Distraction From Social Media and Phones

Developing the power to voluntarily focus attending and control potential distractions (i.e., attentional command; Chin et al., 2020; Diamond, 2013; Tavares & Freire, 2016) is an important task for adolescents (Higgins & Turnure, 1984; Luna, 2009; Luna, Garver, Urban, Lazar, & Sweeney, 2004). Yet, concerns have been raised that adolescents experience more and more difficulty in developing this ability because of the distractions posed by social media. This is hardly surprising given that adolescents are avid users of social media and receive notifications and messages throughout the entire day from diverse social media apps, such as WhatsApp, Instagram, and Snapchat (Anderson & Jiang, 2018; van Driel, Pouwels, Beyens, Keijsers, & Valkenburg, 2019). Notwithstanding, whether and to what extent adolescents' social media use (SMU) goes hand in hand with greater distractions nonetheless largely remains unclear.

Up until now, well-nigh a dozen studies have tapped into the association between SMU and lark, mostly among (young) adults. One role of these studies has investigated to what extent social media are distracting. These studies take shown that even the mere presence of a smartphone, the device adolescents typically apply for social media, can pb to distraction (e.grand., Johannes, Veling, Verwijmeren, & Buijzen, 2019; Kushlev, Proulx, & Dunn, 2016; Stothart, Mitchum, & Yehnert, 2015; Thornton, Faires, Robbins, & Rollins, 2014). A 2d part of these studies has shown that these social media or smartphone distractions are related to diverse outcomes, such equally academic achievement (eastward.m., Play a joke on, Rosen, & Crawford, 2009; McCoy, 2016), task performance (Brooks, 2015), well-being (Johannes et al., 2020), and productivity (Mark, Iqbal, & Czerwinski, 2017). Finally, a third role of these studies has revealed that (social) media use is related to lark and various indicators of failure in attentional control, such as concentration problems (Aalbers et al., 2019; Levine, Waite, & Bowman, 2007; Xie, Rost, Wang, Wang, & Monk, 2021).

While providing important insights into the topic of SMU and lark, the electric current literature leaves 2 of import gaps. A starting time gap is that knowledge virtually the clan betwixt SMU and lark amidst adolescents is scarce, specially about the momentary associations. Whereas the few prior studies predominantly focused on the associations between SMU and trait-like operationalizations of distraction, it has been shown that trait-like levels of attending and distraction tin can too fluctuate on a momentary footing, for example, as a upshot of tiredness (Riley, Esterman, Fortenbaugh, & DeGutis, 2017) or disturbing environmental factors (Vasilev, Kirkby, & Angele, 2018). Thus, the question of whether adolescents' SMU and distraction co-fluctuate on a momentary ground remains unanswered. Therefore, the showtime aim of this study is to examine both the between-person and the momentary within-person associations of adolescents' SMU with distraction.

A second related gap in the literature is that studies have disregarded the idea that the association between SMU and distraction may differ from person to person. Media effects theories and models such every bit the Differential Susceptibility to Media Furnishings Model (DSMM; Valkenburg & Peter, 2013) assume that adolescents differ in how they select and respond to social media. Following this line of thought, the association between SMU and lark may be stronger for certain adolescents than for others, for example, because they may vary in their overall usage of social media or in their trait-similar ability to sustain attending. To truly empathise how SMU and distraction are related within each individual boyish, scholars have recently chosen for a person-specific (or idiographic) arroyo in enquiry on SMU (e.k., Beyens, Pouwels, van Driel, Keijsers, & Valkenburg, 2020b; Odgers & Jensen, 2020; Valkenburg, Beyens, Pouwels, van Driel, & Keijsers, 2021a). Therefore, our 2d aim is to investigate the association between SMU and distraction for every single adolescent and to examine to what extent these person-specific associations differ from adolescent to adolescent.

To address these two gaps in the literature, the current report investigated the between-person, the within-person, and the person-specific associations betwixt SMU and distraction among adolescents. To that end, nosotros conducted an feel sampling report among a sample of 383 adolescents (35,099 observations in full). We focused on adolescents because, on the one manus, their underdeveloped abilities for attentional control make them more vulnerable to experience distractions compared to other age groups, such as young adults (Cohen Kadosh, Heathcote, & Lau, 2014; Stawarczyk, Majerus, Catale, & D'Argembeau, 2014). On the other mitt, adolescents are avid users of social media, and social media platforms form the perfect stage to fulfill developmental tasks, such as forming an identity, bonding with peers, and becoming democratic from parents (Borca, Bina, Keller, Gilbert, & Begotti, 2015; Meeus, Eggermont, & Beullens, 2018).

The Between-Person Clan of Social Media Utilize with Distraction

Young adults who spend more time on social media than others experience more distraction (Aalbers et al., 2019; Levine et al., 2007; Xie et al., 2021). An of import caption for this finding is the fact that some individuals may exist more than alert to social media notifications than others. This alertness, called "online vigilance" (Johannes et al., 2019; Reinecke et al., 2018), may develop automated tendencies to check social media regularly (Bayer, Campbell, & Ling, 2016). These automated checking behaviors, or "connection habits" (Bayer et al., 2016), could brand information technology hard to focus, shift, and sustain attention voluntarily. Hence, adolescents who brandish higher levels of constant alertness may have more difficulties sustaining attention and stay focused than their peers, and may use social media more than their peers.

The first contribution of this report is to shed calorie-free on these individual differences between adolescents. Edifice on the approach of Aalbers et al. (2019), nosotros investigated the between-person association of SMU with distraction past aggregating across all within-person momentary measures of SMU and distraction (i.e., 126 momentary assessments at maximum) toward a trait-like measure (Fleeson, 2001). Such momentary assessments generally reduce recall bias, and, as a result, yield more ecologically valid data than quondam, global measurements (Van Gog, Kirschner, Kester, & Paas, 2012; van Roekel, Keijsers, & Chung, 2019). Birthday, in line with prior studies among young adults and assumptions from online vigilance, we expected to find a positive between-person association of SMU with distraction.

H1: SMU is positively associated with lark at the between-person level, such that adolescents who spend more time using social media (compared to other adolescents) experience more distraction (compared to other adolescents).

The Momentary Within-Person Clan of Social Media Utilise with Distraction

Indications exist that adolescents' preoccupation with social media may fluctuate from moment to moment (Johannes et al., 2020). In fact, online vigilance is typically described every bit a "state of alertness" (Johannes et al., 2019, p. 215). For instance, adolescents may sometimes be more than occupied with receiving messages from friends on WhatsApp or receiving likes on an Instagram post than at other moments. At moments when adolescents experience a heightened state of alertness, they may experience more difficulties to focus their attention and a stronger trend to check their social media, and thus spend more time using social media.

Only one study among immature adults has tapped into the momentary co-fluctuations between SMU and attentional control (Aalbers et al., 2019). Aalbers et al. adopted a dynamic network approach to examine to what extent SMU and concentration problems are associated contemporaneously. They plant that college students experienced more concentration issues during hours when they spent more time on social media, compared to hours when they spent less time on social media. Equally concentration problems and distraction are both indicators of attentional control failure, nosotros hypothesized a positive clan between adolescents' momentary SMU and momentary distraction at the within-person level.

H2: Momentary SMU is positively associated with momentary lark at the within-person level, such that an increase in an adolescent's fourth dimension spent using social media (relative to the boyish's mean) is accompanied by an increment in this adolescent's distraction (relative to the adolescent's hateful).

Heterogeneity in the Association between SMU and Lark

Models of media uses and furnishings suggest that the way adolescents select and respond to media differs essentially between individuals (Valkenburg & Peter, 2013). Likewise, the association between SMU and distraction may non be the same for each adolescent. Recently, scholars have called for research that examines heterogeneity at the person level and provides clarity on how media selection and effects differ from adolescent to adolescent (Lerner, Lerner, & Chase, 2019; Russell & Gajos, 2020). Instead of estimating an average effect size that applies to the overall sample (i.e., nomothetic approach) or subgroups (i.e., group-differential approach; east.g., based on active vs. passive use; Alloway & Alloway, 2012), a person-specific (or idiographic) arroyo estimates unique result sizes for every individual (Lerner et al., 2019). This approach allows cartoon conclusions based on the dispersion of associations that are unique for every person in the sample.

Recent studies have introduced this idiographic approach to social media research. For instance, Beyens et al. (2020b) showed that adolescents varied greatly in the extent to which passive SMU affected their well-existence. The upshot sizes found in this study ranged between −.24 and +.68. Similarly, other studies showed that there was substantial heterogeneity in the person-specific associations between SMU and a wide range of indicators of well-existence and psychosocial performance, such as depressive symptoms, cocky-esteem, friendship closeness, green-eyed, inspiration, and enjoyment (Pouwels, Valkenburg, Beyens, van Driel, & Keijsers, 2021; Rodriguez, Aalbers, & McNally, 2021; Valkenburg et al., 2021a; Valkenburg, Beyens, Pouwels, van Driel, & Keijsers, 2021b). These studies underline that investigating person-specific susceptibilities is essential to obtain a deeper understanding of the scope of various furnishings that might manifest in a given population.

Up to now, studies have shown individual differences in both SMU and lark: Adolescents vary in the extent to which they use social media, how they use it, and for what purposes (Rideout & Pull a fast one on, 2018), and they differ in their attentional control strategies and their level of distraction (Irons & Leber, 2020). However, what remains unknown is whether the within-person association betwixt SMU and distraction besides varies from adolescent to adolescent. The third contribution of this study is to shed light on how the association between SMU and distraction differs from person to person. Since this written report is the first to examine the unique associations between SMU and distraction per adolescent, we had no articulate expectations as to how heterogeneity would manifest itself. Thus, as a first footstep, we investigated the post-obit inquiry question:

RQ1: To what extent does the association between SMU and distraction differ from boyish to adolescent?

Method

The current preregistered study (see https://osf.io/cszk7/) is part of a larger project that investigates the psychosocial consequences of SMU amongst adolescents. The current report uses data from the first 3-week ESM moving ridge of the project, which was conducted in November and December 2019. While other studies in the larger project used data from this first ESM wave to investigate SMU in relation to different aspects of adolescents' psychosocial operation (e.one thousand., well-being, self-esteem), this study was the commencement to investigate SMU in relation to distraction (for a full overview, come across https://osf.io/spuza/).

Participants

A priori power analyses conducted before setting upwards the larger project (see https://osf.io/tk8pw/) suggested that a sample size of 300 participants with at least 63 assessments (i.e., 50% of all assessments) would be sufficient to exist able to detect small outcome sizes and variance around these furnishings with a minimum of 80% power and a significance level of 5%. To account for potential technical failures and participant drib-out, we aimed for a sample size of 400 participants.

In full, 745 7th and 8th graders at a secondary school in the s of the Netherlands were invited to participate in the inquiry project and 387 gave consent (52% consent rate) and signed up for the beginning ESM wave of this project. Following our preregistered plan, we excluded iv participants from the analyses because they did not use social media, which resulted in a final sample of 383 subjects. On average, adolescents were 14.11 years onetime (SD = 0.69), 56% were girls, and 96% were built-in in the Netherlands and self-identified as being Dutch. The educational level of the adolescents varied between the prevocational secondary education runway (42%), the intermediate general secondary educational activity track (31%), and the academic preparatory education rails (27%).

Process

The procedure of this project was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences at the Academy of Amsterdam. Several weeks ahead of data collection, researchers visited the school to provide adolescents and parents with detailed information about the goals of the research project. The students were enrolled after they and their parents gave written consent for participation. At the terminate of November 2019, researchers visited the school to provide participants with procedural instructions and to conduct a baseline survey. After completion of the baseline survey, participants installed the Ethica app on their mobile phones, which was required to collect the ESM data. An initial ESM survey was prompted from the app to check whether the installation was successful and to collect data on adolescents' SMU required for subsequent ESM triggers (i.e., to personalize their trigger schedule based on the school schedule and to tailor the SMU questions in the ESM surveys). Participants were costless to ask questions to the three researchers who were present. The three-week ESM written report started 4 days after this baseline session.

ESM assessments

In full, each participant received 126 ESM surveys distributed over six time points per day, for 21 consecutive days. This total number was based on the recommendation to have at to the lowest degree 50 to 100 assessments per participant to allow a meaningful interpretation of person-specific effects (Molenaar & Campbell, 2009; Voelkle, Oud, von Oertzen, & Lindenberger, 2012). The assessments were semi-randomly distributed throughout the 24-hour interval, that is, at random times within fixed intervals to account for course schedules and sleeping in on Saturdays and Sundays (the notification scheme is bachelor at https://osf.io/9dnjm/). Participants had 30 min to consummate the survey earlier it expired. For the start and concluding surveys of each day, the expiration fourth dimension was extended to threescore min and 120 min, respectively, to account for travel time and evening activities. After 5 to x minutes, an automatic reminder popped upwards if participants had non however completed the survey.

Incentives

Participants received a modest gadget for completing the baseline survey and €0.thirty for every completed ESM survey. In addition, each day we ran a lottery in which 4 winners were drawn from a pool of participants who had completed all surveys on the mean solar day before. The lottery winners all received a bonus of €25. An interactive website for real-time monitoring (built with Shiny R; Chang, Cheng, Allaire, Xie, & McPherson, 2020) allowed participants to audit FAQs, the page where lottery winners were announced, and their personal dashboard including an overview of their full earnings and compliance.

Compliance

In theory, a total of 48,258 surveys could be completed in this study, based on 383 participants who each received 126 assessments. Due to unforeseen technical errors, the Ethica app failed to send out 449 surveys (1%). Equally such, a total of 47,809 surveys was sent successfully (for a compliance overview of the total project, come across https://osf.io/se98b/). With 35,099 completed surveys, the overall compliance in this report was 73%, which is a proficient compliance rate compared to other ESM studies among adolescents (van Roekel et al., 2019). On average, participants completed 91.six surveys each (SD = 23.5, range = eleven–125), which gave sufficient power to comport person-specific furnishings analyses (Molenaar & Campbell, 2009; Voelkle et al., 2012). The group of lowly committed participants (i.e., those who completed less than a quarter of all surveys) was relatively pocket-size (4.ii%) compared to the grouping of highly committed participants (i.e., those who completed more iii-quarters of all surveys; 53.viii%).

Measures

Each ESM survey consisted of 23 items, including questions about SMU, distraction, and other topics not included in the current study. The morning and evening surveys each independent one boosted question.

Social media use

Social media apply was measured with eight items over 3 different social media platforms: Instagram, WhatsApp, and Snapchat. Participants but received questions about a specific platform if they used it more than one time a week, equally indicated in the initial survey that was distributed directly after the baseline measurement. The total number of ESM items was equally balanced across individuals by providing alternative questions to those participants who did not apply Instagram, WhatsApp, or Snapchat. Instagram use was measured with 3 items ("How much time over the past hr take you spent viewing posts/ stories of others on Instagram?" " … reading direct messages on Instagram?" and " … sending direct messages on Instagram?"), WhatsApp employ with two items (" … reading messages on WhatsApp?" and " … sending messages on WhatsApp?"), and Snapchat use with three items (" … viewing stories of others on Snapchat?" " … viewing snaps of others on Snapchat?" and " … sending snaps on Snapchat?"). Participants could choose a value between 0 and 60 min, with 1-min intervals. Sum scores were established by summing the items per platform and were recoded equally 60 min in case they exceeded 60 min (3.9% of all completed assessments). The platform scores were summed to create the full SMU measure, and values were over again recoded as threescore min upon exceedance (10.5% of all completed assessments). Finally, we divided the SMU score past 10 to facilitate the interpretation and comparability with the distraction measure (i.e., scale from 0 to half-dozen), so that an increment of ane unit reflects an increment of 10 min of SMU. To investigate the between-person association of SMU and distraction (H1), the hateful score of all momentary SMU assessments over the iii weeks was used for each participant (see Fleeson, 2001).

Distraction

To assess distraction, participants responded to the question "To what extent were you distracted by something over the past hour?" on a 7-point scale ranging from 0 (not at all) to half-dozen (completely), with 3 (a little) every bit the midpoint. This item was based on the momentary attentional control measure of Chin et al. (2020). To investigate the between-person clan between SMU and distraction (H1), the mean score of all momentary distraction assessments over the 3 weeks was used for each participant (run across Fleeson, 2001).

Statistical Analyses

As preregistered, nosotros examined the between-person, inside-person, and person-specific associations between SMU and distraction using multilevel modeling to account for the nested structure of the information. We conducted the analyses in Mplus 8.four because, unlike other statistical software, it provides standardized parameter estimates for the fixed effects models (Muthén & Muthén, 2017, p. 799). An comeback was made to our original preregistered program: Instead of using Maximum likelihood estimation with robust standard errors (MLR), we used Bayesian Markov Chain Monte Carlo (MCMC) interpretation, for two reasons. Start, in addition to standardizing the fixed effects, Bayesian estimation as well standardizes the parameters for every single person (Schuurman, Ferrer, de Boer-Sonnenschein, & Hamaker, 2016). More than specifically, for each private, Mplus standardizes the coefficients at the inside-person level, thereby providing the person-specific within-person standardized parameters. Nosotros refer to this every bit person-specific associations and interpret the estimates as person-specific consequence sizes. Second, the Bayes estimator uses latent person means and latent person-mean centering for disentangling between- and within-person associations, which is preferred over observed means and observed person-mean centering (McNeish & Hamaker, 2020). Since we did not specify priors, the parameter estimation fully relied on the data. The results of the Bayesian estimation method are therefore practically like to those obtained when using the preregistered MLR estimation method (Van de Schoot et al., 2014; see supplement A on https://osf.io/tzn34/ for the model results obtained with MLR estimation).

We estimated four multilevel models with distraction as outcome variable. The null model (or intercept-only model, Model 0) was estimated with 3 levels, namely observations (level 1) nested within persons (level 2), who were nested within classes (level 3), to determine the corporeality of variance at each level. Since the variance in distraction at the class level was just 5%, nosotros continued the analyses past estimating two-level rather than 3-level models. We used the two-level intercept model to compute the Intra-Class Correlations (ICC) via the statsBy() role from the psych package in R (R Cadre Team, 2020; Revelle, 2020).

The commencement model (Model 1) served as our reference model and included two fixed control variables to detrend the information: the notification number of the day and a dummy variable indicating whether it was a weekday (0) or a weekend twenty-four hour period (1). Detrending was required because we were interested in the within-person associations of SMU with distraction irrespective of the blazon of twenty-four hours or time of the 24-hour interval (Wang & Maxwell, 2015). In the second model (Model 2), SMU was added every bit a fixed effect both at the between-person level (i.due east., latent person means) and the within-person level (i.due east., latent person-mean centered scores) to investigate the between-person (H1) and within-person associations (H2) of SMU with distraction, respectively. Finally, random slopes for SMU were added to the third model (Model three) to examine the heterogeneity in person-specific associations of SMU with distraction (RQ1).

We ran the models with a minimum of v,000 iterations. The models successfully converged when the Potential Scale Reduction (PSR) values approached 1. We and so doubled the number of iterations and ran each model again to exclude the potential of a premature stoppage trouble, equally recommended by Schultzberg and Muthén (2018). While our original preregistered program was to compare models based on the Akaike information criterion (AIC) and Bayesian data criterion (BIC), the Bayesian interpretation arroyo does not provide such indices. Therefore, we compared the explained variance of each model, both at the inside- and between-person level.

For between-person associations, nosotros interpreted .ten, .20, and .thirty as small, medium, and large furnishings, respectively, thereby following Gignac and Szodorai (2016). For the within-person and person-specific associations, we interpreted issue sizes equally pocket-size as .05 as meaningful, every bit recommended by Adachi and Willoughby (2015). Nosotros evaluated the person-specific effect sizes obtained from Model 3 to interpret the amount of heterogeneity and to generate a corresponding distribution plot.

Availability Statement

The codebook (https://osf.io/y85mp/), the preregistration of the design and sampling plan (https://osf.io/327cx), the preregistration of the analysis plan (https://osf.io/xszta), and the syntaxes used to set up, clarify, and visualize the data (https://osf.io/5ts3r/) are publicly available on the Open Science Framework (OSF). The anonymized data gear up that was used for the current written report is published on Figshare (Siebers, Beyens, Pouwels, & Valkenburg, 2021).

Results

Descriptive Statistics and Correlations

As Table i shows, on average, participants reported little distraction (M = one.42, SD = one.72, range = 0–6) and spent 16.97 min on social media (SD = 20.xc) in the preceding hour (encounter supplement B on https://osf.io/g29vb/ for the distributions of both variables). The results demonstrated a strong between-person correlation between SMU and distraction (r = .33, p < .001), meaning that adolescents who spent on average more time using social media than their peers across the three weeks also reported more distraction than their peers. Similarly, there was a small within-person correlation between momentary SMU and distraction (r = .12, p < .001), meaning that, on average, adolescents experienced more than distraction during hours in which they spent more fourth dimension on social media. The ICCs of SMU and distraction were .47 and .38, respectively. This indicates that the larger office of the variance in SMU (53%) and distraction (62%) could be attributed to inside-person fluctuations (and error) and that the remaining role of the variance in SMU and distraction (i.due east., 47% and 38%, respectively) could be explained by differences between adolescents.

Social Media and Distraction: An Feel Sampling Study among Adolescents

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Tabular array 1. Descriptive statistics and within-person, between-person, and intra-class correlations of social media use and lark

Confirmatory Analyses

All models that we used to exam our hypotheses converged after 5,000 iterations. As shown in Table ii, the reference model (Model 1) indicated that lark was higher on weekdays compared to weekend days (β = −.07, p < .001) and increased throughout the day (β = .04, p < .001). Together, the control variables explained 0.2% of the inside-person variance in lark.

Social Media and Lark: An Feel Sampling Study amongst Adolescents

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23 August 2021

Tabular array 2. Bayesian multilevel model estimates of the within-person clan, between-person association, and random furnishings of social media utilise and distraction

Our first hypothesis (H1) predicted a positive between-person association of SMU with lark. Consistent with this hypothesis, the fixed effect of Model two pointed at a strong positive between-person association (β = .31, p < .001), meaning that adolescents who had spent more than fourth dimension on social media, on average, also experienced more distraction, on average, than their peers. Our second hypothesis (H2) predicted a positive within-person clan of momentary SMU with distraction. Supporting this hypothesis, the fixed outcome indicated a minor positive within-person association (β = .12, p < .001), implying that, on boilerplate, adolescents experienced more than distraction during hours when they used more social media. Specifically, after decision-making for beep fourth dimension and weekday, SMU explained 1.4% of the within-person variance in distraction and 9.seven% of the between-person variance.

Heterogeneity Analysis

Our research question (RQ1) asked to what extent the within-person clan of SMU with distraction differs from adolescent to adolescent. Every bit shown in Table 2, the explained within-person variance of lark increased from i.half dozen% (Model 2) to iv% (Model 3) by estimating a random effect of SMU. The variance effectually the slope of SMU (σ² = 0.017, p < .001) indicated the presence of heterogeneity in the inside-person association of momentary SMU with distraction (see Table two). Figure 1 illustrates how the person-specific effect sizes are distributed beyond the sample, with effect sizes ranging from β = −.15 to β = +.46. For virtually adolescents (82.v%) the association between SMU and distraction was positive (i.e., β ≥ .05). The association was not-existent (i.e., −.05 < β < .05) for 15.7% of the adolescents. Finally, for only a few adolescents (1.eight%) there was a negative clan (i.eastward., β ≤ −.05). Figure ii shows 2 fourth dimension series of the co-fluctuation between SMU and distraction, ane for a participant with a positive momentary clan and i for a participant with a negative momentary clan.

Social Media and Lark: An Experience Sampling Report among Adolescents

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23 Baronial 2021

Figure 1. Time series for the association between social media utilise and distraction. The x-axis displays the days of the study (ranging from i to 21) and covers 126 assessments.The anonymized participant'due south ID and the model-based person-specific effect sizes (#β's) are presented above the graphs. The pinnacle time series belong to an boyish for whom a negative association was found, and the bottom time series belong to an boyish for whom a positive association was institute.

Effigy 1. Time series for the association betwixt social media use and distraction. The ten-centrality displays the days of the study (ranging from 1 to 21) and covers 126 assessments.The anonymized participant'southward ID and the model-based person-specific effect sizes (#β'south) are presented to a higher place the graphs. The height time series vest to an boyish for whom a negative clan was found, and the lesser time series belong to an adolescent for whom a positive association was constitute.

Social Media and Lark: An Experience Sampling Written report among Adolescents

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Figure two. Distribution of the model-based person-specific effect sizes for the association between social media use and distraction. The dashed vertical line indicates the average within-person effect size.

Figure 2. Distribution of the model-based person-specific event sizes for the association between social media employ and lark. The dashed vertical line indicates the average within-person effect size.

Exploratory Analyses

In add-on to our preregistered analyses, we conducted ii types of exploratory analyses. First, we investigated whether the heterogeneity in the within-person association between momentary SMU and distraction (Beta) could be explained by adolescents' boilerplate levels of SMU and distraction. As presented in Table 2 (Model iii), there was an interaction betwixt average SMU and momentary SMU, every bit indicated by a negative consequence of average SMU on Beta (β = −.16, p = .020). This interaction suggests that the positive momentary association between SMU and distraction was stronger among adolescents who spent, on average, relatively less fourth dimension using social media than adolescents who spent relatively much time using social media. The cross-level interaction betwixt the average distraction and Beta was nonsignificant (β = .08, p = .143), indicating that the association between SMU and lark did not depend on adolescents' boilerplate level of distraction.

In the 2d exploratory analysis, we ran a lag-1 multilevel Vector Autoregressive (multilevel VAR(1)) model using Dynamic Structural Equation Modeling (DSEM) in Mplus (Asparouhov, Hamaker, & Muthén, 2018) to examine how SMU and distraction would affect each other over fourth dimension (i.due east., 2-60 minutes lag). Since the model did not converge after 5,000 iterations (PSR = 1.388), nosotros increased the number of iterations and simplified the model by excluding the correlations of the random effects with average levels of SMU and distraction. The model converged well later 10,800 iterations (PSR = ane.088). Although the upshot was pocket-sized, we found that SMU predicted distraction 2 hours later, β = .05, p < .001, 95% CI [0.03, 0.07]. The reversed outcome, that is, the issue of distraction on SMU two hours later was also significant, notwithstanding almost not-existent, β = .03, p = .001, 95% CI [0.01, 0.05].

Sensitivity Analyses

Nosotros conducted sensitivity analyses to examine the robustness of the results. To that end, we conducted 3 sets of analyses, past excluding participants based on one of 3 criteria and comparison the output with our main analyses, in which all participants were included. Outset, as preregistered, we excluded ane participant because the participant's mean score on SMU (i.eastward., 59.2 min) was larger than 2 standard deviations above the sample mean of SMU. 2d, post-obit the procedures of van Roekel et al. (2019) and following our preregistration, we excluded four participants because they provided inappropriate responses (i.e., gross comments or jokes) to the open question (i.eastward., the final question of the twenty-four hours: "What was the most pleasant feel that y'all had today?"). Third, we excluded twelve participants considering their sum score of at least i social media platform repeatedly exceeded the maximum usage fourth dimension of threescore min (i.e., in at least one-3rd of all assessments). The results of the sensitivity analyses showed that the primary findings were robust confronting outliers, potentially untrustworthy response patterns, and overreporting of SMU (see Table C1, Table C2, and Table C3 on https://osf.io/5cpdw/).

Discussion

The current three-week ESM report (35,099 assessments in total) investigated the between-person, the within-person, and the person-specific associations betwixt adolescents' SMU and distraction. Although adolescents reported little distraction on average, our results showed that lark fluctuated substantially inside adolescents. Interestingly, nosotros found that these fluctuations in lark co-occurred with fluctuations in adolescents' SMU (β = .12), implying that adolescents experienced more distraction at moments when they spent more fourth dimension on social media, irrespective of the time or blazon of 24-hour interval. This finding is in line with Aalbers et al. (2019), who showed that college students experienced more concentration issues at moments when they used more social media. This association of SMU and lark at the momentary level was too establish at the trait-similar level (β = .31), implying that adolescents who spent, on boilerplate, more than fourth dimension on social media beyond the iii weeks than their peers reported more distraction, on average, than their peers. These results corroborate previous findings among college students (Aalbers et al., 2019; Levine et al., 2007; Xie et al., 2021).

An caption as to why adolescents perceived more distraction when they spent more time using social media could be that they experienced an increased state of online vigilance and preoccupation with social media (Johannes et al., 2019). More specifically, at moments when adolescents felt more than preoccupied with social media, they may have experienced more difficulties focusing their attention and a stronger tendency to check social media regularly. Countless affordances of social media platforms (e.g., keeping in bear on with friends, expressing oneself, learning about social norms) have fabricated these platforms more ubiquitous than ever for adolescents (Anderson & Jiang, 2018). In improver, social media take become more proximal to our body, since "the devices with which we communicate take moved from the desktop (on our desks) to the laptop (in our bags) to the smartphone (in our pockets), and are now actualization on our wrists" (Valkenburg & Piotrowski, 2017, p. 261). This ubiquity and proximity of social media apps, and the idea of permanent connection may have blurred the lines between attention to the chore at hand and attention to social media interferences.

While, overall, our results showed that adolescents felt more distracted when they spent more time on social media, the broad range in person-specific event sizes (β = −.15 to β = +.46) indicates that there is substantial heterogeneity across adolescents. More specifically, the vast majority of all adolescents (82.v%) experienced more distraction when they spent more fourth dimension using social media, a smaller grouping of adolescents (15.7%) experienced no differences in distraction, and only a few adolescents (1.viii%) fifty-fifty experienced less lark. This evidence for heterogeneity aligns with recent results yielding heterogeneity in the furnishings of SMU on well-beingness (Beyens, Pouwels, van Driel, Keijsers, & Valkenburg, 2020a; Rodriguez et al., 2021; Valkenburg et al., 2021b), friendship closeness (Pouwels et al., 2021), and self-esteem (Valkenburg et al., 2021a). However, while the bulk of participants in these studies experienced no effect, most participants in the current study experienced increased distraction when spending more fourth dimension on social media. This predominance of positive person-specific associations was also institute in recent work investigating the effects of social media on procrastination (Aalbers, vanden Abeele, Hendrickson, De Marez, & Keijsers, 2021). Equally such, it seems that dissimilar degrees of heterogeneity exist in the effects of SMU on unlike outcomes: Whereas most adolescents inappreciably feel changes in well-being, cocky-esteem, or friendship closeness when their SMU increases, most adolescents do experience more lark and procrastination when their SMU increases.

Cross-level interactions showed that private differences in adolescents' average level of SMU, but not in their average level of distraction, could explain role of this heterogeneity. More specifically, the positive momentary association betwixt SMU and lark was less pronounced for adolescents who, on average, spent more time using social media than adolescents who spent less time using social media. This finding could point that heavier social media users are more accustomed to switching attending to social media notifications and alerts (Karpinski, Kirschner, Ozer, Mellott, & Ochwo, 2013), and less decumbent to experience distracted when they spend more time using social media. At the same time, it is also possible that heavier users are less aware that they are distracted or less inclined to admit that they experience distracted.

To further investigate what factors explain the heterogeneity in the clan of SMU and distraction, Bolger, Zee, Rossignac-Milon, and Hassin (2019) proposed to focus on the degree of stability of the heterogeneity, that is, whether person-specific associations of SMU with lark remain consistent over time spans such as weeks, months, or fifty-fifty years. Such cognition can provide guidance for introducing relevant moderators. More specifically, if the heterogeneity of the person-specific associations between SMU and distraction is stable, the heterogeneity may be explained by factors that are also relatively stable over time. In this instance, trait-similar characteristics, such as gender (Throuvala et al., 2021), trait levels of fright of missing out (Franchina, Vanden Abeele, van Rooij, Lo Coco, & De Marez, 2018), parental command (Fardouly, Magson, Johnco, Oar, & Rapee, 2018), or proactive cocky-control strategies (Brevers & Turel, 2019) are likely to moderate the effects of social media use on lark. Conversely, if the heterogeneity appears to be unstable beyond time, it is more likely that the heterogeneity in person-specific associations is explained by situational factors, such as mindfulness (Mentum et al., 2020), exhaustion (Reinecke & Hofmann, 2016), sleep quality (Baumeister, Wright, & Carreon, 2019; Roca et al., 2012), or other factors that may fluctuate beyond different weeks, months, or years.

Past investigating the reciprocal effects between SMU and distraction, we found that the effect of SMU on distraction is somewhat stronger than the reversed effect, although the difference in strength was very small. This suggests that SMU leads to reduced attentional control, making it more difficult for adolescents to sustain attention and suppress distractions. One reasonable caption for this directional effect is that SMU evokes "task-irrelevant thoughts" that persist beyond the actual fourth dimension spent on social media (Stothart et al., 2015). For case, after sending a message on WhatsApp or updating a story on Instagram, adolescents might recollect well-nigh replies, comments, or likes to such extent that they fail to focus their attention on subsequent tasks.

Avenues for Future Inquiry

To move the field forward, nosotros propose 4 directions for future research. First, future studies should further investigate the longitudinal clan of SMU and distraction across different time intervals. This may help to meliorate understand the direction and timing of the issue, and it may provide insight into whether an adolescent is likely to end up in a reinforcing screw over time (Slater, 2007; Valkenburg & Peter, 2013). While the time lag between two measurements in the current study was two hours, the effects likely develop at a shorter rate (e.g., within minutes or seconds) since the momentary associations were much stronger than the lagged causal effects. In addition, information technology may be interesting to examine how such short-term effects are related to the development of attentional control in the long run, given that adolescents' long-term developments may unfold from curt-term furnishings (eastward.g., Smith & Thelen, 2003). When noesis almost the direction, timing, and the long-term evolution of the result is combined with the idiographic approach, interventions tin can exist created that address the key cause for those adolescents who need it.

A 2nd avenue for future research is to unravel why SMU is associated with distraction. For instance, information technology could be that adolescents experienced more than distractions because they were distracted by social media, for instance, considering of agonizing factors from their smartphones (e.g., notifications, beeps, and sounds) or because of internal thoughts related to social connectedness (Johannes et al., 2019; Stothart et al., 2015). In addition, it could be that, due to the constant availability of social media, adolescents may have developed a processing style characterized by scattered attention, or so-chosen "latitude biased" attentional control (Baumgartner & Sumter, 2017; Baumgartner, van der Schuur, Lemmens, & te Poel, 2018; Ophir, Nass, & Wagner, 2009; Ralph, Thomson, Cheyne, & Smilek, 2014; van der Schuur, Baumgartner, Sumter, & Valkenburg, 2015). Therefore, adolescents with a more scattered processing style may have more difficulties in filtering out irrelevant environmental stimuli. This may explain why heavier social media users experience more distractions and difficulties focusing attention than their peers.

A third avenue for future research is to examine how fragmented utilize is associated with distraction. Since the experience of "being distracted" denotes a switch in attending from the job at manus toward irrelevant stimuli, the frequency of using social media might be more indicative of attentional command failures than the fourth dimension spent on social media. In other words, the co-fluctuations that we found in the electric current study might have been even more pronounced when participants were asked to report how often they checked social media apps over the by 60 minutes. For instance, checking social media apps xx times for merely 30 s might be more intrusive and distracting than spending ten sequent minutes on social media. Enquiry is needed to understand the differential effects of the time spent on social media versus the fragmented utilise of social media on distraction.

A final avenue for future research is to investigate the consequences of social media-induced distractions. For example, maintaining control over one's online connectivity has been recognized as an important status for achieving digital well-existence, that is, a healthy balance between connectivity and disconnectivity (Vanden Abeele, 2020). As such, existence distracted by social media may undermine this balance. In addition, social media-induced distractions may affect adolescents' well-being more than generally, as a recent written report showed that having more than thoughts about social media messages was associated with decreased melancholia well-beingness (Johannes et al., 2020). Moreover, studies take repeatedly shown that social media-induced distractions have a negative impact on goal attainment, such as academic accomplishment (Fox et al., 2009; Marker, Gnambs, & Appel, 2018). Future enquiry is needed that investigates how and to what extent social media-induced distractions bear upon digital well-being, general well-being, and goal attainment.

Decision

Digital technology is growing rapidly these days, making social media omnipresent in daily lives. This poses a serious challenge for adolescents as they are in a crucial period for developing attentional command abilities, and at the same time, accept social media at their disposal that may distract them numerous times a twenty-four hours. We institute that moments of increased SMU were accompanied by increases in lark and that this applied to the vast majority of all adolescents. Therefore, this study served as an initial step toward understanding the association betwixt SMU and distraction in daily life, and future studies are encouraged to paint a more refined moving-picture show of the dynamics over time.

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