When Smartphones Fragment the Mind: Exploring the Links between Fragmented Smartphone Use and Anxiety among College Students through Distraction and Procrastination

When Smartphones Fragment the Mind: Exploring the Links between Fragmented Smartphone Use and Anxiety among College Students through Distraction and Procrastination | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article When Smartphones Fragment the Mind: Exploring the Links between Fragmented Smartphone Use and Anxiety among College Students through Distraction and Procrastination Hongfa Yi, Xiaoqin Wu, Xin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7600425/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract With the widespread adoption of smartphones among college students, fragmented smartphone use has become a defining feature of their digital lives. Fragmented smartphone use may exacerbate anxiety among college students; however, existing research has yet to provide clear empirical evidence for this relationship, and studies that systematically examine its underlying mechanisms remain scarce. To address this gap, this study combined self-report questionnaires with application logs obtained through a monitoring program installed on participants’ smartphones, which continuously tracked their usage over a full week, yielding valid data from 322 Chinese college students. Using a structural equation model (SEM), we examined the relationship between fragmented smartphone use and anxiety, focusing on the mediating roles of distraction and procrastination. The results revealed that although fragmented smartphone use itself did not exert a significant direct effect on anxiety (β = -0.034, p > 0.05), distraction emerged as a significant mediator in this relationship (β = 0.054, 95% CI: 0.003–0.104, p 0.05). These findings underscore the pivotal influence of distraction, enriching our understanding of how fragmented smartphone use shapes psychological well-being. Importantly, by leveraging objective, real-time behavioral data, this study provides robust empirical evidence to advance theoretical modeling and to inform precise intervention strategies for promoting digital health among college students. Fragmented smartphone use anxiety distraction procrastination Figures Figure 1 Figure 2 1 Introduction With the rapid development of mobile internet technology and the widespread adoption of smartphones, usage patterns have increasingly exhibited a fragmented nature. Today, various media and communication functions are consolidated within smartphone devices. Moreover, smartphones are equipped with numerous applications that enable users to seamlessly switch between multiple tasks while on the move[ 1 ]. This technological advancement is considered to have transformed people’s time management and daily behaviors, making their media use more overlapping and fragmented[ 2 ]. This implies that smartphone usage does not occur at a single point in time but is divided into multiple longer or shorter sessions, scattered throughout the day [ 3 ]. Over time, this has led to audiences exhibiting fragmented and non-linear characteristics in their media consumption and content preferences [ 4 ]. On the other hand, smartphones possess the characteristics of portability and ease of use[ 1 ], which has led users to develop a checking habit—users will immediately conduct brief but repetitive checks of dynamic content on the device[ 2 ]. This habit also reinforces the phenomenon of increasingly fragmented smartphone use. Due to the intense immersion provided by smartphones, their usage has become both fragmented and ubiquitous in daily life. People rarely consciously pay attention to this fragmented usage behavior[ 5 ]. However, this unconscious fragmented usage may erode the cognitive resources of college students, ultimately leading to a decline in their learning efficiency. The mechanisms and impacts behind this phenomenon require further academic attention. Numerous studies have demonstrated a clear correlation between smartphone use and anxiety [ 6 – 8 ]. However, extant literature predominantly focuses on the impacts of problematic smartphone use [ 9 , 10 ], screen time[ 11 , 12 ], and smartphone addiction[ 13 , 14 ] on anxiety, with relatively few studies directly examining the association between fragmented smartphone use and anxiety. Recently, Siebers et al.[ 15 ] identified a significant correlation between fragmented smartphone use and distraction in adolescents by developing a fragmentation index encompassing both screen time and session count. Nevertheless, the relationship between fragmented smartphone use and anxiety remains insufficiently understood. These gaps in the current body of research raise two critical, unresolved questions: Does fragmented smartphone use significantly increase anxiety? What is the mechanism of the impact of fragmented smartphone use on anxiety? At the methodological level, most studies rely primarily on self-report measures. However, traditional self-report methods exhibit notable limitations. Previous research has demonstrated that retrospective evaluations of digital media use are subject to systematic cognitive biases. High-frequency users tend to overestimate their usage duration, Whereas low-frequency users may exaggerate the frequency of use to conform to social expectation [ 16 ]. These social and cognitive biases substantially compromise data validity. To improve existing research, this study focuses on the group of Chinese college students and adopts an objective data collection method based on application logs and a psychological scale measurement method combining self-report to alleviate the limitations and errors of self-report. Additionally, the study employs the Input-Mechanism-Output (I-M-O) Model as its theoretical framework to examine the relationship between fragmented smartphone use and anxiety. At the same time, we introduced two variables, distraction and procrastination, to investigate the mediating effects of distraction and procrastination on the relationship between fragmented smartphone use and anxiety, aiming to provide effective supplements to existing literature. 2 Literature Review 2.1 Fragmented smartphone use and the Input-Mechanism-Output model Fragmented smartphone use refers to a specific pattern of smartphone usage[ 15 ]. It is an inevitable phenomenon in the current era of information explosion and represents a fundamental characteristic of contemporary social communication [ 17 ]. Liao et al. [ 4 ] defined it as a behavior characterized by intermittent user contact with various media or content over time, without a fixed spatial location. Thus, fragmented smartphone use is closely related to media multitasking, task switching, and overall media consumption. It encompasses various smartphone usage indicators, including, but not limited to, problematic smartphone use, phone checking, and screen time. Empirical studies have confirmed the fragmented nature of smartphone use. The results indicate that participants spent an average of 2 hours and 39 minutes using their smartphones each day, with an average session duration of approximately 7 minutes, and they switched applications 101 times [ 16 ]. Additionally, Oulasvirta et al.[ 2 ] found that although the total time spent on smartphones has decreased, the frequency of usage has increased. Research on temporal use may provide deeper insights into fragmented smartphone use. Smartphones have reconstructed traditional temporal structures through functional integration, dissolving clearly demarcated time blocks for work, household chores, and leisure into a fluid mixture. For example, users may browse news and information during work breaks or handle work emails during family dinners. This instantaneous access to diverse media content results in highly dispersed and overlapping usage patterns on smart devices [ 16 ]. Based on the aforementioned characterization of fragmentation and its relation to fragmented smartphone use, this study adopts the I-M-O model proposed by Chib and Lin [ 18 ] as the analytical framework. This model offers theoretical support for analyzing the dynamic relationships among computer-mediated communication, interpersonal communication, and human-computer interaction [ 18 ]. The original model conceptualizes input as accessibility and usability factors associated with technological development, mechanism as theoretical explanations at individual and socio-psychological levels of adoption and compliance, and output as outcomes ranging from healthcare system performance to individual health status [ 18 ]. As a heuristic framework, the I-M-O model is particularly valuable for elucidating how the technical features of mobile applications translate into health outcomes and through which theoretical mechanisms these transformations occur [ 19 ]. From this theoretical perspective, the present study investigates how fragmented smartphone use (input) influences anxiety (output) among college students through distraction and procrastination (mechanisms), as illustrated in Fig. 1 . This research design maintains the analytical logic of the original model while extending its application to the domains of digital behavior and mental health. 2.2 Fragmented smartphone use and anxiety We first focus on the relationship between the input factor (fragmented smartphone use) and the output result (anxiety). Existing literature has consistently confirmed a significant association between smartphone usage and anxiety, providing a theoretical foundation for examining fragmented smartphone use. Specifically, problematic smartphone use[ 9 , 10 ], smartphone addiction[ 13 , 14 ], social media use [ 20 , 21 ], and the intensity of use [ 22 ] have all been identified as positive predictors of increased anxiety. In contrast, although moderate smartphone use, such as social media interaction, may offer some benefits for mental health, excessive use has a more significant and far-reaching adverse impact on mental well-being [ 23 ]. When examining the operational dimensions of fragmented smartphone use, such as the frequency of switching and time spent[ 15 ], researchers have found that task switching data is significantly negatively correlated with emotional well-being[ 8 ]. Frequent phone checking[ 6 ] and excessive screen time [ 24 ] have also been linked to increased anxiety. Moreover, a review study suggests that smartphone screen time may contribute to greater fatigue, loneliness, and anxiety by displacing restorative activities, such as sleep and face-to-face socializing [ 7 ]. This pattern of association has been validated across various cultural contexts. A grounded study investigating social media use among young people in China found that social media has a dual impact on appearance anxiety, one of which is to amplify appearance anxiety through comparisons of physical attractiveness [ 25 ]. In the undergraduate population of the United States, the phenomenon of “Facebook invasion”—defined as addictive behavior resulting from uncontrolled social media use [ 26 ] — has been found to be significantly positively correlated with anxiety [ 27 ]. This relationship between media addiction and anxiety has also been supported by further research [ 28 ]. Logically, fragmented smartphone use, as a type of smartphone use, should be significantly associated with anxiety. Therefore, we propose the following hypothesis: H1: Fragmented smartphone use is significantly and positively correlated with anxiety. 2.3 The mediating role of distraction Distraction is a competing response tendency that diverts an individual’s attention away from a target area, thereby inhibiting or diminishing the processing of information within that area [ 29 ]. This phenomenon essentially reflects a staged breakdown of perceptual self-regulatory mechanisms [ 15 ]. The attention distraction hypothesis posits that frequent social media users may develop generalized but superficial attention patterns due to continuous exposure to rapidly changing information streams, resulting in the depletion of cognitive resources [ 30 ]. Empirical evidence demonstrates that individuals are frequently distracted by the allure of mobile application technologies in various contexts, including during class [ 31 ], in traffic [ 32 ], and during social conversations [ 33 ], all of which have the potential to induce distraction. Based on this, we believe that the primary mechanism by which fragmented smartphone use induces distraction is through frequent attention switching. Users often alternate between online digital activities and offline physical tasks, which imposes switching costs on the cognitive system. This continual shifting requires substantial time and energy to reset attention[ 15 ]. For example, it has been reported that corporate employees are interrupted by social media at least six to eight times per day, with each interruption requiring approximately 30 minutes to fully resume their previous work state [ 29 ]. Studies have demonstrated that, compared to non-Weibo users, frequent Weibo users experience greater difficulty resisting distracting information and are more susceptible to distraction [ 29 ]. Adolescents, as active social media users and a representative cohort of Generation Z, receive frequent notifications and messages from smartphone applications daily. Numerous scholars have empirically investigated the relationship between social media use and distraction. Attention problems arising from distraction have consistently posed challenges, particularly among young adults [ 34 ]. Individual studies have demonstrated that fragmented smartphone use significantly increases the level of distraction among adolescents [ 15 ]. There are divergent perspectives regarding the relationship between distraction and anxiety. Some studies suggest that distraction can alleviate anxiety in real-life situations, positioning it as a low-cost, accessible, and highly effective method for anxiety reduction [ 35 , 36 ]. Conversely, the American Psychiatric Association (2013) reported that attention problems constitute a core component of pathological anxiety related to daily functional impairments. Moreover, trait anxiety has been linked to impaired attention control in the presence of interfering stimuli [ 37 ]. Correspondingly, previous studies have demonstrated that increased distraction can contribute to heightened anxiety levels [ 38 ]. Technology-induced distraction diminishes the attentional resources allocated to ongoing tasks, impedes deep cognitive processing and task execution, and consequently exerts a direct negative effect on performance [ 39 ]. Few studies have examined the relationship between fragmented smartphone use and distraction, and the link between distraction and anxiety remains unclear. To clarify the role of distraction, we propose the following research questions: Based on this evidence, we propose the following research question: RQ1: Does distraction significantly and positively mediate the relationship between fragmented smartphone use and anxiety? 2.4 The mediating role of procrastination Another mediating variable we aim to examine in the relationship between fragmented smartphone use and anxiety is procrastination. Procrastination is defined as the irrational and voluntary delay in initiating or completing a planned task [ 40 ]. According to the short-term mood repair theory, procrastination arises primarily from the need for immediate emotional relief. When individuals experience aversion toward a task, they tend to avoid the task as a way to achieve temporary emotional compensation [ 41 ]. As one of the tools for emotion regulation, smartphones can contribute to the occurrence of procrastination. When smartphones—viewed as immediate temptations—are easily accessible, individuals are more likely to engage in procrastination [ 42 ], using them as a means to escape task-related pressure through activities such as cyberstalking [ 43 ]. For instance, when students use smartphones to manage academic stress and temporarily improve their mood, these readily available entertainment applications become immediate temptations that foster continued procrastination. However, the relationship between smartphone use and procrastination remains inconclusive, as existing research findings are often contradictory. On the one hand, some self-reported data indicate that social media usage is significantly positively correlated with procrastination [ 44 , 45 ]. Another study found that when students experience more frequent notifications and exhibit more fragmented smartphone use patterns, they tend to report increased procrastination behaviors [ 46 ]. In particular, task delay—one of the manifestations of procrastination—is more common among adolescents in the context of widespread digitalization and constant online connectivity [ 47 ]. On the other hand, when analyzing the relationship between fragmented smartphone use and task delay, Siebers et al. [ 15 ] did not find a significant correlation between the two. Although the causal relationship described above requires further verification, procrastination is widely recognized as a typical manifestation of self-regulation failure [ 40 ]. Its impact on anxiety has been extensively documented. Students who engage in procrastination tend to report lower GPAs [ 48 ] and exhibit significant associations with various forms of academic anxiety. Specifically, research has shown that procrastinators are more likely to experience psychological distress related to learning, such as test anxiety [ 49 ] and library anxiety [ 50 ]. Another study demonstrated that procrastinators frequently report experiencing a certain degree of anxiety about the future [ 26 ]. As mentioned earlier, individuals may avoid tasks as a way to manage or repair negative emotions. However, in the long term, this avoidance strategy can have detrimental effects on task completion and overall psychological well-being [ 51 ]. Although existing studies have demonstrated a relatively clear correlation between procrastination and anxiety, the relationship between fragmented smartphone use and procrastination remains unclear. To clarify the role of procrastination, we propose the following research questions: RQ2: Does procrastination significantly and positively mediate the relationship between fragmented smartphone use and anxiety? 3 Methods 3.1 Participants This study was approved by the Institutional Review Board of the author's university. From August to November 2024, recruitment advertisements were posted on forums of 12 universities across eastern and western mainland China, supplemented by offline field recruitment to broaden the participant pool. Ultimately, 334 eligible students were successfully recruited. All participants provided informed consent after receiving a thorough explanation of the study's purpose and procedures. Subsequently, they were instructed to download the smartphone tracking application AppUsage ( https://play.google.com/store/apps/details?id=com.a0soft.gphone.uninstaller&hl=en_SG&pli=1 ), which records and monitors participants' screen time and application-switching frequency in real time. Participants were required to keep the application running in the background for seven consecutive days to ensure comprehensive and objective data collection on smartphone usage. After sharing one week of smartphone tracking data, participants completed an online questionnaire including measures of distraction, procrastination, and anxiety. The smartphone tracking data and survey responses were merged using each participant’s unique identifier and stored in a comprehensive dataset. Sample screening strictly adhered to the predefined criteria, including only full-time undergraduate and postgraduate students who exclusively used Android smartphones as their mobile devices. Participants who completed the study received a reward of RMB 50. Due to incomplete smartphone tracking data, 12 participants were excluded. Consequently, the final sample consisted of 322 participants, resulting in a sample efficiency of 96.4%. 3.2 Smartphone usage data The AppUsage application automatically records app usage data in a usage log. The raw log data comprise the app name, date, time, and duration of each usage. Upon data collection, researchers screened and processed the raw data to derive specific metrics, including the precise end time of each app session, total weekly smartphone usage duration per participant, and the number of usage sessions. To capture users' daily behavior, discrete actions can be organized into continuous and uninterrupted sequences, conceptualized as mobile sessions in empirical research [ 52 ]. To objectively and accurately record fragmented smartphone use, smartphone sessions were employed as the unit of analysis. The maximal timeout period that marks the end of a smartphone session is called a session “threshold” [ 53 ]. Based on prior research findings and practical application requirements, the session threshold was set to 30 seconds [ 15 ]. This threshold resulted in an average of 408 smartphone sessions across 322 subjects. In accordance with the research guidelines established by [ 54 ], fragmented smartphone use was calculated based on both the frequency and temporal distribution of smartphone sessions. Building upon this, Siebers et al. [ 15 ] explored smartphone use fragmentation by examining the number of sessions and the ratio of total session duration to the overall measurement duration. Drawing on their work, we extended the measurement period to one week (168 hours) to yield more comprehensive results. To make fragmented smartphone use have similar scale to anxiety, we set the unit of number of sessions to hundred. Theoretically, given a measurement period of 168 hours and a session threshold of 30 seconds, the maximum achievable fragmented smartphone use score is 3024. Fragmented smartphone use was calculated as follows: 3.3 Survey data Distraction: The Smartphone Distraction Scale (SDS) developed by Throuvala et al. [ 55 ] was utilized. The scale comprises four dimensions: “Attention Impulsiveness,” “Emotion Regulation,” “Online Vigilance,” and “Multitasking.” Each dimension includes four items, such as “I get distracted by just having my phone next to me,” “Using my phone distracts me from tasks that are tedious or difficult,” “I get distracted with what I could post while doing other tasks,” and “I often talk to others while checking what’s on my phone.” Participants responded on a 5-point Likert scale, where 1 indicates strong disagreement and 5 indicates strong agreement. Procrastination: To assess trait procrastination, the General Procrastination Scale (GPS-9) developed and validated by Sirois et al. [ 56 ] was employed. This scale encompasses three dimensions: unnecessary delay, delay of intended tasks, and a general tendency to delay across tasks. Example items include statements such as “In preparing for some deadlines, I often waste time by doing other things” and “I am continually saying I’ll do it tomorrow.” Participants responded on a 5-point Likert scale, where 1 indicates strong disagreement and 5 indicates strong agreement. Anxiety: The anxiety dimension of the Depression, Anxiety, and Stress Scale (DASS), originally developed by Lovibond and Lovibond [ 57 ] and subsequently revised by Wang et al. [ 58 ], was utilized to reflect the specific context of Chinese college students. Four items with high factor loadings were selected for the present study. These items demonstrated strong psychometric properties, and their validity has been confirmed in Chinese samples. Participants responded on a 5-point Likert scale, where 1 indicates strong disagreement and 5 indicates strong agreement. The survey also recorded relevant demographic information of the participants, including gender, ethnicity, educational level, school location, household registration, and average monthly expenditure. 3.4 Statistical analysis We first conducted confirmatory factor analysis (CFA) on the three latent variables—anxiety, distraction, and procrastination—employing the maximum likelihood estimation method to assess factor loadings, composite reliability, and average variance extracted (AVE) for each scale. This procedure ensured that the construct validity of the measurement tools met the standards for academic research. To test our hypotheses and address our research questions, we employed the lavaan package in R software (version 4.4.2) to conduct a structural equation modeling (SEM) analysis. Fragmented smartphone use was set as an exogenous variable, and anxiety, distraction, and procrastination were set as endogenous variables. Distraction and procrastination were also mediating variables. The bootstrap method with 5,000 resamples was employed to conduct bias-corrected confidence interval tests on the mediation effects, ensuring the robustness of the research conclusions. Model fit was evaluated using multiple indices, including the chi-square statistic (χ²), root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI). Gender, ethnicity, educational level, school location, household registration, and average monthly expenditure were included as control variables. 4 Results 4.1 Descriptive statistical analysis A total of 322 college students participated in the study. Among them, 47.8% were male, 95.3% identified as Han ethnicity, and 38.2% were freshmen. Participants were enrolled in ordinary undergraduate colleges (30.4%), “211” universities (34.8%), and “985” universities (34.8%). The mean fragmented smartphone use score was 1.68 (range: 0.17–5.04), with a standard deviation of 0.809. Descriptive statistics are presented in Table 1. Table 1 Descriptive statistics Characteristics N(percentage) or M(SD) Gender male 154 (47.8%) female 168 (52.2%) Ethnicity han 307 (95.3%) minority 15 (4.7%) College year freshman 23 (38.2%) non-freshman 199 (61.8%) Educational level ordinary undergraduate university 98 (30.4%) 211 112 (34.8%) 985 112 (34.8%) The value of fragmented smartphone use 1.68 (0.809) 4.2 Measurement model The reliability analysis of the measurement indicators of distraction, procrastination, and anxiety was conducted, and the results are reported in Table 2. Confirmatory factor analysis demonstrated that the procrastination scale was robust (AVE=0.639, CR=0.841, α=0.825, factor loadings ranging from 0.748 to 0.859), indicating excellent convergent validity. Although the average variance extracted (AVE) values for distraction (AVE=0.495, CR=0.787, α=0.782) and anxiety (AVE=0.493, CR=0.793, α=0.790) were slightly below the conventional threshold of 0.5, they nonetheless exceeded the minimum recommended standard of 0.36 [59]. Consequently, the data remained within acceptable limits, and the composite reliability (CR) and Cronbach’s alpha (α) values met established standards, confirming the overall reliability and validity of the measurements. While the loadings of dis3 and dis4 for distraction and anx2 for anxiety were comparatively weaker, they still met widely accepted standards [60]. The data satisfied the fundamental requirements for empirical research. Simultaneously, the fit indices of the measurement model aligned with our expectations: χ²/df = 1.853, CFI = 0.976, TLI = 0.902, RMSEA = 0.047, and SRMR = 0.037 (see Table 3). These values indicated that the model fit met widely accepted standards [61]. Table 2 Results of the confirmatory factor analysis Construct Item Standardized factor loading Cronbach’s α CR AVE Distraction dis1 0.904 0.782 0.787 0.495 dis2 0.776 dis3 0.527 dis4 0.530 Procrastination pro1 0.859 0.825 0.841 0.639 pro2 0.787 pro3 0.748 Anxiety anx1 0.687 0.790 0.793 0.493 anx2 0.552 anx3 0.776 anx4 0.771 Note: CR = composite reliability; AVE = average variance extracted. dis1 = “Attention Impulsiveness”,dis2 = “Emotion Regulation”,dis3 = “Online Vigilance”, dis4 = “Multitasking”, pro1 = “unnecessary delay”, pro2 = “delay of intended task”, pro3 = “A general tendency to delay across tasks”,anx1 = “I have trouble breathing (for example, I feel short of breath or breathlessness even when I am not exercising)”, anx2 = “I have felt trembling (e.g., shaking hands)”, anx3 = “I'm about to panic.”, anx4 = “I feel scared for no reason”. Table 3 Fitting indicators of structural models Measurement model Structural models χ²/df 1.853 1.696 RMSEA 0.051 0.047 SRMR 0.039 0.037 CFI 0.976 0.954 TLI 0.966 0.937 4.3 Research modeling and hypothesis testing According to the hypothesized model, fragmented smartphone use was potentially associated with anxiety through three pathways. The direct pathway was as follows: fragmented smartphone use → anxiety. The first indirect pathway involved the mediating role of distraction: fragmented smartphone use → distraction → anxiety. The second indirect pathway involved the mediating role of procrastination: fragmented smartphone use → procrastination → anxiety. If any of these indirect pathways were significant, it indicated the presence of a mediating effect. As shown in Figure 2 and Table 4, the direct effect of fragmented smartphone use on anxiety was not significant (β = -0.034, p > 0.05), and H1 was therefore not supported. However, the indirect effect through distraction (fragmented smartphone use → distraction → anxiety) was significant (β = 0.054, 95% CI: 0.003–0.104, p < 0.05). Specifically, the path coefficient of fragmented smartphone use on distraction was significant (β = 0.195, p < 0.01), as was the path coefficient of distraction on anxiety (β = 0.276, p < 0.01). Therefore, we addressed RQ1, indicating that distraction significantly and positively mediated the relationship between fragmented smartphone use and anxiety. The indirect effect through procrastination (fragmented smartphone use → procrastination → anxiety) was weak (β = 0.009, 95% CI: -0.032–0.051, p > 0.05), indicating that procrastination did not mediate the relationship between fragmented smartphone use and anxiety. RQ2 was addressed. Table 4 The mediating effect of distraction and procrastination between fragmented smartphone use and anxiety, as shown by standardized estimates Effect Path Effect value SE Confidence interval LLCI ULCI Mediating effect fragmented smartphone use → distraction →anxiety (indirect1) 0.054 0.026 0.003 0.104 fragmented smartphone use → procrastination →anxiety (indirect2) 0.009 0.021 -0.032 0.051 Total mediating effect indirect1 + indirect2 0.063 0.039 -0.013 0.139 Direct effect fragmented smartphone use →anxiety (direct) -0.034 0.066 -0.163 0.095 Total effect direct effect + indirect effect 0.029 0.071 -0.109 0.167 5 Discussion By combining log-tracking data with survey data, this study employed the I-M-O model to examine the relationships among fragmented smartphone use, distraction, procrastination, and anxiety, thereby elucidating the complexity of psychological adaptation mechanisms in the digital age. The results indicated that fragmented smartphone use was not directly associated with anxiety, distraction significantly and positively mediated the relationship between fragmented smartphone use and anxiety, and procrastination did not significantly mediate the relationship between fragmented smartphone use and anxiety. Overall, the model demonstrated that the impact of fragmented smartphone use on anxiety was primarily mediated by distraction. 5.1 Association between fragmented smartphone use and anxiety These results contribute novel insights into the relationship between fragmented smartphone use and anxiety. First, we found that fragmented smartphone use did not significantly increase anxiety, a finding that diverges from much of the existing literature. This discrepancy may be attributed to the following factors: (1) Prior research has predominantly focused on isolated behavioral indicators—such as frequent checking, screen time, or social media intensity—without adequately addressing the phenomenon of daily smartphone multitasking fragmentation [ 62 ], leading to inconsistent findings. (2) Previous studies have primarily examined the relationship between smartphone use and anxiety through the lens of technology dependence, conceptualizing anxiety as a subjective experience of “loss of control” [ 63 ]. For example, addiction theory often equates repetitive habit patterns with pathological representations, which is superficially similar to the fragmented smartphone use we studied, but what is the definition of good or bad smartphone user habits and to what extent they cause negative effects remain unresolved issues[ 2 ]. Perhaps the usual situation of smartphone use (i.e., usually problematic) [ 64 ] and the fragmented smartphone use pattern should be divided into two different viewpoints, but as for the exact difference, there is no consensus at present. Secondly, our study confirmed that distraction serves as a mediator between fragmented smartphone use and anxiety, providing valuable insights for future research. The apparent contradiction with previous findings—such as Wong and Moulds [ 65 ], who reported that distraction induction can alleviate depression and reduce anxiety—may be attributable to the fact that anxiety is more strongly associated with excessive technology use [ 66 ]. Additionally, adolescents tend to experience increased distraction during periods of fragmented smartphone use [ 15 ]. This differential impact on psychological distress constitutes a key finding of our study. Finally, the study found that although procrastination was statistically associated with anxiety levels, it did not mediate the relationship between fragmented smartphone use and anxiety. This finding diverges from the prevailing view that time spent on smartphone applications can reasonably be regarded as time not invested in goal-directed behavior [ 46 ] and contradicts recent scholarship. Accordingly, it may be necessary to re-examine the perspective that “smartphone users have internalized media narratives” from the standpoint of fragmented smartphone use [ 64 ]. The findings align with the proposition by Aalbers et al. [ 46 ] that an increased number of notifications may reflect procrastination-related patterns of messenger use, rather than notifications themselves causing procrastination. It is important to avoid oversimplifying students’ smartphone usage behaviors by attributing them solely to procrastination tendencies. Furthermore, procrastination is more closely associated with work efficiency and goal orientation. For many adolescents, habitual social media use or phone checking may be unrelated to task delay [ 46 ], which also accounts for why procrastination did not emerge as a significant mediating variable in this study. 5.2 Theoretical and practical implications This study enhances the theoretical understanding of the mechanisms underlying the relationship between fragmented smartphone use and anxiety among college students in the digital age. Its significance is primarily reflected in two aspects: framework introduction and mechanism discovery. Our research adopts the I-M-O model as an analytical framework to clarify the mechanisms of fragmented smartphone use, emphasizes the critical role of the mediating variable (distraction) in the transformation from input to output, and offers an alternative perspective for comprehending the impact of fragmented smartphone use on psychological distress. Our research findings indicate that fragmented smartphone use does not directly lead to anxiety among college students. Instead, it induces anxiety through the mediating effect of distraction. Therefore, the primary focus of attention and intervention should be the distraction caused by smartphone use. In other words, smartphones exert a substantial distracting effect [ 67 ]. This conclusion also reflects the importance of cognitive resource allocation and attention management in the digital environment, and proposes a theoretical correction to simplistic intervention strategies such as "digital detoxification", that is, simply limiting the duration and frequency of use may not effectively alleviate anxiety. Instead, attention should be paid to the continuous distraction during smartphone use. On the other hand, we have ruled out the mediating role of procrastination in the relationship between fragmented smartphone use and anxiety. This finding can assist subsequent researchers in identifying more critical mediating factors and revising existing theoretical models, thereby avoiding the default use of procrastination as a mediating variable. These theoretical advances offer a fresh perspective for research in the fields of smartphones and digital health, effectively enriching the existing literature. In terms of practical value, this study demonstrates that precise measurement of fragmentation can improve the accuracy of assessing the impact of fragmented smartphone use, which is highly significant for quantitative research in this area. On the other hand, this study also reveals that mitigating the negative impact of fragmented smartphone use can be achieved by enhancing attention management. In other words, attention training may be more effective than time management training in counteracting the adverse effects of fragmented smartphone use. This not only provides essential theoretical support for the digital literacy education system but also highlights the importance of incorporating the ability to allocate attention resources into core training dimensions, particularly for college students. Furthermore, this study offers a theoretical foundation for schools, families, and society to enhance digital literacy education. 5.3 Limitations and future research In our study, fragmentation refers specifically to smartphone use and is not further subdivided for analysis. Future research can refine the fragmentation of individual smartphone applications, such as differentiating between information, social, and entertainment applications. Previous studies have shown that the former are more likely to cause mobile phone interference [ 68 ]. Additionally, distinguishing the differences in fragmented smartphone use among college students during study time and non-study time can help address the important social issue of cyberslacking, which contributes to the breakdown of the study-life balance. On the other hand, our study is limited to Android users and does not include iOS system users. Differences in the technical affordances of the iOS system, such as notification management and background refresh, may result in distinct patterns of fragmented smartphone use; however, these specific differences remain unclear. Future research should incorporate iOS users and expand comparative studies across device ecosystems. 6 Conclusion Our study revealed the mechanism by which fragmented smartphone use impacts anxiety. Contrary to traditional assumptions, fragmented smartphone use was not directly and significantly associated with anxiety. Distraction significantly and positively mediated the relationship between fragmented smartphone use and anxiety, while procrastination did not play a significant mediating role between fragmented smartphone use and anxiety, which emphasizes the unique role of distraction in this mechanism and provides important clues for future research. This study enriches the theoretical framework concerning fragmented smartphone use and anxiety, providing a foundation and valuable insights for future research. Moreover, the findings hold practical significance for developing intervention strategies aimed at mitigating the negative effects of fragmented smartphone use on anxiety. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Shanghai University, Shanghai, China (Approval number: ECSHU 2024 − 100). Furthermore, all participants provided informed consent before they participated in the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This research is not funded by any internal or external grants. Author Contribution Conceptualization, H.Y.; Data curation, H.Y., X.W.; Investigation, X.W., X.L.;Project administration, H.Y., X.W., X.L.; Writing—original draft, X.W; Writing—review and editing, H.Y. All authors have read and agreed to the published version of the manuscript. Acknowledgements There is no acknowledgement to declare. Data Availability Data that supports the findings of this study are available from the corresponding author upon reasonable request. References Schrock A R. Communicative affordances of mobile media: Portability, availability, locatability, and multimediality. International journal of communication, 2015, 9: 18. Oulasvirta A, Rattenbury T, Ma L, et al. Habits make smartphone use more pervasive. Personal and Ubiquitous computing, 2012, 16(1): 105-114. Deters, F., & Schoedel, R. Keep on scrolling? Using intensive longitudinal smartphone sensing data to assess how everyday smartphone usage behaviors are related to well-being. 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The Effect of Professional Social Media Usage on Procrastination and Work Engagement in the Face of the COVID‐19 Pandemic. Scandinavian Journal of Psychology, 2025. Aalbers G, Vanden Abeele M M P, Hendrickson A T, et al. Caught in the moment: Are there person-specific associations between momentary procrastination and passively measured smartphone use?. Mobile media & communication, 2022, 10(1): 115-135. Meier A, Reinecke L, Meltzer C E. “Facebocrastination”? Predictors of using Facebook for procrastination and its effects on students’ well-being. Computers in Human Behavior, 2016, 64: 65-76. Goroshit M, Hen M. Academic procrastination and academic performance: Do learning disabilities matter?. Current Psychology, 2021, 40(5): 2490-2498. Wang Y. Academic procrastination and test anxiety: A cross-lagged panel analysis. Journal of Psychologists and Counsellors in Schools, 2021, 31(1): 122-129. Onwuegbuzie A J, Jiao Q G. 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Throuvala M A, Pontes H M, Tsaousis I, et al. Exploring the dimensions of smartphone distraction: Development, validation, measurement invariance, and latent mean differences of the smartphone distraction scale (SDS). Frontiers in psychiatry, 2021, 12: 642634. Sirois F M, Yang S, van Eerde W. Development and validation of the General Procrastination Scale (GPS-9): A short and reliable measure of trait procrastination. Personality and Individual Differences, 2019, 146: 26-33. Lovibond P F, Lovibond S H. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour research and therapy, 1995, 33(3): 335-343. Wang K, Shi H S, Geng F L, et al. Cross-cultural validation of the depression anxiety stress scale–21 in China. Psychological assessment, 2016, 28(5): e88. Chin W W. Commentary: Issues and opinion on structural equation modeling. MIS quarterly, 1998: vii-xvi. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. 2021, 197. Hu L, Bentler P M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 1999, 6(1): 1-55. Hendrickson A, De Marez L, Martens M, et al. How do people use their smartphone? A data scientific approach to describe and identify user-related, system-related and context-related patterns in use. 69th Annual ICA conference: Communication beyond borders. 2019. Marlatt G A, Baer J S, Donovan D M, et al. Addictive behaviors: Etiology and treatment. Annual review of Psychology, 1988, 39(1): 223-252. Lanette S, Chua P K, Hayes G, et al. How much is' too much'? The role of a smartphone addiction narrative in individuals' experience of use. Proceedings of the ACM on Human-Computer Interaction, 2018, 2(CSCW): 1-22. Wong Q J J, Moulds M L. Impact of rumination versus distraction on anxiety and maladaptive self-beliefs in socially anxious individuals. Behaviour research and therapy, 2009, 47(10): 861-867. Elhai J D, Dvorak R D, Levine J C, et al. Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. Journal of affective disorders, 2017, 207: 251-259. Leynes P A, Flynn J, Mok B A. Event-related potential measures of smartphone distraction. Cyberpsychology, Behavior, and Social Networking, 2018, 21(4): 248-253. David P, Kim J H, Brickman J S, et al. Mobile phone distraction while studying. New media & society, 2015, 17(10): 1661-1679. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":76909,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework based on the \"input - mechanisms - output\" model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7600425/v1/9bc096090f3a68d03b46faac.png"},{"id":94782473,"identity":"ee6c7f85-ef88-4fe1-91d1-612f8af8164b","added_by":"auto","created_at":"2025-10-30 15:57:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115205,"visible":true,"origin":"","legend":"\u003cp\u003eModel of serial mediating effect, with standardized estimates displayed (Notes: \u003csup\u003e**\u003c/sup\u003e p\u0026lt;0.01)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7600425/v1/b8784c8b8cedac011a20abcf.png"},{"id":94827302,"identity":"6f4a8f3a-adeb-4bb4-a04b-a360702925fa","added_by":"auto","created_at":"2025-10-31 06:57:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":974830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7600425/v1/8d798326-f7f8-415a-adab-2e0209020838.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"When Smartphones Fragment the Mind: Exploring the Links between Fragmented Smartphone Use and Anxiety among College Students through Distraction and Procrastination","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the rapid development of mobile internet technology and the widespread adoption of smartphones, usage patterns have increasingly exhibited a fragmented nature. Today, various media and communication functions are consolidated within smartphone devices. Moreover, smartphones are equipped with numerous applications that enable users to seamlessly switch between multiple tasks while on the move[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This technological advancement is considered to have transformed people\u0026rsquo;s time management and daily behaviors, making their media use more overlapping and fragmented[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This implies that smartphone usage does not occur at a single point in time but is divided into multiple longer or shorter sessions, scattered throughout the day [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Over time, this has led to audiences exhibiting fragmented and non-linear characteristics in their media consumption and content preferences [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. On the other hand, smartphones possess the characteristics of portability and ease of use[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which has led users to develop a checking habit\u0026mdash;users will immediately conduct brief but repetitive checks of dynamic content on the device[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This habit also reinforces the phenomenon of increasingly fragmented smartphone use. Due to the intense immersion provided by smartphones, their usage has become both fragmented and ubiquitous in daily life. People rarely consciously pay attention to this fragmented usage behavior[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, this unconscious fragmented usage may erode the cognitive resources of college students, ultimately leading to a decline in their learning efficiency. The mechanisms and impacts behind this phenomenon require further academic attention.\u003c/p\u003e\u003cp\u003eNumerous studies have demonstrated a clear correlation between smartphone use and anxiety [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, extant literature predominantly focuses on the impacts of problematic smartphone use [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], screen time[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and smartphone addiction[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] on anxiety, with relatively few studies directly examining the association between fragmented smartphone use and anxiety. Recently, Siebers et al.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] identified a significant correlation between fragmented smartphone use and distraction in adolescents by developing a fragmentation index encompassing both screen time and session count. Nevertheless, the relationship between fragmented smartphone use and anxiety remains insufficiently understood. These gaps in the current body of research raise two critical, unresolved questions: Does fragmented smartphone use significantly increase anxiety? What is the mechanism of the impact of fragmented smartphone use on anxiety? At the methodological level, most studies rely primarily on self-report measures. However, traditional self-report methods exhibit notable limitations. Previous research has demonstrated that retrospective evaluations of digital media use are subject to systematic cognitive biases. High-frequency users tend to overestimate their usage duration, Whereas low-frequency users may exaggerate the frequency of use to conform to social expectation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These social and cognitive biases substantially compromise data validity.\u003c/p\u003e\u003cp\u003eTo improve existing research, this study focuses on the group of Chinese college students and adopts an objective data collection method based on application logs and a psychological scale measurement method combining self-report to alleviate the limitations and errors of self-report. Additionally, the study employs the Input-Mechanism-Output (I-M-O) Model as its theoretical framework to examine the relationship between fragmented smartphone use and anxiety. At the same time, we introduced two variables, distraction and procrastination, to investigate the mediating effects of distraction and procrastination on the relationship between fragmented smartphone use and anxiety, aiming to provide effective supplements to existing literature.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Fragmented smartphone use and the Input-Mechanism-Output model\u003c/h2\u003e\u003cp\u003eFragmented smartphone use refers to a specific pattern of smartphone usage[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It is an inevitable phenomenon in the current era of information explosion and represents a fundamental characteristic of contemporary social communication [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Liao et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] defined it as a behavior characterized by intermittent user contact with various media or content over time, without a fixed spatial location. Thus, fragmented smartphone use is closely related to media multitasking, task switching, and overall media consumption. It encompasses various smartphone usage indicators, including, but not limited to, problematic smartphone use, phone checking, and screen time. Empirical studies have confirmed the fragmented nature of smartphone use. The results indicate that participants spent an average of 2 hours and 39 minutes using their smartphones each day, with an average session duration of approximately 7 minutes, and they switched applications 101 times [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, Oulasvirta et al.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] found that although the total time spent on smartphones has decreased, the frequency of usage has increased. Research on temporal use may provide deeper insights into fragmented smartphone use. Smartphones have reconstructed traditional temporal structures through functional integration, dissolving clearly demarcated time blocks for work, household chores, and leisure into a fluid mixture. For example, users may browse news and information during work breaks or handle work emails during family dinners. This instantaneous access to diverse media content results in highly dispersed and overlapping usage patterns on smart devices [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBased on the aforementioned characterization of fragmentation and its relation to fragmented smartphone use, this study adopts the I-M-O model proposed by Chib and Lin [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] as the analytical framework. This model offers theoretical support for analyzing the dynamic relationships among computer-mediated communication, interpersonal communication, and human-computer interaction [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The original model conceptualizes input as accessibility and usability factors associated with technological development, mechanism as theoretical explanations at individual and socio-psychological levels of adoption and compliance, and output as outcomes ranging from healthcare system performance to individual health status [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As a heuristic framework, the I-M-O model is particularly valuable for elucidating how the technical features of mobile applications translate into health outcomes and through which theoretical mechanisms these transformations occur [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. From this theoretical perspective, the present study investigates how fragmented smartphone use (input) influences anxiety (output) among college students through distraction and procrastination (mechanisms), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This research design maintains the analytical logic of the original model while extending its application to the domains of digital behavior and mental health.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Fragmented smartphone use and anxiety\u003c/h2\u003e\u003cp\u003eWe first focus on the relationship between the input factor (fragmented smartphone use) and the output result (anxiety). Existing literature has consistently confirmed a significant association between smartphone usage and anxiety, providing a theoretical foundation for examining fragmented smartphone use. Specifically, problematic smartphone use[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], smartphone addiction[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], social media use [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and the intensity of use [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] have all been identified as positive predictors of increased anxiety. In contrast, although moderate smartphone use, such as social media interaction, may offer some benefits for mental health, excessive use has a more significant and far-reaching adverse impact on mental well-being [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. When examining the operational dimensions of fragmented smartphone use, such as the frequency of switching and time spent[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], researchers have found that task switching data is significantly negatively correlated with emotional well-being[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Frequent phone checking[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and excessive screen time [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] have also been linked to increased anxiety. Moreover, a review study suggests that smartphone screen time may contribute to greater fatigue, loneliness, and anxiety by displacing restorative activities, such as sleep and face-to-face socializing [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis pattern of association has been validated across various cultural contexts. A grounded study investigating social media use among young people in China found that social media has a dual impact on appearance anxiety, one of which is to amplify appearance anxiety through comparisons of physical attractiveness [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In the undergraduate population of the United States, the phenomenon of \u0026ldquo;Facebook invasion\u0026rdquo;\u0026mdash;defined as addictive behavior resulting from uncontrolled social media use [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] \u0026mdash; has been found to be significantly positively correlated with anxiety [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This relationship between media addiction and anxiety has also been supported by further research [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Logically, fragmented smartphone use, as a type of smartphone use, should be significantly associated with anxiety. Therefore, we propose the following hypothesis:\u003c/p\u003e\u003cp\u003eH1: Fragmented smartphone use is significantly and positively correlated with anxiety.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 The mediating role of distraction\u003c/h2\u003e\u003cp\u003eDistraction is a competing response tendency that diverts an individual\u0026rsquo;s attention away from a target area, thereby inhibiting or diminishing the processing of information within that area [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This phenomenon essentially reflects a staged breakdown of perceptual self-regulatory mechanisms [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The attention distraction hypothesis posits that frequent social media users may develop generalized but superficial attention patterns due to continuous exposure to rapidly changing information streams, resulting in the depletion of cognitive resources [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Empirical evidence demonstrates that individuals are frequently distracted by the allure of mobile application technologies in various contexts, including during class [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], in traffic [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and during social conversations [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], all of which have the potential to induce distraction. Based on this, we believe that the primary mechanism by which fragmented smartphone use induces distraction is through frequent attention switching. Users often alternate between online digital activities and offline physical tasks, which imposes switching costs on the cognitive system. This continual shifting requires substantial time and energy to reset attention[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, it has been reported that corporate employees are interrupted by social media at least six to eight times per day, with each interruption requiring approximately 30 minutes to fully resume their previous work state [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Studies have demonstrated that, compared to non-Weibo users, frequent Weibo users experience greater difficulty resisting distracting information and are more susceptible to distraction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Adolescents, as active social media users and a representative cohort of Generation Z, receive frequent notifications and messages from smartphone applications daily. Numerous scholars have empirically investigated the relationship between social media use and distraction. Attention problems arising from distraction have consistently posed challenges, particularly among young adults [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Individual studies have demonstrated that fragmented smartphone use significantly increases the level of distraction among adolescents [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere are divergent perspectives regarding the relationship between distraction and anxiety. Some studies suggest that distraction can alleviate anxiety in real-life situations, positioning it as a low-cost, accessible, and highly effective method for anxiety reduction [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Conversely, the American Psychiatric Association (2013) reported that attention problems constitute a core component of pathological anxiety related to daily functional impairments. Moreover, trait anxiety has been linked to impaired attention control in the presence of interfering stimuli [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Correspondingly, previous studies have demonstrated that increased distraction can contribute to heightened anxiety levels [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Technology-induced distraction diminishes the attentional resources allocated to ongoing tasks, impedes deep cognitive processing and task execution, and consequently exerts a direct negative effect on performance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Few studies have examined the relationship between fragmented smartphone use and distraction, and the link between distraction and anxiety remains unclear. To clarify the role of distraction, we propose the following research questions: Based on this evidence, we propose the following research question:\u003c/p\u003e\u003cp\u003eRQ1: Does distraction significantly and positively mediate the relationship between fragmented smartphone use and anxiety?\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 The mediating role of procrastination\u003c/h2\u003e\u003cp\u003eAnother mediating variable we aim to examine in the relationship between fragmented smartphone use and anxiety is procrastination. Procrastination is defined as the irrational and voluntary delay in initiating or completing a planned task [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. According to the short-term mood repair theory, procrastination arises primarily from the need for immediate emotional relief. When individuals experience aversion toward a task, they tend to avoid the task as a way to achieve temporary emotional compensation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. As one of the tools for emotion regulation, smartphones can contribute to the occurrence of procrastination. When smartphones\u0026mdash;viewed as immediate temptations\u0026mdash;are easily accessible, individuals are more likely to engage in procrastination [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], using them as a means to escape task-related pressure through activities such as cyberstalking [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. For instance, when students use smartphones to manage academic stress and temporarily improve their mood, these readily available entertainment applications become immediate temptations that foster continued procrastination. However, the relationship between smartphone use and procrastination remains inconclusive, as existing research findings are often contradictory. On the one hand, some self-reported data indicate that social media usage is significantly positively correlated with procrastination [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Another study found that when students experience more frequent notifications and exhibit more fragmented smartphone use patterns, they tend to report increased procrastination behaviors [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In particular, task delay\u0026mdash;one of the manifestations of procrastination\u0026mdash;is more common among adolescents in the context of widespread digitalization and constant online connectivity [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. On the other hand, when analyzing the relationship between fragmented smartphone use and task delay, Siebers et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] did not find a significant correlation between the two.\u003c/p\u003e\u003cp\u003eAlthough the causal relationship described above requires further verification, procrastination is widely recognized as a typical manifestation of self-regulation failure [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Its impact on anxiety has been extensively documented. Students who engage in procrastination tend to report lower GPAs [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and exhibit significant associations with various forms of academic anxiety. Specifically, research has shown that procrastinators are more likely to experience psychological distress related to learning, such as test anxiety [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and library anxiety [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Another study demonstrated that procrastinators frequently report experiencing a certain degree of anxiety about the future [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As mentioned earlier, individuals may avoid tasks as a way to manage or repair negative emotions. However, in the long term, this avoidance strategy can have detrimental effects on task completion and overall psychological well-being [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Although existing studies have demonstrated a relatively clear correlation between procrastination and anxiety, the relationship between fragmented smartphone use and procrastination remains unclear. To clarify the role of procrastination, we propose the following research questions:\u003c/p\u003e\u003cp\u003eRQ2: Does procrastination significantly and positively mediate the relationship between fragmented smartphone use and anxiety?\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Participants\u003c/h2\u003e\n \u003cp\u003eThis study was approved by the Institutional Review Board of the author\u0026apos;s university. From August to November 2024, recruitment advertisements were posted on forums of 12 universities across eastern and western mainland China, supplemented by offline field recruitment to broaden the participant pool. Ultimately, 334 eligible students were successfully recruited. All participants provided informed consent after receiving a thorough explanation of the study\u0026apos;s purpose and procedures. Subsequently, they were instructed to download the smartphone tracking application AppUsage (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://play.google.com/store/apps/details?id=com.a0soft.gphone.uninstaller\u0026amp;hl=en_SG\u0026amp;pli=1\u003c/span\u003e\u003c/span\u003e), which records and monitors participants\u0026apos; screen time and application-switching frequency in real time. Participants were required to keep the application running in the background for seven consecutive days to ensure comprehensive and objective data collection on smartphone usage. After sharing one week of smartphone tracking data, participants completed an online questionnaire including measures of distraction, procrastination, and anxiety. The smartphone tracking data and survey responses were merged using each participant\u0026rsquo;s unique identifier and stored in a comprehensive dataset. Sample screening strictly adhered to the predefined criteria, including only full-time undergraduate and postgraduate students who exclusively used Android smartphones as their mobile devices. Participants who completed the study received a reward of RMB 50. Due to incomplete smartphone tracking data, 12 participants were excluded. Consequently, the final sample consisted of 322 participants, resulting in a sample efficiency of 96.4%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Smartphone usage data\u003c/h2\u003e\n \u003cp\u003eThe AppUsage application automatically records app usage data in a usage log. The raw log data comprise the app name, date, time, and duration of each usage. Upon data collection, researchers screened and processed the raw data to derive specific metrics, including the precise end time of each app session, total weekly smartphone usage duration per participant, and the number of usage sessions.\u003c/p\u003e\n \u003cp\u003eTo capture users\u0026apos; daily behavior, discrete actions can be organized into continuous and uninterrupted sequences, conceptualized as mobile sessions in empirical research [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. To objectively and accurately record fragmented smartphone use, smartphone sessions were employed as the unit of analysis. The maximal timeout period that marks the end of a smartphone session is called a session \u0026ldquo;threshold\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. Based on prior research findings and practical application requirements, the session threshold was set to 30 seconds [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. This threshold resulted in an average of 408 smartphone sessions across 322 subjects. In accordance with the research guidelines established by [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e], fragmented smartphone use was calculated based on both the frequency and temporal distribution of smartphone sessions. Building upon this, Siebers et al. [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] explored smartphone use fragmentation by examining the number of sessions and the ratio of total session duration to the overall measurement duration. Drawing on their work, we extended the measurement period to one week (168 hours) to yield more comprehensive results. To make fragmented smartphone use have similar scale to anxiety, we set the unit of number of sessions to hundred. Theoretically, given a measurement period of 168 hours and a session threshold of 30 seconds, the maximum achievable fragmented smartphone use score is 3024. Fragmented smartphone use was calculated as follows:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1761839398.png\" width=\"908\" height=\"87\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Survey data\u003c/h2\u003e\n \u003cp\u003eDistraction: The Smartphone Distraction Scale (SDS) developed by Throuvala et al. [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e] was utilized. The scale comprises four dimensions: \u0026ldquo;Attention Impulsiveness,\u0026rdquo; \u0026ldquo;Emotion Regulation,\u0026rdquo; \u0026ldquo;Online Vigilance,\u0026rdquo; and \u0026ldquo;Multitasking.\u0026rdquo; Each dimension includes four items, such as \u0026ldquo;I get distracted by just having my phone next to me,\u0026rdquo; \u0026ldquo;Using my phone distracts me from tasks that are tedious or difficult,\u0026rdquo; \u0026ldquo;I get distracted with what I could post while doing other tasks,\u0026rdquo; and \u0026ldquo;I often talk to others while checking what\u0026rsquo;s on my phone.\u0026rdquo; Participants responded on a 5-point Likert scale, where 1 indicates strong disagreement and 5 indicates strong agreement.\u003c/p\u003e\n \u003cp\u003eProcrastination: To assess trait procrastination, the General Procrastination Scale (GPS-9) developed and validated by Sirois et al. [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e] was employed. This scale encompasses three dimensions: unnecessary delay, delay of intended tasks, and a general tendency to delay across tasks. Example items include statements such as \u0026ldquo;In preparing for some deadlines, I often waste time by doing other things\u0026rdquo; and \u0026ldquo;I am continually saying I\u0026rsquo;ll do it tomorrow.\u0026rdquo; Participants responded on a 5-point Likert scale, where 1 indicates strong disagreement and 5 indicates strong agreement.\u003c/p\u003e\n \u003cp\u003eAnxiety: The anxiety dimension of the Depression, Anxiety, and Stress Scale (DASS), originally developed by Lovibond and Lovibond [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e] and subsequently revised by Wang et al. [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e], was utilized to reflect the specific context of Chinese college students. Four items with high factor loadings were selected for the present study. These items demonstrated strong psychometric properties, and their validity has been confirmed in Chinese samples. Participants responded on a 5-point Likert scale, where 1 indicates strong disagreement and 5 indicates strong agreement.\u003c/p\u003e\n \u003cp\u003eThe survey also recorded relevant demographic information of the participants, including gender, ethnicity, educational level, school location, household registration, and average monthly expenditure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eWe first conducted confirmatory factor analysis (CFA) on the three latent variables\u0026mdash;anxiety, distraction, and procrastination\u0026mdash;employing the maximum likelihood estimation method to assess factor loadings, composite reliability, and average variance extracted (AVE) for each scale. This procedure ensured that the construct validity of the measurement tools met the standards for academic research. To test our hypotheses and address our research questions, we employed the lavaan package in R software (version 4.4.2) to conduct a structural equation modeling (SEM) analysis.\u003c/p\u003e\n \u003cp\u003eFragmented smartphone use was set as an exogenous variable, and anxiety, distraction, and procrastination were set as endogenous variables. Distraction and procrastination were also mediating variables. The bootstrap method with 5,000 resamples was employed to conduct bias-corrected confidence interval tests on the mediation effects, ensuring the robustness of the research conclusions. Model fit was evaluated using multiple indices, including the chi-square statistic (\u0026chi;\u0026sup2;), root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI). Gender, ethnicity, educational level, school location, household registration, and average monthly expenditure were included as control variables.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Results","content":"\u003ch2\u003e4.1 Descriptive statistical analysis\u003c/h2\u003e\n\u003cp\u003eA total of 322 college students participated in the study. Among them, 47.8% were male, 95.3% identified as Han ethnicity, and 38.2% were freshmen. Participants were enrolled in ordinary undergraduate colleges (30.4%), \u0026ldquo;211\u0026rdquo; universities (34.8%), and \u0026ldquo;985\u0026rdquo; universities (34.8%). The mean fragmented smartphone use score was 1.68 (range: 0.17\u0026ndash;5.04), with a standard deviation of 0.809. Descriptive statistics are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Descriptive statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"83%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eN(percentage) or M(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e154 (47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e168 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ehan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e307 (95.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eminority\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e15 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eCollege year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003efreshman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e23 (38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003enon-freshman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e199 (61.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eordinary undergraduate university\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e98 (30.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e112 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e112 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eThe value of fragmented smartphone use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1.68 (0.809)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e4.2 Measurement\u0026nbsp;model\u003c/h2\u003e\n\u003cp\u003eThe reliability analysis of the measurement indicators of distraction, procrastination, and anxiety was conducted, and the results are reported in Table 2. Confirmatory factor analysis demonstrated that the procrastination scale was robust (AVE=0.639, CR=0.841, \u0026alpha;=0.825, factor loadings ranging from 0.748 to 0.859), indicating excellent convergent validity. Although the average variance extracted (AVE) values for distraction (AVE=0.495, CR=0.787, \u0026alpha;=0.782) and anxiety (AVE=0.493, CR=0.793, \u0026alpha;=0.790) were slightly below the conventional threshold of 0.5, they nonetheless exceeded the minimum recommended standard of 0.36\u0026nbsp;[59]. Consequently, the data remained within acceptable limits, and the composite reliability (CR) and Cronbach\u0026rsquo;s alpha (\u0026alpha;) values met established standards, confirming the overall reliability and validity of the measurements. While the loadings of dis3 and dis4 for distraction and anx2 for anxiety were comparatively weaker, they still met widely accepted standards [60]. The data satisfied the fundamental requirements for empirical research. Simultaneously, the fit indices of the measurement model aligned with our expectations: \u0026chi;\u0026sup2;/df = 1.853, CFI = 0.976, TLI = 0.902, RMSEA = 0.047, and SRMR = 0.037 (see Table 3). These values indicated that the model fit met widely accepted standards [61].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Results of the confirmatory factor analysis\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eStandardized factor loading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCronbach\u0026rsquo;s \u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 99px;\"\u003e\n \u003cp\u003eDistraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003edis1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003edis2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003edis3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003edis4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 99px;\"\u003e\n \u003cp\u003eProcrastination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003epro1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003epro2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003epro3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eanx1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eanx2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eanx3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eanx4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: CR = composite reliability; AVE = average variance extracted. dis1 = \u0026ldquo;Attention Impulsiveness\u0026rdquo;,dis2 = \u0026ldquo;Emotion Regulation\u0026rdquo;,dis3 = \u0026ldquo;Online Vigilance\u0026rdquo;, dis4 = \u0026ldquo;Multitasking\u0026rdquo;, pro1 = \u0026ldquo;unnecessary delay\u0026rdquo;, pro2 = \u0026ldquo;delay of intended task\u0026rdquo;, pro3 = \u0026ldquo;A general tendency to delay across tasks\u0026rdquo;,anx1 = \u0026ldquo;I have trouble breathing (for example, I feel short of breath or breathlessness even when I am not exercising)\u0026rdquo;, anx2 = \u0026ldquo;I have felt trembling (e.g., shaking hands)\u0026rdquo;, anx3 = \u0026ldquo;I\u0026apos;m about to panic.\u0026rdquo;, anx4 = \u0026ldquo;I feel scared for no reason\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Fitting indicators of structural models\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eMeasurement model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eStructural models\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;/df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e4.3 Research modeling and hypothesis testing\u003c/h2\u003e\n\u003cp\u003eAccording to the hypothesized model, fragmented smartphone use was potentially associated with anxiety through three pathways. The direct pathway was as follows: fragmented smartphone use \u0026rarr; anxiety. The first indirect pathway involved the mediating role of distraction: fragmented smartphone use \u0026rarr; distraction \u0026rarr; anxiety. The second indirect pathway involved the mediating role of procrastination: fragmented smartphone use \u0026rarr; procrastination \u0026rarr; anxiety. If any of these indirect pathways were significant, it indicated the presence of a mediating effect.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2 and Table 4, the direct effect of fragmented smartphone use on anxiety was not significant (\u0026beta; = -0.034, p \u0026gt; 0.05), and H1 was therefore not supported. However, the indirect effect through distraction (fragmented smartphone use \u0026rarr; distraction \u0026rarr; anxiety) was significant (\u0026beta; = 0.054, 95% CI: 0.003\u0026ndash;0.104, p \u0026lt; 0.05). Specifically, the path coefficient of fragmented smartphone use on distraction was significant (\u0026beta; = 0.195, p \u0026lt; 0.01), as was the path coefficient of distraction on anxiety (\u0026beta; = 0.276, p \u0026lt; 0.01). Therefore, we addressed RQ1, indicating that distraction significantly and positively mediated the relationship between fragmented smartphone use and anxiety. The indirect effect through procrastination (fragmented smartphone use \u0026rarr; procrastination \u0026rarr; anxiety) was weak (\u0026beta; = 0.009, 95% CI: -0.032\u0026ndash;0.051, p \u0026gt; 0.05), indicating that procrastination did not mediate the relationship between fragmented smartphone use and anxiety. RQ2 was addressed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e The mediating effect of distraction and procrastination between fragmented smartphone use and anxiety, as shown by standardized estimates\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 35px;\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003eEffect value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003eConfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMediating effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003efragmented smartphone use \u0026rarr; distraction \u0026rarr;anxiety (indirect1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003efragmented smartphone use \u0026rarr; procrastination \u0026rarr;anxiety (indirect2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eTotal mediating effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eindirect1 + indirect2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003efragmented smartphone use \u0026rarr;anxiety (direct)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003edirect effect + indirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eBy combining log-tracking data with survey data, this study employed the I-M-O model to examine the relationships among fragmented smartphone use, distraction, procrastination, and anxiety, thereby elucidating the complexity of psychological adaptation mechanisms in the digital age. The results indicated that fragmented smartphone use was not directly associated with anxiety, distraction significantly and positively mediated the relationship between fragmented smartphone use and anxiety, and procrastination did not significantly mediate the relationship between fragmented smartphone use and anxiety. Overall, the model demonstrated that the impact of fragmented smartphone use on anxiety was primarily mediated by distraction.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Association between fragmented smartphone use and anxiety\u003c/h2\u003e\u003cp\u003eThese results contribute novel insights into the relationship between fragmented smartphone use and anxiety. First, we found that fragmented smartphone use did not significantly increase anxiety, a finding that diverges from much of the existing literature. This discrepancy may be attributed to the following factors: (1) Prior research has predominantly focused on isolated behavioral indicators\u0026mdash;such as frequent checking, screen time, or social media intensity\u0026mdash;without adequately addressing the phenomenon of daily smartphone multitasking fragmentation [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], leading to inconsistent findings. (2) Previous studies have primarily examined the relationship between smartphone use and anxiety through the lens of technology dependence, conceptualizing anxiety as a subjective experience of \u0026ldquo;loss of control\u0026rdquo; [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. For example, addiction theory often equates repetitive habit patterns with pathological representations, which is superficially similar to the fragmented smartphone use we studied, but what is the definition of good or bad smartphone user habits and to what extent they cause negative effects remain unresolved issues[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Perhaps the usual situation of smartphone use (i.e., usually problematic) [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and the fragmented smartphone use pattern should be divided into two different viewpoints, but as for the exact difference, there is no consensus at present.\u003c/p\u003e\u003cp\u003eSecondly, our study confirmed that distraction serves as a mediator between fragmented smartphone use and anxiety, providing valuable insights for future research. The apparent contradiction with previous findings\u0026mdash;such as Wong and Moulds [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], who reported that distraction induction can alleviate depression and reduce anxiety\u0026mdash;may be attributable to the fact that anxiety is more strongly associated with excessive technology use [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Additionally, adolescents tend to experience increased distraction during periods of fragmented smartphone use [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This differential impact on psychological distress constitutes a key finding of our study.\u003c/p\u003e\u003cp\u003eFinally, the study found that although procrastination was statistically associated with anxiety levels, it did not mediate the relationship between fragmented smartphone use and anxiety. This finding diverges from the prevailing view that time spent on smartphone applications can reasonably be regarded as time not invested in goal-directed behavior [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and contradicts recent scholarship. Accordingly, it may be necessary to re-examine the perspective that \u0026ldquo;smartphone users have internalized media narratives\u0026rdquo; from the standpoint of fragmented smartphone use [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The findings align with the proposition by Aalbers et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] that an increased number of notifications may reflect procrastination-related patterns of messenger use, rather than notifications themselves causing procrastination. It is important to avoid oversimplifying students\u0026rsquo; smartphone usage behaviors by attributing them solely to procrastination tendencies. Furthermore, procrastination is more closely associated with work efficiency and goal orientation. For many adolescents, habitual social media use or phone checking may be unrelated to task delay [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which also accounts for why procrastination did not emerge as a significant mediating variable in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Theoretical and practical implications\u003c/h2\u003e\u003cp\u003eThis study enhances the theoretical understanding of the mechanisms underlying the relationship between fragmented smartphone use and anxiety among college students in the digital age. Its significance is primarily reflected in two aspects: framework introduction and mechanism discovery. Our research adopts the I-M-O model as an analytical framework to clarify the mechanisms of fragmented smartphone use, emphasizes the critical role of the mediating variable (distraction) in the transformation from input to output, and offers an alternative perspective for comprehending the impact of fragmented smartphone use on psychological distress. Our research findings indicate that fragmented smartphone use does not directly lead to anxiety among college students. Instead, it induces anxiety through the mediating effect of distraction. Therefore, the primary focus of attention and intervention should be the distraction caused by smartphone use. In other words, smartphones exert a substantial distracting effect [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This conclusion also reflects the importance of cognitive resource allocation and attention management in the digital environment, and proposes a theoretical correction to simplistic intervention strategies such as \"digital detoxification\", that is, simply limiting the duration and frequency of use may not effectively alleviate anxiety. Instead, attention should be paid to the continuous distraction during smartphone use. On the other hand, we have ruled out the mediating role of procrastination in the relationship between fragmented smartphone use and anxiety. This finding can assist subsequent researchers in identifying more critical mediating factors and revising existing theoretical models, thereby avoiding the default use of procrastination as a mediating variable. These theoretical advances offer a fresh perspective for research in the fields of smartphones and digital health, effectively enriching the existing literature.\u003c/p\u003e\u003cp\u003eIn terms of practical value, this study demonstrates that precise measurement of fragmentation can improve the accuracy of assessing the impact of fragmented smartphone use, which is highly significant for quantitative research in this area. On the other hand, this study also reveals that mitigating the negative impact of fragmented smartphone use can be achieved by enhancing attention management. In other words, attention training may be more effective than time management training in counteracting the adverse effects of fragmented smartphone use. This not only provides essential theoretical support for the digital literacy education system but also highlights the importance of incorporating the ability to allocate attention resources into core training dimensions, particularly for college students. Furthermore, this study offers a theoretical foundation for schools, families, and society to enhance digital literacy education.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Limitations and future research\u003c/h2\u003e\u003cp\u003eIn our study, fragmentation refers specifically to smartphone use and is not further subdivided for analysis. Future research can refine the fragmentation of individual smartphone applications, such as differentiating between information, social, and entertainment applications. Previous studies have shown that the former are more likely to cause mobile phone interference [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Additionally, distinguishing the differences in fragmented smartphone use among college students during study time and non-study time can help address the important social issue of cyberslacking, which contributes to the breakdown of the study-life balance. On the other hand, our study is limited to Android users and does not include iOS system users. Differences in the technical affordances of the iOS system, such as notification management and background refresh, may result in distinct patterns of fragmented smartphone use; however, these specific differences remain unclear. Future research should incorporate iOS users and expand comparative studies across device ecosystems.\u003c/p\u003e\u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eOur study revealed the mechanism by which fragmented smartphone use impacts anxiety. Contrary to traditional assumptions, fragmented smartphone use was not directly and significantly associated with anxiety. Distraction significantly and positively mediated the relationship between fragmented smartphone use and anxiety, while procrastination did not play a significant mediating role between fragmented smartphone use and anxiety, which emphasizes the unique role of distraction in this mechanism and provides important clues for future research. This study enriches the theoretical framework concerning fragmented smartphone use and anxiety, providing a foundation and valuable insights for future research. Moreover, the findings hold practical significance for developing intervention strategies aimed at mitigating the negative effects of fragmented smartphone use on anxiety.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Shanghai University, Shanghai, China (Approval number: ECSHU 2024\u0026thinsp;\u0026minus;\u0026thinsp;100). Furthermore, all participants provided informed consent before they participated in the study.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research is not funded by any internal or external grants.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization, H.Y.; Data curation, H.Y., X.W.; Investigation, X.W., X.L.;Project administration, H.Y., X.W., X.L.; Writing\u0026mdash;original draft, X.W; Writing\u0026mdash;review and editing, H.Y. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThere is no acknowledgement to declare.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData that supports the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSchrock A R. Communicative affordances of mobile media: Portability, availability, locatability, and multimediality. International journal of communication, 2015, 9: 18.\u003c/li\u003e\n \u003cli\u003eOulasvirta A, Rattenbury T, Ma L, et al. Habits make smartphone use more pervasive. Personal and Ubiquitous computing, 2012, 16(1): 105-114.\u003c/li\u003e\n \u003cli\u003eDeters, F., \u0026amp; Schoedel, R. Keep on scrolling? 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New media \u0026amp; society, 2015, 17(10): 1661-1679.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fragmented smartphone use, anxiety, distraction, procrastination","lastPublishedDoi":"10.21203/rs.3.rs-7600425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7600425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the widespread adoption of smartphones among college students, fragmented smartphone use has become a defining feature of their digital lives. Fragmented smartphone use may exacerbate anxiety among college students; however, existing research has yet to provide clear empirical evidence for this relationship, and studies that systematically examine its underlying mechanisms remain scarce. To address this gap, this study combined self-report questionnaires with application logs obtained through a monitoring program installed on participants\u0026rsquo; smartphones, which continuously tracked their usage over a full week, yielding valid data from 322 Chinese college students. Using a structural equation model (SEM), we examined the relationship between fragmented smartphone use and anxiety, focusing on the mediating roles of distraction and procrastination. The results revealed that although fragmented smartphone use itself did not exert a significant direct effect on anxiety (β = -0.034, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), distraction emerged as a significant mediator in this relationship (β\u0026thinsp;=\u0026thinsp;0.054, 95% CI: 0.003\u0026ndash;0.104, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas procrastination did not play a significant role (β\u0026thinsp;=\u0026thinsp;0.009, 95% CI: -0.032\u0026ndash;0.051, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings underscore the pivotal influence of distraction, enriching our understanding of how fragmented smartphone use shapes psychological well-being. Importantly, by leveraging objective, real-time behavioral data, this study provides robust empirical evidence to advance theoretical modeling and to inform precise intervention strategies for promoting digital health among college students.\u003c/p\u003e","manuscriptTitle":"When Smartphones Fragment the Mind: Exploring the Links between Fragmented Smartphone Use and Anxiety among College Students through Distraction and Procrastination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 15:57:46","doi":"10.21203/rs.3.rs-7600425/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-16T11:24:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T03:40:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-22T09:22:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-20T14:03:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-20T13:59:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a865243d-9d6b-42c3-841d-61afd4a072ad","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-30T15:57:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 15:57:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7600425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7600425","identity":"rs-7600425","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}