Bangla validation of the smartphone addiction scale and the academic underachievement scale: The associative role of classroom mindful attention in the relationship between smartphone addiction and underachievement among adolescents and young adults

Figures
Abstract
This study aimed to validate the Smartphone Addiction Scale–Short Version (SAS-SV) and the Perceived Academic Underachievement Scale (PAUS) for Bangladeshi adolescents and young adults. In addition, the study investigated whether classroom mindful attention has an indirect association with the relationship between smartphone addiction and academic underachievement in the target population. A cross-sectional survey using convenience sampling was conducted with 712 participants recruited from different educational institutions in Bangladesh. Bangla versions of SAS-SV, Classroom Mindful Attention Regulation Scale, and PAUS were administered in collecting data. Factor structure, reliability, and validity of the measures were assessed, and statistical mediation to determine the indirect association was conducted to achieve the study aims. Both SAS-SV and PAUS showed excellent internal consistency (Cronbach’s α = 0.95 and 0.91, respectively) and confirmed the predefined factor structures with good model fits (CFI = 0.99, TLI = 0.99, RMSEA = 0.05, SRMR = 0.03; PAUS: CFI = 0.99, TLI = 0.99, RMSEA = 0.04, SRMR = 0.03). While determining the convergent validity of the measures, both smartphone addiction and perceived academic underachievement were associated with lower GPAs and reduced classroom mindful attention. Moreover, the SAS-SV and PAUS were found to be invariably applicable across age, gender, and data collection mode. Mediation analyses indicate that the self-awareness component of classroom mindful attention had an indirect association with the relationship between smartphone addiction and perceived academic underachievement in young adults (B = 0.014, 95% CI [0.001, 0.028], p = .033) and the combined sample (B = 0.020, 95% CI [0.005, 0.036], p = .009), whereas no significant indirect association was observed among adolescents. These findings highlight the associative role of classroom self-awareness in the relationship between smartphone addiction and perceived academic underachievement and underscore the potential role of mindfulness-based interventions in educational settings.
Citation: Khatun S, Ridoy MMH, Noman AH, Sharon MMH, Mia MR, Islam J, et al. (2026) Bangla validation of the smartphone addiction scale and the academic underachievement scale: The associative role of classroom mindful attention in the relationship between smartphone addiction and underachievement among adolescents and young adults. PLoS One 21(7): e0353865. https://doi.org/10.1371/journal.pone.0353865
Editor: Gladys Dzansi, University of Ghana, GHANA
Received: October 20, 2025; Accepted: June 30, 2026; Published: July 16, 2026
Copyright: © 2026 Khatun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The minimal data set is fully available and without restriction at Zenodo via https://doi.org/10.5281/zenodo.19685114.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Over the past decade, smartphone ownership has risen dramatically, with approximately half of the global population now using these devices [1,2]. In high-income regions such as North America and Europe, ownership exceeds 80%, while usage in low- and middle-income countries continues to expand rapidly [3]. In this digital age, smartphones have become integral to daily life, shaping how people communicate, access information, and interact with the world around them [4]. By the end of 2022, over 6.6 billion people worldwide were using smartphones, and the number of users is expected to continue rising steadily over the coming years, approaching 7.8 billion by 2028 [5]. This global trend is also reflected in Bangladesh where smartphone access has expanded significantly due to improved internet availability and affordability [6]. Despite the growing availability of digital technologies, smartphones remain insufficiently integrated into structured educational practices in Bangladesh, a gap attributed to infrastructural limitations, lack of teacher training, and concerns about excessive screen exposure, particularly in rural areas [7]. This pattern is consistent with the evidence in developing countries, where, despite widespread smartphone access, their pedagogical use in higher education remains limited and underdeveloped [8]. In contrast, the global literature suggests that smartphone-based-e-learning has been systematically integrated into the educational systems through blended learning models, mobile applications, and interactive digital platforms, thereby enhancing accessibility and engagement in the teaching-learning process [9]. This highlights a clear gap between access to technology-based education and its effective implementation across cultures. In Bangladesh context, more than 90% of adolescents use smartphones, reflecting their widespread adoption and increasing reliance on digital technologies [6,10].
Smartphone usage among Bangladeshi students is restricted to entertainment and communication activities such as social media, gaming, and messaging rather than educational purposes [11]. A large percentage of young adult students, spending three to six hours daily on smartphones, has been found to be scolded for smartphone use [11], indicating teacher and parental concerns about inappropriate or excessive usage. Consistent with this, global literature suggests that parents could not control the smartphone overuse by their adolescent children despite the awareness of the detrimental impacts of smartphone overuse [12]. The majority of young adult students in higher educational institutions of Bangladesh use smartphones on an average of 6.5 hours per day for entertainment purposes only [13]. Similar patterns have also been observed among younger populations, where a substantial proportion of early adolescents spend at least three hours per day using smartphones or other digital devices [14]. Although smartphones offer numerous advantages, including communication, access to information, education, and entertainment [15,16], their excessive use has increasingly been linked to poor academic performance in urban Bangladeshi students due to the prioritization of social networking over academic applications [17,18].
Smartphone overuse leading to smartphone addiction has not been formally classified as a distinct clinical disorder by the World Health Organization; instead, it is often merged within a broader perspective of behavioral addictions, such as gaming disorder. But smartphone addiction is an emerging public health concern, indicating that up to 37% of adolescents show signs of it and exhibit maladaptive behavioral patterns characterized by withdrawal symptoms and interference with daily activities [19,20]. Excessive smartphone use has been linked to a range of psychological and behavioral difficulties, including depression, stress, fatigue, headaches, poor concentration, social isolation, and reduced sleep quality [21–26]. It may also reduce academic performance and engagement in learning [27–31]. In educational settings, smartphone use disrupts the learning process, as students engage in social media or other online communications during classes [32–34], leading to decreased attention, lower productivity, and impaired learning efficiency [35,36]. Higher levels of smartphone usage have also been associated with decreased concentration and ineffective time management [37]. Thus, despite its access to educational resources and effective communication, smartphone overuse frequently results in distraction, procrastination, and reduced academic engagement [38,39].
Parental guidance plays a crucial role in shaping adolescents’ smartphone use, with effective monitoring and balanced control helping to prevent excessive or addictive behaviors [40]. Specifically, authoritative parenting—characterized by high warmth combined with firm control—is associated with lower risks of smartphone addiction among adolescents [41]. Alongside parental influence, students’ self-control is equally important. Smartphone addiction is strongly linked to poor self-regulation, as individuals struggle to limit their usage despite its negative consequences [42]. Empirical evidence suggests that individuals with lower self-control—the inability to resist impulses and complete necessary tasks—are more likely to respond immediately to mobile notifications and have difficulty resisting the urge to check their smartphones [43,44]. Low self-control has also been linked to problematic smartphone behaviors, including late-night usage and sleep disruptions [45,46], as well as increased procrastination on social media platforms such as Facebook [47]. Prolonged social media engagement has been found to intensify smartphone dependency, further undermining academic performance [48–51]. Research suggests that such impulsive engagements are linked to deficits in attentional control, emotional regulation, and cognitive functioning in classroom settings [51,52].
In classroom settings, mindful attention has been shown to counteract distractions and enhance students’ ability to regulate attention, maintain focus, and engage fully with learning activities [53,54]. Mindfulness practices foster present-moment awareness, sensory engagement, and empathetic interactions in classrooms, supporting creativity, motivation, adaptability, and attentional control, ultimately promoting sustained engagement [55–59]. Students who do not engage mindfully often become inattentive to teacher instructions, discontinue academic tasks prematurely, lose focus during lessons, struggle to follow classroom norms, and display behaviors that disrupt learning [53,60,61]. Classroom mindfulness enhances awareness of thoughts, emotions, and bodily sensations, which can help reduce addictive behaviors and manage cravings [62]. Research also indicates that mindfulness practices can significantly reduce anxiety [35], thereby improving attention regulation, information retrieval, and the ability to perform goal-directed tasks through self-regulatory skills [63–65]. Taken together, practicing mindfulness is particularly important for promoting self-awareness, acceptance, self-regulation, and coping with academic stress [66,67]. A growing body of research has consistently linked mindfulness to lower levels of compulsive smartphone use [68,69]. By enhancing self-awareness and emotional regulation, mindfulness-based approaches have been shown to reduce maladaptive patterns of technology use [70]. From a theoretical perspective, self-regulation theory helps explain this relationship, as mindfulness supports attentional control, emotional regulation, and reflective awareness—components essential for strengthening self-regulatory mechanisms [71]. Individuals with higher mindfulness tend to demonstrate greater self-control and a more active regulatory system, reducing impulsive technology-related behaviors [72]. In addition, mindfulness has been found to be positively associated with self-control and negatively associated with rumination, both of which contribute to lower levels of problematic smartphone use among college students [69]. In addition, mindfulness has been shown to partially mediate the relationship between emotion regulation and mobile phone addiction, suggesting an important pathway linking mindfulness to technology use behaviors [73]. Previous studies have highlighted the need for more in-depth research on the role of mindfulness, especially in relation to academic stress and self-regulation [66]. Research has also documented a negative relationship between smartphone addiction and academic performance [51,74,75], with smartphone multitasking contributing to lower academic achievement [51]. However, the potential mediating role of classroom mindful attention in this relationship remains underexplored. Therefore, the present study aims to examine whether mindful attention in the classroom has an associative role in the relationship between smartphone addiction and academic underachievement among adolescents and young adults.
To examine this relationship, two standardized scales have been validated for use in Bangladesh. The Smartphone Addiction Scale (SAS; [76]) and its short version (SAS-SV; [76]) are among the most widely used instruments for assessing problematic smartphone use. Developed in collaboration with clinicians, these scales assess multiple aspects of behavioral addiction, including withdrawal, tolerance, loss of control, and continued use despite harmful consequences [77]. The full version demonstrates strong shared variance with related constructs, while the short form reliably predicts clinical judgments of smartphone addiction [76]. The 10-item SAS-SV, derived from the original 33-item version, has been translated and validated across multiple cultural contexts, such as Spain and France [78], Switzerland [16], Morocco [79], Turkey [80], Egypt [81], Indonesia [82], and Italy [83].The Perceived Academic Underachievement Scale (PAUS) was developed by Snyder and Adelson [84] in the USA to assess perceived academic underachievement among adults and was later adapted for use in Turkey by Bozgün et al. [85]. To ensure cultural appropriateness for Bangladeshi adolescents and young adults, this study validated the Bangla versions of the Smartphone Addiction Scale–Short Version (SAS-SV; [76]) and the Perceived Academic Underachievement Scale (PAUS; [84]). Therefore, the specific aims of the present study were: (1) to evaluate the factor structures and reliabilities of the Bangla versions of the Smartphone Addiction Scale–Short Form (SAS-SV) and the Perceived Academic Underachievement Scale (PAUS); (2) to assess the convergent validity of the scales by determining their relationships with relevant criterion measures; (3) To test whether the validated Bangla versions of the scales can be invariably be applied across age, gender, and data collection mode; and (4) to investigate whether mindful attention in the classroom has an indirect association with the relationship between smartphone addiction and academic underachievement among adolescents, young adults, or the combined sample.
Materials and methods
Participants
Procedure
Prior to data collection, the original English versions of the Smartphone Addiction Scale–Short Version (SAS-SV) and the Perceived Academic Underachievement Scale (PAUS) were translated into Bangla following the International Test Commission (ITC) guidelines for translating and adapting tests [87]. Initially, four bilingual experts, proficient in both English and Bangla as well as experienced in psychological test development and adaptation, independently translated the scales. Their translations were reviewed, compared, and consolidated into a single Bangla version that preserved the original meaning and cultural relevance. To ensure semantic and conceptual equivalence, three additional bilingual experts back-translated the Bangla version of the scales into English. The back-translations were reviewed, edited, and carefully compared with the original English version of the scales. Necessary revisions were made to ensure that the scales accurately reflect both the concepts and the meaning of the original instruments. A pilot study was then conducted on 30 students (16 adolescents and 14 young adults) representing the same population of the main study (M = 22.43, SD = 1.63). Students assessed the linguistic clarity, cultural relevance, and contextual suitability of the translated scales. In the pilot sample, the presence of demographic characteristics such as age, gender, academic discipline, and others were the same as those of the main study. Most participants reported that the key concepts were clearly expressed, and the items were generally easy to understand and accurately captured the intended constructs. Based on their feedback, minor modifications in the measures were made to enhance clarity and cultural fit. Following the pilot study, the final instruments used for the main study included the SAS-SV (10 items, 6-point Likert scale), the PAUS (6 items, 5-point scale), and the Classroom Mindful Attention Regulation Scale (CMARS, 10 items), administered to the total sample (n = 712).
Measures
Smartphone Addiction Scale-Short Version (SAS-SV). The Smartphone Addiction Scale–Short Version (SAS-SV; [76]) is a unidimensional self-report instrument designed to assess the risk of smartphone addiction and identify high-risk individuals. It conceptualizes smartphone addiction as excessive use that interferes with daily responsibilities and shows addiction-like symptoms such as tolerance, withdrawal, loss of control, cravings, and emotional instability [48,49]. The items of the SAS-SV were derived from the original 33-item Smartphone Addiction Scale (SAS; [48]). The SAS-SV consists of 10 items, each rated on a six-point Likert scale: 1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = slightly agree, 5 = agree, and 6 = strongly agree. Total scores on the scale can range between 10 and 60. Higher scores indicate a greater risk of smartphone addiction. During its initial development, the scale showed excellent internal reliability, with a Cronbach’s alpha of 0.97. In the present study, the SAS-SV demonstrated excellent internal consistency reliabilities, with Cronbach’s alpha and McDonald’s omega values of 0.92 and 0.91 for the combined sample and 0.95 and 0.94 for the adolescent and young adult samples, respectively.
Perceived Academic Underachievement Scale (PAUS). The Perceived Academic Underachievement Scale (PAUS; [84]) assesses perceived academic underachievement among individuals in a particular course or overall coursework. Although originally developed for adults aged 18 and above, the scale was adapted for use with adolescents aged 12 and older in this study. It captures situations in which individuals feel that their performance does not reflect their true abilities, even though they know they could perform better [84]. The scale consists of 6 items rated on a 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree), resulting in total scores ranging from 6 to 30. Lower scores reflect minimal or no perception of academic underachievement, whereas higher scores indicate a stronger perception of academic difficulties. In the PAUS, item 2 is reverse-coded. Prior to the main study, a pilot study conducted with 16 adolescents indicated that the items were clear, understandable, and appropriate for the target age group, with minor adjustments made to improve clarity without altering the original meaning. In the original study, the scale demonstrated excellent internal consistency, with a Cronbach’s alpha of 0.91. For the present sample, the scale also exhibited good internal consistency reliabilities with Cronbach’s alpha and McDonald’s omega values of 0.91 for the combined sample and 0.93 and 0.91 for the adolescent and young adult samples, respectively.
Classroom Mindful Attention Regulation Scale (CMARS). The Classroom Mindful Attention Regulation Scale (CMARS; [67]), consists of 10 items designed to assess emerging adults’ ability to nonjudgmentally regulate attention to present-moment stimuli in classroom settings. The scale captures two dimensions of mindful attention regulation: self-awareness and emotional regulation. Responses are recorded on a 5-point Likert scale, ranging from 1 (almost never) to 5 (almost always). Negatively worded items (2, 4, 5, 8, and 10) are reverse-scored to maintain consistency with the scale’s positive orientation. Higher scores indicate greater mindful attention regulation, while lower scores reflect weaker attentional control. In its original development, the CMARS demonstrated strong internal consistency, with ω = 0.83 for the total scale and ω = 0.82 and 0.75 for the emotional regulation and self-awareness subscales, respectively. In the present study, the total scale showed excellent reliability, with alpha and omega values of 0.85 for the total sample and 0.93 and 0.82 for the adolescent and young adult samples, respectively.
Data analysis
The psychometric evaluation of the SAS-SV and PAUS incorporated both Classical Test Theory (CTT) and Item Response Theory (IRT) frameworks to ensure comprehensive scale validation. The validation process involved several components, including reliability assessment (internal consistency), construct validation through EFA and CFA, evaluation of convergent validity, and item-level examination using IRT models. All instruments used in this study—the SAS-SV, PAUS, and CMARS—recorded self-reported responses from adolescents and young adults, reflecting participants’ subjective assessment of smartphone overuse, perceived academic underachievement, and mindful attention in the classroom. To assess the data normality, the values of skewness (< 2) and kurtosis (< 7) were considered acceptable indicators [88]. Internal consistency was evaluated using Cronbach’s alpha (α) and McDonald’s omega (ω), with values ≥ 0.70 considered acceptable [89]. The mean inter-item correlations (ranging from 0.15 to 0.50 [90]) and corrected item-total correlations (≥ 0.30 [91]) indicated satisfactory item discrimination. Composite reliability (≥ 0.70) and average variance extraction (≥ 0.50) were obtained from CFA factor loadings [92].
Prior to exploratory factor analysis (EFA), sampling adequacy and data suitability were assessed. For the SAS-SV and PAUS, the Kaiser-Meyer-Olkin (KMO) measure (> 0.60 [93]), Bartlett’s test of sphericity value (p < .001 [93]), and determinant value (>.00001 [94]) satisfied the prerequisites for factor analysis. EFA with geominQ rotation was conducted for both scales, retaining factors with eigenvalues greater than one [95]. A parallel analysis was also performed to confirm the number of retained factors [96,97]. In confirmatory factor analysis (CFA), model fit was evaluated using multiple indices: χ²/df < 5 [98], Comparative Fit Index (CFI), Tucker-Lewis Index (TLI ≥ 0.95 [99]), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR, ≤ 0.08 [100]). Multi-group CFA was run to check measurement invariance across age, gender, and data collection mode with ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 [101].
After establishing the optimal factor structure using confirmatory factor analysis (CFA), item response theory (IRT) analyses were performed using the graded response model (GRM; [102]) to evaluate item discrimination and threshold parameters after confirming IRT assumptions through local independence, monotonicity, and scalability of the SAS-SV and PAUS. Item discrimination and threshold parameters were estimated to assess how well each item differentiated between respondents at different levels of the latent traits. According to Baker and Kim [103], discrimination values of 0.10–0.34 indicate very low, 0.35–0.64 low, 0.65–1.34 moderate, 1.35–1.69 high, and values above 1.70 indicate very high discrimination. Threshold parameters represent the level of the latent trait (θ) required to move from one response category to the next [104]. To ensure that the items functioned independently, local independence was evaluated using residual correlation coefficients (Q3 statistics; [105]), with values below 0.20 indicating no local dependence [106]. Item scalability was assessed using Loevinger’s H coefficients [107], where values ≥ 0.50 indicate strong scalability, 0.40–0.49 moderate, and 0.30–0.39 weak [108]. Monotonicity was examined via the monotonicity index, with values below 40 considered acceptable [109]. The GRM was performed using R package ltm version 1.2.0. The scalability and monotonicity were determined using the R package mokken version 3.1.2. Local independence was checked through the R package mirt version 1.44.0. In addition, item information curves (IICs) and the test information function (TIF) were generated to assess the precision of the scales across different levels of the latent traits. Convergent validity of the SAS-SV and PAUS was assessed by examining their associations with the Bangla version of the Classroom Mindful Attention Regulation Scale (CMARS) and the obtained GPAs of the participants. Furthermore, to have a more nuanced interpretation of results, we have examined whether smartphone addiction, perceived academic underachievement, and mindful attention in the classroom differ by age (12–18/19–27), gender (male/female), academic field (science/non-science), and academic achievement (high/medium/low achievers). Statistical mediation analysis was used to assess whether mindful attention in the classroom has an indirect association with the relationship between smartphone addiction and perceived academic underachievement. Data were processed and analyzed using the IBM SPSS (version 26), RStudio (version 2025.09.1 + 401), JASP (version 0.95.2, Intel) and Microsoft Excel 365.
Results
Descriptive statistics and item-level properties of the Bangla versions of SAS-SV and PAUS
Descriptive statistics presented in Table 1 showed that the mean scores for individual items on the SAS-SV ranged from 3.35 (SD = 1.41) to 3.80 (SD = 1.53), whereas for the PAUS, item means varied between 3.22 (SD = 1.04) and 3.48 (SD = 1.19; 1.24). Analyses of item-level distribution characteristics revealed that skewness values were within acceptable limits, ranging from −0.19 to 0.05 for SAS-SV and from −0.48 to −0.15 for PAUS. Similarly, kurtosis values fall between −1.09 and −0.69 for SAS-SV and between −0.85 and −0.52 for PAUS, all within the recommended thresholds of less than 2 for skewness and less than 7 for kurtosis [60]. Corrected item-total correlations demonstrated good item discrimination, ranging between 0.73 (Item 8) and 0.80 (Item 6) for SAS-SV, and from 0.65 (Item 1) to 0.81 (Item 4) for PAUS (Table 1).
Reliability evidence
The internal consistency reliabilities of both scales were excellent (Table 2), as reflected by Cronbach’s alphas (0.95 for the Bangla SAS-SV and 0.91 for the Bangla PAUS) and McDonald’s omegas (0.95 for SAS-SV and 0.91 for PAUS). The mean item-total correlations were 0.64 for SAS-SV and 0.62 for PAUS. Composite reliability estimates were also high (0.96 for SAS-SV and 0.91 for PAUS; Table 2).
Dimensionality
An exploratory factor analysis (EFA) was conducted to investigate the underlying factor structure of the Bangla versions of SAS-SV and PAUS. Prior to this, the Kaiser-Meyer-Olkin (KMO) measures of sampling adequacy were tested and found excellent (0.96 for SAS-SV; 0.91 for PAUS). Bartlett’s tests of sphericity values were also significant (χ² = 5298.62, p < .001 for SAS-SV; χ² = 2638.98, p < .001 for PAUS), indicating that the data were appropriate for factor analysis. The EFA results revealed that both instruments demonstrated a unidimensional factor structure, accounting for 63.8% and 62.9% of the variances for SAS-SV and PAUS, respectively. The analyses employed the minimal residual method with oblique (geominQ) rotation. To confirm the number of factors retained, parallel analysis was conducted, which demonstrated that the observed eigenvalues from the real data exceeded those from simulated datasets, confirming a single-factor solution for both measures [68,69].
Fit indices of measurement models
To validate the factor structure of the SAS-SV and PAUS, confirmatory factor analysis (CFA) was performed using the diagonally weighted least square (DWLS) estimation method (Table 3). The fit indices indicated excellent model fits according to established criteria. Specifically, the single-factor models demonstrated a good fit to the data, with values as follows: for SAS-SV, χ²/df = 2.63, CFI = 0.99, TLI = 0.99, RMSEA = 0.05, and SRMR = 0.03; for PAUS, χ²/df = 2.26, CFI = 0.99, TLI = 0.99, RMSEA = 0.04, and SRMR = 0.03. Item factor loadings from the EFA for SAS-SV ranged from 0.75 to 0.83, while CFA loadings were slightly higher, ranging from 0.77 to 0.84. For PAUS, EFA factor loadings ranged from 0.69 to 0.86, whereas CFA factor loadings ranged between 0.71 and 0.87 (Table 4).
IRT assumptions and model evaluation
Following the confirmatory factor analysis, the assumptions of Item Response Theory (IRT) were evaluated to assess the unidimensional structure of the SAS-SV and PAUS. The residual correlation coefficients (Q3 statistics) ranged from −0.28 to 0.12 for SAS-SV and from −0.34 to 0.07 for PAUS, with none exceeding the commonly used threshold of 0.20. This indicates that the items met the assumption of local independence. Loevinger’s H coefficients (item scalability) ranged from 0.63 to 0.69 for the SAS-SV, with a scale-level H of 0.66 (SE = 0.01), and from 0.60 to 0.70 for the PAUS, with a scale-level H of 0.66 (SE = 0.01), suggesting strong scalability and acceptable item discrimination. The monotonicity analysis showed that all items in both scales demonstrated acceptable monotonicity, with minimal and no critical violations observed for most items. These findings collectively support the application of an IRT model for the SAS-SV and PAUS, confirming that the items perform well in measuring the underlying latent traits of smartphone addiction and perceived academic underachievement. In the graded response model (GRM), item discrimination parameters ranged from 2.01 to 2.65 for the SAS-SV (Mean α = 2.55), and from 1.70 to 3.12 for the PAUS (Mean α = 2.51; Table 5). All items on both measures showed very high discrimination power.
The difficulty parameters in Table 5 reveal that the negative thresholds (b1–b2 for both scales) represent easier endorsement, whereas the positive thresholds (b3–b5 for SAS-SV; b3–b4 for PAUS) indicate progressively more difficult endorsement. Among the SAS-SV items, Item 1 had the lowest thresholds and was the easiest to endorse, while Item 7 had the highest thresholds, reflecting higher levels of the trait. For the PAUS, Item 6 had the lowest thresholds, indicating easiest endorsement, whereas Item 3 showed the highest thresholds, reflecting greater trait levels (Table 5). Overall, the distribution of thresholds across items demonstrates that both scales effectively capture responses from individuals with lower and higher levels of the underlying constructs.
Fig 1 presents the item information curves (IICs), which illustrate how much information each item contributes across different levels of the underlying latent trait. In Panel A (SAS-SV), Item 6 emerged as the most informative, although other items also provided considerable information, particularly around the mid-range of the trait continuum. In Panel B (PAUS), Item 4 yielded the highest level of information, with the remaining items contributing meaningful information across a broad range of the latent trait.
Fig 2 illustrates the test information functions (TIFs) for the Bangla versions of SAS-SV and PAUS. As shown in Panel A, the Bangla SAS-SV offers the highest level of information for θ values between −2.0 and 2.0. Similarly, Panel B shows that the Bangla PAUS also provides the most information within this θ range. Both scales demonstrate strong measurement precision at moderate levels of the latent trait, indicating their effectiveness in capturing variance around the average trait levels.
Group-based invariance testing
A multi-group confirmatory factor analysis was conducted to examine the measurement invariance of the Bangla versions of SAS-SV and PAUS across age, gender, and data collection mode. The ΔCFI (≤ 0.010) and ΔRMSEA (≤ 0.015) values reported in Table 6 were within the recommended cutoffs, indicating that both scales are invariant and can be applied consistently across different age groups, between males and females, and irrespective of mode effects (online/offline data collection mode).
Differences in SAS-SV, PAUS, and CMARS across academic achievement level, gender age, and academic field
A one-way ANOVA was conducted to examine differences in smartphone addiction (SAS-SV), classroom mindful attention regulation (CMARS), and perceived academic underachievement (PAUS) across levels of academic achievement (high/medium/low) of the participants. Results indicated no significant differences in SAS scores among high (M = 34.94, SD = 12.27), medium (M = 36.64, SD = 11.14), and low achievers (M = 36.35, SD = 11.87; F (2, 709) = 1.47, p = .231, η²p = .004]. Similarly, CMARS scores did not differ significantly across high (M = 27.34, SD = 8.63), medium (M = 26.06, SD = 6.97), and low achiever groups (M = 26.95, SD = 6.99; F (2, 709) = 1.52, p = .219, η²p = .004]. PAUS scores revealed no significant differences across achievement levels (high: M = 19.91, SD = 5.94; medium: M = 20.64, SD = 5.04; low: M = 20.81, SD = 5.55; F (2, 709) = 1.56, p = .210, η²p = .004]. Post hoc comparisons using Bonferroni corrections revealed no significant pairwise differences.
Independent samples t-tests were conducted to examine differences by gender, age group, and academic field. Male and female students did not differ significantly in SAS [t (710) = −0.776, p = .438], CMARS [t (710) = −0.171, p = .864], and PAUS scores [t (710) = −0.328, p = .743]. In respect of age group, adolescents (12–18 years) and young adults (19–24 years) did not differ significantly in SAS [t (710) = −1.219, p = .223], CMARS [t (710) = 0.035, p = .972], and PAUS scores [t (710) = −0.334, p = .739]. While considering academic filed, students from science and non-science streams had no differences in SAS [t (710) = 0.791, p = .429], CMARS [t (710) = 1.272, p = .204], and PAUS scores [t (710) = 0.332, p = .740]. Overall, these results suggest that smartphone addiction, classroom mindful attention, and perceived academic underachievement remain relatively stable by demographic characteristics and academic achievement levels.
Convergent validity evidence
While considering the convergent validity of Bangla versions of the SAS-SV and PAUS, CMARS (classroom mindful attention regulation) and GPAs were used as criterion variables to determine the relationships of SAS-SV and PAUS with these variables. The results showed strong negative associations of CMARS with SAS-SV (r = −0.62, p < .001) and PAUS (r = −0.60, p < .001), indicating that higher smartphone addiction and perceived academic underachievement are associated with lower classroom mindful attention. Additionally, both SAS-SV (r = −0.08, p < .05) and PAUS (r = −0.08, p < .05) showed weak but statistically significant negative correlations with GPAs, indicating that higher smartphone use and perception of academic underachievement are linked to slightly lower academic performance (GPAs). These findings support the convergent validity of SAS-SV and PAUS, as they are meaningfully related to classroom mindful attention and GPAs. In addition, the AVE values for both scales were 0.66, aligning with the recommended thresholds, and further indicate the convergent validity of the measures.
Indirect association of classroom mindful attention with the relationship between smartphone addiction and perceived underachievement
Statistical mediation analysis was conducted to examine whether the two components of classroom mindful attention—self-awareness (CMARS-SA) and emotional regulation (CMARS-ER)—were indirectly associated with the relationship between smartphone addiction (SAS-SV) and perceived academic underachievement (PAUS) across adolescents, young adults, or the combined sample. Parameters were estimated using the full information maximum likelihood (FIML), which provides unbiased estimates under the assumption of missing at random (MAR; [110,111]). Indirect effects were evaluated using the bootstrap resampling with percentile confidence intervals [112].
Among adolescents, the indirect associations through CMARS-SA (B = 0.051, 95% CI [−0.017, 0.127], p = .082) and CMARS-ER (B = 0.040, 95% CI [−0.019, 0.111], p = .163) remained statistically non-significant, indicating that classroom mindful attention components were not significantly associated with the relationship between smartphone addiction and perceived academic underachievement. However, the direct association between smartphone addiction and perceived academic underachievement was significant (B = 0.337, 95% CI [0.257, 0.403], p < .001).
Among young adults, the indirect association through CMARS-SA was statistically significant (B = 0.014, 95% CI [0.001, 0.027], p = .033), indicating that smartphone addiction was linked with perceived academic underachievement via classroom self-awareness in this cross-sectional model. This finding should not be interpreted as evidence that smartphone addiction causes lower classroom self-awareness, or that reduced classroom self-awareness leads to perceived academic underachievement. However, the indirect association through CMARS-ER was not significant (B = −0.001, 95% CI [−0.011, 0.010], p = .948). The direct association between smartphone addiction and perceived academic underachievement remained significant (B = 0.401, 95% CI [0.380, 0.423], p < .001).
In the combined sample, the indirect association through CMARS-SA was significant (B = 0.020, 95% CI [0.005, 0.035], p = .009), whereas the indirect association through CMARS-ER was not significant (B = 0.005, 95% CI [−0.008, 0.019], p = .428). The direct association between smartphone addiction and perceived academic underachievement was quite strong and significant (B = 0.393, 95% CI [0.372, 0.414], p < .001). Overall, these findings indicate that self-awareness is indirectly associated with the relationship between smartphone addiction and perceived academic underachievement, particularly among young adults and in the combined sample, whereas emotional regulation was not significantly associated with this relationship across adolescents, young adults, or the combined sample.
Discussion
This study aimed to explore whether classroom mindful attention has an indirect association with the relationship between smartphone addiction and academic underachievement of adolescents and young adults in Bangladesh. To address this, two standardized instruments were used, the Smartphone Addiction Scale–Short Version (SAS-SV) for assessing smartphone addiction and the Perceived Academic Underachievement Scale (PAUS) for measuring self-perceived academic underachievement. The findings demonstrated that the higher levels of smartphone addiction were found to be associated with greater perceived academic underachievement, while classroom mindful attention—particularly self-awareness—was indirectly associated with this relationship. Although the SAS-SV and PAUS have been validated in multiple cultural contexts—such as Italian, Turkish, Chinese, and Serbian populations for the SAS-SV [83,113–115], and American and Turkish populations for the PAUS [84,85]—they have not yet been validated in Bangladesh.
To this end, the present study examined the factor structure and validated the Smartphone Addiction Scale–Short Version (SAS-SV; [76]) and the Perceived Academic Underachievement Scale (PAUS; [84]) among adolescents and young adults in Bangladesh. In the current study, the instruments were first translated and back-translated, followed by a pilot study with a small group of students to ensure linguistic equivalence, item clarity, and appropriate item directionality. Subsequently, a larger sample of adolescent and young adult students, recruited through convenience sampling, participated in the main validation study. The results indicated excellent internal consistency for both scales, consistent with previous research demonstrating high reliability for the SAS-SV across different cultural contexts, including the Iranian version (Cronbach’s α = 0.85; [116]), the Spanish version (α = 0.88; [78]), the Mexican version (α = 0.89; [117]) and the Serbian version (α = 0.89; [113]). Similarly, the PAUS showed strong reliability in the Turkish version (Cronbach’s α = 0.80; McDonald’s ω = 0.80; [85]) and the American version (α = 0.91; [84]). The excellent reliability estimates in this study further suggest that the Bangla SAS-SV and PAUS captured their intended constructs with a high degree of homogeneity. However, very high reliability coefficients, particularly those approaching or exceeding 0.95, may suggest some overlap in item content. Although the present psychometric evidence, including the factor analytic and IRT findings, supports retaining all items, future studies may further examine the potential for item redundancy using complementary psychometric approaches and independent samples. Exploratory factor analysis (EFA) confirmed that the data for both the SAS-SV and PAUS were suitable for factor analysis, with all items loading strongly onto a single factor. Parallel analysis further supported a unidimensional structure for both scales. These results are consistent with prior research demonstrating robust unidimensional structures for the SAS-SV in Chinese [115], Turkish [80], and Italian versions [118], as well as for the PAUS in the American version [84]. Confirmatory factor analysis (CFA) further validated the factor structures, with fit indices indicating an excellent fit to the data. These findings are consistent with previous studies reporting good model fit for the SAS-SV in Turkish [80], Chinese [84], and Korean versions [76], and for the PAUS in the American version [84]. Finally, the IRT analyses conducted in this study further support the psychometric robustness of both the SAS-SV and the PAUS. For the SAS-SV, the items demonstrated high scalability and discrimination, and the test information function showed that the scale provided the most information across varying ability ranges of the latent trait, indicating optimal measurement precision within this range. This pattern aligns with the findings of Hidalgo-Fuentes et al. [119], who also observed that the SAS-SV maintains consistent information across the latent continuum, with the highest reliability observed around the mean to two standard deviations above it. Although no previous studies have applied IRT analysis to the PAUS, the current findings suggest that it performed effectively in capturing the latent construct at varying levels of perceived academic underachievement.
Furthermore, multi-group confirmatory factor analysis confirmed measurement invariance across gender, age, and data collection mode, indicating that the Bangla versions of SAS-SV and PAUS function invariably across these groups. This suggests that the instruments maintain consistent psychometric properties regardless of mode effects (online/offline data collection), thereby strengthening their applicability for cross-cultural validations across diverse populations. In addition, the study findings are consistent with prior research supporting the factorial stability of the SAS-SV across different cultural and demographic contexts. For instance, a Honduran validation study reported full invariance across sex and age categories [119], while a Peruvian adolescent sample similarly demonstrated invariance across age, sex, and region [120]. Likewise, Servidio et al. [118] identified configural and metric invariance—and partial scalar invariance—across gender and age for a 9-item version of the SAS-SV, which closely aligns with the present findings. With regard to the PAUS, although this study is among the first to examine its measurement invariance, the results indicated that it operates equivalently across gender and age groups. However, previous research suggests that gender differences may influence how students perceive their academic performance. For example, Vera Gil [121] found that gender moderates the relationship between psychological resilience and academic performance, with female students often perceiving their academic abilities differently than male students. Similarly, Wang and Yu [122] reported that gender moderates the relationship between academic self-concept and achievement, motivation, and self-efficacy, indicating that males and females may evaluate their academic competence in distinct ways. Behera and Seth [123] reported no significant gender differences in smartphone addiction among university students, although academic discipline was identified as a significant predictor. This suggests that smartphone use patterns and perceived academic abilities vary across cultural and demographic contexts. The present study findings further revealed that smartphone addiction, classroom mindful attention, and perceived academic underperformance did not differ significantly across academic achievement levels, gender, age groups, or academic streams of the participants, highlighting that these variables were relatively consistent across key student subgroups.
This study revealed a strong negative correlation of classroom mindful attention regulation with the SAS-SV and PAUS, indicating that higher levels of smartphone addiction and perceived academic underachievement are associated with lower classroom mindful attention. This finding aligns with meta-analytic and empirical evidence showing that problematic smartphone use is inversely related to mindfulness and attentional control [124–126]. Additionally, previous research has demonstrated that greater mindfulness is significantly associated with better academic achievement, highlighting the role of attentional regulation in academic performance [127]. Studies have also shown that smartphone addiction can lead to decreased mindful attention in classroom settings [128]. Engagement with social media for entertainment can increase cognitive distractions, further reducing mindful attention in classroom settings [15,129–131]. Although the associations of SAS-SV and PAUS with academic performance (i.e., GPAs) were statistically significant, the effect sizes were small, indicating a weaker association. This suggests that higher smartphone addiction and perceived academic underachievement are associated with lower GPAs, but the strength of these relationships is limited. In a broader sense, these findings are consistent with prior research linking higher smartphone addiction and perceived academic underachievement to lower GPAs and exam scores [84,131–138], although effect sizes reported in the literature vary across studies. Collectively, these results support the convergent validity of the SAS-SV and PAUS, suggesting that both scales are meaningfully associated with attentional regulation and academic performance.
Furthermore, smartphone addiction was found to be significantly associated with perceived academic underachievement, with classroom mindful attention showing an indirect association in this relationship. Specifically, smartphone addiction was significantly associated with perceived academic underachievement through the self-awareness component of classroom mindful attention among young adults and in the combined sample. Contrary to it, emotional regulation did not show a significant relationship within the same analytical framework. This finding aligns with prior research indicating that excessive smartphone use is associated with negative academic outcomes, such as increased academic burnout and decreased engagement [139,140]. Previous studies have also reported that higher levels of smartphone addiction are associated with poorer concentration and lower academic performance among students [141,142]. Similarly, research evidence suggests that higher levels of mindfulness are linked to lower problematic smartphone use, greater self-awareness, better emotional regulation, and more favorable academic outcomes [73,143]. In addition, higher self-awareness has been associated with better self-monitoring of smartphone use and more favorable academic outcomes [144,145]. These associations are consistent with self-regulation theory, which emphasizes the capacity to manage attention, prioritize long-term goals, and inhibit distractions [146,147]. Developmental differences in self-regulation have been reported across age groups, and evidence suggests that age may moderate the relationship between self-regulation and smartphone addiction, with older adolescents and young adults typically demonstrating stronger regulatory capacities [148]. Additionally, the association between self-regulation and academic achievement tends to strengthen with age, particularly among university students. This is consistent with research indicating that self-regulatory abilities develop alongside cognitive maturity [149,150].
Older adolescents and young adults have also been reported to exhibit stronger self-monitoring, greater perceived social support, and better behavioral regulation than younger individuals [149,150]. Although these characteristics may support more effective self-regulation, the absence of a significant indirect association of emotional regulation influencing smartphone addiction and perceived academic underachievement in this study suggests that managing emotions alone may be insufficient to offset the academic challenges linked to smartphone overuse, particularly when self-regulatory awareness is low. Overall, these findings suggest that self-awareness serves as a correlate linking smartphone addiction and perceived academic underachievement. However, given the cross-sectional nature of the study, this relationship should not be explained as a causal mechanism.
The findings of this study have practical implications for educational and psychological interventions aimed at mitigating the negative effects of smartphone addiction on academic performance. This highlights the importance of fostering self-awareness in students, which can be achieved through mindfulness-based activities such as guided attention exercises, reflective journaling, or classroom mindfulness practices. Although the indirect effects through classroom self-awareness were statistically significant, their relatively small magnitude (B = 0.014 in young adults and B = 0.020 in the combined sample) indicates that classroom self-awareness accounts for only a modest statistical association within the observed relationships. Therefore, classroom self-awareness should be considered a modest contributing factor statistically associated with perceived academic underachievement, alongside other psychological, educational, and contextual variables. The results also suggest that interventions focusing solely on emotional regulation may be insufficient, emphasizing the need to prioritize self-awareness alongside emotional skills. Educators, school counselors, and psychologists can incorporate training programs that help students monitor and regulate their smartphone use, set academic goals, and maintain focus during learning activities. Furthermore, these findings can inform curriculum design and school policies, promoting the integration of mindfulness and self-regulation strategies to enhance academic engagement, support mental well-being, and improve overall student performance.
Limitations and future directions
The present study has several limitations. The use of a convenience sample of Bangladeshi adolescent and young adult students limits the generalizability of the findings to the broader youth and adult populations. Future research should recruit more diverse samples, including participants from different regions, educational backgrounds, and cultural contexts, to improve generalizability. The cross-sectional design restricts causal interpretations, highlighting the need for longitudinal or experimental studies to establish temporal and causal relationships among smartphone addiction, mindful attention, and academic outcomes. Data were collected through self-reported measures, which may be influenced by social desirability or recall biases; future research should incorporate objective assessments such as digital usage tracking or academic records to enhance validity. Although classroom mindful attention was examined as an associating factor, other factors such as parental control, motivation, and sleep quality—which research indicates can be negatively impacted by smartphone addiction and subsequently affect academic performance [131,151]—were not investigated. Future studies should examine the roles of these additional mediating and moderating factors. Additionally, smartphone addiction was treated as a single construct, without differentiating between types of use, such as social media, gaming, or educational activities [76,152]. Future research should explore how specific patterns of smartphone use differentially influence mindful attention and academic underperformance. Finally, both EFA and CFA were conducted using the same dataset rather than independent exploratory and confirmatory subsamples. Although the sample size was adequate for psychometric analyses, using the same sample for both exploratory and confirmatory procedures may have capitalized on chance and resulted in somewhat optimistic estimates of the factor structure and model fit. Future studies are encouraged to replicat these findings using independent samples or split-sample cross-validation to provide stronger evidence for the stability and generalizability of the measurement models.
Acknowledgments
The authors thank the participants for their involvement in responding to the survey.
References
- 1.
Newzoo. Global esports & live streaming market report. 2021.
- 2. Olson JA, Sandra DA, Colucci ÉS, Al Bikaii A, Chmoulevitch D, Nahas J, et al. Smartphone addiction is increasing across the world: A meta-analysis of 24 countries. Comput Hum Behav. 2022;129:107138.
- 3.
Newzoo. Global mobile market report. 85. 2018.
- 4. Chen B, Liu F, Ding S, Ying X, Wang L, Wen Y. Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students. BMC Psychiatry. 2017;17(1):341. pmid:29017482
- 5. Ericsson. Ericsson Mobility Report 2022. 2022. https://d110erj175o600.cloudfront.net/wp-content/uploads/2022/11/29180338/Ericsson-Mobility-Report-November-2022.pdf
- 6. Rashid SMM, Mawah J, Banik E, Akter Y, Deen JI, Jahan A, et al. Prevalence and impact of the use of electronic gadgets on the health of children in secondary schools in Bangladesh: A cross-sectional study. Health Sci Rep. 2021;4(4):e388. pmid:34622022
- 7. Munira S, bin Ahsan W. E-Reading adoption and digital literacy in rural bangladesh: overcoming barriers and improving educational outcomes. UJ. 2024.
- 8. Iqbal S, Bhatti ZA. A qualitative exploration of teachers’ perspective on smartphones usage in higher education in developing countries. Int J Educ Technol High Educ. 2020;17(1):29.
- 9. Naveed QN, Choudhary H, Ahmad N, Alqahtani J, Qahmash AI. Mobile Learning in Higher Education: A Systematic Literature Review. Sustainability. 2023;15(18):13566.
- 10. Huang S, Lai X, Xue Y, Zhang C, Wang Y. A network analysis of problematic smartphone use symptoms in a student sample. J Behav Addict. 2020;9(4):1032–43. pmid:33372911
- 11. Afroz S, Sajib AS, Ahmed F, Ghosh AK, Rahman M-U. Exploring Smartphone Use Among Secondary School Students in a Rural School in Bangladesh: A Cross-Sectional Pilot Study. Public Health Chall. 2025;4(4):e70158. pmid:41140533
- 12. Buabbas A, Hasan H, Shehab AA. Parents’ Attitudes Toward School Students’ Overuse of Smartphones and Its Detrimental Health Impacts: Qualitative Study. JMIR Pediatr Parent. 2021;4(2):e24196. pmid:33878024
- 13. Sharmin Sultana, Md Ruknuzzaman, Md. Ayub Ali, Aysha Seddeque, Salma Siddiqua, Nibas Kumar Pal, et al. Smartphone usage and academic performance: A cross-sectional investigation among statistics students at MBSTU. World J Adv Res Rev. 2025;27(1):1577–92.
- 14. Mondal SK, Paul GK, Islam MA, Sharna MP, Akter M, Islam MR. Use and addiction of smartphone in adolescent students in Bangladesh: Social networking and gaming service. Int J Sci Basic Appl Res. 2020;50(2):65–74.
- 15. Al-Khlaiwi TM, Habib SS, Meo SA, Alqhtani MS, Ogailan AA. The association of smart mobile phone usage with cognitive function impairment in Saudi adult population. Pak J Med Sci. 2020;36(7):1628–33. pmid:33235587
- 16. Haug S, Castro RP, Kwon M, Filler A, Kowatsch T, Schaub MP. Smartphone use and smartphone addiction among young people in Switzerland. J Behav Addict. 2015;4(4):299–307. pmid:26690625
- 17. Haq MT, Hasan M, Rahman SN. Smartphone addiction and its impact on academic performance and health among adolescents in Kotalipara Upazila, Gopalganj, Bangladesh. Int J Public Health. 2024;10(1):45–58.
- 18. Islam B. Exploring the Impact of Smartphone Addiction on Adolescent Well-being in Dhaka City: A Sociological Study. JDIU. 2024;13(1).
- 19. Bhanderi DJ, Pandya YP, Sharma DB. Smartphone Use and Its Addiction among Adolescents in the Age Group of 16-19 Years. Indian J Community Med. 2021;46(1):88–92. pmid:34035584
- 20. Lin LY, Sidani JE, Shensa A, Radovic A, Miller E, Colditz JB, et al. Association between social media use and depression among u.s. young adults. Depress Anxiety. 2016;33(4):323–31. pmid:26783723
- 21. Ikeda K, Nakamura K. Association between mobile phone use and depressed mood in Japanese adolescents: a cross-sectional study. Environ Health Prev Med. 2014;19(3):187–93. pmid:24347468
- 22. Khan MM. Adverse effects of excessive mobile phone use. Int J Occup Med Environ Health. 2008;21(4):289–93. pmid:19228576
- 23. Prasad M, Patthi B, Singla A, Gupta R, Saha S, Kumar JK, et al. Nomophobia: a cross-sectional study to assess mobile phone usage among dental students. J Clin Diagn Res. 2017;11(2):ZC34–9. pmid:28384977
- 24. Yayan EH, Suna Dağ Y, Düken ME. The effects of technology use on working young loneliness and social relationships. Perspect Psychiatr Care. 2019;55(2):194–200. pmid:30044497
- 25. Zhang K, Guo H, Wang T, Zhang J, Yuan G, Ren J, et al. A bidirectional association between smartphone addiction and depression among college students: A cross-lagged panel model. Front Public Health. 2023;11:1083856. pmid:36761134
- 26. Jniene A, Errguig L, El Hangouche AJ, Rkain H, Aboudrar S, El Ftouh M, et al. Perception of sleep disturbances due to bedtime use of blue light-emitting devices and its impact on habits and sleep quality among Young Medical Students. Biomed Res Int. 2019;2019:7012350. pmid:31950050
- 27. Baert S, Vujić S, Amez S, Claeskens M, Daman T, Maeckelberghe A, et al. Smartphone Use and Academic Performance: Correlation or Causal Relationship?. Kyklos. 2019;73(1):22–46.
- 28. Han S, Yi YJ. How does the smartphone usage of college students affect academic performance?. J Comput Assist Learn. 2019;35(1):13–22.
- 29. Kates AW, Wu H, Coryn CL. The effects of mobile phone use on academic performance: A meta-analysis. Comput Educ. 2018;127:107–12.
- 30. Kibona L, Mgaya G. Smartphones’ effects on academic performance of higher learning students. J Multidiscip Eng Sci Technol. 2015;2(4):777–84.
- 31. Yang Z, Asbury K, Griffiths MD. An Exploration of Problematic Smartphone Use among Chinese University Students: Associations with Academic Anxiety, Academic Procrastination, Self-Regulation and Subjective Wellbeing. Int J Ment Health Addiction. 2018;17(3):596–614.
- 32. Gupta N, Irwin JD. In-class distractions: The role of Facebook and the primary learning task. Comput Hum Behav. 2016;55:1165–78.
- 33. Sana F, Weston T, Cepeda NJ. Laptop multitasking hinders classroom learning for both users and nearby peers. Comput Educ. 2013;62:24–31.
- 34. Tindell DR, Bohlander RW. The Use and Abuse of Cell Phones and Text Messaging in the Classroom: A Survey of College Students. College Teaching. 2011;60(1):1–9.
- 35. Hofmann SG, Sawyer AT, Witt AA, Oh D. The effect of mindfulness-based therapy on anxiety and depression: A meta-analytic review. J Consult Clin Psychol. 2010;78(2):169–83. pmid:20350028
- 36. Lepp A, Barkley JE, Karpinski AC. The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Comput Hum Behav. 2014;31:343–50.
- 37. Amez S, Baert S. Smartphone use and academic performance: A literature review. Int J Educ Res. 2020;103:101618.
- 38. Junco R. The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Comput Educ. 2012;58(1):162–71.
- 39. Singh MKK, Samah NA. Impact of Smartphone: A Review on Positive and Negative Effects on Students. ASS. 2018;14(11):83.
- 40. Yogesh M, Ladani H, Parmar D. Associations between smartphone addiction, parenting styles, and mental well-being among adolescents aged 15-19 years in Gujarat, India. BMC Public Health. 2024;24(1):2462. pmid:39256701
- 41. Koning IM, Peeters M, Finkenauer C, van den Eijnden RJJM. Bidirectional effects of Internet-specific parenting practices and compulsive social media and Internet game use. J Behav Addict. 2018;7(3):624–32. pmid:30273047
- 42. Afshan N, Alim F. An assessment of smartphone addiction among adolescents in relation to their mental health and self-control. Int J Educ Res Dev. 2023;5(1):32–6.
- 43.
Carver CS. Personality. In: Finkel IEJ, Baumeister RF, editors. Advanced Social Psychology: The State of the Science. 2nd ed. Oxford: Oxford University Press. 2019. p. 471–98.
- 44. Berger S, Wyss AM, Knoch D. Low self-control capacity is associated with immediate responses to smartphone signals. Comput Hum Behav. 2018;86:45–51.
- 45. Gökçearslan Ş, Mumcu FK, Haşlaman T, Çevik YD. Modelling smartphone addiction: The role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students. Comput Hum Behav. 2016;63:639–49.
- 46. Van Deursen AJAM, Bolle CL, Hegner SM, Kommers PAM. Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Comput Hum Behav. 2015;45:411–20.
- 47. Meier A, Reinecke L, Meltzer CE. Facebocrastination? Predictors of using Facebook for procrastination and its effects on students’ well-being. Comput Hum Behav. 2016;64:65–76.
- 48. Rozgonjuk D, Kattago M, Täht K. Social media use in lectures mediates the relationship between procrastination and problematic smartphone use. Comput Hum Behav. 2018;89.
- 49. Domoff SE, Foley RP, Ferkel R. Addictive phone use and academic performance in adolescents. Hum Behav Emerg Technol. 2020;2(1):33.
- 50. Giunchiglia F, Zeni M, Gobbi E, Bignotti E, Bison I. Mobile social media usage and academic performance. Comput Hum Behav. 2018;82:177–85.
- 51. Rosen LD, Carrier LM, Cheever NA. Facebook and texting made me do it: Media-induced task-switching while studying. Comput Hum Behav. 2013;29(3):948–58.
- 52. Kuznekoff JH, Titsworth S. The impact of mobile phone usage on student learning. Commun Educ. 2013;62(3):233–52.
- 53. Napoli M, Krech PR, Holley LC. Mindfulness training for elementary school students: The attention academy. J Appl Sch Psychol. 2005;21(1):99–125.
- 54. Zeidan F, Johnson SK, Diamond BJ, David Z, Goolkasian P. Mindfulness meditation improves cognition: evidence of brief mental training. Conscious Cogn. 2010;19(2):597–605. pmid:20363650
- 55. Roeser RW, Schonert-Reichl KA, Jha A, Cullen M, Wallace L, Wilensky R, et al. Mindfulness training and reductions in teacher stress and burnout: Results from two randomized, waitlist-control field trials. J Educ Psychol. 2013;105(3):787.
- 56. Cicekci MA, Sadik F. Teachers’ and students’ opinions about students’ attention problems during the lesson. J Educ Learn. 2019;8(6):15–30.
- 57. Jennings PA. Early Childhood Teachers’ Well-Being, Mindfulness, and Self-Compassion in Relation to Classroom Quality and Attitudes Towards Challenging Students. Mindfulness. 2014;6(4):732–43.
- 58. Sharp JE, Jennings PA. Strengthening Teacher Presence Through Mindfulness: What Educators Say About the Cultivating Awareness and Resilience in Education (CARE) Program. Mindfulness. 2015;7(1):209–18.
- 59. Posner MI, Rothbart MK. Research on attention networks as a model for the integration of psychological science. Annu Rev Psychol. 2007;58:1–23. pmid:17029565
- 60. Granziera H, Liem GA, Chong WH, Martin AJ, Collie RJ, Bishop M, et al. The role of teachers’ instrumental and emotional support in students’ academic buoyancy, engagement, and academic skills: A study of high school and elementary school students in different national contexts. Learn Instr. 2022;80:101619.
- 61. Xu X, Wu D, Zhao X, Chen J, Xia J, Li M, et al. Relation of perceptions of educational environment with mindfulness among Chinese medical students: a longitudinal study. Med Educ Online. 2016;21:30664. pmid:27118582
- 62. Bowen S, Chawla N, Collins SE, Witkiewitz K, Hsu S, Grow J, et al. Mindfulness-based relapse prevention for substance use disorders: a pilot efficacy trial. Subst Abus. 2009;30(4):295–305. pmid:19904665
- 63. Ainley J, Luntley M. The role of attention in expert classroom practice. J Math Teach Educ. 2007;10(1):3–22.
- 64. Mahfouz J, Levitan J, Schussler D, Broderick T, Dvorakova K, Argusti M, et al. Ensuring college student success through mindfulness-based classes: Just breathe. Coll Stud Aff J. 2018;36(1):1–6.
- 65. Huey M, Giguere D. The Impact of Smartphone Use on Course Comprehension and Psychological Well-Being in the College Classroom. Innov High Educ. 2023;48(3):527–37. pmid:36440453
- 66. Hj Ramli NH, Alavi M, Mehrinezhad SA, Ahmadi A. Academic Stress and Self-Regulation among University Students in Malaysia: Mediator Role of Mindfulness. Behav Sci (Basel). 2018;8(1):12. pmid:29342910
- 67. Shahrier MohdA, Naher L, Parvin M, Ali MdM, Sarkar N, Rahman MdL, et al. Development and Validation of the Classroom Mindful Attention Regulation Scale (CMARS) for Emerging Adults. Mindfulness. 2025;16(4):1015–27.
- 68. Jeong J-H, Bae S-M. The Relationship Between Perceived Stress and Smartphone Addiction: The Mediating Effect of Rumination and The Mediated Moderating Effect of Mindfulness. Psychiatry Investig. 2024;21(4):340–51. pmid:38695041
- 69. Cheng S-S, Zhang C-Q, Wu J-Q. Mindfulness and Smartphone Addiction before Going to Sleep among College Students: The Mediating Roles of Self-Control and Rumination. Clocks Sleep. 2020;2(3):354–63. pmid:33089209
- 70. Zhang Z, Wu L, Lu C, Guan T. Effectiveness of brief online mindfulness-based intervention on different types of mobile phone addiction: mechanisms of influence of trait mindfulness. Front Psychol. 2025;16:1400327. pmid:40034944
- 71.
Baumeister RF, Vohs KD. Self-regulation. In: Peterson C, Seligman MEP, editors. Character strengths and virtues: A handbook and classification. New York: Oxford University Press. 2004. p. 499–516.
- 72. Tan Z, Chen W-W, Wang Z. How Is Self-efficacy Linked to Procrastination? The Mediating Role of Attentional Control. J Rat-Emo Cognitive-Behav Ther. 2025;43(2).
- 73. Gülden Ç, Polat K. Mobile Phone Addiction, Emotion Regulation, Mindfulness, and Self-Control Among Adolescents: A Serial Mediation Analysis. Erzincan Üniversitesi Eğitim Fakültesi Dergisi. 2024;26(4):565–78.
- 74. Judd T. Making sense of multitasking: The role of Facebook. Comput Educ. 2014;70:194–202.
- 75. Karpinski AC, Kirschner PA, Ozer I, Mellott JA, Ochwo P. An exploration of social networking site use, multitasking, and academic performance among United States and European university students. Computers in Human Behavior. 2013;29(3):1182–92.
- 76. Kwon M, Lee J-Y, Won W-Y, Park J-W, Min J-A, Hahn C, et al. Development and validation of a smartphone addiction scale (SAS). PLoS One. 2013;8(2):e56936. pmid:23468893
- 77. Sohn SY, Rees P, Wildridge B, Kalk NJ, Carter B. Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry. 2019;19(1):356. pmid:31779637
- 78. Lopez-Fernandez O. Short version of the Smartphone Addiction Scale adapted to Spanish and French: Towards a cross-cultural research in problematic mobile phone use. Addict Behav. 2017;64:275–80. pmid:26685805
- 79. Sfendla A, Laita M, Nejjar B, Souirti Z, Touhami AAO, Senhaji M. Reliability of the Arabic Smartphone Addiction Scale and Smartphone Addiction Scale-Short Version in Two Different Moroccan Samples. Cyberpsychol Behav Soc Netw. 2018;21(5):325–32. pmid:29762065
- 80. Akın A, Altundağ Y, Turan ME, Akın U. The Validity and Reliability of the Turkish Version of the Smart Phone Addiction Scale-short Form for Adolescent. Procedia - Social and Behavioral Sciences. 2014;152:74–7.
- 81. Fathalla MM. Egyptian validation of smartphone addiction scale short version for adolescents (SAS-SV). Psycho-Educ Res Rev. 2019;8(3):7–10.
- 82. Arthy CC, Effendy E, Amin MM, Loebis B, Camellia V, Husada MS. Indonesian Version of Addiction Rating Scale of Smartphone Usage Adapted from Smartphone Addiction Scale-Short Version (SAS-SV) In Junior High School. Open Access Maced J Med Sci. 2019;7(19):3235–9. pmid:31949522
- 83. De Pasquale C, Sciacca F, Hichy Z. Italian validation of smartphone addiction scale short version for adolescents and young adults (SAS-SV). Psychol. 2017;8(10):1513.
- 84. Snyder KE, Adelson JL. The development and validation of the perceived academic underachievement scale. J Exp Educ. 2017;85(4):614–28.
- 85. Bozgün K, Can F, Baytemir KE. Adaptation of the triarchic model of grit scale and perceived academic underachievement scale into Turkish. Int J Psychol Educ Stud. 2022;9(3).
- 86. World Medical Association. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191–4.
- 87. Gregoire J. ITC guidelines for translating and adapting tests. Int J Test. 2018;18(2):101–34.
- 88. Kim H-Y. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restor Dent Endod. 2013;38(1):52–4. pmid:23495371
- 89.
Nunnally JC. An overview of psychological measurement. Clinical diagnosis of mental disorders: A handbook. 1978. p. 97–146.
- 90. Clark LA, Watson D. Constructing validity: Basic issues in objective scale development. In: Kazdin AE, editor. Methodological issues and strategies in clinical research. 4th ed. American Psychological Association. 2016. p. 187–203.
- 91.
Field A. Discovering statistics using SPSS. 5th ed. Sage Publications. 2017.
- 92. Bagozzi RP, Yi Y. On the evaluation of structural equation models. J Acad Mark Sci. 1988;16(1):74–94.
- 93.
Hair JF, Anderson RE, Babin BJ, Black WC. Multivariate data analysis: A global perspective. 7th ed. Pearson. 2010.
- 94. Yong AG, Pearce S. A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis. TQMP. 2013;9(2):79–94.
- 95. Kaiser HF. The application of electronic computers to factor analysis. Educ Psychol Meas. 1960;20(1):141–51.
- 96. Courtney MG. Determining the number of factors to retain in EFA: Using the SPSS R-Menu v2.0 to make more judicious estimations. Pract Assess Res Eval. 2013;18(1):8.
- 97. O’Connor BP. SPSS and SAS programs for determining the number of components using parallel analysis and velicer’s MAP test. Behav Res Methods Instrum Comput. 2000;32(3):396–402. pmid:11029811
- 98. Marsh HW, Hocevar D. A new, more powerful approach to multitrait-multimethod analyses: application of second-order confirmatory factor analysis. J Appl Psychol. 1988;73(1):107.
- 99. Hu L, Bentler PM. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods. 1998;3(4):424–53.
- 100. Schreiber JB, Nora A, Stage FK, Barlow EA, King J. Reporting structural equation modeling and confirmatory factor analysis results: A review. J Educ Res. 2006;99(6):323–38.
- 101. Chen FF. Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance. Structural Equation Modeling: A Multidisciplinary Journal. 2007;14(3):464–504.
- 102. Samejima F. Estimation of Latent Ability Using a Response Pattern of Graded Scores. Psychometrika. 1969;34(S1):1–97.
- 103. Baker FB, Kim SH. The basics of item response theory using R. New York: Springer. 2017.
- 104.
Baker FB. The basics of item response theory. 2001.
- 105. Yen WM. Effects of local item dependence on the fit and equating performance of the three-parameter logistic model. Appl Psychol Meas. 1984;8(2):125–45.
- 106. Chen WH, Thissen D. Local dependence indexes for item pairs using item response theory. J Educ Behav Stat. 1997;22(3):265–89.
- 107. Loevinger JE. A systematic approach to the construction and evaluation of tests of ability. Psychol Monogr. 1947;61(4):i.
- 108.
Sijtsma K, Molenaar IW. Introduction to nonparametric item response theory. Sage. 2002.
- 109.
Molenaar IW, Sijtsma K, Boer P. MSP5 for Windows: A program for Mokken scale analysis for polytomous items: Version 5.0: User’s manual. Iec ProGAMMA. 2000.
- 110.
Enders CK. Applied missing data analysis. New York: Guilford Press. 2010.
- 111.
Kaplan D. Structural equation modeling: foundations and extensions. 2nd ed. Thousand Oaks (CA): Sage Publications. 2009.
- 112. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879–91. pmid:18697684
- 113. Nikolic A, Bukurov B, Kocic I, Soldatovic I, Mihajlovic S, Nesic D, et al. The Validity and Reliability of the Serbian Version of the Smartphone Addiction Scale-Short Version. Int J Environ Res Public Health. 2022;19(3):1245. pmid:35162268
- 114. Noyan CO, Darcin AE, Nurmedov S, Yilmaz O, Dilbaz N. Validity and reliability of the Turkish version of the Smartphone Addiction Scale-Short version among university students/Akilli Telefon Bagimliligi Olceginin Kisa Formunun universite ogrencilerinde Turkce gecerlilik ve guvenilirlik calismasi. Anadolu Psikiyatri Dergisi. 2015;16(S1):73–82.
- 115. Zhao H, Rafik-Galea S, Fitriana M, Song T-J. Translation and psychometric evaluation of Smartphone Addiction Scale-Short Version (SAS-SV) among Chinese college students. PLoS One. 2022;17(11):e0278092. pmid:36445890
- 116. Esmaeilpour F, Letafatkar A, Baker JS, Dutheil F, Khazaei O, Rabiei P, et al. Reliability and construct validity of the smartphone addiction scale short version (SAS-SV) in Iranian students. J Public Health (Berl). 2021;31(3):345–53.
- 117. Escalera-Chávez ME, Rojas-Kramer CA. SAS‐SV Smartphone Addiction Scale in Mexican University Students. Educ Res Int. 2020;2020(1):8832858.
- 118. Servidio R, Griffiths MD, Di Nuovo S, Sinatra M, Monacis L. Further exploration of the psychometric properties of the revised version of the Italian Smartphone Addiction Scale–Short Version (SAS-SV). Curr Psychol. 2023;42(31):27245–58.
- 119. Hidalgo-Fuentes S, Martínez-Álvarez I, Llamas-Salguero F, Pineda-Zelaya IS, Merino-Soto C, Chans GM. Psychometric properties of the smartphone addiction scale-short version (SAS-SV) in Honduran university students. PLoS One. 2025;20(7):e0327226.
- 120. Figueroa-Quiñones J, Cjuno J, Morales-García WC, Salinas-Arias S. Psychometric properties of the Smartphone Addiction Scale (SAS-SV) in adolescents in Peru. Humanit Soc Sci Commun. 2025;12(1):1–7.
- 121. Vera Gil S. The Influence of Gender on Academic Performance and Psychological Resilience, and the Relationship Between Both: Understanding the Differences Through Gender Stereotypes. Trends in Psychol. 2024.
- 122. Wang L, Yu Z. Gender-moderated effects of academic self-concept on achievement, motivation, performance, and self-efficacy: A systematic review. Front Psychol. 2023;14:1136141. pmid:37057158
- 123. Behera RK, Seth MK. Smartphone addiction among university students: difference in gender and academic streams. Online J Distance Educ e-Learn. 2023;11(4):3114–22.
- 124. Aldbyani A, Chuanxia Z, Alhimaidi A, Li Y. Mindfulness and problematic smartphone use: indirect and conditional associations via self-regulated learning and digital detox. BMC Psychol. 2025;13(1):1131. pmid:41074205
- 125. Kim M, Seong G, Jeon M-J, Jung Y-C, Lee D. The mediating effect of attentional impulsivity between mindfulness and problematic smartphone use. BMC Psychiatry. 2024;24(1):294. pmid:38637786
- 126. Ru Y, Norlizah HC, Nasuha Burhanuddin NA, Liu H, Dong J. The correlation between mindfulness and problematic smartphone use: A meta-analysis. Addict Behav. 2025;164:108272. pmid:39923383
- 127. Caballero C, Scherer E, West MR, Mrazek MD, Gabrieli CFO, Gabrieli JDE. Greater Mindfulness is Associated With Better Academic Achievement in Middle School. Mind Brain and Education. 2019;13(3):157–66.
- 128. Şimşek H. The effect of smartphone addiction on attention level in high school students. Bağımlılık Dergisi. 2023;24(2):113–22.
- 129. Adelantado-Renau M, Moliner-Urdiales D, Cavero-Redondo I, Beltran-Valls MR, Martínez-Vizcaíno V, Álvarez-Bueno C. Association Between Screen Media Use and Academic Performance Among Children and Adolescents: A Systematic Review and Meta-analysis. JAMA Pediatr. 2019;173(11):1058–67. pmid:31545344
- 130. Gutiérrez-Puertas L, Márquez-Hernández VV, Gutiérrez-Puertas V, Granados-Gámez G, Aguilera-Manrique G. The Effect of Cell Phones on Attention and Learning in Nursing Students. Comput Inform Nurs. 2020;38(8):408–14. pmid:32349025
- 131. Rathakrishnan B, Bikar Singh SS, Kamaluddin MR, Yahaya A, Mohd Nasir MA, Ibrahim F, et al. Smartphone Addiction and Sleep Quality on Academic Performance of University Students: An Exploratory Research. Int J Environ Res Public Health. 2021;18(16):8291. pmid:34444042
- 132. Chaudhury P, Tripathy HK. A study on impact of smartphone addiction on academic performance. Int J Eng Technol. 2018;7(2.6):50–3.
- 133. Jackson LA, Von Eye A, Witt EA, Zhao Y, Fitzgerald HE. A longitudinal study of the effects of Internet use and videogame playing on academic performance and the roles of gender, race and income in these relationships. Comput Hum Behav. 2011;27(1):228–39.
- 134. Jang Y, Ryu S. The role of parenting behavior in adolescents’ problematic mobile game use. Soc Behav Pers. 2016;44(2):269–82.
- 135. Lin TT, Chiang YH. Investigating predictors of smartphone dependency symptoms and effects on academic performance, improper phone use and perceived sociability. Int J Mob Commun. 2017;15(6):655–76.
- 136. Liu F, Xu Y, Yang T, Li Z, Dong Y, Chen L, et al. The Mediating Roles of Time Management and Learning Strategic Approach in the Relationship Between Smartphone Addiction and Academic Procrastination. Psychol Res Behav Manag. 2022;15:2639–48. pmid:36148284
- 137. Zhang J, Zeng Y. Effect of College Students’ Smartphone Addiction on Academic Achievement: The Mediating Role of Academic Anxiety and Moderating Role of Sense of Academic Control. Psychol Res Behav Manag. 2024;17:933–44. pmid:38464809
- 138. Zeerak Q, Imran M, Azeez K, Lokanathan TH, Ismail IM. The Effects of Smartphone Addiction on Academic Performance Among Undergraduate Medical Students in Karnataka, India: A Multi-centric Study. Cureus. 2024;16(6):e62796. pmid:39036273
- 139. Yang G-H, Cao X-X, Fu Y-Y, Wang N-D, Lian S-L. Mobile phone addiction and academic burnout: the mediating role of technology conflict and the protective role of mindfulness. Front Psychiatry. 2024;15:1365914. pmid:38501091
- 140. Wei Z, Hassan NC, Hassan SA, Ismail N, Gu X, Dong J. The relationship between Internet addiction and academic burnout in undergraduates: a chain mediation model. BMC Public Health. 2025;25(1):1523. pmid:40275249
- 141. Farchakh Y, Hallit R, Akel M, Chalhoub C, Hachem M, Hallit S, et al. Nomophobia in Lebanon: Scale validation and association with psychological aspects. PLoS One. 2021;16(4):e0249890. pmid:33878132
- 142. Hawi NS, Samaha M, Griffiths MD. Internet gaming disorder in Lebanon: Relationships with age, sleep habits, and academic achievement. J Behav Addict. 2018;7(1):70–8. pmid:29486571
- 143. Bilgiz S, Peker A. The mediating role of mindfulness in the relationship between school burnout and problematic smartphone and social media use. Int J Progress Educ. 2021;17(1):68–85.
- 144. Kong L, Zhao M, Huang W, Zhang W, Liu J. The impact of academic anxiety on smartphone addiction among college students: the mediating role of self-regulatory fatigue and the moderating role of mindfulness. BMC Psychol. 2025;13(1):354. pmid:40205575
- 145. Zhao X, Wang H, Ma Z, Zhang L, Chang T. Smartphone addiction and academic procrastination among college students: a serial mediation model of self-control and academic self-efficacy. Front Psychiatry. 2025;16:1572963. pmid:40511461
- 146.
Shapiro SL, Schwartz GE. The role of intention in self-regulation: Toward intentional systemic mindfulness. In: Boekaerts M, Pintrich PR, Zeidner M, editors. Handbook of self-regulation. San Diego (CA): Academic Press. 2000. p. 253–73.
- 147. Luszczynska A, Diehl M, Gutiérrez-Doña B, Kuusinen P, Schwarzer R. Measuring one component of dispositional self-regulation: Attention control in goal pursuit. Pers Individ Dif. 2004;37(3):555–66.
- 148. Wang C-Y, Chou C. Investigating the mediating role of self-regulation in the association between smartphone addiction and academic performance: a nationally representative study of Taiwanese students from primary to university levels. Chinese Journal of Communication. 2026;:1–15.
- 149. Theurel A, Gentaz E. The regulation of emotions in adolescents: Age differences and emotion-specific patterns. PLoS One. 2018;13(6):e0195501. pmid:29879165
- 150. Zimmer-Gembeck MJ, Skinner EA. The development of coping across childhood and adolescence: An integrative review and critique of research. Int J Behav Dev. 2011;35(1):1–17.
- 151. Geng Z, Liu R. The impact of smartphone addiction and negative emotions on parent-child relationships among elementary school students. Front Psychiatry. 2025;16:1582741. pmid:40530061
- 152. Samaha M, Hawi NS. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Comput Hum Behav. 2016;57:321–5.