a research paper about cell phones

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Open Access

Peer-reviewed

Research Article

Mobile phones: The effect of its presence on learning and memory

Roles Conceptualization, Data curation, Investigation, Writing – original draft

Affiliation Department of Psychology, Sunway University, Selangor, Malaysia

Roles Formal analysis, Investigation, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Clarissa Theodora Tanil, 
• Min Hooi Yong

• Published: August 13, 2020
• https://doi.org/10.1371/journal.pone.0219233
• See the preprint
• Peer Review
• Reader Comments

Our aim was to examine the effect of a smartphone’s presence on learning and memory among undergraduates. A total of 119 undergraduates completed a memory task and the Smartphone Addiction Scale (SAS). As predicted, those without smartphones had higher recall accuracy compared to those with smartphones. Results showed a significant negative relationship between phone conscious thought, “how often did you think about your phone”, and memory recall but not for SAS and memory recall. Phone conscious thought significantly predicted memory accuracy. We found that the presence of a smartphone and high phone conscious thought affects one’s memory learning and recall, indicating the negative effect of a smartphone proximity to our learning and memory.

Citation: Tanil CT, Yong MH (2020) Mobile phones: The effect of its presence on learning and memory. PLoS ONE 15(8): e0219233. https://doi.org/10.1371/journal.pone.0219233

Editor: Barbara Dritschel, University of St Andrews, UNITED KINGDOM

Received: June 17, 2019; Accepted: July 30, 2020; Published: August 13, 2020

Copyright: © 2020 Tanil, Yong. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: MHY received funding from Sunway University (GRTIN-RRO-104-2020 and INT-RRO-2018-49).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Smartphones are a popular communication form worldwide in this century and likely to remain as such, especially among adolescents [ 1 ]. The phone has evolved from basic communicative functions–calls only–to being a computer-replacement device, used for web browsing, games, instant communication on social media platforms, and work-related productivity tools, e.g. word processing. Smartphones undoubtedly keep us connected; however, many individuals are now obsessed with them [ 2 , 3 ]. This obsession can lead to detrimental cognitive functions and mood/affective states, but these effects are still highly debated among researchers.

Altmann, Trafton, and Hambrick suggested that as little as a 3-second distraction (e.g. reaching for a cell phone) is adequate to disrupt attention while performing a cognitive task [ 4 ]. This distraction is disadvantageous to subsequent cognitive tasks, creating more errors as the distraction period increases, and this is particularly evident in classroom settings. While teachers and parents are for [ 5 ] or against cell phones in classrooms [ 6 ], empirical evidence showed that students who used their phones in class took fewer notes [ 7 ] and had poorer overall academic performance, compared to those who did not [ 8 , 9 ]. Students often multitask in classrooms and even more so with smartphones in hand. One study showed no significant difference in in-class test scores, regardless of whether they were using instant messaging [ 10 ]. However, texters took a significantly longer time to complete the in-class test, suggesting that texters required more cognitive effort in memory recall [ 10 ]. Other researchers have posited that simply the presence of a cell phone may have detrimental effects on learning and memory as well. Research has shown that a mobile phone left next to the participant while completing a task, is a powerful distractor even when not in use [ 11 , 12 ]. Their findings showed that mobile phone participants could perform similarly to control groups on simple versions of specific tasks (e.g. visual spatial search, digit cancellation), but performed much poorer in the demanding versions. In another study, researchers controlled for the location of the smartphone by taking the smartphones away from participants (low salience, LS), left the smartphone next to them (high salience/HS), or kept the smartphones in bags or pockets (control) [ 13 ]. Results showed that participants in LS condition performed significantly better compared to HS, while no difference was established between control and HS conditions. Taken together, these findings confirmed that the smartphone is a distractor even when not in use. Further, smartphone presence also increases cognitive load, because greater cognitive effort is required to inhibit distractions.

Reliance on smartphones has been linked to a form of psychological dependency, and this reliance has detrimental effect on our affective ‘mood’ states. For example, feelings of anxiety when one is separated from their smartphones can interfere with the ability to attend to information. Cheever et al. observed that heavy and moderate mobile phone users reported increased anxiety when their mobile phone was taken away as early as 10 minutes into the experiment [ 14 ]. They noted that high mobile phone usage was associated with higher risk of experiencing ‘nomophobia’ (no mobile phone phobia), a form of anxiety characterized by constantly thinking about one’s own mobile phones and the desire to stay in contact with the device [ 15 ]. Other studies reported similar separation-anxiety and other unpleasant thoughts in participants when their smartphones were taken away [ 16 ] or the usage was prohibited [ 17 , 18 ]. Participants also reported having frequent thoughts about their smartphones, despite their device being out of sight briefly (kept in bags or pockets), to the point of disrupting their task performance [ 13 ]. Taken together, these findings suggest that strong attachment towards a smartphone has immediate and lasting negative effects on mood and appears to induce anxiety.

Further, we need to consider the relationship between cognition and emotion to understand how frequent mobile phone use affects memory e.g. memory consolidation. Some empirical findings have shown that anxious individuals have attentional biases toward threats and that these biases affect memory consolidation [ 19 , 20 ]. Further, emotion-cognition interaction affects efficiency of specific cognitive functions, and that one’s affective state may enhance or hinder these functions rapidly, flexibly, and reversibly [ 21 ]. Studies have shown that positive affect improves visuospatial attention [ 22 ], sustained attention [ 23 ], and working memory [ 24 ]. The researchers attributed positive affect in participants’ improved controlled cognitive processing and less inhibitory control. On the other hand, participants’ negative affect had fewer spatial working memory errors [ 23 ] and higher cognitive failures [ 25 ]. Yet, in all of these studies–the direction of modulation, intensity, valence of experiencing a specific affective state ranged widely and primarily driven by external stimuli (i.e. participants affective states were induced from watching videos), which may not have the same motivational effect generated internally.

Present study

Prior studies have demonstrated the detrimental effects of one’s smartphone on cognitive function (e.g. working memory [ 13 ], visual spatial search [ 12 ], attention [ 11 ]), and decreased cognitive ability with increasing attachment to one’s phone [ 14 , 16 , 26 ]. Further, past studies have demonstrated the effect of affective state on cognitive performance [ 19 , 20 , 22 – 25 , 27 ]. To our knowledge, no study has investigated the effect of positive or negative affective states resulting from smartphone separation on memory recall accuracy. One study showed that participants reporting an increased level of anxiety as early as 10 minutes [ 14 ]. We also do not know the extent of smartphone addiction and phone conscious thought effects on memory recall accuracy. One in every four young adults is reported to have problematic smartphone use and this is accompanied by poor mental health e.g. higher anxiety, stress, depression [ 28 ]. One report showed that young adults reached for their phones 86 times in a day on average compared to 47 times in other age groups [ 29 ]. Young adults also reported that they “definitely” or “probably” used their phone too much, suggesting that they recognised their problematic smartphone use.

We had two main aims in this study. First, we replicated [ 13 ] to determine whether ‘phone absent’ (LS) participants had higher memory accuracy compared to the ‘phone present’ (HS). Second, we predicted that participants with higher smartphone addiction scores (SAS) and higher phone conscious thought were more likely to have lower memory accuracy. With regards to separation from their smartphone, we hypothesised that LS participants will experience an increase of negative affect or a decrease in positive affect and that this will affect memory recall negatively. We will also examine whether these predictor variables–smartphone addiction, phone conscious thought and affect differences—predict memory accuracy.

Materials and methods

Participants.

A total of 119 undergraduate students (61 females, M age = 20.67 years, SD age = 2.44) were recruited from a private university in an Asian capital city. To qualify for this study, the participant must own a smartphone and does not have any visual or auditory deficiencies. Using G*Power v. 3.1.9.2 [ 30 ], we require at least 76 participants with an effect size of d = .65, α = .05 and power of (1-β) = .8 based on Thornton et al.’s [ 11 ] study, or 128 participants from Ward’s study [ 13 ].

Out of 119 participants, 43.7% reported using their smartphone mostly for social networking, followed by communication (31.1%) and entertainment (17.6%) (see Table 1 for full details on smartphone usage). Participants reported an average smartphone use of 8.16 hours in a day ( SD = 4.05). There was no significant difference between daily smartphone use for participants in the high salience (HS) and low salience groups (LS), t (117) = 1.42, p = .16, Cohen’s d = .26. Female participants spent more time using their smartphones over a 24-hour period ( M = 9.02, SD = 4.10) compared to males, ( M = 7.26, SD = 3.82), t (117) = 2.42, p = .02, Cohen’s d = .44.

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https://doi.org/10.1371/journal.pone.0219233.t001

Ethical approval and informed consent

The study was conducted in accordance with the protocol approved by the Department of Psychology Research Ethics Committee at Sunway University (approval code: 20171090). All participants provided written consent before commencing the study and were not compensated for their participation in the study.

Study design

Our experimental study was a mixed design, with smartphone presence (present vs absent) as a between-subjects factor, and memory task as a within-subjects factor. Participants who had their smartphone out of sight formed the ‘Absent’ or low-phone salience (LS) condition, and the other group had their smartphone placed next to them throughout the study, ‘Present’ or high-phone salience (HS) condition. The dependent variable was recall accuracy from the memory test.

Working memory span test.

A computerized memory span task ‘Operation Span (OS)’ retrieved from software Wadsworth CogLab 2.0 was used to assess working memory [ 31 ]. A working memory span test was chosen as a measure to test participants’ memory ability for two reasons. First, participants were required to learn and memorize three types of stimuli thus making this task complex. Second, the duration of task completion took approximately 20 minutes. This was advantageous because we wanted to increase separation-anxiety [ 16 ] as well as having the most pronounced effect on learning and memory without the presence of their smartphone [ 9 ].

The test comprised of three stimulus types, namely words (long words such as computer, refrigerator and short words like pen, cup), letters (similar sound E, P, B, and non-similar sound D, H, L) and digits (1 to 9). The test began by showing a sequence of items on the left side of the screen, with each item presented for one second. After that, participants were required to recall the stimulus from a 9-button box located on the right side of the screen. In order to respond correctly, participants were required to click on the buttons for the items in the corresponding order they were presented. A correct response increases the length of stimulus presented by one item (for each stimulus category), while an incorrect response decreases the length of the stimulus by one item. Each trial began with five stimuli and increased or decreased depending on the participants’ performance. The minimum length possible was one while the maximum was ten. Each test comprised of 25 trials with no time limit and without breaks between trials. Working memory ability was measured through the number of correct responses over total trials: scores ranged from 0 to 25, with the highest score representing superior working memory.

Positive and Negative Affect Scale (PANAS).

We used PANAS to assess the current mood/affective state of the participants with state/feeling-descriptive statements [ 32 ]. PANAS has ten PA statements e.g. interested, enthusiastic, proud, and ten NA statements e.g. guilty, nervous, hostile. Each statement was measured using a five-point Likert scale ranging from very slightly or not at all to extremely, and then totalled to form overall PA or NA score with higher scores representing higher levels of PA or NA. In the current study, the internal reliability of PANAS was good with a Cronbach’s alpha coefficient of .819, and .874 for PA and NA respectively.

Smartphone Addiction Scale (SAS)

SAS is a 33-item self-report scale used to examine participants’ smartphone addiction [ 33 ]. SAS contained six sub-factors; daily-life disturbance that measures the extent to which mobile phone use impairs one’s activities during everyday tasks (5 statements), positive anticipation to describe the excitement of using phone and de-stressing with the use of mobile phone (8 statements), withdrawal refers to the feeling of anxiety when separated from one’s mobile phone (6 statements), cyberspace-oriented relationship refers to one’s opinion on online friendship (7 statements), overuse measures the excessive use of mobile phone to the extent that they have become inseparable from their device (4 statements), and tolerance points to the cognitive effort to control the usage of one’s smartphone (3 statements). Each statement was measured using a six-point Likert scale from strongly disagree to strongly agree, and total SAS was identified by totalling all 33 statements. Higher SAS scores represented higher degrees of compulsive smartphone use. In the present study, the internal reliability of SAS was identified with Cronbach's alpha correlation coefficient of .918.

Phone conscious thought and perceived effect on learning

We included a one-item question for phone conscious thought: “During the memory test how often do you think of your smartphone?”. The aim of this question was two-fold; first was to capture endogenous interruption experienced by the separation, and second to complement the smartphone addiction to reflect current immediate experience. Participants rated this item on a scale of one (none to hardly) to seven (all the time). We also included a one-item question on how much they perceived their smartphone use has affected their learning and attention: “In general, how much do you think your smartphone affects your learning performance and attention span?”. This item was similarly rated on a scale of one (not at all) to seven (very much).

We randomly assigned participants to one of two conditions: low-phone salience (LS) and high-phone salience (HS). Participants were tested in groups of three to six people in a university computer laboratory and seated two seats apart from each other to prevent communication. Each group was assigned to the same experimental condition to ensure similar environmental conditions. Participants in the HS condition were asked to place their smartphone on the left side of the table with the screen facing down. LS participants were asked to hand their smartphone to the researcher at the start of the study and the smartphones were kept on the researcher’s table throughout the task at a distance between 50cm to 300cm from the participants depending on their seat location, and located out of sight behind a small panel on the table.

At the start of the experiment, participants were briefed on the rules in the experimental lab, such as no talking and no smartphone use (for HS only). Participants were also instructed to silence their smartphones. They filled in the consent form and demographic form before completing the PANAS questionnaire. They were then directed to CogLab software and began the working memory test. Upon completion, participants were asked to complete the PANAS again followed by the SAS, phone conscious thought, and their perception of their phone use on their learning performance and attention span. The researcher thanked the participants and returned the smartphones (LS condition only) at the end of the task.

Statistical analysis

We examined for normality in our data using the Shapiro-Wilk results and visual inspection of the histogram. For the normally distributed data, we analysed our data using independent-sample t -test for comparison between groups (HS or LS), paired-sample t test for within groups (e.g. before and after phone separation), and Pearson r for correlation. Non-normally distributed or ranked data were analysed using Spearman rho for correlation.

Preliminary analyses

Our female participants reported using their smartphone significantly longer than males, and so we examined the effects of gender on memory recall accuracy. We found no significant difference between males and females on memory recall accuracy, t (117) = .18, p = .86, Cohen’s d = .03. Subsequently, data were collapsed, analysed and reported on in the aggregate.

Smartphone presence and memory recall accuracy

An independent-sample t- test was used to examine whether participants’ performance on a working memory task was influenced by the presence (HS) or absence (LS) of their smartphone. Results showed that participants in the LS condition had higher accuracy ( M = 14.21, SD = 2.61) compared to HS ( M = 13.08, SD = 2.53), t (117) = 2.38, p = .02, Cohen’s d = .44 (see Fig 1 ). The effect size ᶇ 2 = .44 indicates that smartphone presence/salience has a moderate effect on participant working memory ability and a sensitivity power of .66.

https://doi.org/10.1371/journal.pone.0219233.g001

Relationship between Smartphone Addiction Score (SAS), higher phone conscious thought and memory recall accuracy

Sas and memory recal..

We first examined participants’ SAS scores between the two conditions. Results showed no significant difference between the LS (M = 104.64, SD = 24.86) and HS (M = 102.70, SD = 20.45) SAS scores, t (117) = .46, p = .64, Cohen’s d = .09. We predicted that those with higher SAS scores will have lower memory accuracy, and thus we examined the relationship between SAS and memory recall accuracy using Pearson correlation coefficient. Results showed that there was no significant relationship between SAS and memory recall accuracy, r = -.03, n = 119, p = .76. We also examined the SAS scores between the LS and HS groups on memory recall accuracy scores. In the LS group, no significant relationship was established between SAS score and memory accuracy, r = -.04, n = 58, p = .74. Similarly, there was no significant relationship between SAS score and memory accuracy in the HS group, r = .10, n = 61, p = .47. In the event that one SAS subscale may have a larger impact, we examined the relationship between each subscale and memory recall accuracy. Results showed no significant relationship between each sub-factor of SAS scores and memory accuracy, all p s > .12 (see Table 2 ).

https://doi.org/10.1371/journal.pone.0219233.t002

Phone conscious thought and memory accuracy.

We found a significant negative relationship between phone conscious thought and memory recall accuracy, r S = -.25, n = 119, p = .01. We anticipated a higher phone conscious thought for the LS group since their phone was kept away from them during the task and examined the relationship for each condition. Results showed a significant negative relationship between phone conscious thought and memory accuracy in the HS condition, r S = -.49, n = 61, p = < .001, as well as the LS condition, r S = -.27, n = 58, p = .04.

Affect/mood changes after being separated from their phone

We anticipated that our participants may have experienced either an increase in negative affect (NA) or a decrease in positive affect (PA) after being separated from their phone (LS condition).

We first computed the mean difference (After minus Before) for both positive ‘PA difference’ and negative affect ‘NA difference’. A repeated-measures 2 (Mood change: PA difference, NA difference) x 2 (Conditions: LS, HS) ANOVA was conducted to determine whether there is an interaction between mood change and condition. There was no interaction effect of mood change and condition, F (1, 117) = .38, p = .54, n p 2 = .003. There was a significant effect of Mood change, F (1, 117) = 13.01, p < .001, n p 2 = .10 (see Fig 2 ).

https://doi.org/10.1371/journal.pone.0219233.g002

Subsequent post-hoc analyses showed a significant decrease in participants’ positive affect before ( M = 31.12, SD = 5.79) and after ( M = 29.36, SD = 6.58) completing the memory task in the LS participants, t (57) = 2.48, p = .02, Cohen’s d = .28 but not for the negative affect, Cohen’s d = .07. A similar outcome was also shown in the HS condition, in which there was a significant decrease in positive affect only, t (60) = 3.45, p = .001, Cohen’s d = .37 (see Fig 2 ).

PA/NA difference on memory accuracy.

We predicted that LS participants will experience either an increase in NA and/or a decrease in PA since their smartphones were taken away and that this will affect memory recall negatively. Results showed that LS participants who experienced a higher NA difference had poorer memory recall accuracy ( r s = -.394, p = .002). We found no significant relationship between NA difference and memory recall accuracy for HS participants ( r s = -.057, p = .663, n = 61) and no significant relationship for PA difference in both HS ( r s = .217, p = .093) and LS conditions ( r s = .063, p = .638).

Relationship between phone conscious thought, smartphone addiction scale and mood changes to memory recall accuracy

Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity and homoscedasticity. There was a significant positive relationship between SAS scores and phone conscious thought, r S = .25, n = 119, p = .007. Using the enter method, we found that phone conscious thought explained by the model as a whole was 19.9%, R 2 = .20, R 2 Adjusted = .17, F (4, 114) = 7.10, p < .001. Phone conscious thought significantly predicted memory recall accuracy, b = -.63, t (114) = 4.76, p < .001, but not for the SAS score, b = .02, t (114) = 1.72, p = .09, PA difference score, b = .05, t (114) = 1.29, p = .20, and NA difference score, b = .06, t (114) = 1.61, p = .11.

Perception between phone usage and learning

For the participants’ perception of their phone usage on their learning and attention span, we found no significant difference between LS ( M = 4.22, SD = 1.58) and HS participants ( M = 4.07, SD = 1.62), t (117) = .54, p = .59, Cohen’s d = .09. There was also no significant correlation between perceived cognitive interference and memory accuracy, r = .07, p = .47.

We aimed [ 1 ] to examine the effect of smartphone presence on memory recall accuracy and [ 2 ] to investigate the relationship between affective states, phone conscious thought, and smartphone addiction to memory recall accuracy. For the former, our results were consistent with prior studies [ 11 – 13 ] in that participants had lower accuracy when their smartphone was next to them (HS) and higher accuracy when separated from their smartphones (LS). For the latter, we predicted that the short-term separation from their smartphone would evoke some anxiety, identified by either lower PA or higher NA post-test. Our results showed that both groups had experienced a decrease in PA post-test, suggesting that the reduced PA is likely to have stemmed from the prohibited usage (HS) and/or separation from their phone (LS). Our results also showed lower memory recall in the LS group who experienced higher NA providing some evidence that separation from their smartphone does contribute to feelings of anxiety. This is consistent with past studies in which participants reported increased anxiety over time when separated from their phones [ 14 ], or when smartphone usage was prohibited [ 17 ].

We also examined another variable–phone conscious thought–described in past studies [ 11 , 13 ], as a measure of smartphone addiction. Our findings showed that phone conscious thought is negatively correlated to memory recall in both HS and LS groups, and uniquely contributed 19.9% in our regression model. We propose that phone conscious thought is more relevant and meaningful compared to SAS as a measure of smartphone addiction [ 15 ] because unlike the SAS, this question can capture endogenous interruptions from their smartphone behaviour and participants were to simply report their behaviour within the last hour. The SAS is better suited to describe problematic smartphone use as the statements described behaviours over a longer duration. Further, SAS statements included some judgmental terms such as fretful, irritated, and this might have influenced participants’ ability in recalling such behaviour. We did not find any support for high smartphone addiction to low memory recall accuracy. Our participants in both HS and LS groups had similar high SAS scores, and they were similar to Kwon et al. [ 33 ] study, providing further evidence that smartphone addiction is relatively high in the student population compared to other categories such as employees, professionals, unemployed. Our participants’ high SAS scores and primary use of the smartphone was for social media signals potential problematic users [ 34 ]. Students’ usage of social networking (SNS) is common and the fear of missing out (FOMO) may fuel the SNS addiction [ 35 ]. Frequent checks on social media is an indication of lower levels of self-control and may indicate a need for belonging.

Our results for the presence of a smartphone and frequent phone conscious thought on memory recall is likely due to participants’ cognitive load ‘bandwidth effect’ that contributed to poor memory recall rather than a failure in their memory processes. Past studies have shown that participants with smartphones could generally perform simple cognitive tasks as well as those without, suggesting that memory failure in participants themselves to be an unlikely reason [ 1 , 3 , 5 ]. Due to our study design, we are unable to tease apart whether the presence of the smartphone had interfered with encoding, consolidation, or recall stage in our participants. This is certainly something of consideration for future studies to determine which aspects of memory processes are more susceptible to smartphone presence.

There are several limitations in our study. First, we did not ask the phone conscious thought at specific time points during the study. Having done so might have determined whether such thoughts impaired encoding, consolidating, or retrieval. Second, we did not include the simple version of this task as a comparison to rule out possible confounds within the sample. We did maintain similar external stimuli in their environment during testing, e.g. all participants were in one specific condition, lab temperature, lab noise, and thereby ruling out possible external factors that may have interfered with their memory processes. Third, the OS task itself. This task is complex and unfamiliar, which may have caused some disadvantages to some participants. However, the advantage of an unfamiliar task requires more cognitive effort to learn and progress and therefore demonstrates the limited cognitive load capacity in our brain, and whether such limitation is easily affected by the presence of a smartphone. Future studies could consider allowing participants to use their smartphone in both conditions and including eye-tracking measures to determine their smartphone attachment behaviour.

Implications

Future studies should look into the online learning environment. Students are often users of multiple electronic devices and are expected to use their devices frequently to learn various learning materials. Because students frequently use their smartphones for social media and communication during lessons [ 34 , 36 ], the online learning environment becomes far more challenging compared to a face-to-face environment. It is highly unlikely that we can ban smartphones despite evidence showing that students performed poorer academically with their smartphones presented next to them. The challenge is then to engage students to remain focused on their lessons while minimising other content. Some online platforms (e.g. Kahoot and Mentimeter) create a fun interactive experience to which students complete tasks on their smartphones and allow the instructor to monitor their performance from a computer. Another example is to use Twitter as a classroom tool [ 37 ].

The ubiquitous nature of the smartphone in our lives also meant that our young graduates are constantly connected to their smartphones and very likely to be on SNS even at work. Our findings showed that the most frequently used feature was the SNS sites e.g. Instagram, Facebook, and Twitter. Being frequently on SNS sites may be a challenge in the workforce because these young adults need to maintain barriers between professional and social lives. Young adults claim that SNS can be productive at work [ 38 ], but many advise to avoid crossing boundaries between professional and social lives [ 39 , 40 ]. Perhaps a more useful approach is to recognise a good balance when using SNS to meet both social and professional demands for the young workforce.

In conclusion, the presence of the smartphone and frequent thoughts of their smartphone significantly affected memory recall accuracy, demonstrating that they contributed to an increase in cognitive load ‘bandwidth effect’ interrupting participants’ memory processes. Our initial hypothesis that experiencing higher NA or lower PA would have reduced their memory recall was not supported, suggesting that other factors not examined in this study may have influenced our participants’ affective states. With the rapid rise in the e-learning environment and increasing smartphone ownership, smartphones will continue to be present in the classroom and work environment. It is important that we manage or integrate the smartphones into the classroom but will remain a contentious issue between instructors and students.

Acknowledgments

We would like to thank our participants for volunteering to participate in this study, and comments on earlier drafts by Louisa Lawrie and Su Woan Wo. We would also like to thank one anonymous reviewer for commenting on the drafts.

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Article Contents

Smartphone (over-)use and brain structure (structural mri studies), smartphone (over-)use and functional mri, summary of the current status and a roadmap for future research, conclusions, conflict of interest, acknowledgments and funding.

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Neuroimaging the effects of smartphone (over-)use on brain function and structure—a review on the current state of MRI-based findings and a roadmap for future research

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Christian Montag, Benjamin Becker, Neuroimaging the effects of smartphone (over-)use on brain function and structure—a review on the current state of MRI-based findings and a roadmap for future research, Psychoradiology , Volume 3, 2023, kkad001, https://doi.org/10.1093/psyrad/kkad001

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The smartphone represents a transformative device that dramatically changed our daily lives, including how we communicate, work, entertain ourselves, and navigate through unknown territory. Given its ubiquitous availability and impact on nearly every aspect of our lives, debates on the potential impact of smartphone (over-)use on the brain and whether smartphone use can be “addictive” have increased over the last years. Several studies have used magnetic resonance imaging to characterize associations between individual differences in excessive smartphone use and variations in brain structure or function. Therefore, it is an opportune time to summarize and critically reflect on the available studies. Following this overview, we present a roadmap for future research to improve our understanding of how excessive smartphone use can affect the brain, mental health, and cognitive and affective functions.

At the time of writing, more than six billion smartphone subscriptions have been estimated for the year 2022 (Statista, 2022 ). This tremendously high number reflects that over the last 15 years—since the inception of the iPhone in 2007 (Macedonia, 2007 )—a global mobile digital revolution happened leading to ubiquitously and permanently available smartphone technologies around the world. Smartphones enable us to find our way in unknown territory, to initiate and maintain social communications, to join and provide content for social networks, and find information on whatever we are interested in as long a network signal is available. Due to the numerous applications in occupational and recreational contexts the smartphone has attracted many users around the globe. The gigantic rise of social media over the last 15 years is highly interwoven with the digital mobile revolution (Jurgenson, 2012 ; Korolija, 2020 ).

Adverse consequences of excessive smartphone use

Despite the many obvious improvements in our daily life by the smartphone, the scientific and public debates have drawn attention toward potential detrimental effects of smartphone (over-)use on different levels ranging from the societal to the individual level. Initial evidence suggests negative effects of excessive smartphone use on cognitive and affective domains, such that excessive smartphone use has being linked to lower productivity (Duke & Montag, 2017 ), lower learning outcomes/academic achievements (Sapci et al ., 2021 ; Sunday et al ., 2021 ), and elevated levels of negative emotionality (Elhai et al ., 2019 ; Montag et al ., 2016 ). Moreover, being distracted by the smartphone in the traffic represents a considerable danger on the road (Jannusch et al ., 2021 ; Rosenthal et al ., 2022 ). Although the cross-sectional nature of many of the available studies does not allow to make causal interpretations with respect to the associations—as it is, for example, also conceivable that higher negative emotionality renders individuals at an increased risk to develop escalating smartphone use—the reported associations between excessive smartphone use and adverse outcomes led to scientific and societal debates around the globe. Most of the research endeavors to date have been trying to understand how the digital revolution and the ubiquitous use of digital platforms may affect mental health, daily life functioning, social interactions, and also the brain (Firth et al ., 2019 ; Montag & Diefenbach, 2018 ). In the context of smartphone use, much research has focused in recent years on the question of how (excessive) smartphone use is linked to altered cognition (Liebherr et al ., 2020 ) and whether excessive smartphone use resembles an addictive behavior.

Debate on the medium versus function of smartphones

One critical debate concerns the differentiation of the medium versus function in the discussion on “smartphone addiction.” From the perspective of substance addiction, it would clearly be misleading to refer to being addicted to bottles, but reasonably the content of the bottle matters (Kuss & Griffiths, 2017 ; Panova & Carbonell, 2018 ). Applying this example to the area of excessive smartphone use or “smartphone addiction,” one would need to disentangle which specific contents the smartphone transmits and ultimately which specific needs these contents fulfill. Initial evidence suggests that drivers of excessive smartphone use likely are social media applications (Montag, 2021 ; Rozgonjuk et al ., 2020 ; Sha et al ., 2019 ) promoted through highly immersive app designs (Montag, Lachmann, et al ., 2019 ). Of note, many social media apps need to be considered in the context of the so-called data business model, which aims to prolong online time to enlarge the digital footprints of their users (Montag & Hegelich, 2020 ). Other drivers of excessive smartphone use might be video games on the phone (Leung et al ., 2020 ) and other functions such as unregulated access to work-related e-mails (Sadeghi et al ., 2022 ), which in turn might lower well-being (Kushlev & Dunn, 2015 ). In the context of ongoing debates about the conceptualization of excessive and potentially harmful smartphone use a very recent systematic meta-analysis reported global prevalence rates as high as 26.99% (Meng et al ., 2022 ) and another work mentioned an increase of “smartphone addiction” around the world (Olson et al ., 2022 ). Interestingly, the work by Meng et al . ( 2022 ) observed that prevalence rates of smartphone addiction are particularly high among adults (26.84%), followed by adolescents (21.62%), and children (15.19%). Males and females did not differ significantly. For a review on antecedents and consequences of smartphone addiction, see a recent overview by Busch & McCarthy ( 2021 ).

Is the actual nature of excessive smartphone use “addictive”?

In addition to the need for a more detailed examination with respect to which specific contents on smartphones drive excessive use, a large and growing number of studies (primarily employing self-report questionnaires) has examined excessive smartphone behavior within an addiction framework. Within the addiction framework, one would hypothesize that excessive—and ultimately problematic—use of the smartphone would be characterized by symptoms such as loss of control and preoccupation with use, reflected in, for example, unsuccessful attempts to reduce use or continued use despite negative consequences, as well as social problems and functional impairments in daily life—which represent key diagnostic symptoms of both substance and behavioral addictions (e.g. see also the new Gaming Disorder criteria by the WHO; Montag, Schivinski et al ., 2021 ). Of note, the duration of use, so spending a considerable amount of time on the smartphone, is not a distinct criterion per se. On the one hand, not everyone who spends much time on the smartphone shows problematic smartphone use behavior (for instance, the time could reflect constant job-related smartphone use); however, on the other hand, those using the smartphone in an “addictive” way will inherently spend much of their time on the smartphone. The most prominent self-report measures to assess problematic use of the smartphone are currently perhaps Kwon's Smartphone Addiction Scale (SAS) (Kwon et al ., 2013 ) and the Korean Smartphone Addiction Proneness Scale (SAPS) (D. Kim et al ., 2014 ). This notion is also supported by their use in many of the brain imaging studies discussed in the present review.

While the introduced scales and several of the current studies refer to smartphone addiction some researchers argue that the term “addiction” should not be simply transferred into the context of smartphone use (Panova & Carbonell, 2018 ) or propose that the term should be abandoned (Carbonell et al ., 2022 ) “to refocus clinical and research efforts on real disorders” (p. 2). Further, many researchers refer in the context of smartphone addiction research to problematic smartphone use as a more neutral term (Elhai et al ., 2017 ; Fischer-Grote et al ., 2019 ), while others prefer terms such as Smartphone Use Disorder (SmUD) to align the terminology with substance addictions and addictive behaviors in the ICD-11 of the World Health Organization (Gao, Jia, et al ., 2020 ; Gao, Sun, et al ., 2020 ). We currently consider the term SmUD as most suitable as it aligns well with the WHO terminology of Gaming Disorder in the realm of addictive behaviors (Marengo et al ., 2020 ; Montag et al ., 2021 ), but explicitly state that SmUD (or the other terms used) are not officially recognized at the moment and in many studies at best tendencies toward SmUD are investigated. Moreover, we have iterated already on the many problem areas when using terms such as smartphone addiction or SmUD. It is important to not overpathologize everyday behavior, and this is what needs to be kept in mind in this research field (Billieux et al ., 2015 ) until evidence from different areas comes up to understand the actual nature of excessive behavior (Brand et al ., 2020 )—here, excessive smartphone use. A final thought on the terminology: the umbrella term “Smartphone Use Disorder”—instead of focusing exclusively on excessive use of single app groups such as game apps or social-media apps—might also be relevant, when people show problematic use behavior in several categories on their phones. In this case, the SmUD terminology can summarize the problematic behavior being observed across several apps on the smartphone.

Although research on the detrimental effects of smartphone (over-)use or SmUD has only recently begun, initial studies have employed neuroimaging techniques, in particular magnetic resonance imaging (MRI), to explore associations between excessive smartphone use and individual variations in brain structure and function. These variations may reflect adaptations that neurally underlie excessive use or mediate the association between problematic smartphone use and affective, cognitive, and behavioral dysregulations. The initial yet rapidly growing literature has used different MRI-based approaches to examine variations in the structural organization of the brain using e.g. voxel-based morphometry (VBM) or diffusion tensor imaging (DTI), the functional organization of the brain using resting state functional MRI (fMRI) techniques, and functional alterations during cognitive and affective processes, using task-based fMRI (Ahn et al ., 2021 ; Arató et al ., 2023 ; Cho et al ., 2021 ; Choi et al ., 2021 ; Chun et al ., 2017 , 2018 ; Han & Kim, 2022 ; Hirjak et al ., 2022 ; Horvath et al ., 2020 ; Hu et al ., 2017 ; Kwon et al ., 2022 ; Lee et al ., 2019 ; D. Liu et al ., 2022 ; Lou et al ., 2019 ; Paik et al ., 2019 ; Pyeon et al ., 2021 ; Rashid et al ., 2021 ; Schmitgen et al ., 2020 , 2022 ; Tymofiyeva et al ., 2020 ; Wang et al ., 2016 ; Yoo et al ., 2021 ; Zou et al ., 2021 , 2022 ). Please note that the papers cited here will be reviewed in the next section of the present work (for a summary, see Tables  1 and  2 ; see also an overview in Fig.  1 ).

Structural and functional MRI studies on SmUD tendencies (also known as smartphone addiction or problematic smartphone use) as reviewed in the present work.

Findings from structural MRI studies on smartphone (over-)use (in alphabetic order following the surname of the first author); gray shaded boxes represent DTI studies, the Wang et al . ( 2016 ) and Zou et al . ( 2021 ) studies in the table include both a VBM and DTI approach.

GMV = gray matter volume, SPA = smartphone addiction, PSU = problematic smartphone use (smartphone use disorder tendencies), SPAI = Smartphone Addiction Inventory, TBSS = tract-based spatial statistics, SAS-SV = SAS-Short Version, MPAI = Mobile Phone Addiction Index, MPD = Mobile Phone Dependent group.

Findings from functional MRI studies on smartphone (over-)use (in alphabetic order following the surname of the first author); gray colored parts of the table represent task-based fMRI studies, the remaining studies represent resting state fMRI studies.

Meanwhile >20 studies on MRI-neuroimaging and excessive smartphone use or SmUD have been published and the number has strongly increased since 2020. Within this context, it is an opportune time for a review that (i) critically reflects on where the field stands and how strong the evidence is for smartphone-use associated brain changes, and (ii) provides a roadmap that outlines critical issues in the field and next steps that can help to shed light on the cognitive, affective, and neurobiological basis of smartphone (over-)use.

Neuroimaging of SmUD

The last few years have seen a strong increase in studies investigating individual differences in SmUD and associated brain variations by means of MRI. As depicted in Fig.  1 , the studies encompass structural MRI focusing on use-associated variations in gray and white matter as well as functional MRI studies examining associations with the intrinsic functional organization of the brain or during engagement in cognitive and affective tasks.

Associations between SmUD and the structural organization of the brain have been examined on the level of gray and white matter. Further, different methodological strategies including the use of individual differences association designs (e.g. examining linear relationships between the level of SmUD and brain structural variations) as well as between group designs aiming to examine brain structural differences between groups of individuals with high and low SmUD levels have been implemented. Differences in the gray matter organization of the brain are commonly examined by means of voxel based morphometry of T1 images [Ashburner & Friston ( 2000 ); for recent methodological aspects see the work by Zhou et al . ( 2022 ) and for information on cortical thickness or cortical folding patterns the work by Chen et al . ( 2013 ); additional insights can be derived from the work by Jiang et al . ( 2022 )]. Investigations on the level of the white matter tract organization are for instance examined using DTI (and the application of tract based spatial statistics: Bach et al ., 2014 ). While structural brain imaging provides insights into the structural brain architecture, fMRI is applied to study the intrinsic functional organization of the brain (resting state fMRI) or the engagement of specific brain regions during cognitive or affective task paradigms (task-based fMRI). To better understand individual differences in SmUD tendencies both task-based fMRI and resting state fMRI have been applied. During task-based fMRI studies, the individuals engage into specific cognitive or affective processes of interest, e.g. viewing a smartphone stimulus that can trigger "cue-reactivity." Cue-reactivity is a process during which a stimulus that is frequently paired with the addictive substance or the addictive behavior gains strong incentive salience (e.g. Yu et al ., 2020 ; X. Zhou et al ., 2019 ). In contrast, resting state fMRI aims to gain insights into the intrinsic functional architecture of the brain ("at rest") while the participants do not engage in a specific mental operation ("do not think of something in particular") (Gonzalez-Castillo et al ., 2021 ; Markett et al ., 2018 ).

The structural and functional MRI-approaches have been extensively used—either separately or in combination—to determine the brain basis of substance-related and (established) behavioral addictions (for quantitative and qualitative reviews, see also Klugah-Brown et al ., 2021 ; Taebi et al ., 2022 ; Tolomeo & Yu, 2022 ; and Zilverstand et al ., 2018 for examples). The combination of the different imaging approaches can allow a more holistic evaluation at different levels and allow to examine different research questions with respect to potential addiction-related changes. The present review aims to provide a brief overview on the smartphone-(over-)use MRI literature, and is divided in both structural and functional MRI sections summarizing the results of the current literature (Fig. 1 ). Next, the review presents a roadmap for future studies in the field of SmUD and associated brain changes.

As becomes apparent in Table  1 , several studies examined differences in gray matter brain volumes in the context of SmUD tendencies. Overall deriving a consistent picture of SmUD tendency associated variations in brain structure is currently limited by the use of varying SmUD assessments in these studies. Moreover, differences in MRI analysis strategy may further limit direct comparisons. In this context, recent studies have shown that the specific brain structural variations that are identified may strongly depend on the choice of processing pipeline (see Zhou et al ., 2022 for a methodological evaluation of the effects of choice of processing pipeline on brain structural analyses). Moreover, some of the identified studies focused in their analysis on specific hypothesis-driven brain regions such as the brain stem (Cho et al ., 2021 ) or specifically on striatal morphology (Yoo et al ., 2021 ), whereas other studies used whole-brain analytic approaches (Horvath et al ., 2020 ; Rashid et al ., 2021 ). Further complicating matters is the use of different rigor in the brain analysis and in the level of description of the analyses used, such that for several studies the exact multiple comparisons approach used remains unclear.

An initial overview of the reported associations between brain volume and SmUD tendencies mostly suggests an association of inverse nature (such that higher SmUD tendencies associated with lower volumes in specific brain regions, e.g. Wang et al ., 2016 ; Zou et al ., 2021 ). Accordingly, between-group designs comparing participants with high versus low SmUD often revealed decreased regional brain volumes in the group of excessive smartphone users compared to control individuals (e.g. Lee et al ., 2019 ; Yoo et al ., 2021 ). With respect to the volumetric gray matter approach, studies reported lower gray matter volume in regions such as the anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), fusiform gyrus, parahippocampal regions, and the striatum (caudate). These regions partly resemble regions that have been identified in previous works examining brain volumetric alterations in substance and behavioral addictions (e.g. see the following studies: Klugah-Brown et al ., 2021 ; Koester et al ., 2012 ; Qin et al ., 2020 ; Yu et al ., 2022 ; Zhang et al ., 2021 ). However, none of the regions consistently replicated across SmUD studies and, together with the lack of standardized SmUD assessments and brain structural analyses strategies, the previous studies currently do not allow to draw clear conclusions with respect to the specific regions that might show brain structural alterations associated with excessive smartphone use. While the lack of consistently reported regions and the methodological limitations do not allow a clear interpretation of the underlying brain structural variations, previous studies in behavioral addictions have associated volumetric decreases in some of the mentioned regions with, for example, the severity of problematic online gaming or social media engagement (Montag et al ., 2018 ; F. Zhou et al ., 2019 ) or higher impulsivity in cocaine dependent individuals (Moreno-López et al ., 2012 ), which may suggest an association with key features of addiction. For details on the specific regions reported in the SmUD studies, please see also Table 1 .

Although the reviewed number of structural studies - from our perspective - is currently too small and heterogenous to support an overarching picture, we further refer to studies that examined either white matter tract integrity (Hu et al ., 2017 ; Tymofiyeva et al ., 2020 ; Zou et al ., 2021 ) or variations in cortical folding (Hirjak et al ., 2022 ) in the context of SmUD tendencies.

Table  2 shows studies investigating the neural correlates of SmUD tendencies using fMRI. Most previous studies employed a resting state fMRI approach examining associations between SmUD tendencies and the intrinsic functional organization of the brain (Ahn et al ., 2021 ; Chun et al ., 2018 ; Horvath et al ., 2020 ; Kwon et al ., 2022 ; D. Liu et al ., 2022 ; Lou et al ., 2019 ; Paik et al ., 2019 ; Pyeon et al ., 2021 ; Rashid et al ., 2021 ; Schmitgen et al ., 2022 ; Zou et al ., 2022 ). A direct comparison between the studies is hindered by differences in the methodological approaches, ranging from different preprocessing methods to different network analytic strategies. The studies reported potential associations between SmUD tendencies and variations in the intrinsic architecture of a number of brain systems and large-scale networks, including for instance altered connectivity of striatal, limbic, and frontal regions (e.g. Pyeon et al ., 2021 ; Zou et al ., 2022 ; Chun et al ., 2018 ; Paik et al ., 2019 ), as well as altered functional interaction between and within large scale networks including the default mode network and the salience network (Ahn et al ., 2021 ; Kwon et al ., 2022 ). While the different approaches employed in these studies and the methodological limitations prevent clear conclusions at the present stage, the identified pathways partly overlap with the intrinsic pathways and networks that have been identified in substance and behavioral addictions (Zhou et al ., 2018 ; Taebi et al ., 2022 ; Tolomeo & Yu, 2022 ; Yan et al ., 2021 ; Zimmermann et al ., 2018 ). Within the context of the previous literature on the role of dysregulations in the intrinsic functional organization of the brain in addiction, it is conceivable that alterations in specific systems may promote different symptomatic features. Alterations in salience and executive control systems may, for instance, underly dysregulations in several affective and cognitive domains, while alterations in the striato-frontal organization may reflect the development of compulsive behavior and alterations in the default mode network may promote dysfunctional self-related decision-making (Taebi et al ., 2022 ; Tolomeo & Yu, 2022 ; Yan et al ., 2021 ; Yu et al ., 2022 ; Zhang & Volkow, 2019 ; X. Zhou et al ., 2019 ; Zimmermann et al ., 2018 ). However, clear conclusions with respect to consistent and robust effects of excessive smartphone use or SmUD on the functional architecture of the brain remain to be determined.

Five studies investigated the neural basis of altered cognitive and affective behaviors related to SmUD tendencies by means of task-based functional MRI (Arató et al ., 2023 ; Choi et al ., 2021 ; Chun et al ., 2017 ; Han & Kim, 2022 ; Schmitgen et al ., 2020 ). An early study by Chun et al . ( 2017 ) examined facial emotion processing alterations in participants with high SmUD versus participants with low SmUD and found that the high SmUD group displayed decreased activiation in the dorsolateral prefrontal cortex and dorsal ACC during the presentation of angry faces. Schmitgen et al . ( 2020 ) used a cue reactivity paradigm during which participants were presented with smartphone or neutral images and reported group differences between participants with high and low SmUD tendencies in several regions including anterior cingulate, medial prefrontal, and temporal regions. Choi et al . ( 2021 ) used a modified version of a cognitive conflict task and reported that participants with high SmUD exhibited lower task performance accompanied by enhanced recruitment of fronto-parietal regions. Han and Kim ( 2022 ) used a modified oddball task in participants with high versus low risk for SmUD and observed attention filtering impairments and a lower engagement of the frontopolar cortex in participants at high risk for SmUD. The most current study by Arató et al . ( 2023 ) applied a facial emotion recognition paradigm, where higher scores in the smartphone application-based addiction scale were associated with higher functional connectivity among brain regions related to emotional/cognitive control. While these findings may reflect that (social) cognitive and addiction-related changes in SmUD are accompanied by changes in corresponding brain systems, the low number of available studies and some other methodological limitations, such as the comparably small samples and lack of replication designs, do not allow to draw conclusions at present with respect to robust task-based brain functional alterations related to excessive smartphone use. Please note that two studies are not listed in Table 2 as they did not investigate SmUD tendencies, but either directly investigated links between objective smartphone use measures and resting state fMRI (Huckins et al ., 2019 ) or applied a general screen time self-report measure/time spent on reading to investigate functional connectivity in children (Horowitz-Kraus & Hutton, 2018 ).

From the literature review, it becomes apparent that although the smartphone technology has now been available for over 15 years and the detrimental consequences of excessive smartphone use have been increasingly debated, the present knowledge about how smartphone use affects our neurobiology or is linked to variations in brain structure and function still is very limited. The available literature—although growing—does currently not allow us to draw firm conclusions with respect to potential effects of excessive smartphone use on the brain. This is partly because no consensus exists on which inventories to best use to assess smartphone (over-)use and a lack of studies including “objective” tracked smartphone data in the available MRI literature (see exceptions in studies such as those by Huckins et al ., 2019 and Montag et al ., 2017 ). While the conventional (neuroimaging) studies in this field employ a combination of self-report data for determining the severity or the extent of smartphone (over-)use with respect to symptoms or actual duration of use, “objective” in this context refers to tracked behavior on smartphones providing quantifiable and precise data of actual use, including, for example, how often a person checks the phone, what apps are used in particular, etc. (for a tracking app, see Montag, Baumeister, et al ., 2019 ). This approach is often described as digital phenotyping or mobile sensing in the literature (Baumeister & Montag, 2023 ). In this context, it is of importance to mention that SmUD tendencies not necessarily need to strongly overlap with time spent on the phone [“not everyone spending much time on the phone is addicted”; see also lack of association between fear of missing out (FOMO) with actual phone behavior (Rozgonjuk et al ., 2021 )]. With respect to the design of the studies, several of the studies cited here are underpowered for determining robust brain alterations in SmUD and some studies do not adhere to multiple correction procedures, which are the current standard in the field. Finally, the predominance of retrospective cross-sectional study designs limits the conclusions that can be drawn with respect to disentangling predisposing brain variations from effects that are directly linked to smartphone (over-)usage.

Nevertheless, the available literature suggests a potential association between smartphone (over-)use and variations in brain structure and function that may mediate cognitive and behavioral changes, as well as detrimental effects on mental health and probably even addictive usage. Going forward, it will be essential to apply strategies and experimental designs that have been evaluated in the context of other mental disorders to disentangle the potential impact of different factors and thus to describe potential smartphone (over-)use associated brain changes. In contrast to other fields of research on the neurobiological basis of addiction, i.e. substance addiction, animal models for SmUD have not been developed and it will be challenging—or even impossible—to develop corresponding mechanistic animal models. Within this context, neuroimaging potential brain changes in humans will be even more important as a strategy to determine the underlying neurobiological and potentially neuropathological pathways. Based on the present overview, we outline the following key questions and strategies as a roadmap on the way forward to determining brain changes associated with smartphone (over-)use.

SmUD (or smartphone addiction/problematic smartphone use) consists of many symptoms such as loss of control, functional impairments due to excessive use of the smartphone, and preoccupation with the smartphone, etc. Therefore, it is of importance to not only understand how overall SmUD scores are linked to brain structure and function, but also the different symptoms/facets. Initial studies have begun to determine separable and common brain alterations associated with different facets of general internet gaming behavior (e.g. Yu et al ., 2022 ). Within the SmUD context similar approaches may allow to better describe associations between specific symptomatic and behavioral dysregulations and associated brain changes. Moreover, we mention that several studies are hard to compare because different inventories to assess SmUD have been applied, as no agreement exists regarding a conceptual framework (but see Billieux's framework to understand problematic smartphone use; Billieux, 2012 ). Beyond this, it will be vital to disentangle brain changes that are specifically associated with SmUD and to separate these from other psychological processes that might be associated with or even inherently linked with SmUD. The construct fear of missing out (FOMO) for instance has gained increasing interest in the field of digital addictions (Elhai, Yang, Montag, et al ., 2020 ; Elhai, Yang, Rozgonjuk, et al ., 2020 ) and has been associated with individual variations in brain morphology (Wang et al ., 2022 ). Moreover, it will be critical to further separate brain changes related to specific or unspecific pathology relevant domains such as depression and anxiety—which have been related to brain structural and functional variations (X. Liu et al ., 2021 , 2022 ; Serra-Blasco et al ., 2021 ; Wise et al ., 2017 )—from variations that specifically associate with SmUD.

The literature search demonstrated (see also Fig.  1 ), that only few studies applied task-based fMRI methods in the field of SmUD. While resting state and brain structural approaches may allow to determine variations in the intrinsic architecture of the brain, task-based fMRI studies will further allow to determine the neural alterations that underlie dysregulations in domains that have been found to be disrupted across other addictive disorders. Promising underlying domains in this respect may be to examine whether: (a) smartphone-associated stimuli have gained an increased salience or even engage habit and compulsive use associated circuits during cue reactivity paradigms (for substance related addictions, see e.g. Vollstädt-Klein et al ., 2010 ; X. Zhou et al ., 2019 ; for behavioral addictions, see e.g. L. Liu et al ., 2017 ; for SmUD see Schmitgen et al ., 2020 ); (b) whether cognitive functions, in particular executive functions and the underlying fronto-parietal networks, show alterations in SmUD (for studies in other addictions please see Klugah-Brown et al ., 2021 ; Zheng et al ., 2019 ); and whether brain systems involved in (c) emotion and stress reactivity; or (d) natural reward processing are affected by SmUD (for studies in other addictions see e.g. Luijten et al ., 2017 ; J. Zhang et al ., 2020 ; Zhao et al., 2020 ).

Aside from self-reported SmUD tendencies, more studies need to correlate objective tracked smartphone use with brain data to add a further data layer to the neuroscientific study on smartphone use (Montag, Elhai, et al ., 2021b ). Meanwhile, it became clear that humans have problems in correctly assessing their technology use, in particular regarding the quantity of technology use (Parry et al ., 2021 ).

To our knowledge no study in the field investigated potential changes of the brain due to smartphone use in term of structure and function with repeated MRI measures. This will be of particular importance within prospective longitudinal designs that hold the promise to separate predisposing brain alterations that render participants at an increased risk of developing SmUD from effects that are rather a consequence of escalating smartphone use or develop in association with the transition to addictive use (for prospective longitudinal designs in substance addiction research, see also the following studies: Becker et al ., 2013 , 2015 ; Jager et al ., 2007 ). The implementation of prospective longitudinal designs would allow to draw stronger conclusions with respect to whether and how the smartphone technology affects human neurobiology (for comparable approaches in the field of behavioral addictions, see also previous studies: Gleich et al ., 2017 ; Kühn et al ., 2018 ; Yu et al ., 2020 ; Zhou et al ., 2019 ).

The scientific works available in the field usually study the different MRI sources in an independent fashion, hence they correlate the smartphone behavior or SmUD scores with the brain data without shedding light on what differences in structure mean for functionality of the brain when studying smartphone use. Bringing these different brain sources together in a meaningful fashion would open interesting research avenues.

As already mentioned, to understand how smartphone (over-)use affects human neurobiology, a closer look needs to be taken on what smartphone applications humans use in what intensity and in what context. A taxonomy of different smartphone use patterns will be needed to be taken into account to better grasp the nature of smartphone (over-)use and potential brain changes (Marengo et al ., 2021 ; Montag et al ., 2021 ). Generalized views on overusing the smartphone might be helpful to get a bird's eye view on the topic, but consuming different contents might lead to different results when one is trying to understand the neurobiology of smartphone (over-)use. See also exemplary research in related areas investigating general social media (over-)use, specific social media (over-)use or e-mail (over-)use (He et al ., 2017 ; Lee et al ., 2021 ; Montag et al ., 2017 , 2018 ; Nasser et al ., 2020 ; Sadeghi et al ., 2022 ; Sherman et al ., 2016 ; Turel et al ., 2014 , 2018 ), which at best would directly be also put in the context of smartphone (over-)use research (seldom done at the moment). We also mention highly interesting work investigating smartphone touchscreen use and the brain (Balerna & Ghosh, 2018 ; Gindrat et al ., 2015 ).

Finally, the present review showed that most studies (to our knowledge) focused on the study of SmUD or related topics by means of MRI. There is so much else to be studied in the context of smartphone use—which likely will result in the study of so called digital biomarkers (Montag, Elhai, et al ., 2021a ). By this, we mean that the digital footprints left on smartphones (and other devices of the Internet of Things) can help us to get insights into the neurobiology of a person (Montag, Elhai, et al ., 2021b ). Given that the smartphone is our companion with whom we interact in many everyday life situations, it is not surprising that the smartphone can provide a detailled characterization of behavioral, cognitive and affective domains and this could inform not only psychological but also neuroscientific approaches.

An increasing number of studies suggests sex differences in the brain correlates of addiction (see e.g. Grace et al ., 2021 ) and going forward it will be important to explore potential differences in the effects of smartphone (over-)use in men and women. Moreover, it will be important to determine the effects of smartphone (over-)use on the brain over the life span, it is e.g. conceivable that in particular developing brains are more sensitive to the impact of excessive usage (but see interesting opposing prevalence numbers as mentioned above; Meng et al ., 2022 ).

In terms of a general challenge of the MRI-based research field it will be vital to better address and enhance the replicability of MRI research (see e.g. Klugah-Brown et al . ( 2022 ) for an example of replicable brain structural markers in behavioral addictions), employing designs that extend the view of the traditional case-control designs in neuroimaging of mental disorders (Etkin, 2019 ), and implement transparent data and code sharing as well as preregistration of studies that allow an a priori specification of brain-based hypotheses (see also Nichols et al ., 2017 ; Poldrack et al ., 2017 ).

The present paper provides an initial overview on research examining potential brain changes related to smartphone (over-)use from studies applying MRI techniques (studies are of cross-sectional nature though). While many studies observed functional and structural differences and associations with SmUD in brain systems spanning cortical and subcortical regions involved in reward/motivational processes, affective and cognitive domains, and the development of addictive behavior, the current evidence remains patchy and overarching neuroscientific frameworks uniquely touching on smartphone (over-)use are lacking. The studies do not allow us to determine whether the observed brain characteristics are a result of a certain kind of smartphone use or merely represent a predisposition to use the smartphone in a certain kind of way. Many study findings are based on small study populations. In this context, prospective longitudinal designs and replication studies are needed soon to determine the direction of the associations and the robustness of the findings. Finally, MRI, although being a powerful tool to understand the human brain, comes with technical limitations—among others a limited temporal resolution. Therefore, multimodal assessments that integrate the advantages of different brain imaging methods are warranted. Within this context, we shortly hint toward already existing literature investigating the present smartphone use complex with means of electroencephalography (S.-K. Kim et al ., 2015 ; Weon, 2017 ), fNIRS (Li et al ., 2022 ; Xiang et al ., 2023 ) as well as positron emission tomography (Westbrook et al ., 2021 ). The combination of advanced prospective study designs with different neuroscientific techniques (also hormones and genetics) can promote a better and more complete understanding of the neurobiological changes related to smartphone (over-)use.

The author B.B. is editorial-board member of Psychoradiology . He was blinded from the review process and making decisions on the manuscript.

C.M. reports no conflict of interest. Nevertheless, for reasons of transparency, C.M. mentions that he has received (to Ulm University and earlier University of Bonn) grants from agencies such as the German Research Foundation (DFG). C.M. has performed grant reviews for several agencies; has edited journal sections and articles; has given academic lectures in clinical or scientific venues or companies; and has generated books or book chapters for publishers of mental health texts. For some of these activities he received royalties, but never from the gaming or social media industry. C.M. was part of a discussion circle (Digitalität und Verantwortung: https://about.fb.com/de/news/h/gespraechskreis-digitalitaet-und-verantwortung/ ) debating ethical questions linked to social media, digitalization, and society/democracy at Meta. In this context, he received no salary for his activities. C.M. currently functions as independent scientist on the scientific advisory board of the Nymphenburg group (Munich, Germany). This activity is financially compensated. Further, he is on the scientific advisory board of Applied Cognition (Redwood City, CA, USA), an activity that is also compensated.

The present study was partly supported by the China Brain Project (MOST2030, grant no. 2022ZD0208500), National Natural Science Foundation of China (grant no. NSFC 82271583; 32250610208) and the National Key Research and Development Program of China (grant no. 2018YFA0701400).

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• 29 May 2023

Episode 27: Our mobile world: How the cell phone is changing science and research

• Subhra Priyadarshini

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A researcher documenting ant colonies. Credit: Subhra Priyadarshini

Does the mobile phone have a place in the lab?

The smartphone is a great example of technology leapfrog in countries like India, where a vast majority of phone users never had a landline. The increasing penetration of affordable mobile phones in developing countries is now making it possible for scientists to conduct meaningful and timely research, in the lab, field or while working from home.

Nature India's 'Our mobile world' podcast series will look at the many ways in which the smartphone has changed India’s science-society dynamics and the way researchers work. We will look at themes ranging from smartphones as enablers of science and research in India, to digital health, digital illiteracy, research around mobile phone e-waste, the gender digital divide and innovations in healthcare, medicine, agriculture and governance. We've chosen stories predominantly from India but also have examples from other counties in the global south.

Host: Subhra Priyadarshini, production and script: Aroma Warsi, sound editing: Prince George.

doi: https://doi.org/10.1038/d44151-023-00061-9

(Lightly edited for readability)

Speakers : Subhajit Bandyopadhyay, Preethi Jyothi, Jayashree Balasubramaniam, Subhra Priyadarshini

00:02 Support announcement : This episode is produced with support from DBT Wellcome Trust India Alliance.

00:30 Subhra Priyadarshini : The mobile phone. Yes, that’s the subject of our new podcast season. It’s ubiquitous, its indispensable, it’s almost like an extension of your hand. In many countries of the global south, such as India, the smartphone is a great example of technology leapfrog, as a vast majority of phone users never had a landline and were introduced to phones with the handheld phone.

And, of course, the increasing penetration of affordable mobile phones in developing countries is also making it possible for scientists to conduct meaningful and timely research, in the lab, in the field or while working from home, especially what we saw during the COVID-19 pandemic.

I am your host Subhra Priyadarshini, and in this new season of the Nature India podcast, I will explore how the mobile phone has changed India’s science-society dynamics as well as the way scientists, researchers and policy makers work. In today’s episode we will specifically look at smartphones as enablers of science and research. We will talk about the use of mobile phones for research and data collection, crowdsourcing and science education.

In short, does the lab have a place for the mobile phone? Let’s find out.

Up first, we talk of the use of mobile phones in a science laboratory setting. Convenient, right? When you don’t have a laptop handy. But can they also replace bulky, expensive scientific instruments in the lab or help set up labs, for instance, in remote places? We ask Subhajit Bandyopadhyay, a professor in the Department of Chemical Sciences at the Indian Institute of Science, Education and Research, Kolkata.

2:37 Subhajit Bandyopadhyay : Oh, yes, of course. A mobile phone can be used as a great tool, because it has so many features. I teach chemistry, and we deal with a lot of problems that are associated with chemistry. So quite often, you use instruments called spectrophotometers. And what it does is, it would tell you, very simplistically, a lot about the intensity of light and how it various wavelengths and so on. Typical spectrophotometric would be quite expensive. So if in village schools where you don't really have a stable power supply, and if the funding situation is not that great. We have developed programs, which could be used by schoolchildren, to supplement spectrophotometers. And they can do certain experiments like chemical kinetics and stuff with these cell phones. So it's basically free. And it's really easy to use. And, you know, the precision would not be as good as the spectrophotometer. But it's pretty good.

3:38 Subhra Priyadarshini : Right. And while mobile apps can provide easy access to scientific information, analysis, or simulations, or making learning and experimentation more engaging and accessible, imagine if you are colour blind or have impaired vision and can’t differentiate between all the colourful liquids in a chemistry lab. Subhajit and his team developed a smartphone app that helps colour-blind and visually impaired students detect colour change in a routine lab experiment, thereby ensuring their active participation and independence in the lab.

6:11 Subhajit Bandyopadhyay : We developed this a few years ago. About 8% of the male population of the world is colour blind. And about 0.5% of the female population of the world is colour blind. Now that's, that's really a big number. I'm thinking of a classroom of 80 students or, or sometimes in big colleges, it's over 100 students, you have a large number of students who are colour blind. Now, these students cannot really perform the chemistry experiments, because very often this chemistry experiments would involve colours. For example, the basic experiment of titration, acid base titration, or redox titration would involve colours. So what we did was we basically use this mobile phone camera and translated the colour data to something which was easy for a student with color blindness to perceive. For example, when the there is a change in the colour from colourless to red, the screen would indicate the colour change. At the same time, there will be other indicators like beeping sound, or it would vibrate.

Really was a very rewarding experience for me. So a few years ago, I went to Vietnam and one of the students told me that he was colour blind. And he said, he uses a particular programme that helps him greatly, and he takes out the phone and shows me my programme. So it was really a wonderful experience for me.

The application records the colour information. Hue Saturation and Value colour space and when there is a change in colour, it basically says there is a colour change by various means like beep sounds or vibration pulses.

6:11 Subhra Priyadarshini : One of Subhajit’s students Balraj Rathod, now a PhD scholar at the University of British Columbia in Canada, helped the team make this app.

Now, mobile phones have also emerged as supplementary teaching methods by providing access to educational resources, remote communication and multimedia learning. Preethi Jyothi, a faculty member in the Department of Computer Science at IIT Bombay uses it as a teaching aid.

6:53 Preethi Jyothi : So to give an example, smartphones now have lots of these built-in sensors. And using the sensors, you could teach fundamental concepts in physics, like, motion, and pressure, and so on. Typically abstract concepts, but using smartphones to make lab lessons applications involving these concepts would really reinforce the student's interest in learning,specific concepts. and also language learning. when you're trying to speak a new language, how to pronounce words, and so on, if you have apps on your smartphones, which will record what you're saying, and then give you instant feedback about how you're pronouncing certain words. That's a very powerful kind of tool. So I think science education, certainly mobile phones have a place.

7:35 Subhra Priyadarshini : And Preethi tell us a bit about the crowd sourced research, which has been your forte, along with your colleague Kameswari Chebrolu.

7:45 Preethi Jyothi : These days smartphones can also be used to gather data from people. And this could be because smartphones have GPS systems enabled, you could use it to gather data from people for various applications, like say traffic forecasting, or route planning and so on. I work on applying machine learning techniques for speech and language. And I'm specifically interested in building technologies for Indian languages. And so this app that we built that it's called clap, it's available on the Google Play Store. So this is an app via which you can be collected speech data from anyone who downloads this app. the volunteers would be asked to just read out these prompts. what we get immediately is parallel text with the corresponding speech from different speakers. unlike maybe other crowdsourcing platforms, which are very well known like Amazon's Mechanical Turk, and so on, which actually have many users from India, what we have found is that platforms like Mechanical Turk, most of the users are urban users, this automatically excludes a large fraction of users. Smartphones, now the reach is so much wider. And so our idea was to be able to reach users across a very broad spectrum, spanning multiple demographics they're all already very comfortable with using mobile phones. And this is currently a big area of interest across kind of machine learning technologies that you don't want to be catering just to very small sections of users. And if you're building machine learning applications, it all everything that is driving the accuracy of the such applications is the data that is being used to train these applications.That was the motivation behind building such an app on a smartphone so that we could get data from diverse users, and then use that to train speech recognition and language technologies.

9:40 Subhra Priyadarshini : Certainly, phones are the new trainers and teachers. They also play a crucial role in disseminating scientific knowledge for various end users. Take the instance of farmers as consumers of scientific knowledge. Jayashree Balasubramaniam, who works in the business of communication at Reliance Foundation tells us more.

10:06 Jayashree Balasubramaniam : The whole context of using mobile phones to bridge a number of gaps, I think that's something that's really picked up, especially post-COVID, where people have not only broken down their own personal barriers, but I think technology has grown immensely. What has also happened is that we see a large number of people, especially from communities, like small and marginal farmers, looking at ways in which they can explore this, take, for instance, you know, something that's related to crop practices, or, you know, pests and disease or a package of practices that developed by agricultural research institutions, and that's actually to be used by farmers. So what's been happening is that the typical agricultural extension services has managed to reach out to farmers through physical modes, but given the limitations that, you know, situations, such as the COVID pandemic brought in, what happened was that farmers also had to kind of look at other ways to gather the same information. During, you know, the 2020, I think this was the only sector in India that actually kind of had a positive growth. And this was primarily thanks to the way that they had, you know, kind of leveraged their knowledge.

11:27 Subhra Priyadarshini : Agriculture sciences have been a great beneficiary of mobile phone use for data collection and surveys, crowdsourcing, education and dissemination. We’ll, of course, dedicate a full episode to talk about this unique use case. But Jayashree, do talk us through a few of these use cases in this field as you have been at the forefront of this use.

11:53 Jayashree Balasubramaniam : Take for instance, you know, access to mobile-based advisories. Now, one of the biggest barriers in actually reaching information to a community like a small and marginal farmer has been internet connectivity or mobile connectivity, or actually just the use of technology, the ability to use technology,we work with millions of farmers across the country, when we actually need to send out a message, it's not just given to them in a simple localized context and format, it's also given in multiple languages. So, I think breaking the language barrier has been like one of you know, the most important steps in reaching this information, besides of course, the penetration in internet connectivity, The second is actually looking at ways in which with low mobile connectivity or low internet connectivity areas, you can use simple methods, these could be you know, chatbots this could be voice messages, this could also be some sort of audio conferencing that happens, where with a limited bandwidth and with a limited physical presence, you can still kind of get your message across, what we found through you know, our work in in a number of locations is that not only is the knowledge used, but you know, 75% or most of the farmers who have actually received these you know, pieces of information at different points of time have reported that they have actually improved their livelihoods.

13:18 Subhra Priyadarshini : And you see an easy uptake of this scientific information by people who may not have been exposed to science at all?

13:27 Jayashree Balasubramaniam : The second part of this whole process is adding to the scientific information with some sort of, you know, physical demonstration, new seed varieties, crop practices,water efficient , climate resilient, practices that can help rural communities.For instance, we're looking at something like Go. And DVIR are like a normalized difference vegetation index, which is you using, you know, satellite imagery.How it can predict something like drought or other crop stresses, even before that, it actually happens, it makes a big difference in actually transmitting this information. So this information is not just, you know, looked at, as somebody who's watching it, observing it, and recording it in a lab with the use of satellite imagery, this is actually getting translated through mobile or messaging or through, you know, mobile platforms, it's also like, you know, rural communities, we're using it for micro entrepreneurship and other things, but here translating the scientific information in simple, digestible nuggets, that has made a big difference to the way they actually adapt it on the field.

Now, we look at how integrated information like, weather, there is some sort of an impending natural disaster, you know, floods or cyclones, for instance, there are fishing communities who are actually exposing themselves to risk on a day to day basis,we found that 97% of the fishing communities were who actually received preventive information about the weather, said that actually, they not just, you know, minimize their losses, but actually, a lot of them were able to take preventive action to save their livelihood.

15:07 Subhra Priyadarshini : 10 years back Abhijit Pakhare, a community medicine specialist at the All India Institute of Medical Sciences at Bhopal and his colleagues analysed the use of mobile phones as research instruments for data collection in household surveys, clinical trials, surveillance and spatial data in global south countries. They inferred that mobile phones enabled economical, environment-friendly, faster and more accurate data collection for research. The limitations, however, were data entry errors, connectivity issues and of course the digital divide – all of which we will have a closer look at in our next episodes.

Ten years later, due to their widespread availability, affordability and connectivity, mobile phones are becoming extremely important to the process of science as much as science’s connect to society, as we have just heard through examples in the lab, in classrooms, in farming, fishing, rural communities. While urban users have to actually use apps for digital detox to keep away from potential negative effects of mobile use, science certainly benefits from these tiny devices. We will hear more on various aspects of scientific research benefitting from during this season.

Stay tuned, and give us a listen at your favourite podcast platform. This is Subhra Priyadarshini signing off from the Nature India podcast.

16:56 Support announcement : This episode was brought to you with support from DBT Wellcome Trust India Alliance.

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• The Best (and Worst) of Mobile Connectivity
• Part III: The Impact of Mobile Phones on People’s Lives

Table of Contents

• Part I: The Good and Bad of Cell Ownership
• Part II: Barriers to Adoption
• Part IV: Cell Phone Attachment and Etiquette
• Part V: Cell Phone Usage
• Methodology

In an effort to examine the broader impact of mobile devices on people’s lives, we presented cell phone owners with six separate impacts that might result from mobile phone ownership and these impacts were equally balanced between positive and negative ones. These responses indicate that mobile users see mostly positive benefits to mobile technologies — but also some drawbacks related to the constant connectivity (and mental temptations) that cell phones offer.

When it comes to the positive impacts of cell phone ownership, fully two-thirds (65%) of cell owners say that mobile phones have made it “a lot” easier to stay in touch with the people they care about, while just 6% say that their phone has not improved their connections with friends and family at all. Roughly half of cell owners say that their phone has made it at least somewhat easier to plan and schedule their daily routine, and to be productive while doing things like sitting in traffic or waiting in line.

When it comes to the “dark side” of cell phone ownership, roughly one in five cell owners say that their phone has made it at least somewhat harder to forget about work at home or on the weekends; to give people their undivided attention; or to focus on a single task without being distracted. A majority of cell owners say that their phone has had no impact at all on their life in any of these negative ways.

Compared with their elders, younger cell owners are especially attuned to both the positive and negative impacts of mobile connectivity. Low- and high-income cell owners also have divergent attitudes towards the benefits and challenges posed by ubiquitous mobility. Those from higher income households are more likely to say that their cell phone makes it easier to schedule their daily routine, and to be productive throughout the day. At the same time, cell owners with a household income of more than $75,000 per year are significantly more likely than other cell owners to say that their phone makes it harder to disconnect from the demands of the workplace. Some 17% of these high-income earners say that their phone makes it “a lot” harder to do this (compared with 7% for those earning less than $30,000 per year, 6% for those earning $30,000-$49,999, and 8% for those earning $50,000-$74,999). Overall, nearly one third (29%) of high-income cell owners say that their phone makes it at least somewhat harder to disconnect from work at home and on the weekends.

One third of cell owners say that overall, their cell phone saves them time — while just 3% say it costs them time.

Overall, cell owners are far more likely to view their phone as a time-saver than as a time-waster. Some 33% of cell owners agree with the statement that their phone “saves you time because you can always access the information you need,” while just 3% agree with the statement that their phone “costs you time because you are constantly distracted or interrupted.” The largest proportion of cell owners (56%) say that the time costs and time savings offered by cell phones pretty much balance each other out.

Smartphone owners have especially positive attitudes towards their phones’ time-saving capabilities. Some 44% of smartphone owners say that their phone saves them time because they can access the information they need at all times—double the 20% of non-smartphone owners who say the same. And despite saying that their cell phone makes it hard to escape the demands of employment, cell owners with high levels of income and education are generally quite positive about the time-saving capabilities of their mobile devices. Some 42% of cell owners with a college degree (and 43% of those with an annual household income of $75,000 or more) say that their cell phone saves them time overall, a significantly higher percentage than those with lower levels of income or education.

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Studies find teens with problematic smartphone use are twice as likely to have anxiety

by King's College London

PSU (problematic smartphone use) describes a pattern of behaviors, thoughts and feelings linked to smartphones that resembles an addiction, such as feeling panicky or upset when the phone is unavailable, finding it difficult to control the amount of time spent on the phone, using it for longer without feeling satisfied, and using the phone to the detriment of other enjoyable or meaningful activities.

Researchers at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) at King's College London conducted two studies at schools investigating the association between PSU and mental health in young people. One study was with young people aged 16–18 years and the other was with 13–16 year-olds. These studies are among the first to evaluate PSU and mental health outcomes in adolescents.

Problematic smartphone use is linked to mental health

Overall, 18.7% of 16–18 year-olds and 14.5% of 13-16 year-olds self-reported PSU, with higher prevalence among girls.

Findings published in Acta Paediatrica revealed 16–18 year-olds who reported PSU were twice as likely to experience anxiety and almost three times as likely to experience depression compared to those who did not report PSU.

Findings published in BMJ Mental Health revealed nearly half of 13–16 year-olds with PSU reported symptoms of anxiety (44.4%) compared to 26.4% without PSU. Over half of 13–16 year-olds with PSU reported symptoms of depression (55.6%) compared to 35.8% without PSU.

This study also investigated if PSU was associated with mental health over time and showed increases in PSU scores over a four-week period were linked to increases in self-reported anxiety, depression and insomnia.

In the first study, conducted from January 31st to 8th March 2020, 657 16–18 year-olds completed assessments of PSU, anxiety, depression and insomnia. In the second, researchers measured PSU and changes in anxiety, depression and insomnia in 69 13–16 year-olds over a four-week period in 2022.

Many young people want to cut down time spent on smartphones

Both studies also found that many young people wish to spend less time on their phones. Almost two-thirds of 16–18 year-olds reported that they have tried to cut down on their smartphone use, and one in eight said they wanted help to reduce their use. Those with PSU were five times more likely to say they want help to cut down on their smartphone use compared to their peers without PSU.

Similarly, nearly 90% of 13–16 year-olds reported that they had attempted at least one strategy to limit their smartphone use, including putting it on silent or turning off notifications.

The researchers say the findings reveal that adolescents are aware that their smartphone use needs to be managed and are receptive to the idea of boundaries around use.

"Adolescent smartphone use is a huge concern for parents and caregivers. We found that problematic smartphone use was linked with anxiety, depression and insomnia across two separate adolescent age groups using two different research methods," says Professor Ben Carter.

"By revealing the link between problematic use of smartphones and poorer mental health , and demonstrating that young people are aware of this problem and are eager to manage their use, these studies highlight the need for evidence-based interventions to help adolescents struggling with difficult behaviors around their smartphone use."

Sixteen to 18 year-olds were recruited from five secondary schools across London, East-Midlands and South-West England; 13–16 year-olds were recruited from two secondary schools in London.

Distinction between smartphone use and screentime

In the first study, researchers also found TikTok and Instagram usage was higher among 16-18-year-olds who reported PSU, compared to those who did not. There was little difference in usage of WhatsApp, general gaming or general internet usage.

The study highlighted a distinction between PSU and screentime, described as the number of minutes spent on the smartphone rather than problematic behaviors surrounding its use. They found screentime was not associated with anxiety or depression in 16–18 year-olds, although did directly link to increased insomnia.

Strategies to reduce smartphone usage

Further analyses, also published by the researchers in Acta Paediatrica , revealed that putting their smartphone on "do not disturb," turning off notifications, and leaving the smartphone in another room at bedtime were reported to be the most effective strategies for reducing PSU.

In contrast, restricting access to specific apps, using a locked box during revision, and turning on grayscale were considered to be the least effective strategies.

"The good news is that adolescents are reflective and insightful about their use—they understand that smartphones bring downsides as well as benefits. Many young people in our studies employed reduction strategies, showing they are already taking active steps to manage their smartphone use," says Dr. Nicola Kalk.

"They found silent mode, removing notifications and placing the phone in another room at bedtime as the most effective. These are the same strategies which university students found helpful to reduce smartphone use.

"We hope these findings encourage parents and caregivers to have a conversation with their adolescents about their smartphone use which acknowledges both benefits and harms, and allows them to explore reasons why their adolescent might want to reduce their use, as well as the most effective tools to do so."

Ben Carter et al, 'There's more to life than staring at a small screen': a mixed methods cohort study of problematic smartphone use and the relationship to anxiety, depression and sleep in students aged 13–16 years old in the UK, BMJ Mental Health (2024). DOI: 10.1136/bmjment-2024-301115

Ben Carter et al, A multi‐school study in England, to assess problematic smartphone usage and anxiety and depression, Acta Paediatrica (2024). DOI: 10.1111/apa.17317

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Should Students Have Cell Phones at School?

Exploring the impact of cell phones on success in education..

Posted July 26, 2024 | Reviewed by Davia Sills

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• Cell phones can be used for accessibility and hands-on learning.
• However, cell phones are still too distracting to have free access to in class.
• Students may be able to have their cell phones in school for contact but not during class time.
• Administrators, students, teachers, and families need to collaborate on any phone policy.

Cell phones in classrooms—yes or no?

Do you know how long it takes a child to refocus after being on their phone?

With July cruising into its last week, the start of the school year rapidly approaches. Supplies, class lists, meetings, and more begin filling up family schedules. A new cell phone might be on your child’s supplies or wish list—but should they have access to it in school?

For parents, how would you feel if your district made a policy forbidding cell phones? Thrilled for them to get off their phones for a while? Anxious about not being able to reach them?

For teachers, would you miss having phones for some assignments, or would you breathe a huge sigh of relief? Currently, few districts or schools have explicit and/or effectively supported policies, leaving many teachers and parents to create their own systems. These systems often add up to a hodgepodge of inconsistent expectations that don’t support students or teachers.

What does the science show us about smartphones in school? Unfortunately, not much. Research has been slow to catch up on these concerns despite the urgent need for answers.

Still, some conclusions can be drawn from the existing studies combined with accumulating experiences from parents, teachers, mental health professionals, and students. Cell phones in school—and specifically in the classroom—appear to offer both potential benefits and significant risks to learning. It turns out the subject isn’t a simple choice between banning them entirely or letting kids have access without guardrails.

Some of the biggest potential benefits of cell phones in school or classrooms include improving access to and engagement in learning. Teachers can meet kids where they are and build creative lesson plans and activities that use students’ phones. For some students with disabilities, phones can be powerful tools for increasing accessibility to learning materials and classroom participation.

The other benefit that many parents value is being able to reach their child during the school day. Often, parents and students worry that in an extreme situation—such as a shooting or lockdown—they want to be able to reach each other. At the same time, easy access to phones has led to some normalizing of routine communications between students and parents during the day, which can disrupt student learning and classroom function.

Even with these potential positives, the presence of cell phones in the classroom brings significant negatives, but one is the most fundamental— distraction . “Multi-tasking” doesn’t work—especially in learning situations.

Some research has shown that once a student is on their phone (or even has a phone in their vicinity), it takes about 20 minutes for them to refocus on the material being presented. That’s about one-half of a typical high school class period—meaning students can be missing 50 percent of what is being taught. Not only are they missing material, but they also aren’t engaging in the learning process with teachers and peers. Effective learning is active, and being on their phone takes that away.

The content on cell phones effectively keeps our eyes on the screen and discourages looking away. Big Tech makes big money by creating algorithms that are meant to steal our attention . They excel at it. It’s hard enough for adults to regulate phone use, but children and teen brains don’t have anywhere near fully developed impulse control abilities. Asking them to just stop doing it—without carefully designed barriers and supports—is simply not going to work.

And for students with learning differences and disorders such as ADHD —or who are struggling with learning for any reason—the temptation to look at their phone and not participate in class is tremendous. While these students often disengage in other ways ( sleeping , staring out the window, going to the bathroom), having access to constant, algorithm-driven content creates a rabbit hole that’s all too easy for them to get deeply lost in. While phones might be helpful for specific learning tasks for students with disabilities, free-range access hurts learning and engagement.

As schools become more aware of how harmful cell phones can be to learning, attempts to limit this harm often face strong headwinds from parent concerns about being able to reach their children. Schools and districts must integrate these concerns when developing policies that would limit children’s access to their devices during the day. It’s easy to say that we can go back to calling the office if you need to get a message to your child, but fears of something like a school shooting are powerful—and, while extremely rare, difficult to dismiss.

Hammering out systems for keeping kids off phones when they are supposed to be learning will require effective communication between everyone involved, including administration, teachers, families, and (older) students. Administrators must develop mechanisms to support the implementation of policies. Expectations need to apply to all students because kids worry that if they are the only ones not on their phones, they miss out on critical social connections. Without universal policy implementation, even with children who would prefer to put their phones away (and many would), their very normal need to stay connected would win out.

Any policy enacted needs to be dynamic, flexible, and responsive to feedback. Students with disabilities will require reasonable accommodations. Well-defined and effective systems for feedback from all stakeholders will be critical to keep policies viable and successful. Evolution in technology will create new needs and opportunities.

Learning is a fundamental right for our children. Participating in school and learning is key to kids’ physical and mental health. While the debate rages on about phones in kids’ lives outside of school, it seems clear that, as hard as it will be, cell phones don’t belong in the classroom.

UNESCO. 2023. Global Education Monitoring Report 2023: Technology in education - A tool on whose terms? Paris, UNESCO

Candida Fink, M.D. , is board certified in child/adolescent and general psychiatry. She practices in New York and has co-authored two books— The Ups and Downs of Raising a Bipolar Child and Bipolar Disorder for Dummies.

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The American Society for Cell Biology (ASCB) celebrates these remarkable individuals for their various achievements in the realm of life sciences.

ASCB takes immense pride in presenting honorific awards to honor our extraordinary members. These accolades hold special significance as they symbolize the brilliance of our peers in research, education, mentoring, and their unwavering dedication to the Society. Please join us in congratulating our colleagues for their remarkable contributions and for serving as an endless source of inspiration and motivation.

The following people were recognized by either receiving an honorific award or being invited to present a keynote speech or lecture.

WICB Sandra K. Masur Senior Leadership Award:  Bestowed by the Women in Cell Biology Committee (WICB) to any later-stage career scientist with outstanding scientific achievements and a record of active leadership in mentoring women and individuals from underrepresented groups.

• Mary Dasso , Senior Investigator, NICHD-DIR; Associate Scientific Director, Division of Molecular and Cellular Biology, NICHD-DIR; and Scientific Co-director, NIH Oxford-Cambridge Scholars Program

Public Service Award:  Honoring national leadership and outstanding public service in support of biomedical research or advocacy of sound research policies. Awardee selected by the Public Policy Committee.

• Peter Hotez , Dean, National School of Tropical Medicine at Baylor College of Medicine

E.B. Wilson Medal : Presented to distinguished researchers for their far-reaching contributions to cell biology over a lifetime in science.

• Denise Montell , Distinguished Professor and Robert and Patricia Duggan Professor of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara

Bruce Alberts Award for Excellence in Science Education : Given to an individual who has demonstrated innovative and sustained contributions to science education, prioritizing the national impact of the nominee’s activities.

• Sara Brownell , President’s Professor, Arizona State University

E.E. Just Lecture:  Honoring the early 20th-century biologist Ernest Everett Just, who made foundational contributions to cell and developmental biology, to recognize the outstanding scientific achievements of a U.S. researcher belonging to a historically excluded racial or ethnic group. Awardee selected by the Maximizing Access to Cell Biology for PEERS Committee.

• Jorge Torres , Professor, Department of Chemistry and Biochemistry, Vice Chair of Undergraduate Education, Department of Chemistry and Biochemistry, Director, NIH T32 Graduate Student Training Program in Cell and Molecular Biology, University of California Los Angeles

Keith R. Porter Lecture:  Named in memory of Keith R. Porter and presented to an outstanding and innovative leader at the forefront of cell biology, actively contributing fundamental new knowledge to our understanding of cell biology.

• Sergio Grinstein , Senior Scientist, Cell Biology Program, Research Institute, The Hospital for Sick Children

The David Burgess Award for Excellence in Inclusivity: Recognizing one scientist who has a track record of excellence in research or serves a critical role in fostering cell biology research and, has demonstrated the importance of inclusion and diversity in science through mentoring, cultural change, outreach, or community service.

• Katherine Wilson , Professor, Johns Hopkins School of Medicine

Mentoring Keynote:  An invited speaker who exemplifies mentoring by their impact on training scientists and scholars belonging to underrepresented groups, particularly racial and ethnic minorities. Awardee selected by the Minorities Affairs Committee.

• Sonya Neal , Associate Professor, HHMI Freeman Hrabowski Scholar, UC San Diego, HHMI

ASCB Award for Excellence in Research by an Historically Excluded Person (HEP): Presented to an early career ASCB member who self-identifies as a Historically Excluded Person (HEP) for making exceptional scientific contributions to cell biology, developing a strong independent research program, and exhibiting the potential for continuing at a high level of scientific endeavor and leadership.

• Lilian Kabeche , Assistant Professor of Molecular Biophysics and Biochemistry, Member, Yale Cancer Biology Institute, Yale University

WICB Junior Award for Excellence in Research:  Presented to a woman or non-binary person in an early stage of their career making exceptional scientific contributions to cell biology, developing a strong independent research program, and exhibiting the potential for continuing at a high level of scientific endeavor and leadership.

• Minna Roh-Johnson , Associate Professor, University of Utah

Günter Blobel Early Career Award:  Given to an outstanding early career life scientist who has served as an independent investigator for no more than seven years at the time of nomination.

• Monther Abu-Remaileh , Assistant Professor of Chemical Engineering and of Genetics, Stanford University

Innovation in Research:  Recognizes early and mid-career scientists for their new and innovative research in cell biology. For this purpose, innovation is defined as an accomplishment that significantly impacts progress in advancing the field of cell biology and is based on work done within three years before the nomination.

• Siyuan Wang , Associate Professor, Yale University

Porter Prizes for Research Excellence:  Prizes are given to graduate students and postdoctoral researchers based on scientific excellence. In the spirit of Keith Porter, the emphasis will be on their contributions to the advancement of science and the novelty and creativity of their findings.

• Sichen Yang , Graduate Fellow, The Rockefeller University

Postdoctoral

• Mengxiao (Mandi) Ma , Postdoctoral Fellow, Stanford University

Merton Bernfield Memorial Award:  Established to honor outstanding postdocs or graduate students with member donations in memory of pediatrician and cell biologist Merton Bernfield.            

• Dhiman Sankar Pal , Postdoctoral Fellow, Department of Cell Biology and Center for Cell Dynamics, School of Medicine, Johns Hopkins University

MBoC Paper of the Year Award:  Awarded to the first author on a paper (grad student or postdoc) chosen by the Editorial Board of ASCB’s science research journal,  Molecular Biology of the Cell (MBoC) , as the best papers published from June of the previous year to May.

• Laura Schenkel, ETH Zurich “ A dedicated cytoplasmic container collects extrachromosomal DNA away from the mammalian nucleus ”
• John Brooks, Research Assistant, The University of Queensland “ Caveola mechanotransduction reinforces the cortical cytoskeleton to promote epithelial resilience “
• Virginia King, Associate, Cure Ventures F-actin–rich territories coordinate apoptosome assembly and caspase activation during DNA damage–induced intrinsic apoptosis

MBoC Early Career Paper Award

• So Youn Moon for the paper: Cell contractility drives mechanical memory of oral squamous cell carcinoma
• Tamás Maruzs for the paper: Interaction of the sorting nexin 25 homolog Snazarus with Rab11 balances endocytic and secretory transport and maintains the ultrafiltration diaphragm in nephrocytes
• Dalma Feil-Börcsök for the paper: Interaction of the sorting nexin 25 homolog Snazarus with Rab11 balances endocytic and secretory transport and maintains the ultrafiltration diaphragm in nephrocytes
• Michael Tobin for the paper: Differences in cell shape, motility, and growth reflect chromosomal number variations that can be visualized with live-cell ChReporters
• Charlotte Pfeifer for the paper: Differences in cell shape, motility, and growth reflect chromosomal number variations that can be visualized with live-cell ChReporters
• Matthew Flagg for the paper: Exploring The “Misfolding Problem” by Systematic Discovery and Analysis of Functional-But-Degraded Proteins

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• Int J Pharm Investig
• v.7(3); Jul-Sep 2017

Smartphone usage and increased risk of mobile phone addiction: A concurrent study

Subramani parasuraman.

Unit of Pharmacology, AIMST University, Kedah, Malaysia

Aaseer Thamby Sam

1 Unit of Pharmacy Practice, Faculty of Pharmacy, AIMST University, Kedah, Malaysia

Stephanie Wong Kah Yee

Bobby lau chik chuon.

This study aimed to study the mobile phone addiction behavior and awareness on electromagnetic radiation (EMR) among a sample of Malaysian population.

This online study was conducted between December 2015 and 2016. The study instrument comprised eight segments, namely, informed consent form, demographic details, habituation, mobile phone fact and EMR details, mobile phone awareness education, psychomotor (anxious behavior) analysis, and health issues. Frequency of the data was calculated and summarized in the results.

Totally, 409 respondents participated in the study. The mean age of the study participants was 22.88 (standard error = 0.24) years. Most of the study participants developed dependency with smartphone usage and had awareness (level 6) on EMR. No significant changes were found on mobile phone addiction behavior between the participants having accommodation on home and hostel.

Conclusion:

The study participants were aware about mobile phone/radiation hazards and many of them were extremely dependent on smartphones. One-fourth of the study population were found having feeling of wrist and hand pain because of smartphone use which may lead to further physiological and physiological complication.

INTRODUCTION

Mobile/hand phones are powerful communication devices, first demonstrated by Motorola in 1973, and made commercially available from 1984.[ 1 ] In the last few years, hand phones have become an integral part of our lives. The number of mobile cellular subscriptions is constantly increasing every year. In 2016, there were more than seven billion users worldwide. The percentage of internet usage also increased globally 7-fold from 6.5% to 43% between 2000 and 2015. The percentage of households with internet access also increased from 18% in 2005 to 46% in 2015.[ 2 ] Parlay, the addiction behavior to mobile phone is also increasing. In 2012, new Time Mobility Poll reported that 84% people “couldn't go a single day without their mobile devices.”[ 3 ] Around 206 published survey reports suggest that 50% of teens and 27% of parents feel that they are addicted to mobiles.[ 4 ] The recent studies also reported the increase of mobile phone dependence, and this could increase internet addiction.[ 5 ] Overusage of mobile phones may cause psychological illness such as dry eyes, computer vision syndrome, weakness of thumb and wrist, neck pain and rigidity, increased frequency of De Quervain's tenosynovitis, tactile hallucinations, nomophobia, insecurity, delusions, auditory sleep disturbances, insomnia, hallucinations, lower self-confidence, and mobile phone addiction disorders.[ 6 ] In animals, chronic exposure to Wi-Fi radiation caused behavioral alterations, liver enzyme impairment, pyknotic nucleus, and apoptosis in brain cortex.[ 7 ] Kesari et al . concluded that the mobile phone radiation may increase the reactive oxygen species, which plays an important role in the development of metabolic and neurodegenerative diseases.[ 8 ]

In recent years, most of the global populations (especially college and university students), use smartphones, due to its wide range of applications. While beneficial in numerous ways, smartphones have disadvantages such as reduction in work efficacy, personal attention social nuisance, and psychological addiction. Currently, the addiction to smartphones among students is 24.8%–27.8%, and it is progressively increasing every year.[ 9 ] Mobile phone is becoming an integral part to students with regard to managing critical situations and maintaining social relationships.[ 10 ] This behavior may reduce thinking capabilities, affect cognitive functions, and induce dependency. The signs of smartphone addiction are constantly checking the phone for no reason, feeling anxious or restless without the phone, waking up in the middle of night to check the mobile and communication updates, delay in professional performance as a result of prolonged phone activities, and distracted with smartphone applications.[ 11 ]

Mobile phone is the most dominant portal of information and communication technology. A mental impairment resulting from modern technology has come to the attention of sociologists, psychologists, and scholars of education on mobile addiction.[ 12 ] Mobile phone addiction and withdrawal from mobile network may increase anger, tension, depression, irritability, and restlessness which may alter the physiological behavior and reduce work efficacy. Hence, the present study was planned to study the addiction behavior of mobile phone usage using an online survey.

This study was approved by Human and Animal Ethics Committee of AIMST University (AUHAEC/FOP/2016/05) and conducted according to the Declaration of Helsinki. The study was conducted among a sample of Malaysian adults. The study participants were invited through personal communications to fill the online survey form. The study was conducted between December 2015 and 2016. The study instrument comprised eight segments, namely, informed consent information, consent acceptance page, demographic details, habituation, mobile phone fact and electromagnetic radiation (EMR) details, mobile phone awareness education, psychomotor (anxious behavior) analysis, and health issues. If any of the participants were not willing to continue in the study, they could decline as per their discretion.

Totally, 450 participants were informed about the study and 409 participated in the study. The demographic details of the study participants are summarized in Table 1 . The incomplete forms were excluded from the study. The participants' details were maintained confidentially.

Demographic details of the study participants

Statistical analysis

Frequency of the data was calculated and the data were analyzed using two-sided Chi-square test with Yate's continuity correction.

Totally, 409 individuals participated in the study, of which 42.3% were males and 57.7% were females, between the age group of 18 and 55 years. Nearly 75.6% of the respondents were between the age group of 21 and 25 years. The mean age of the study participants was 22.88 (standard error = 0.24) years. The study participants' demographic details are summarized in Table 1 .

About 95% of the study participants were using smart phones, with 81.7% of them having at least one mobile phone. Most of the study participants used mobile phone for more than 5 years. Around 64.3% of the study participants use mobile phone for an hour (approximately) and remaining use it for more than an hour. Nearly 36.7% of the study participants have the habit of checking mobile phones in between sleep, while 27.1% felt inconvenience with mobile phone use. Majority of the respondents were using mobile phone for communication purposes (87.8%), photo shooting (59.7%), entertainment (58.2%), and educational/academic purposes (43.8%). Habits of mobile phone usage among the study participants are summarized in Table 2 .

Habituation analysis of mobile phone usage

The study results indicate that 86.8% of the participants are aware about EMR and 82.6% of the study participants are aware about the dangers of EMR. The prolonged use/exposure to EMR may cause De Quervain's syndrome, pain on wrist and hand, and ear discomfort. Among the study participants, 46.2% were having awareness on De Quervain's syndrome, 53.8% were feeling ear discomfort, and 25.9% were having mild-to-moderate wrist/hand pain. Almost 34.5% of the study participants felt pain in the wrist or at the back of the neck while utilizing smartphones [ Table 3a ]. Many of the study participants also agreed that mobile phone usage causes fatigue (12% agreed; 67.5% strongly agreed), sleep disturbance (16.9% agreed; 57.7% strongly agreed), and psychological disturbance (10.8% agreed; 54.8% strongly agreed) [ Table 3b ]. The study participants were having level 6 of awareness on mobile phone usage and EMR.

Analysis of awareness of mobile phone hazards

The behavioral analysis of the smartphone usage revealed that 70.4% of the study participants use smartphone longer than intended and 66.5% of the study participants are engaged for longer duration with smartphone. Nearly 57.7% of the study participants exercise control using their phones only for specific important functions. More number of study participants (58.2%) felt uncomfortable without mobile and were not able to withstand not having a smartphone, feeling discomfort with running out of battery (73.8%), felt anxious if not browsing through their favorite smartphone application (41.1%), and 50.4% of the study participants declared that they would never quit using smartphones even though their daily lifestyles were being affected by it. The study also revealed another important finding that 74.3% of smartphone users are feeling dependency on the use of smartphone. The addiction behavior analysis data of mobile phone are summarized in Table 4 .

Addiction behavior analysis of mobile phone

The study results also suggest that female participants were having more awareness than male participants ( P < 0.001) [ Table 5a ] and were more dependent on smartphones than male participants ( P < 0.05) [ Table 5b ]. Female participants were ready to quit using smartphones, if it affected daily lifestyle compared with male participants ( P < 0.05) [ Table 5b ]. Habituation of mobile phone use and addiction behavior were compared between both genders of the study participants and are summarized in Table 5a and ​ andb, b , respectively.

Comparison of habituation of mobile phone usage between genders

Comparison of addiction behavior between genders

A total of 297 participants were having accommodation in hostel, among them 39.6% of the study participants checked their mobile phone on an average of 21–30 times, a day, and 11.7% of the study participants checked their mobile phone more than 30 times a day. A total of 112 participants have accommodation in home, among them 28.6% of the study participants checked their mobile phone 21–30 times a day, and 13.4% of the study participants checked their mobile phone more than 30 times a day.

A total of 66.1% of participants having accommodation in home use their phones longer than intended, whereas 71.8% of participants having accommodation in hostel are using phone longer than intended. Forty-one (36.6%) and 109 (36.6%) participants from home and hotel checked mobile phone in-between sleep, respectively. About 67.9% of participants having accommodation in home felt dependent on mobile and it was the same for participants having accommodation in hostel (76.5%).

The study results suggest that a significant number of the participants had addiction to mobile phone usage, but were not aware on it, as mobile phones have become an integral part of life. No significant differences were found on addiction behavior between the participants residing in hostel and homes. Mobile phone abuse is rising as an important issue among the world population including physical problems such as eye problems, muscular pain, and psychological problem such as tactile and auditory delusions.[ 13 ] Along with mobile phone, availability of Wi-Fi facility in residence place and work premises also increases mobile phone dependence. The continuous and constant usage of mobile phone reduces intellectual capabilities and work efficacy. A study conducted in Chinese population (160 million out of the total 1.3 billion people) showed that people affected by mobile phone dependence have difficulty in focusing on work and are unsociable, eccentric, and use phones in spite of facing hazards or having knowledge of harmful effects of this form of electromagnetic pollution.[ 14 ]

The statement “I will never quit using my smartphone even though my daily lifestyles are affected by it” was statistically significant ( P = 0.0229). This points to a trend of mobile phone addiction among the respondents. This finding was discussed by Salehan and Negahban. They stated that this trend is due to the fast growth in the use of online social networking services (SNS). Extensive use of technology can lead to addiction. The use of SNS mobile applications is a significant predictor of mobile addiction. Their result showed that the use of SNS mobile applications is affected by both SNS network size and SNS intensity of the user. It has implications for academia as well as governmental and non-for-profit organizations regarding the effect of mobile phones on individual's and public health.[ 15 ] The health risks associated with mobile phones include increased chances of low self-esteem, anxiety or depression, bullying, eye strain and “digital or mobile phone thumb,” motor vehicle accidents, nosocomial infections, lack of sleep, brain tumors and low sperm counts, headache, hearing loss, expense, and dishonesty. The prevalence of cell phone dependence is unknown, but it is prevalent in all cultures and societies and is rapidly rising.[ 16 ] Relapse rate with mobile phone addiction is also high, which may also increase the health risk and affect cognitive function. Sahin et al . studied mobile phone addiction level and sleep quality in 576 university students and found that sleep quality worsens with increasing addiction level.[ 17 ]

The statement “Feeling dependent on the use of smartphone” was also statistically significant ( P = 0.0373). This was also explored by Richard et al . among 404 university students regarding their addiction to smartphones. Half of the respondents were overtly addicted to their phones, while one in five rated themselves totally dependent on their smartphones. Interestingly, higher number of participants felt more secure with their phones than without. Using their phones as an escapism was reported by more than half of the respondents. This study revealed an important fact that people are not actually addicted to their smartphones per se ; however, it is to the entertainment, information, and personal connections that majority of the respondents were addicted to.[ 18 ]

The 2015 statistical report from the British Chiropractic Association concluded that 45% of young people aged 16–24 years suffered with back pain. Long-term usage of smart phone may also cause incurable occipital neuralgia, anxiety and depression, nomophobia, stress, eyesight problem, hearing problems, and many other health issues.[ 19 ]

A study conducted among university students of Shahrekord, Iran, revealed that 21.49% of the participants were addicted to mobile phones, 17.30% participants had depressive disorder, 14.20% participants had obsessive-compulsive disorder, and 13.80% had interpersonal sensitivity.[ 12 ] Nearly 72% of South Korean children aged 11–12 years spend 5.4 h a day on mobile phones, 25% of those children were considered addicts to smartphones.[ 20 ] Thomée et al . collected data from 4156 adults aged between 20 and 24 years and observed no clear association between availability demands or being awakened at night and the mental health outcomes.[ 21 ] Overuse of mobile phone can lead to reduced quality of interpersonal relationships and lack of productivity in daily life. The study outcome from different studies showed variable results on addictive behavior on mobile phone usage. The fact is over-/long-time usage of mobile phone may cause behavioral alteration and induce addictive behavior.

This study suggests that most of the study participants are aware about mobile phone/radiation hazards and many of them developed dependent behavior with smartphone. No significant changes were found on mobile phone dependency behavior between participants having accommodation in house and hostel. One-fourth of the study population is having a feeling of wrist and hand because of smartphone usage which may lead to further physiological and physiological complications.

Limitations

• Cluster sampling from a wider population base could have provided a more clear idea regarding the topic of interest
• Increasing the time frame and number of study phases was not possible due to logistical issues
• Impact of smartphone addiction on sleep pattern could have been studied in-depth.

Financial support and sponsorship

Conflicts of interest.

There are no conflflicts of interest.