the use of smartphones to satisfy their affective needs. Consequently, social support weakens the intensity of the relationship between nomophobia and psychological distress rather than directly explaining its occurrence [26]. In this relationship, therefore, the negative impact of nomophobia on anxiety and depression should be mitigated in individuals whose perceived social support is high and who may have other, healthier announced coping strategies [27]. On the other hand, individuals with a low perceived social support tend to use smartphones as a main tool of emotional regulation, thus making the psychological effects of nomophobia even more severe. This distinction provides a stronger theoretical justification for modelling perceived social support as a moderating variable (Hypotheses H8 and H9), consistent with stress-buffering theory rather than mediation logic.

Overall, the Attachment Theory explains the psychological process that occurs internally and connects the use of a smartphone to nomophobia and, consequently, to anxiety and depression, and the Stress-Buffering Model of Social Support explains why and under what conditions these effects vary across individuals. By integrating these two frameworks, the study proposes a theoretically grounded explanatory model in which nomophobia serves as the underlying psychological mechanism (mediator), and perceived social support functions as a boundary condition (moderator). The model is therefore theoretically consistent, parsimonious, and empirically consistent in the modern studies of psychological and digital behaviour.

Hypotheses development

Smartphone addiction and smartphone phobia

According to attachment theory, people also desire emotional safety by having available and consistent attachment figures. In modern digital environments, smartphones may play this role of attachment, and they can provide instant access to communication, connection, and reassurance, something that is traditionally related to interpersonal attachment relationships [28]. The extensive use of smartphones among the students thus adds strength to emotional dependence since the gadget turns out to be the main source by which the students help sustain social identity, belonging, entertainment, and confirmation [29]. In situations where accessibility is limited, such as because of battery depletion, loss of connectivity, or institutional constraints, users can get stressed, worried, and uncomfortable, which fits the construct of nomophobia.

Therefore, the more people use smartphones, the more prone they are to developing dependence, which leads to their vulnerability to nomophobia.

• H1: The use of Smartphones positively predicts nomophobia in college students.

Nomophobia and anxiety

Nomophobia would induce emotional distress because it would create the fear of being disconnected from the main sources of belonging, confirmation, and communication. In case the smartphone is a psychological safe base to students, its loss can induce anxiety-like feelings that are like the ones experienced during the separation described in the process of attachment disruption [6]. Feelings such as hypervigilance in relation to connectivity, fear of missing out (FOMO), and constant checking might also be provoked by nomophobia, and this is linked to generalized anxiety symptoms [20].

• H2: Nomophobia has a positive impact on anxiety among college students.

Nomophobia and depression

Constant reliance on smartphones as a coping process has a depressive connotation. When the emotional aspect is delegated to a machine instead of interpersonal connections, people might lose the capacity to control themselves and be less resilient to stressors [7, 30]. In case a student loses his/her access to a smartphone temporarily or unpredictably, the student risks experiencing emotional withdrawal, a low mood, and helplessness, all of which are symptoms associated with depressive tendencies [31]. Empirical studies also indicate the correlation between nomophobia and depressive affect in university students.

• H3: Nomophobia has a positive effect on depression in college students.

Smartphone usage could lead to greater emotional dependence, ongoing social comparison, fear of missing out, and stress of constant connectivity, which can all result in increased anxiety symptoms [32, 33]. People who use their smartphones excessively tend to be more emotionally unstable and less likely to regulate their stress levels, according to previous studies. Hence, it is hypothesized that there is a direct positive relationship between the use of a smartphone and anxiety.

• H4: The use of Smartphones has a direct effect on anxiety.

There is also some evidence that excessive use of smartphones can contribute to depression symptoms, as it is associated with social isolation, sleep disturbances, emotional withdrawal, and less interaction with other people in real life. Overuse of smartphones might have negative effects on emotional health and may make people more prone to depression [9, 14]. Extended smartphone use may lead to lower psychological balance among students and lower satisfaction with life, which can lead to depression symptoms. Therefore, it is assumed that there is a direct positive relationship between the use of smartphones and depression.

• H5: The use of Smartphones has a direct effect on depression.

The mediating role of nomophobia

The theory of attachment explains that the use of smartphones is not necessarily bad. There is a psychological harm in the case where the use of the device changes to an emotional dependence, and the device itself turns into an alternate attachment figure [33]. Nomophobia, in this case, is the emotional channel that connects the use of smartphones with anxiety and depression. Teenagers who spend most of their time on smartphones have a higher chance of getting attachment-based dependency, and this dependency, in turn, leads to psychological distress whenever it becomes unavailable [34].

Thus, the use of nomophobia is an intermediary between smartphone use and the association with psychological symptoms.

• H6: Nomophobia mediates the relationship between smartphone use and anxiety.

• H7: Nomophobia mediates the relationship between smartphone use and depression.

Perceived social support

The stress-buffering model of social support argues that when people have strong emotional social support from family, friends, and other important people, they are better placed to cope with stress and tend to evade compensatory coping styles like excessive reliance on smartphones [27, 35]. In case of high perceived social support, students have other available emotional resources and do not rely on smartphones as much as a means of security. As a result, the mental trauma caused by nomophobia is alleviated. On the other hand, in case perceived social support is low, nomophobia is more likely to convert to an increase in anxiety and depressive symptoms since the smartphone becomes the main tool of emotional coping [13, 36].

Perceived social support is therefore supposed to moderate the psychological impact of nomophobia.

• H8a: Perceived social support moderates the relationship between nomophobia and anxiety.

• H8b: Perceived social support moderates the relationship between nomophobia and depression.

Conceptual model

This study presents a mechanism-based model (Fig. 1) wherein smartphone usage induces psychological distress via the mediation of nomophobia. Using a smartphone frequently can make people emotionally dependent on it, meaning they rely on it for comfort and social connection. This dependence manifests as nomophobia, the fear of being without access to one’s smartphone, which serves as a proximal psychological stressor, resulting in heightened anxiety and depression. Additionally, perceived social support is regarded as a moderating variable that mitigates this relationship. Specifically, individuals with elevated levels of social support are less impacted by the adverse psychological effects of nomophobia, whereas those with diminished support experience more pronounced effects. Nomophobia elucidates the correlation between smartphone usage and distress, while social support influences the intensity of this association.

Fig. 1.

Presents the conceptual framework along with the hypotheses' direction

Methodology

The research design used in this study was a quantitative, cross-sectional, and explanatory design to examine the structural relationship between smartphone use, nomophobia, and its psychological correlates, that is, generalized anxiety, depression, and social support among college students in Oman. The explanatory design applies to the study of causal inferences in behavioural studies, where controlled experimentation is not possible and also not ethically viable [37]. Latent variable modeling was also used, which allowed estimation of measurement and structural relationships simultaneously, minimizing measurement error and increasing the model's precision [38]. The study population comprised undergraduate students at institutions of higher learning in Oman. To obtain differences in geographic region, specialization of academic programs, and institutional governance (public/private), institutions were chosen. This variety was necessary to improve external validity and examine whether the results can apply to the heterogeneous population of Omani college students.

A multi-stage stratified random sampling method was employed to have a more representative sample [39]. At the initial level, universities were divided into public and private types. Institutions in both strata were chosen at random and in proportion to their size through the probability proportional method (PPS). At the second phase, academic faculties of individual selected institutions were stratified (i.e., by discipline clusters) (e.g., humanities, business, engineering/technology, and health sciences). Lastly, students were randomly chosen in their respective faculties, with class lists being used to sample the students. This method of sampling decreased sampling bias and provided proportional representation of academic units [40].

To ensure that the research procedure is sound and to avoid any potential bias in the sampling, clear guidelines were set for who could and couldn't be included. Only full-time students who were enrolled during the data collection period and had consistent smartphone access were included. Also, a response rate of about 77% was reached (n = 262 usable responses out of N = 340 distributed questionnaires). This is an acceptable rate for behavioural research and eases concerns about non-response bias. An evaluation of non-response bias was performed by contrasting early and late respondents, revealing no significant disparities in key constructs, which suggests negligible response bias. Furthermore, the sample's representativeness was assessed by combining the demographic distribution (e.g., gender, academic discipline, and institutional type) of respondents with existing national higher education statistics in Oman, thereby validating that the sample sufficiently represents the target population. The flow diagram for the sample is shown in Fig. 2.

Fig. 2.

Sample flow diagram

A priori power analysis was calculated using G*Power 3.1. In the case of structural equation modelling with an expected medium effect size (f2 = 0.15), a = 0.05, and power 0.95, a sample size of N = 178 was found to be the minimum. Nevertheless, the sample was enlarged to make covariance structures more stable and enable the multi-group analysis. After the analysis, the final sample to be analysed was n = 262 students, which is higher than the recommended ratio of 10:1 between sample size and estimated parameters in SEM [41].

Instead of limiting the use of the mode to reduce mode bias, data collection involved both digital and classroom-based paper administration to increase the response participation. The administrative units of the institutions were involved to make access to clusters of the faculty and the sessions of the students systematic. Respondent participation was non-incentivised and voluntary to avoid manipulation of the response.

The data on the demographics of the respondents are summarized in Table 1. The sample size was composed of female students (62.6), mainly those between 16–24 years (61.1). Most possible participants were single, and a significant percentage of them were pursuing MBA degrees (38.2%). The University of Technology and Applied Sciences provided the largest number of respondents (36.3%). With respect to education, most of the participants were currently or had successfully attained a bachelor's degree (62.2%). In general, it can be observed that the demographic map describes a mainly young and academically active student population of various disciplines and institutions in Oman.

Table 1.

Demographic characteristics

Characteristics	Categories	Frequency	Percent
Gender	Male	98	37.4
	Female	164	62.6
	Total	262	100
Age	16- 24	160	61.1
	25- 34	44	16.8
	more than 35	58	22.1
	Total	262	100
Subjects	Medicine	3	1.1
	MBA	100	38.2
	Education	46	17.6
	Engineering	35	13.4
	Others	78	29.8
	Total	262	100
University	SQU	70	26.7
	University of Technology and Applied Sciences	95	36.3
	Nizwa University	20	7.6
	Al Buraimi University College	39	14.9
	A'Sharqiyah University	38	14.5
	Total	262	100
Qualification	Diploma	45	17.2
	Bachelor	163	62.2
	Postgraduate	54	20.6
	Total	262	100

Measurement instrument

The instrument comprised a structured questionnaire that included already tested scales to indicate the study constructs: smartphone use, nomophobia, generalized anxiety, depression, and perceived social support. The Smartphone Use was measured using the Smartphone Addiction Scale- Short Version (SAS-SV), a 10-item tool, which measures excessive and compulsive smartphone use, withdrawal symptoms, and functional impairment [42]. For conceptual simplicity, we refer to the construct as “smartphone use” throughout the manuscript, but the scale operationalizes maladaptive or addictive patterns of smartphone use, not general usage behaviour. Therefore, the results should be interpreted in the context of problematic smartphone dependence and not routine smartphone activity. This distinction is consistent with the measurement approach and the theoretical framework linking emotional dependency, nomophobia, and psychological distress. Each item was rated on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree) to keep items consistent across variables and increase sensitivity to variation in responses. The Nomophobia Questionnaire (NMP-Q), a 14-item scale, was used to measure nomophobia to capture emotional discomfort and anxiety that is caused by a lack of access to a mobile phone [21]. Anxiety and depressive symptoms were measured using two well-established scales: the Generalized Anxiety Disorder Scale (GAD-7) and the Beck Depression Inventory-II (BDI-II) to assess psychological outcome, respectively [43]. Perceived social support was measured with the help of the Multidimensional Scale of Perceived Social Support, which measures the perceived social support of family, friends, and other important others [44]. The questionnaire was reviewed by academic experts in behavioural and psychological research to achieve contextual applicability and clarity, and then it was pilot tested (n = 32). Pilot reliability coefficients were greater than 0.83 in all scales, which shows the strong internal consistency of the reliability and the appropriateness of the instrument to use in full-scale data collection.

Descriptive statistics of participants

Table 2 indicates the descriptive statistics of the important constructs of the study. The Frequency of Smartphone Use (M = 3.21, SD = 0.67) and Nomophobia (M = 3.58, SD = 0.72) were moderate, which means that the participants actively use smartphone devices and experience anxiety when not being in their presence. The anxiety (M = 3.12, SD = 0.81) and Depression (M = 2.87, SD = 0.76) were both of a mild-to-moderate level on average. Perceived Social Support (M = 3.94, SD = 0.62) was meanwhile relatively high, which premises that the students tend to think that they are supported by their social networks. These descriptive trends help to give an initial indication of the possible psychological interaction between the use of technology and mental health. Table 2 provides the descriptive results.

Table 2.

Descriptive statistics

	Mean (M)	Standard Deviation (SD)	Minimum	Maximum
Smartphone Use (SU)	3.21	0.67	1.00	5.00
Nomophobia (NMP)	3.58	0.72	1.14	5.00
Anxiety (ANX)	3.12	0.81	1.00	5.00
Depression (DEP)	2.87	0.76	1.00	5.00
Perceived Social Support (PSS)	3.94	0.62	1.57	5.00

Common method bias

To reduce possible common method bias (CMB), various procedural solutions were taken, such as anonymity of respondents, the elimination of evaluative language, and the separation of the measurement of the predictor and outcome variables into separate parts of the questionnaire. CMB was also evaluated empirically by two known methods in addition to the procedures. To start with, the single factor test by Harman showed that the first unrotated factor explained a variance of only 29.4 per cent, which is far below the 50 per cent mark, and it did not mean that a single factor was dominant in the data. Second, a complete collinearity evaluation based on variance inflation factors (VIFs) had a value of all less than 3.3, indicating that there was no significant common method variance [45]. A combination of these processes and statistical tests gives excellent support that CMB is not a serious threat to the validity of the findings. Table 3 explains the results of VIF.

Table 3.

Multicollinearity

Path	VIF	Interpretation
Smartphone Use → Nomophobia	1.32	No collinearity issue
Nomophobia → Anxiety	1.48	Acceptable
Nomophobia → Depression	1.45	Acceptable
Smartphone Use → Anxiety	1.21	Acceptable
Smartphone Use → Depression	1.19	Acceptable

Data analysis method

This study utilised Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS to evaluate the proposed relationships among the study variables. There are several methodological reasons why PLS-SEM is the best choice. First, PLS-SEM is especially good for research that is focused on making predictions and for complex mediation models that include more than one latent construct [46]. The main goal of this study is to investigate at the predictive links between smartphone use, nomophobia, and psychological distress. PLS-SEM is a good way to do this. Second, PLS-SEM is useful when the research model has both mediating and moderating relationships, since it lets researchers estimate complex structural paths at the same time while still being statistically efficient [46]. Third, PLS-SEM works well with moderate sample sizes and data distributions that aren't normal. This is common in behavioural and psychological research that uses survey data [46]. PLS-SEM makes fewer assumptions about data normality than covariance-based SEM (CB-SEM), so it is better for exploratory and predictive research settings. Also, recent research on methods stresses that PLS-SEM and covariance-based SEM are not competing methods but rather complementary ones. The choice of which method to use should be based on the research goals, not on which method is better [47]. Since this study was meant to predict things and had mediation and moderation effects, PLS-SEM was thought to be the best way to analyse the data.

Measurement model fit indices

Model assessment relied on the Standardized Root Mean Square Residual (SRMR), the normal fit index (NFI) [46, 48]. The SRMR value was less than the threshold value of 0.08, which is recommended for an acceptable model fit in the PLS-SEM framework. Also, the Normed Fit Index (NFI) values for both the saturated (0.944) and estimated model (0.913) are higher than the suggested cutoff of 0.90, which means that the model fits well.

Reliability and validity

Table 4 indicates that all constructs had acceptable levels of internal reliability, with Cronbach Alpha (CA) and Composite Reliability (CR) being above the recommended coefficient of 0.70. In addition, the value of Average Variance Extracted (AVE) of all constructs was over 0.50, which proves sufficient convergent validity and the fact that each construct explains more variance in indicators than error of measurement. These findings support that the measurement scales followed in the study are reliable as well as conceptually consistent. Table 4 explains the reliability and convergent validity.

Table 4.

Reliability and convergent validity

`Construct	Cronbach’s Alpha (CA)	DG rho	Composite Reliability (CR)	AVE
Anxiety	0.733	0.853	0.813	0.625
Depression	0.899	0.929	0.929	0.773
Perceived Social Support	0.836	0.855	0.892	0.676
Nomophobia	0.828	0.829	0.886	0.661
Smartphone Use	0.805	0.806	0.885	0.719
Anxiety	0.867	0.870	0.909	0.715

Table 4 shows the Heterotrait-Monotrait (HTMT) ratios to be used in determining discriminant validity between constructs. The values of all the HTMT were less than the conservative level of 0.85 and the acceptable level of 0.90. Even though the relationships of Anxiety and Depression (HTMT = 0.815) and Anxiety and Nomophobia (HTMT = 0.830) were comparatively stronger, they were still lower than the cutoff. These findings affirm that the constructs are empirically different and that the multicollinearity is not an issue in the model. Table 5 provides HTMT results.

Table 5.

Heterotrait–Monotrait Ratio (HTMT)

Constructs	Anxiety	Depression	Social Support	Nomophobia	Smartphone Use
Anxiety	—	0.815	0.399	0.830	0.095
Depression		—	0.435	0.741	0.140
Perceived Social Support			—	0.400	0.232
Nomophobia				—	0.244
Smartphone Use					—

Structural model assessment

The results of the structural model are summarized in Table 6. The use of a smartphone was positively and statistically significant with nomophobia (β = 0.226, p < 0.001), which was supported (H1). The small effect size didn't affect the relationship's statistical significance, suggesting that higher smartphone use is associated with greater nomophobia. H2 and H3 were supported as the effect of nomophobia on anxiety (β = 0.630, p < 0.001) and depression (β = 0.601, p < 0.001) were positive and statistically significant with large effect sizes. The results showed that nomophobia is a significant predictor of psychological distress in university students. The results showed a negative but significant (β = −0.088, p = 0.036) direct effect of smartphone use on anxiety that supports H4. The effect was statistically significant, but small (f² = 0.013), indicating that while it was statistically significant, it does not have much practical significance. The direct association between the use of a smartphone and depression, however, was negative and statistically insignificant (β = −0.041, p = 0.189), and thus H5 was rejected. Fig. 3 presents the coefficient paths.

Table 6.

Path coefficient

Hypothesis	Relationship	β	t-value	p-value	Lower Bound	Upper Bound	Decision
H1	Smartphone Use → Nomophobia	0.226	3.421	0.000	0.117	0.334	Accepted
H2	Nomophobia → Anxiety	0.630	13.960	0.000	0.548	0.699	Accepted
H3	Nomophobia → Depression	0.601	13.581	0.000	0.529	0.674	Accepted
H4	Smartphone Use → Anxiety	−0.088	1.804	0.036	−0.167	−0.006	Accepted
H5	Smartphone Use → Depression	−0.041	0.883	0.189	−0.117	0.036	Rejected
Mediation Analysis
Hypothesis	Relationship	β	t-value	p-value	Lower Bound	Upper Bound	Decision
H6	Smartphone Use → Nomophobia → Anxiety	0.142	3.206	0.001	0.072	0.218	Accepted
H7	Smartphone Use → Nomophobia → Depression	0.136	3.255	0.001	0.070	0.208	Accepted
Hypothesis	Relationship	β	t-value	p-value	Lower Bound	Upper Bound	Decision
Moderation Analysis
H8a	Social Support × Nomophobia → Anxiety	−0.056	1.499	0.067	−0.114	0.008	Rejected
H8b	Social Support × Nomophobia → Depression	0.063	1.681	0.046	0.002	0.125	Accepted

Fig. 3.

Structural model

In addition, the mediation analysis showed that nomophobia mediated the relationship between smartphone use and anxiety (β = 0.142, p = 0.001) and between smartphone use and depression (β = 0.136, p = 0.001), supporting the H6 and H7. This indicates that smartphone use has an indirect impact on psychological distress via emotional dependence on smartphones. The interaction between perceived social support and nomophobia on anxiety was negative, but statistically non-significant (β = −0.056, p = 0.067), rejecting H8a. The direction of the relationship indicated a buffering tendency, but this effect was not statistically significant. However, perceived social support strongly moderated the relationship between nomophobia and depression (β = 0.063, p = 0.046), which lent support to H8b. The result suggests that social support could buffer the negative impact of nomophobia on depression. The structural path coefficients are given in Fig. 3 and Table 6.

Table 7 shows the R² values, which show how much of the variation in the endogenous constructs can be explained by their predictors. [49]assert that R² values of 0.25, 0.50, and 0.75 are weak, moderate, and strong, respectively. The R² value for Anxiety is 0.461, which indicates that the predictor variables explain 46.1% of the variance in anxiety. This shows that they have a moderate level of explanatory power. In the same way, Depression has an R² value of 0.457, which means that 45.7% of its variance is explained, which is also a moderate level of explanation. On the other hand, nomophobia has an R² value of 0.051, which means that the model only explains 5.1% of its variance. This provides a weak explanation, but does not mean that the relation is not statistically significant. Instead, it implies that other variables than smartphone use could be useful in explaining nomophobia.

Table 7.

R² results

Variables	R-square
Anxiety	0.461
Depression	0.457
Nomophobia	0.051

Table 8 shows the predictive relevance (Q²) values that were identified using the blindfolding method. If the Q² value is greater than zero, it indicates that the model can predict a value for a certain endogenous construct [49]. The Q² value for anxiety is 0.312, which means it is very predictive, while the Q² value for depression is 0.298, which means it is moderately predictive. The Q² value for nomophobia is 0.041, which means it is not very good at predicting things. This implies that the model is not very good at predicting this construct [48].

Table 8.

Predictive relevance (Q²)

Construct	Q2 Value	Predictive Relevance
Anxiety	0.312	Strong predictive relevance
Depression	0.298	Moderate predictive relevance
Nomophobia	0.041	Weak predictive relevance

Table 9 indicates the effect sizes (f²), which show how much each exogenous construct affects the endogenous variables on its own. Cohen [50] states that f² values of 0.02, 0.15, and 0.35 show small, medium, and large effects, respectively. The findings indicate that Nomophobia significantly influences Anxiety (f² = 0.602) and Depression (f² = 0.544), establishing it as a considerable predictor of both psychological outcomes. On the other hand, using a smartphone has a small effect on anxiety (f² = 0.013) and a very small effect on depression (f² = 0.003). Interestingly, the direct path from smartphone use to anxiety was statistically significant, but the effect size was small, indicating little practical significance. This means that it doesn't have a big impact on mental health. Nonetheless, smartphone use exerts a minor influence on nomophobia (f² = 0.054), thereby affirming its function as an antecedent variable within the model. These results bolster the mediating function of nomophobia in conveying the impacts of smartphone usage on psychological outcomes.

Table 9.

Effect size (f²)

Predictor → Outcome	f2 Value	Effect Size
Nomophobia → Anxiety	0.602	Large
Nomophobia → Depression	0.544	Large
Smartphone Use → Anxiety	0.013	Small
Smartphone Use → Depression	0.003	Negligible
Smartphone Use → Nomophobia	0.054	Small

Mediation hypotheses

Finally, the hypothesis of mediation was also assessed (H6 and H7). It was revealed that the relationship between smartphone use and anxiety is mediated by nomophobia (H6), and the mediation effect is significantly positive (β = 0.142, t = 3.206, p = 0.001). Similarly, the relationship between smartphone use and depression is mediated by nomophobia (H7), and the mediation effect is significantly positive (β = 0.136, t = 3.255, p = 0.001). The results indicate that the use of smartphones indirectly leads to psychological disorders through increased nomophobic tendencies rather than the direct effect of smartphone use alone. Details are provided in Table 7.

Moderation hypotheses

To assess the significance of moderation, t-statistics for the relationships created by SmartPLS 4.1 with a bootstrapping function. The bootstrapping settled at a 0.05 level of significance and a one-tailed estimate. Using the bootstrapping approach as shown in Table 4.18, the results of the second-order model moderation indicated that MSPSS weakens the relationship between Nomophobia and Anxiety, but this moderation is not significant (H6: β = −0.056, p = 0.067); thus, the hypothesis was rejected. The interaction term had a buffering tendency, but the moderation effect was not strong enough to produce a statistically significant anxiety reduction. Conversely, perceived social support was an important moderator of the association between nomophobia and depression (β = 0.063, p = 0.046), supporting H8b. This finding suggests that higher social support may buffer the long-term depressive outcomes of nomophobia. The summary of the structural model assessment is shown in Fig. 4, and the detailed results are presented in Table 7.

Fig. 4.

MSPSS moderates the relationship between nomophobia and depression

Discussion

This paper examined the interrelationship between smartphone use, nomophobia, anxiety, depression, and perceived social support in university students. The results show that the positive influence of problematic smartphone use significantly increased nomophobia, which supports the previous studies that have shown that an increased dependence on smartphones for communication, entertainment, and access to information technology increases anxiety in situations when the phone is unavailable or otherwise inaccessible [13, 21]. In addition, the phenomenon of nomophobia became one of the most important predictors of anxiety and depression, suggesting that psychological distress is more strongly associated with emotional dependency on smartphones than with smartphone use itself. These findings provide empirical evidence of the Core Self-Regulation Framework, which suggests that maladaptive coping behaviours contribute to cyclical dependency and emotional susceptibility [51].

The direct impact of smartphone use on anxiety was statistically significant but negative, which means that regular smartphone use can in the first place also be a coping response to cope with stress or maintain being connected.

Nonetheless, this protective power seems to be limited; as the usage transforms into emotional dependency (nomophobia), it adds to the anxiety and depression. However, this protective role appears temporary, as excessive emotional reliance gradually develops into nomophobia, which subsequently increases anxiety and depression [52]. This trend is consistent with previous research that demonstrates the role of mobile devices in supporting short-term social connectedness and excessive dependence on mobile devices in creating long-term mental health stress. The correlation between smartphone usage and depression was not significant, and it could mean that depression can be more strongly generated due to lifelong psychological dependence, rather than frequency of use [53]. These findings imply that interventions should focus not only on reducing screen time but also on addressing emotional attachment and maladaptive dependency patterns.

The mediation effect of nomophobia between smartphone use and psychological outcomes was confirmed, which proved the fact that nomophobia is the mechanism by means of which smartphone use affects well-being. Students who are highly reliant on smartphones to be validated, to communicate, and to manage tasks might develop emotional distress in situations where they expect to lose connection. This result is consistent with the studies that show psychological dependency on digital equipment is a more powerful predictor of mental distress than the frequency of use. This finding extends prior research by demonstrating that emotional dependency is a more critical predictor of mental health outcomes than simple usage behaviour.

The moderating role of perceived social support produced some varied results. Social support played no significant part in alleviating the association between nomophobia and anxiety; nevertheless, it played a significant part in alleviating the association between nomophobia and depression. This suggests that social support may be more effective in reducing long-term emotional distress, such as depression, rather than immediate anxiety responses associated with fear of disconnection. This trajectory is indicative of the fact that those who have a high perceived social support have a higher level of protection against the depressive symptoms of nomophobia, and anxiety can be more immediate and responsive to the sense of being disconnected. These findings are related to the literature, which suggests that depression is more sensitive to long-term protective emotion resources, including supportive social networks, and that anxiety is more likely to be a short-term reaction to threat perceptions [54].

The findings also suggest the rising levels of smartphone addiction and nomophobia among university students in digitally connected societies, consistent with prior Arab and Gulf research [21, 32]. However, the current study adds to the regional literature by empirically examining nomophobia as a mediating variable and perceived social support as a moderating variable in the context of Oman, where empirical evidence is limited.

Theoretical contribution

This study adds to the growing literature in the field of digital behaviour and psychological well-being by extending the existing theoretical models rather than confirming the existing relationships. In particular, the results show that nomophobia is an important mediating process in the relationship between problematic smartphone use and anxiety and depression. The finding adds to attachment-based perspectives, showing that emotional dependence on smartphones is an important psychological pathway that links digital behaviour to mental health outcomes. Furthermore, the inclusion of perceived social support as a moderating variable helps to clarify the contextual conditions under which nomophobia influences psychological distress. Results suggest that social support buffers depressive symptoms more effectively than anxiety, highlighting differential psychological processes underlying chronic emotional distress and immediate threat-related responses. This research integrates attachment theory and the stress-buffering model in the Omani setting to offer a more comprehensive perspective on the psychological consequences of smartphone addiction in non-Western cultures. Such distinction between mediation (nomophobia as causal mechanism) and moderation (social support as buffering condition) adds to the conceptual clarity and theoretical contribution of the model.

Practical contribution

The results of this research have several significant practical implications for the educational institutions, mental health professionals, and policy makers who are interested in the psychological health of university students. Since nomophobia is at the heart of smartphone use as a cause of anxiety and depression, any intervention ought to focus more on the emotional dependency and attachment that are formed with smartphones and not just trying to limit the time spent using them. The mindful use of smartphone programmes can help students to create a healthier relationship with their phones. Digital wellness modules could be introduced to orientation programmes and classroom tasks in universities with a particular focus on encouraging people to be aware of their emotional triggers that lead to the occurrence of compulsive checking behaviours and fear of disconnection. In the case of mental health professionals, the findings highlight the importance of measuring nomophobia and attachment to smartphones when conducting a clinical assessment, particularly in the treatment of students exhibiting anxiety symptoms or depressive ones. Traditional treatment methods may be modified to incorporate the discussion of emotional dependence on digital devices. Indicatively, the cognitive-behavioural interventions may assist students to pinpoint and contest beliefs that associate the availability of smartphones all the time with feeling safe or popular. Also, counsellors can teach students new coping principles, such as relaxation methods, face-to-face social interaction, and thoughtful self-reflection activities to reduce the feeling that they need comfort from smartphones. The results of the study also point to the protective effect of social support and suggest that programmes that enhance a sense of belonging and peer affiliation among the students can make them less vulnerable to nomophobia or its mental health consequences. Universities might help to organise peer-support networks, group-based academic mentors, and community-building activities that would promote meaningful offline engagement. These programmes can create the warm atmosphere of the campuses where students can feel emotionally safe due to the authenticity of interpersonal connections, which ultimately minimises the chances of the development of compensatory attachment bonds with their smartphones.

Limitations and future research directions

This study offers significant insights into the correlation between smartphone usage, nomophobia, and psychological well-being; nonetheless, it is essential to recognise its limitations. First, the cross-sectional research design limits inferences regarding causation, as the observed results indicate associations rather than temporal effects. Second, data were gathered via self-report instruments, potentially affected by social desirability or recall bias. Third, the sample was exclusively composed of university students in Oman, perhaps constraining the generalisability of the findings to other cultural contexts or age demographics. Finally, although the study investigated primary psychological outcomes, supplementary emotional and behavioural variables may elucidate the mechanisms underpinning nomophobia. Future studies might utilise longitudinal or experimental methodologies to elucidate causal relationships among smartphone attachment, nomophobia, and psychological consequences. Research ought to encompass larger and more heterogeneous samples spanning different age cohorts, professional domains, and cultural contexts. Qualitative methods, such as interviews or digital diaries, may yield deeper insights on the emotional experiences associated with smartphone reliance. Additional research may investigate intervention options, assessing the efficacy of digital wellness training, social support enhancement, or therapeutic programs designed to mitigate nomophobia.

Conclusion

The present study adds to the growing corpus of research on the psychological effects of university students' increasing dependence on smartphones. Based on Attachment Theory, the research illustrates that emotional attachment to smartphones is a crucial factor contributing to nomophobia, which is significantly linked to increased anxiety and despair. Although smartphone usage alone did not reliably forecast adverse psychological consequences, its indirect impacts via nomophobia were significant, indicating that the emotional significance attributed to the technology is more pivotal than the frequency or duration of use. This indicates that smartphones may function as a maladaptive coping mechanism, satisfying needs for comfort, reassurance, and immediate connectivity, especially when perceived offline support is inadequate.

Nomophobia evolved as a principal explanatory framework, validating the function of smartphones as psychological attachment entities in modern student life. These results highlight the necessity of emphasising emotional regulation, identity, and interpersonal connectivity in the context of technology-related stress, rather than merely promoting decreased gadget usage. The study underscores the complex role of perceived social support: although robust social support did not markedly mitigate the impact of nomophobia on anxiety, it did affect depressive outcomes, indicating that offline interpersonal relationships may serve to alleviate the emotional repercussions of smartphone attachment in certain contexts. This research enhances theoretical comprehension by integrating Attachment Theory into the realm of digital behaviour, illustrating that psychological dependencies can develop around technology in manners analogous to interpersonal attachments. The findings underscore the necessity for educational institutions and mental health practitioners to integrate digital well-being measures that mitigate emotional dependence on cell phones, foster healthy coping mechanisms, and strengthen real-world social connections. As digital connectivity continues to spread and smartphones become ever more deeply integrated into everyday life, the psychological dynamics described in this study are expected to become increasingly significant. To promote mental health in a society that is quickly becoming more digital, it will be important to help students build stable attachment bonds and learn how to control their emotions strongly.

Authors’ contributions

Iman Mohammed Al Yazidi—original writing, analysis, interpretation Aysin Sinal —supervision, review and editing, discussion and conclusion.

Funding

The authors received no financial support for the research, authorship, or publication of this article.

Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and adhered to all ethical principles for research involving human participants. Ethical approval was obtained from the Research Ethics Committee (Approval No. 2024–2025/-HUM037A). All participants were informed about the purpose of the study and provided written informed consent before data collection.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.