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. Author manuscript; available in PMC: 2021 Mar 10.
Published in final edited form as: Hum Behav Emerg Technol. 2019 Apr 26;1(2):161–168. doi: 10.1002/hbe2.143

Impact of adolescent media multitasking on cognition and driving safety

Despina Stavrinos 1, Benjamin McManus 1, Andrea T Underhill 1, Maria T Lechtreck 1
PMCID: PMC7946084  NIHMSID: NIHMS1026760  PMID: 33709071

Abstract

Adolescence is a critical period in brain development particularly in regions related to attention and executive function (EF). As the use of electronics and media in daily activities increases, one essential question is how adolescent attention development and related executive and speed processes are impacted by media multitasking (MM), or the simultaneous use of media (e.g., text messaging while watching television). This review examines current literature concerning (a) the prevalence of MM during adolescence; (b) relations between MM and adolescent cognitive development, specifically attention, speed of processing, and EF; and (c) real-world implications of MM including adolescents and driver distraction. Finally, future challenges and opportunities in MM research are explored with special attention given to overcoming the limitations of current research in this area and the critical need to advance our understanding of the impact of MM on adolescent driver safety.

Keywords: adolescents, attention, driving, executive function, media multitasking, motor vehicle safety, speed of processing

1 ∣. IMPACT OF ADOLESCENT MEDIA MULTITASKING ON COGNITION AND DRIVING SAFETY

A person listening to music with earbuds in their ears while looking down at their smartphone to peruse social media sites. A teenager using a smartphone while driving. Sitting on the couch to watch television while looking at a tablet. These sights are so common in society that they are cliche. Media multitasking (MM), the simultaneous use of media (Baumgartner, Lemmens, Weeda, & Huizinga, 2017), is quickly becoming the new norm across generations, particularly younger generations (Carrier, Cheever, Rosen, Benitez, & Chang, 2009). At least two types of MM have been identified: (a) using multiple media simultaneously, and (b) using media while engaging in a nonmedia activity (e.g., using a cell phone while driving). The effects of MM are of concern across the lifespan, but its effect during adolescence, defined as ages 10–19 years (Age limits and adolescents, 2003) are of particular concern. Not only is the brain still developing throughout adolescence, but also this is a time of great physical and psychological change as well as a time of marked independence that typically coincides with major life events. One such life event occurring during adolescence is obtaining a driver's license.

Understanding the impact MM during adolescence has on the cognitively complex task of driving a vehicle is of critical importance. With the more pervasive use of electronics and media in daily activities, a central question remains how adolescent attention development and related executive and speed processes are impacted by MM. In this review, the impact of MM on underlying cognitive processes of executive function (EF) and speed of processing is considered, as well as its resulting impact on a real world activity (driving). For brevity and convenience in this review, MM refers to all types as identified in the definition above. Differences may exist between the types of MM on cognition and driving. However, research in this area is currently sparse, and for the purposes of this review, any mention of MM refers to a universal, or combined, definition.

2 ∣. EPIDEMIOLOGY OF MEDIA MULTITASKING

MM is becoming increasingly prevalent among adolescents in particular (Stichting ter Promotie en Optimalisatie van Televisiereclame (SPOT), 2012). In 2010, young people (ages 8–18 years) in the United States spent over 52 hr a week (7 hr, 32 min per day) with some form of media and 29% of that time was spent MM (Jacobsen & Forste, 2011; Rideout, Foehr, & Roberts, 2010). When MM time was considered independently, findings revealed that young people spent over 75 hr a week (10 hr, 45 min per day) with media. More recent estimates suggest MM has become pervasive earlier in development, with sixth- to eighth-grade students reporting approximately 25% of their total media time was spent MM (Cain, Leonard, Gabrieli, & Finn, 2016). It also seems that MM increases over development, with students’ ages 12–15 years reporting MM during nearly half of the time they spent watching television (Christensen, Bickham, Ross, & Rich,2015). MM is more than a leisure activity; nearly a third of adolescents use task-related and task-unrelated media “most of the time” while studying (Rideout et al., 2010), and a survey of university students indicated over half used electronic media during academic activities (Jacobsen & Forste, 2011).

Findings concerning which adolescents are more likely to engage in MM are mixed; though in general, young people who frequently media multitask are more likely to have media in their bedroom, be an owner of a cell phone, and have access to wireless internet (Rideout et al., 2010). Among 7th to 12th grade students, MM is consistent across ethnic groups, despite the differences seen across ethnicities for media consumption (Blacks and Hispanics consume greater quantities of media compared to Whites) (Rideout et al., 2010). Socioeconomic status (SES) predicted MM among a sample of sixth to eighth graders, with those in a lower SES more likely to engage in MM than those in a higher SES (Cain et al., 2016). Although some research has found girls (7th–12th grade), more so than boys, report that they media multitask “most of the time” (17% vs. 11%, respectively), other research has found no differences between males and females in MM engagement (Cain et al., 2016). Research suggests early adolescents (13–16 year olds) are more likely to engage in MM as compared to other age groups (Voorveld & van der Goot, 2013). Still others have found no differences in MM based on basic demographics (e.g., age, gender, and/or ethnicity) (Christensen et al., 2015; Moisala et al., 2016). Among university students, the frequency of online MM was greater among those who were more educated, younger, and Asian (compared to White) (Srivastava, Nakazawa, & Chen, 2016). MM increased between 1999 and 2009, primarily attributable to increases in texting behavior (Rideout et al., 2010), and there is no indication decreases occurred between 2009 and 2019.

MM has been associated with a myriad of outcomes negatively affecting child and adolescent learning and development (Wallis, 2010), including poor academic assessments (Cain et al., 2016; Martin-Perpina, Vinas Poch, & Malo Cerrato, 2019; Patterson, 2017); poor attention (Gorman & Green, 2016), less face-to-face communication (Pea et al., 2012); problems with task switching (Baumgartner, Weeda, van der Heijden, & Huizinga, 2014; Ophir, Nass, & Wagner, 2009); problems staying focused (Baumgartner et al.,2014; lesser growth mindsets (i.e., the belief that intelligence is malleable) (Cain et al., 2016); difficulty with implicit learning (Edwards & Shin, 2017); and working memory deficits (Cain et al., 2016; Ralph, Thomson, Cheyne, & Smilek, 2014; Uncapher, Thieu, & Wagner, 2016). MM has also been related to problems with cognitive control (Ophir et al., 2009); increased distractibility (Baumgartner et al., 2014; Srivastava et al., 2016); lower impulsivity control (Baumgartner et al., 2014; Cain et al., 2016; Minear, Brasher, McCurdy, Lewis, & Younggren, 2013; Sanbonmatsu, Strayer, Medeiros-Ward, & Watson, 2013; Uncapher et al., 2016); problems ignoring irrelevant stimuli (Ophir et al., 2009); and is associated with higher levels of sensation seeking (Sanbonmatsu et al., 2013).

Brain imaging findings are consistent with these behavioral deficits in cognitive control and distractibility, suggesting attentional and inhibitory control areas are taxed during MM (Moisala et al., 2016), and result in structural changes in the brain (e.g., smaller gray matter density in the anterior cingulate cortex) (Loh & Kanai, 2014). The rate of development in cognitive function varies across different cognitive domains, and it is not currently clear whether MM affects cognition at a constant rate (i.e., linearly) over development or whether it slows as age increases (Cromer, Schembri, Harel, & Maruff, 2015; Luna, Garver, Urban, Lazar, & Sweeney, 2004). MM in everyday life does not seem to translate to performance benefits in multitasking in laboratory settings (Moisala et al., 2016). These findings readily demonstrate why it is important to understand how MM impacts adolescents and their driving safety, especially in light of motor vehicle crashes being the leading killer of individuals ages 5–24 (Centers for Disease Control Prevention [CDC], 2018). Below, we discuss relations between specific cognitive processes and MM.

3 ∣. MEDIA MULTITASKING AND ADOLESCENT COGNITIVE DEVELOPMENT

Adolescence is a developmental period marked by significant brain changes (Blakemore & Choudhury, 2006; Giedd, 2008; Institute of Medicine and National Research Council, 2011), though existing literature on the development of cognition in middle adolescence is primarily cross-sectional with small sample sizes and is surprisingly sparse (Cromer et al., 2015; Luna et al., 2004). Although significant cognitive development has occurred by late childhood (i.e., age 10) and the brain has grown to near adult level in size and weight by that time (Giedd, 2008), cognitive functions continue to develop and corresponding neural networks are refined over adolescence and into adulthood (Cromer et al., 2015). Some work has begun to consider the role of MM on basic cognitive processes such as attention, speed of processing, and EF and is summarized briefly below. The rate of development in cognitive functions varies across different cognitive domains (Cromer et al., 2015; Luna et al., 2004) and the trajectory of cognitive development as a function of MM remains unclear.

3.1 ∣. Media multitasking and attention

Findings regarding the association between MM and attention are mixed. On the one hand, research suggests that MM is associated with performance decrements on tasks of sustained attention (Ralph, Thomson, Seli, Carriere, & Smilek, 2014) and self-reported attentional failures (Ralph, Thomson, Cheyne, & Smilek, 2014). One potential contributing agent is the tendency of persons with high levels of MM, compared to those with lower levels, to more commonly integrate incidental and irrelevant environmental cues during perception and evaluation (Lopez, Salinger, Heatherton, & Wagner, 2018). One study showed no association between MM and performance on selective sustained attention (Jun, Remington, Koutstaal, & Jiang, 2019), and a recent meta-analysis found no association between MM and distractibility after correcting for small-study effects (Wiradhany & Nieuwenstein, 2017). Longitudinal research is needed to discern how MM impacts development of attention over the course of adolescence.

3.2 ∣. Media multitasking and speed of processing

Speed of processing (visual processing speed) varies by age, improving into young adulthood and declining later in life (Roenker, Cissell, Ball, Wadley, & Edwards, 2003). It improves with practice (Ball, Beard, Roenker, Miller, & Griggs, 1988; Ball, Edwards, Ross, & McGwin Jr., 2010; Roenker et al., 2003), even in younger individuals (Burge et al., 2013), and has been consistently found to predict motor vehicle crashes in older and younger adults (Ball et al., 1988; McManus, Cox, Vance, & Stavrinos, 2015).

One study considering whether MM predicts cognitive processing speed performance (Cain et al., 2016) suggested no link between MM and cognitive processing speed primarily using simple (psychomotor) tasks. A later study found that those with high levels of MM displayed poorer processing speed than those with lower levels of MM (Martin-Perpina et al., 2019). More complex (perceptual) speed tasks may show a different pattern as they are more closely linked to executive control processes (Cepeda, Blackwell, & Munakata, 2013) which seem to be most impacted by MM.

3.3 ∣. Media multitasking and executive function

EF, the management of cognitive processes, continues to develop beyond adolescence (Diamond, 2013; Luciana, Conklin, Hooper, & Yarger, 2005). On the whole, the research to date suggests that those who engage in heavy MM suffer greater cognitive performance deficits compared to those who engage in light MM (Uncapher et al., 2017; Uncapher & Wagner, 2018). Deficits in EF may give rise to increased MM (Baumgartner et al., 2014) as such deficits result in poorly planned behavior. Adolescents with EF deficits may have difficulties self-regulating appropriate times to use media (i.e., while completing homework). MM appears to have negative consequences on executive processes and cognitive control (Ophir et al., 2009) particularly on tasks involving mental manipulation (e.g., working memory span) (Baumgartner et al., 2014; Cain et al., 2016; Uncapher et al., 2016). Task switching is another EF process that may be impacted by MM. One might posit the inherent task of switching between multiple types of media on a continual basis might “train” the brain to become more efficient at multitasking. Indeed, some work has shown that MM is associated with better performance on task switching (Alzahabi & Becker, 2013) or no differences in switch-costs between heavy and light MM (Minear et al., 2013; Ralph, Thomson, Cheyne, & Smilek, 2014). A recent review, however, noted a mix of positive, negative, and null effects of MM on switching such that with increasing MM, there was significant underperformance in all other cognitive domains (Uncapher & Wagner, 2018).

4 ∣. MULTITASKING AND DRIVING: REAL WORLD IMPLICATIONS OF MEDIA MULTITASKING

Safe driving is a cognitively complex task that relies on high levels of cognitive functioning, specifically attention, speed of processing, and EF. Although research in the area is still young, it is becoming increasingly clear, as evidenced above, that MM results in deficits in all the areas mentioned. Because their brains are still developing, adolescents may be more susceptible to effects of MM through a vulnerability to distraction. Critical safety outcomes may be negatively impacted due to cognitive failures resulting from MM. It is unclear how MM translates to multitasking in a real world context like driving, but given that effective multitasking is difficult or impossible if the two tasks require the resources of the attention network at the same time (Rothbart & Posner, 2015), it is posited that MM does not give one an advantage in multitasking while driving. Herein, driving safety is discussed as motor vehicle collisions (MVCs) because MVCs are the leading cause of death in adolescents (CDC, 2018), and the cognitive factors necessary for safe driving are often impacted by MM (i.e., distracted driving).

4.1 ∣. Adolescent driver distraction

Adolescents are at an increased risk for MVCs due to a number of factors, including willingness to take risks, lack of necessary skill and judgment, and poor hazard recognition and response (Borowsky, Shinar, & Oron-Gilad, 2007; Lee, 2007; McGwin Jr. & Brown, 1999). However, the primary contributor to MVCs is driver inattention manifesting as vulnerability to distraction (Klauer, Ensani, McGehee, & Manser, 2015), where the driver fails to allocate sufficient attention to the driving task (Regan, Hallett, & Gordon, 2011) in comparison with tasks that compete for the driver's attention (e.g., cell phones) (Romer, Lee, McDonald, & Winston, 2014). Distracted driving occurs whenever a driver's attention is diverted from the primary driving task to an object, person, task, or event not related to driving (Olsen, Shults, & Eaton, 2013).

Although dangerous for any driver, distractions are especially detrimental to teen drivers because the task of driving demands more of their cognitive resources (Goodwin, Foss, Harrell, & O'Brien, 2012) compared to older, more experienced drivers. Distractions while driving can encompass many different activities, but the use of a cell phone (whether interacting, dialing, answering) is the most commonly studied and, according to some researchers, possibly the most dangerous form of distracted driving (Goodwin et al., 2012). This is largely due to the fact that cell phone use has become increasingly more common over the past few decades, and more people are interacting with devices in new ways while driving (CDC, 2014; Insurance Institute for Highway Safety [IIHS], 2019).

When adolescents use a single device, such as a cell phone, while driving, driving performance is severely compromised (Caird, Willness, Steel, & Scialfa, 2008; Drews, Pasupathi, & Strayer, 2008; Horrey, Wickens, & Consalus, 2006; Reimer, Mehler, D'Ambrosio, & Fried, 2010). Neyens and Boyle (2008) concluded cell phone distraction may greatly increase the risk and severity of MVC-related injury and death for novice, teen drivers because of their relative inexperience and diminished attentional capacities. Drivers are exposed to many distractions (Bingham, 2014), but those which take drivers' eyes off the forward roadway (Decker et al., 2015; Harbluk, Noy, Trbovich, & Eizenman, 2007; Stavrinos et al., 2016), reduce visual scanning (Recarte & Nunes, 2003), and increase cognitive load (Lee, Caven, Haake, & Brown, 2001; Strayer & Drews, 2004; Strayer, Drews, & Crouch, 2006; Strayer, Drews, & Johnston, 2003; Strayer, Turrill, Coleman, Ortiz, & Cooper, 2014) are among the most dangerous. Eye glances away from the forward roadway lasting 2 s or more double the risk of a crash or near crash (Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006). For adolescents, the longer the glance, the greater the risk, regardless of type of secondary task (Simons-Morton, Guo, Klauer, Ehsani, & Pradhan, 2014).

4.2 ∣. Media multitasking and driving

To date, only one study has considered MM (specifically, the simultaneous use of multiple media) in the context of driving. Sanbonmatsu et al. (2013) surveyed 277 college students and found that self-reported frequency of distracted driving was related to more self-reported MM, providing evidence that multitasking occurs across contexts. Additionally, the perceived ability to multitask was associated with more self-reported frequency of distracted driving. Although an important step in investigating the impact of MM in real world contexts, these findings are limited with consideration to (a) self-reported frequency of MM and self-reported frequency of distracted driving; (b) small effect sizes (r = .20) (Cohen, 1988); and (c) a sample limited to 18 years of age and older. As the frequency of MM specifically while driving was not assessed, it remains unknown how MM influences driving attention development in young novice drivers (e.g., age 16), which is a critical skill acquired in midadolescence as individuals begin to operate a motor vehicle.

5 ∣. FUTURE OPPORTUNITIES AND CHALLENGES IN MEDIA MULTITASKING RESEARCH

Given that engagement in technology is becoming more pervasive in society, especially at younger ages, future research should continue to elucidate the mechanisms of MM itself, and the subsequent effect of MM on driving. A few important considerations are noted below to spawn future research in this area.

5.1 ∣. Overcoming limitations of current media multitasking research

To date, MM studies have primarily relied upon cross-sectional self-report survey data gathered from small samples of participants. Further, due to these small sample sizes, most analyses have compared participants at the highest levels of MM activity to those at the lowest levels, as opposed to examining the entire continuum. Future studies should address these limitations and further examine individual and situational differences in MM behavior. For example, some adolescents may be predisposed to engage in higher levels of MM and this behavior may make them more sensitive to distracting or irrelevant information (Baumgartner, van der Schuur, Lemmens, & te Poel,2017). Previous cross-sectional work suggests that young drivers with executive functioning difficulties and poorer speed of processing abilities are at risk for negative driving outcomes (McManus et al., 2015; Pope, Ross, & Stavrinos, 2016). Longitudinal studies are needed to better explore questions of the directionality between MM, cognitive development and the resulting potential impact on driving safety. Self-report survey data should be supplemented by real-time experience sampling methodology. Finally, larger and more diverse samples could provide more accurate measures of effect sizes. In these future samples, it will be increasingly important to note the initial onset of MM activity as well as the complexity of that activity, as each generation has earlier access to a greater variety of media systems, and thus, participants could be considered to be from different populations.

5.2 ∣. Impact of media multitasking on adolescent driver safety

A clear gap exists in the literature on MM and driving. Descriptive work to advance our understanding of the prevalence and use of MM within the context of driving is critical. Future studies should consider the frequency of MM specifically while driving, as well as the role of embedded vehicle technologies (e.g., infotainment systems) in the decision to engage in MM. At the time of this review, only one study was noted to examine MM and driving (Sanbonmatsu et al., 2013). This study did not explicitly measure MM during driving and only associated MM in general with frequency of distracted driving among emerging adults. The comprehensive literature investigating the impact of distracted driving more broadly suggests significant performance decrements among young drivers who attempt to multitask and a sharp increase in motor vehicle crash risk, especially among young drivers in their first months of independent driving (Stavrinos, Pope, Shen, & Schwebel, 2017). One might presume that using multiple devices at once while driving would further increase the risks adolescents would face.

Future experimental work to examine how MM while driving impacts performance, especially in the driving attention domain, would prove useful. Sources of distraction may be embedded within the vehicle (e.g., touchscreen infotainment systems). Physical controls are increasingly being replaced with touchscreens, with some vehicles' interior controls being entirely presented in a single touchscreen unit. These touchscreen interfaces frequently lack haptic, sensory feedback making them visually demanding, therefore requiring more visual attention compared to interfaces with physical controls (Kim & Song, 2014; Suh & Ferris, 2018). The growing prevalence of in-vehicle touchscreen units coinciding with increased cell phone interaction while driving (IIHS, 2019) may present the potential for a “perfect storm” in adolescents, where adolescents' vulnerability to distraction (Klauer et al., 2015) may result in MM via an in-vehicle touchscreen infotainment unit in combination with a handheld cell phone while driving. It is currently unknown, if provided the opportunity to MM within a vehicle, whether adolescents would use two devices in conjunction with one another (e.g., cell phone in addition to infotainment system) or use a single device in place of one another.

Ideally, studies utilizing a driving simulator are recommended to allow a high degree of experimental control while safely and ethically exposing adolescents to real world driving scenarios and hazards. Given the high visual demand of distractions and the subsequent impact on driving safety (Klauer et al., 2006; Simons-Morton et al., 2014), research on visual attention (e.g., eye tracking) while engaging in MM is needed to determine the extent to which visual attention is degraded in the driving environment, thereby providing insight into attention allocation during MM while driving (Castro, 2009). With inefficient attention allocation as a major factor in hazard recognition and driving performance in adolescents (Borowsky et al., 2007; Rizzo & Kellison, 2010; Zhang, Kaber, Rogers, Liang, & Gangakhedkar, 2014), it is important to understand how MM influences attention allocation while driving.

Finally, MVCs are burdensome and costly to society. With inattention as the primary contributor to MVCs, it is critical to gain a clear understanding of how dangerous behaviors such as MM while driving affect the riskiest developmental period for drivers: adolescence (ages 16–18). Using a mechanistic approach to understand the impact of MM on adolescent driver safety could improve translational work aimed at developing evidence-based interventions to curb such dangerous behaviors. Some early work shows promise for short-term mindfulness meditation intervention in ameliorating the cognitive and attentional deficits of MM. This might be especially beneficial for individuals considered to be “heavy multitaskers” (e.g., the most frequent users of MM) (Gorman & Green, 2016), though additional work is warranted to determine whether such interventions translate to safer driving behaviors and to adolescents who are considered the most frequent media multitaskers and at the highest risk for involvement in MVCs.

6 ∣. CONCLUSION

Current trends suggest media use among adolescents will continue to increase. It is expected that concurrent use of more than one type of media will grow rapidly as access to multiple media increases and begins at younger ages. Future research will need to develop innovative measures to quantify MM and should consider longitudinal work to better understand the impact of MM on cognitive development as well as on real world activities such as driving.

ACKNOWLEDGMENTS

This work was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R01HD089998 and the UAB Edward R. Roybal Center for Translational Research in Aging and Mobility (NIH/NIA Grant 5 P30 AG022838-09). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Funding information

Eunice Kennedy Shriver National Institute of Child Health and Human Development, Grant/Award Number: R01HD089998; National Institute on Aging, Grant/Award Number: 5 P30 AG022838-09

Biography

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Despina Stavrinos is an Associate Professor of psychology at the University of Alabama at Birmingham where she directs the Translational Research for Injury Prevention (TRIP) laboratory. The TRIP lab houses a fully immersive, state-of-the-art driving simulator. Dr. Stavrinos focuses on the cognitive aspects of pediatric unintentional injury—particularly injuries resulting from motor vehicle collisions related to distracted driving. Her research addresses applied injury prevention issues from a developmental psychology perspective. Dr. Stavrinos has received grant funding from NIH-NICHD, CDC, DOT, and private industry to support her work.

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Benjamin McManus graduated from the University of Alabama at Birmingham (UAB) with a bachelor of science in psychology and biology. He also earned his MA and PhD in developmental psychology from UAB. Dr. McManus' work primarily focuses on occupational populations whose work may impact sleep and cognitive factors (e.g., attention) which, in turn, impact driving safety. His interests include use of actigraphy, eye tracking, and driving simulation.

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Andrea T. Underhill earned her PhD and MPH in epidemiology from the University of Alabama at Birmingham. She has a MS in psychology from Auburn University at Montgomery, a BS in chemistry from Georgia Southern University, and taught high school science. Prior to her work with the TRIP Lab, Dr. Underhill was the Associate Director for Administration and Finance for the UAB University Transportation Center and the project coordinator for the UAB Injury Control Research Center's longest running research project, A Longitudinal Study of Rehabilitation Outcomes. Her diverse background and experience, along with a gnawing curiosity, have resulted in an interdisciplinary array of research interests, the center of which is injury prevention.

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Maria T. Lechtreck received her bachelor of science from the University of Montevallo. Currently, she is a doctoral student in behavioral neuroscience in the department of psychology at the University of Alabama at Birmingham. Maria's primary research interests are in Attention-Deficit/Hyperactivity Disorder (ADHD) and emotional self-regulation. She is interested in how difficulties to inhibit frustration, impatience, and anger impact ability to interact with one's environment, including the dangers and stresses of driving a motor vehicle.

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