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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]

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  • Clarissa Theodora Tanil, 
  • Min Hooi Yong

PLOS

  • Published: August 13, 2020
  • https://doi.org/10.1371/journal.pone.0219233
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Table 1

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.

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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 ).

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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 ).

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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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Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure

Affiliation.

  • 1 Department of Psychology, University of Gothenburg, 405 30 Gothenburg, Sweden. [email protected].
  • PMID: 30501032
  • PMCID: PMC6314044
  • DOI: 10.3390/ijerph15122692

The purpose of this study was to carry out a review of observational studies that consider links between mobile phone use and mental health from a psychological or behavioral perspective. Systematic literature searches in PubMed and PsycINFO for articles published until 2017 were done. Exclusion criteria included: papers that considered radiofrequency fields, attention, safety, relational consequences, sexual behavior, cyberbullying, and reviews, qualitative, and case or experimental studies. A total of 4738 papers were screened by title and abstract, 404 were retrieved in full text, and 290 were included. Only 5% had any longitudinal design. Self-reporting was the dominating method of measurement. One third of the studies included children or youth. A majority of adult populations consisted of university students and/or self-selected participants. The main research results included associations between frequent mobile phone use and mental health outcomes, such as depressive symptoms and sleep problems. Mobile phone use at bedtime was associated with, e.g., shorter sleep duration and lower sleep quality. "Problematic use" (dependency) was associated with several negative outcomes. In conclusion, associations between mobile phone use and adverse mental health outcomes are found in studies that take a psychological or behavioral perspective on the exposure. However, more studies of high quality are needed in order to draw valid conclusions about the mechanisms and causal directions of associations.

Keywords: behavioral addiction; cell phone; depression; epidemiology; psychology; sleep.

Publication types

  • Systematic Review
  • Behavior, Addictive / epidemiology*
  • Cell Phone Use / statistics & numerical data*
  • Depression / epidemiology*
  • Mental Health*
  • Observational Studies as Topic
  • Sleep Wake Disorders / epidemiology*
  • Sleep Wake Disorders / psychology
  • Text Messaging / statistics & numerical data*

REVIEW article

Problematic mobile phone and smartphone use scales: a systematic review.

\r\nBethany Harris

  • 1 Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, United States
  • 2 Department of Psychology, Texas A&M University, College Station, TX, United States

The popularity of smartphones is undeniable in nearly all facets of society. Despite the many benefits attributed to the technology, concern has grown over the potential for excessive smartphone use to become problematic in nature. Due to the growing concerns surrounding the recognized and unrecognized implications of smartphone use, great efforts have been made through research to evaluate, label and identify problematic smartphone use mostly through the development and administration of scales assessing the behavior. This study examines 78 existing validated scales that have been developed over the past 13 years to measure, identify or characterize excessive or problematic smartphone use by evaluating their theoretical foundations and their psychometric properties. Our review determined that, despite an abundance of self-report scales examining the construct, many published scales lack sufficient internal consistency and test-retest reliability. Additionally, there is a lack of research supporting the theoretical foundation of many of the scales evaluated. Future research is needed to better characterize problematic smartphone use so that assessment tools can be more efficiently developed to evaluate the behavior in order to avoid the excessive publication of seemingly redundant assessment tools.

Introduction

Smartphone ownership has become increasingly more prevalent over the past decade since Apple’s first iPhone smartphone device was launched in 2007 ( Apple Inc, 2007 ). In 2018, the Consumer Technology Association (CTA) revealed that smartphones were owned in 87% of United States homes and predicted that smartphone ownership could reach household TV ownership rates (96%) within 5 years ( Twice Staff, 2018 ). However, in the fields of psychology and cognition, it is not the mere ownership of the technological devices that is causing increased concern. It is, instead, the potential for dysfunction associated with smartphone use that is leading researchers to stress the importance of investigating the behavior. Therefore, the purpose of this paper is 3-fold. First, we review literature examining psychological and behavioral dysfunctions related to smartphone use as well as probe the potential role problematic smartphone usage may occupy within the realm of addiction research. Second, we present an exhaustive review of assessment scales that measure problematic smartphone or mobile phone use including an overview of reliability (i.e., internal consistency and test-retest reliability) and criterion-related validity by each scale. Third, we will provide specific recommendations for moving the field forward including furthering research in order to standardize conceptualization of the behavior.

Associated Dysfunction

It is important to note that a standard cut-off point to determine when smartphone use becomes problematic has yet to be established. Due to insufficient research investigating problematic smartphone use in order to effectively and consistently characterize it, it is currently unclear whether “problematic use” ought to be defined by use quantity, patterns of use, or by the negative consequences of the use. Billieux (2012) conducted a frequently cited literature review of dysfunctional mobile phone use and defined the problematic use of mobile phones as “an inability to regulate one’s use of the mobile phone, which eventually involves negative consequences in daily life” (pg. 1). Numerous research studies indicating that smartphone use is related to various facets of dysfunction support Billieux’s (2012) conceptualization of problematic use being contingent upon negative consequences associated with the use. Evidence has accumulated showing strong links between smartphone use and social, interpersonal, mental health, cognition and academic dysfunction, suggesting that smartphone use can result in significant negative consequences for some individuals (see review, Billieux, 2012 ).

For example, although smartphones provide unique opportunities for social interaction, Scott et al. (2016) found that problematic attachment to technology such as smartphone devices was associated with lowered social skills, emotional intelligence and empathy, as well as increased conflict with others. Additionally, Laramie (2007) identified social anxiety and loneliness as being associated with heavy use of and reliance upon mobile phones, suggesting smartphone overuse may result in interpersonal dysfunction. Relatedly, self-reported subjective smartphone addiction has been shown to be negatively correlated with psychological well-being ( Kumcagiz and Gündüz, 2016 ). Several studies have revealed evidence that low self-esteem ( Bianchi and Phillips, 2005 ; Hong et al., 2012 ). and depression and anxiety ( De-Sola et al., 2017b ; Elhai et al., 2017 ; Matar Boumosleh and Jaalouk, 2017 ) are associated with problematic smartphone use, especially in populations of adolescents and young adults. The results of these studies present rationale for a justified concern surrounding potential negative psychological consequences of smartphone overuse.

Similarly, concern has grown over the potential negative impacts smartphone use might have on users’ behavior and cognitive abilities. Research has shown that problematic smartphone use is related to impulsivity ( Contractor et al., 2017 ; De-Sola et al., 2017b ; Hadar et al., 2017 ), impaired attention ( Roberts et al., 2015 ; Hadar et al., 2017 ), and compromised inhibitory control ( Chen et al., 2016 ). These associated cognitive deficits have spurred researchers to investigate the potential for dysfunction in academic performance, as well. Smartphone use has been shown to negatively correlate with academic progress and success ( Alosaimi et al., 2016 ; Hawi and Samaha, 2016 ; Samaha and Hawi, 2016 ). Findings from these studies suggest the cognitive dysfunction associated with smartphone overuse may result in real-world consequences for some individuals.

To reiterate, despite research efforts characterizing associated dysfunction, a standardized conceptualization of problematic smartphone use has yet to be established in the field. However, the previously described areas of dysfunction (e.g., social, interpersonal, mental health, cognition, and academia) found to be associated with smartphone use support Billieux’s (2012) conceptualization of problematic smartphone use being contingent upon negative consequences associated with the use. As such, many assessment tools for problematic use tap into these types of negative life consequences as they are likely to identify individuals for which excessive smartphone use is especially harmful.

Is Smartphone Addiction a Real Concept?

The American Psychiatric Association (APA) broadly defines addiction as “a complex condition, a brain disease that is manifested by compulsive substance use despite harmful consequences” (pg. 1; American Psychiatric Association, 2017 ). In this definition, the use of substances is a requirement of the condition in that, to be “addicted,” one must have a substance to which to be addicted. But, what about behavioral addictions? Both the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013 ) and the International Classification of Diseases (ICD-11; World Health Organization [WHO], 2018 ) have grouped behavioral addictions within their respective substance dependence categories. Re-categorization of addictions was seen in the DSM-5 resulting in gambling disorder being recognized as a non-substance-related addictive disorder ( American Psychiatric Association, 2013 ). Additionally, Internet Gaming Disorder (IGD) is included in the DSM-5 as a condition for further study ( American Psychiatric Association, 2013 ). Finally, both gambling disorder and gaming disorder are grouped together in the ICD-11 ( World Health Organization [WHO], 2018 ), suggesting behavioral addictions share some common ground with substance use disorders (SUD).

Despite this conceptual similarity, Billieux et al. (2015) argue that, while addictive behaviors like problematic smartphone use is associated with several types of associated dysfunction, research in this arena is inconsistent in documenting significant behavioral and neurobiological similarities and correlates with more widely recognized substance addictions. For example, there are many features of substance addiction that do not appear to be present when considering excessive smartphone use. Very little research has documented the presence of loss of control (i.e., trouble consciously limiting one’s smartphone use), tolerance (i.e., increasing smartphone use to achieve satisfaction), and withdrawal (i.e., negative symptoms that occur after smartphone use discontinuation; Billieux et al., 2015 ). Also, dependence symptoms such as tolerance and withdrawal, theoretically based in physiological adaptation to increasing amounts of a drug, are often absent in behavioral addictions. In their review for IGD, Kaptsis et al. (2016) did not find consistent answers to questions inquiring about withdrawal symptoms, such as effects on mood (i.e., feeling “irritable,” “dissatisfied,” or “moody” when unable to play a game) for IGD. Similarly, physiological and neurobiological adaptations to increasing amounts of smartphone use have yet to be documented, suggesting researchers may need to use other criterion to define problematic smartphone use. Some researchers have argued that “borrowing” such criteria from more recognized addictive behaviors, like substance abuse or problematic gambling, might not fit for certain problematic or excessive behaviors ( Starcevic, 2016 ). Thus, although sharing common ground, problematic smartphone use may substantially differ from substance addiction in regards to loss of control, tolerance, and withdrawal.

Some other criteria for addiction map on better. Aforementioned associated life dysfunction is becoming increasingly documented, meaning the problematic use of smartphone devices has real-world negative consequences for some individuals. Compulsive use has been documented: Parasuraman et al. (2017) found that over 50% of participants would not quit using their smartphones even though their daily lifestyles were being negatively affected by their excessive use. This irresistible impulse to use one’s smartphone despite wanting to stop is reminiscent of individuals with SUDs, in which the drive to use drugs overrides other executive control processes. Six symptom criteria were even proposed to diagnose smartphone “addiction” and related functional impairment, which were based on guidelines for SUDs and IGD. Lin et al. (2016) dropped tolerance from their final criterion, due to low diagnostic accuracy. However, they included withdrawal, as subjects who used their smartphones excessively enough to be considered “addicted” displayed feelings of dysphoria, anxiety, and/or irritability after a period without their smartphones.

Dependency appears, to some extent, in excess smartphone users, although again this is based on subjective self-report. In a study conducted by Parasuraman et al. (2017) analyzing smartphone use behavior, almost 75% of smartphone users reported feeling dependent upon smartphone devices and 58% of users felt as though they were “unable to withstand” not having their smartphone with them. Additionally, over 70% of participants indicated that they use their smartphone longer than they intended. Similarly, results from a research study released by The Sun newspaper in March of 2013 indicated that one in ten college students say that they are “addicted” to their smartphones ( Hope, 2013 ). Upon surveying 2,000 college students, 85% of the students endorsed the question about constantly checking their smartphones to figure out what time it is and 75% of the students responded that they sleep with their smartphones lying beside them. These data indicate, when used excessively, smartphones can become problematic and users report feeling as though they have an addiction to them.

Laws have even been enacted in many states to combat problematic use. Phone use while driving a vehicle has become a major concern and it has been shown that it is the anticipation of incoming calls, messages and notifications that directly correlates with greater in-vehicle phone use ( O’Connor et al., 2013 , 2017 ). Additionally, recently, the city of Honolulu, Hawaii has even gone so far as to enact a law making it illegal for pedestrians to use their phones when crossing a street or highway (Honolulu, Hawaii, Ordinance 17-39, Bill 6, 2017) due to the significant increase in pedestrian fatalities in the city partially attributed to smartphone distraction ( Ellis, 2017 ). Thus, more and more individuals are using their smartphones in risky and physically hazardous situations. This is conceptually similar to some more recognized addiction criteria for SUDs in DSM-5.

DSM-5 Criteria and Considerations

As reviewed, problematic smartphone use shares some conceptual similarities with more typically recognized addictions, including excessive use, failure of impulse control, feelings of dependency, use in risky and/or physically hazardous situations, and potential for negative affect when not using one’s smartphone. The term “addiction” is typically characterized by these criterion, but the question of whether “behavioral addictions” must contain all of these same criterion to be considered a true “addiction” is still under debate.

Starcevic (2013) suggested behavioral addictions are characterized by salient behaviors which promote craving and neglect of other life activity, loss of control, tolerance and withdrawal manifestations, and negative consequences from overuse. Gambling disorder, considered an impulse control disorder in the DSM-IV ( American Psychiatric Association, 2000 ), is now characterized and grouped with SUDs in the most recent DSM-5 ( American Psychiatric Association, 2013 ) in a new category of psychopathology entitled “Substance-Related and Addictive Disorders.” This transition was the result of a wide body of research demonstrating clinical, phenomenological, genetic, neurobiological, and other similarities between gambling disorder and SUDs ( Potenza, 2014 ). While gambling disorder is currently the only representative member of the “Non-Substance-Related Disorders” subsection, this transition was an important shift for the recognition of “behavioral addictions” more broadly. Many researchers now advocate for the similar recognition of problematic smartphone use (e.g., Potenza et al., 2018 ).

Support for recognition of problematic smartphone use has also been motivated by the growing body of research literature on Internet addiction seen since the late 1990s. Kimberly Young is considered to be the “founder” of the concept of Internet addiction due to her publication of a case study in 1996 involving a 43-year-old female with no addiction or psychiatric history who abused the Internet causing significant impairment ( Young, 1996 ). This led to her development and validation of the Young Internet Addiction Scale (Y-Scale; Young, 1998 ) assessing self-reported preoccupation with the Internet, need to use the Internet with increasing amounts of time, unsuccessful efforts to stop use, restlessness associated with decreased use, longer than intended use, associated life impairments, concealment of involvement, and use of the Internet to relieve a dysphoric mood. The scale’s items were derived from the DSM-IV’s criteria for Pathological Gambling ( American Psychiatric Association, 1995 ) due to her conceptualization of the behavior as being similar to other impulse-control disorders. It seems likely that the development of this scale and the subsequent research that has been conducted on Internet addiction have greatly influenced the investigation of problematic smartphone use as a similar disorder.

In light of growing concerns surrounding the known and unknown implications of smartphone use as well as these recent changes in the conceptualization of non-substance-related addictions, great efforts have been made through research to identify, label and evaluate problematic smartphone use mostly through the development and administration of scales measuring and characterizing the behavior. Researchers within the past 13 years have set out to develop assessment tools based upon varying diagnostic criteria for officially recognized disorders and addictions such as SUDs and gambling disorder as well as unofficial criteria associated with Internet addiction. The aim of the present review is to examine existing validated scales that have been developed to measure, identify or characterize problematic smartphone use by evaluating their theoretical foundations and their psychometric properties.

Literature Collection

All studies (published between January 1994 and May 2019) validating standardized measures of varying forms of problematic smartphone use were identified by searching two databases (PsycINFO and MEDLINE Complete) through EBSCOhost. The date range was decided upon after conducting a preemptive literature search utilizing the search terms listed in Appendix A and concluding that the earliest study was published in 1994 ( Clifford et al., 1994 ). For the EBSCOhost literature collection, language was limited to English. Further studies, including those in other languages, were identified by reviewing the bibliographies of relevant studies and reviews.

Search Terms

Due to inconsistencies in the field regarding the conceptualization of the technology being used and the use of the technology, various terms were used in order to ensure that all relevant studies would be identified and reviewed. In addition to searching for studies identifying problematic use of smartphones, terms such as “smart phone,” “cellular phone,” “cell phone,” “mobile device,” and “mobile phone” were used. Additionally, because of the conceptualization of the problematic use has also been shown to be inconsistently described in research studies, terms such as “dependence,” “dependency,” “overuse,” “nomophobia,” “attachment,” and “compulsive” were used during the literature collection process. Finally, terms such as “questionnaire,” “scale,” “inventory,” measurement,” and “validation” were used to ensure all studies validating measurement scales were identified. The full search strategy is presented in Appendix A .

Inclusion/Exclusion Criteria

Scales were selected for inclusion if: (a) their development and validation were investigated in the identified study, or (b) they were described in the methods section of a research study as being used to identify or evaluate the behavior. Scales were excluded from the systematic review when they were used to measure behavior not specific to smartphone or cellular phone problematic use.

Data Extraction

Once a measurement scale was identified through the review of a study, a structured process was used to extract data on the scale (title, abbreviation, and the author(s) of original development/validation study), items (total number, format, and scale range), sample and norms (validation study participant count and descriptions), reliability (internal consistency and temporal stability), validity (content domains and criterion-related validity), and construct being measured. If a scale was mentioned in a research study as being used to measure the behavior, the study used to validate the scale and discuss its development was found in the study’s references and used to extract these data.

Format of the scale items was identified and described as either Likert scale (range of potential responses on a continuum) or dichotomous (yes or no response options). Internal consistency (the degree of the interrelatedness among the items; Mokkink et al., 2013 ) was assessed and the Cronbach’s alpha (α) value was recorded for each scale if provided in the validation study. Reported temporal stability, or test-retest reliability, measuring the stability of the responses to items over time was assessed and were recorded for each scale, as well. Content domains were often identified by using the factors listed by the author indicated through factor analysis of their scale’s items. The content domains often reflected similar criteria used to assess disorders or conditions claimed by the researchers to be similar in nature to the problematic behavior being assessed. The criterion-related validity (the degree to which the scores of the instrument are an adequate reflection of a “gold standard;” Mokkink et al., 2013 ) of each scale was identified by assessing the scales and criteria used by the researchers to validate their instruments. Finally, the purported construct being measured by each scale was typically identified by evaluating the title assigned to the scale by the researchers and their description of the purpose of developing the scale.

Identification of Measurement Scales

The process for the identification and selection of the problematic smartphone use scales is displayed in the flow diagram (see Figure 1 ). The combined search strategy using PsycINFO and MEDLINE Complete databases and the search terms displayed in Appendix A yielded 2452 potentially relevant articles. From them, 379 duplicate articles were excluded leaving 2073 remaining articles identified as being unique. By screening the titles of the articles, 1567 articles were excluded leaving 506 articles identified as being potentially relevant. Next, through an abstract screening process, a single, broad exclusion criteria was utilized to evaluate article relevance and inclusion. Articles were eliminated if there was no mention of either development and/or validation or utilization of assessment tools examining use of smartphone or mobile phone devices in their abstract. For example, articles were eliminated if researchers utilized smartphone devices to administer assessments of unrelated constructs (e.g., depression, anxiety). This resulted in the removal of 40 articles. The remaining 466 articles were identified as being eligible studies requiring further examination in order to identify applicable measures. Finally, through an in-depth examination process, 78 total scales were identified as being unique and relevant. These scales are organized by purpose and can be found in Table 1 (Problematic Smartphone Use Measurement Scales; 70 scales), Table 2 (Smartphone Use Frequency Scales; 3 scales), and Table 3 (Smartphone Use Motivations and Attitudes Scales; 6 scales), with one scale (MTUAS; Rosen et al., 2013 ) appearing in both Tables 2 , 3 due to overlapping constructs being measured.

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Figure 1. Study flow diagram showing review process on measures of problematic smartphone use.

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Table 1. Problematic smartphone use measurement scales.

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Table 2. Smartphone use frequency scales.

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Table 3. Smartphone use motivations and attitudes scales.

Description of Measurement Scales

Following the development of the first mobile phone use measurement scales ( Toda et al., 2004 ), mobile phone ownership began decreasing as smartphones became increasingly more popular. However, during this transition from mobile phone use to the “smartphone era,” the terms “mobile phone” and “smartphone” were used interchangeably across studies and, often, within studies. Because smartphones have significantly more components and capabilities than mobile phones and the problematic use of the two different forms of technology should be viewed differently, comparisons of published scales should be made in light of the distinctive differences between the two types of technology. The scales are arranged based upon the date that they were published within each of the three tables starting with the first developed scale in 2004 to the most recently published scales in 2019. Thus, it is likely that the more recently developed scales involved specific analyses of smartphone use and behavior as opposed to that of more contemporary mobile phone devices.

Scales included in Table 1 are those that were specifically developed and validated to identify problematic smartphone or mobile phone use or to diagnose individuals with smartphone addiction, overuse, dependency, attachment, etc. [e.g., Smartphone Addiction Scale (SAS; Kwon et al., 2013b ); Smartphone Addiction Inventory (SPAI; Lin et al., 2014 )]. Although the construct being claimed to be measured by each of these individual scales may differ, many of them are similar in their theoretical foundations and even item content. For example, while Kwon et al. (2013b) utilized DSM-IV criteria for substance dependence to develop the item content for the SAS with the goal of assessing “addiction,” Merlo et al. (2013) utilized the same criteria to develop the Problematic Use of Mobile Phones (PUMP) scale. In Table 1 , validated shortened versions of originally validated scales were included in the review if identified in the literature review. Table 1 includes the majority of the scales identified in the review (70 of the 78).

Table 2 , on the other hand, contains three scales. This table includes scales assessing smartphone use frequency as opposed to general problematic smartphone use behavior. It is important to note the differences between these two constructs. As described earlier, smartphone use frequency can be very heterogeneous due to differing motivations and purposes for use ( Elhai et al., 2018 ). Higher frequency of smartphone use may not indicate the presence of problematic smartphone use if, for example, associated life dysfunction is not identified ( Billieux, 2012 ). Scales included in this table include: the Media and Technology Usage and Attitudes Scale (MTUAS; Rosen et al., 2013 ) which assesses both use of technological devices – including smartphones – and attitudes surrounding technology use (see Table 3 ); the Smartphone Use Frequency (SUF; Elhai et al., 2016 ) assessing use of smartphone devices in areas such as calling, messaging, emailing, etc.; and the Mobile Usage Scale (MUS; Konok et al., 2016 ) examining differences in use of smartphones and traditional mobile phones. These scales were included in the review so as to provide researchers with options for examining problematic smartphone use and/or smartphone use frequency.

Finally, additional scales assessing motivations for as well as attitudes surrounding use of smartphone devices are included in Table 3 . For example, the MTUAS ( Rosen et al., 2013 ) is included in both Tables 2 , 3 because, although it assesses frequency of smartphone and technology usage, it also examines attitudes associated with this usage. Additionally, the Process vs Social Smartphone Usage Scale (PSSU; van Deursen et al., 2015 ) was included in Table 3 due to its examination of motivations for use of smartphone devices. Finally, a third example of a scale included in Table 3 is the Mobile Phone Affinity Scale (MPAS; Bock et al., 2016 ) which evaluates motivations of smartphone use, including connectedness, productivity, empowerment, etc. It was important to include these six scales in Table 3 because their inclusion further exemplifies the robust nature of the research and development of measurement scales focusing on smartphone use. The inclusion of each of these three domains makes this review a useful tool for researchers studying smartphone use behavior – problematic or otherwise – as well as associated benefits and dysfunction.

Psychometric Characteristics

Elements of criterion-related validity, content domains, internal consistency, temporal stability, and purported construct were listed or briefly described for each of the scales in Tables 1 – 3 . Because of this, these tables can be used to compare the individual scales. Additionally, in the following analysis, the psychometric properties and conceptual foundations of the scales included in this review will be further dissected. This analysis will help researchers and practitioners alike to consider the psychometric properties and theoretical foundations of potential assessment tools before deciding which scale should be utilized in their research or practice.

The term “addiction” was used frequently when naming many of the problematic smartphone use scales. This is due to the choice of framework and criterion-related validity used when developing and validating the scales. Many scale developers used either the DSM-IV or DSM-5 criteria for substance use to examine criterion-related validity during development. Others chose to use Griffiths’ (2005) components descriptive model of addiction, which includes the following core components: salience, mood modification, tolerance, withdrawal, conflict and relapse. Similarly, Internet addiction was frequently used to establish criterion-related validity. Before the release of the first smartphone, problematic Internet use was being observed, identified, and subsequently labeled as Internet addiction. Addiction scales were quickly developed to assist identifying this behavior such as Young’s Internet Addiction Test (IAT or Y-Scale; Young, 1998 ). Once smartphones were developed and made available to the public, problematic smartphone use similarly became a concern. Many researchers utilized various Internet addiction scales to validate their scales (e.g., SMS-PUDQ; ECPUS; MPAI; SAPS).

One of the final ways that scale developers established criterion-related validity was by utilizing quantified smartphone use as a criterion to determine whether the scales could be used to identify smartphone addiction. However, most of these scale development processes involved self-reported and self-estimated smartphone use. Because they were unable to utilize concrete and exact documentation of participants’ smartphone use time, they relied upon estimation which can be unreliable ( Andrews et al., 2015 ) and, therefore, should not be considered to be a practical or accurate means of validating a scale.

Even if accurate data were being obtained from participants concerning time spent using their phones, there is no established cut-off point that can be used to validate accuracy of a scale in indicating dependency, problematic use or addiction based upon extensiveness of use alone since it has not been determined at what point phone use becomes problematic. It is likely that a cut-off point for quantitative smartphone use may not be feasible. Elhai et al. (2018) explains that smartphone use frequency can be very heterogeneous due to differing motivations and purposes for use. They describe how a high frequency of smartphone use can be functional for some (e.g., productive smartphone use for purposes of work or school) and dysfunctional for others (e.g., excessive gaming and social media use).

Additionally, a significant number of scales described in Tables 1 – 3 relied upon existing measurement scales for problematic phone use in order to establish concurrent validity for the scale they were developing. This is due to the recognized issue of currently not having a gold standard for criterion-related validity for problematic phone use or addiction. However, this is concerning considering the existing assessments used to validate the new scale likely also used problematic criteria to establish criterion-related validity. For example, when developing the Smartphone Impact Scale (SIS), Pancani et al. (2019) included the widely used Smartphone Addiction Scale (SAS; Kwon et al., 2013b ) in their study to validate the SIS with an Italian adult sample. This could be problematic for two reasons. Firstly, to our knowledge, the SAS has yet to be validated for use with Italian adults as it was developed using a population of Korean adults. Secondly, to our knowledge, the temporal stability of the SAS has yet to be investigated.

Selection of content domains by the researchers in their validation studies stemmed from their conceptual foundation for the scale’s development and their criterion-related validity. For example, regarding the scales in Table 1 , DSM-IV pathological gambling criteria or DSM-5 gambling disorder criteria were used to establish criterion-related validity for seven of the scales (COS, MPAI, CERM, KBUTK, MAT, AMPUH, and ATeMo). Therefore, these scales’ content domains were shown to reflect the diagnostic criteria associated with problematic gambling disorder. The DSM-5 indicates that, to be diagnosed with gambling disorder, an individual must exhibit four or more of the following symptoms: tolerance; withdrawal; lack of control; preoccupation; escape from problems; “chasing” losses; deception; and associated life dysfunction in areas such as relationships, job, education, or finances ( American Psychiatric Association, 2013 ). Excluding “chasing” losses, these factors were shown to be consistently reflected across those seven scales in terms of their established content domains. DSM-IV, DSM-IV-TR or DSM-5 criteria for SUDs were frequently used to validate these problematic smartphone use scales, and their diagnostic criteria were similarly, reflected in the content domains established in the validation studies (e.g., CERM, PCPU-Q, and SAS).

Internal consistency is the degree of interrelatedness among scale items ( Mokkink et al., 2013 ). This measure of reliability was reported for most of the scales in the form of a Cronbach’s α value. However, despite its importance in scale development, an internal consistency value was not reported for seven of the scales in their validation studies (STDS, DENA, MPIQ, MAT, MP-UQ, MIUI, and SAMI). The Cronbach’s α values that were reported ranged from 0.53 (MPUS) to 0.97 (PMUM, MTUAS, and SAS). Although there is inconsistency in the field regarding at what point Cronbach’s α values should be considered to be adequate or acceptable, acceptable values of alpha have been reported to range from 0.70 to 0.95 ( Nunnally and Bernstein, 1994 ; Bland and Altman, 1997 ; DeVellis, 2016 ). Using the lowest value reported as being acceptable or adequate as a cutoff, four of the scales identified in this review (PMPUQ, IMAT, MPUS, and MTUAS) would not meet that standard.

Although internal consistency is important in scale development, most of the scale developers failed to account for temporal stability in guaranteeing reliability. Upon analyzing the psychometric properties of the various scales, it was discovered that only in the scale development of ten scales were test-retest reliability coefficients provided to indicate that the scales have temporal stability. This is a cause for concern because even some of the most frequently used scales have failed to ensure temporal stability in their development (e.g., SAS, NMP-Q, and SABAS).

This review is the first to identify and report the method of development for all problematic smartphone use scales as well as those developed to assess smartphone use frequency, motivations, and attitudes. After conducting a systematic search and identifying all relevant measures, we analyzed the psychometric properties and criterion-related validity of each scale. However, despite identifying 78 validated scales, we were not able to fully determine the most efficient scales for measuring problematic phone usage due to several issues: (1) most of the scales established criterion validity using DSM-IV or DSM-5 criteria for gambling disorder or substance-use disorders, even though there is still considerable controversy over whether problematic smartphone use should be considered an “addiction”; (2) test-retest reliability coefficients were not reported in the development articles for 68 of the 78 scales, and both internal consistency and test-retest reliability were not available for seven of the scales, which causes concern for future analyses that attempt to identify the most efficient scale(s); (3) the gold-standard criteria and cut-off scores for problematic smartphone use has yet to be established; in other words, these scales cannot accurately be compared and contrasted since there is no validated, gold-standard criteria to which they can strive to incorporate. Therefore, we will primarily discuss practical ideas and recommendations for future research.

Scale Content

The addition of gambling disorder to the substance-related and addictive disorders section of the DSM-5 as a non-substance-related addictive disorder has subsequently opened the door for other behaviors to be researched, evaluated, and identified through developed and validated scales. Another example of this would be the behavioral condition known as internet gaming disorder (IGD). While this area of research warrants further study according to the DSM-5 ( American Psychiatric Association, 2013 ), the proposed criteria for IGD as a behavioral addiction involving the problematic use of video game technology closely resembles the criteria for SUD and are very similar to how researchers are conceptualizing problematic smartphone use ( Lin et al., 2016 ). Further, based on the development methods of the majority of the reviewed scales, many researchers feel as though it could be time to start assessing smartphone use with an addictive framework in mind, arguably with the exception of “tolerance” symptoms ( Lin et al., 2016 , 2017 ). This may in part be due to a belief that problematic smartphone use, as well as potentially other problematic behaviors, should be similarly characterized and defined as diagnosable behavioral or non-substance-related addictions. The majority of reviewed scales reflect this viewpoint. The content domain of most scales (see Table 1 ) are related to dependence-related concepts including craving, tolerance, withdrawal, excessive time spent using, and negative life consequences.

Other scales have moved away from this content domain in their development and have attempted to measure more specific and different aspects of problematic smartphone use. For example, The Mobile Phone Involvement Questionnaire ( Walsh et al., 2010 ) and the Media and Technology Usage and Attitudes Scale ( Rosen et al., 2013 ) examine smartphone use involvement through items assessing euphoria, salience, and overall usage. This perhaps reflects the rationale that smartphones may be especially cognitively and behaviorally salient to some, resulting in more usage, but without this usage necessarily being pathological, uncontrollable, or addictive in nature. Such scales perhaps measure the construct of “liking,” or the pleasurable impact of habitual smartphone use, compared to other scales measuring the construct of “wanting,” or the compulsive motivation to engage in smartphone use resulting in negative life consequences. This reflects an important distinction considering the behavioral addiction framework: more and more in today’s society, smartphones are linked with several forms of reward and social value. It makes sense people would “like” smartphones, feel they are important, and use them many times a day. This does not necessarily reflect addiction to them, despite individual’s tendency to self-report this. Some of the reviewed scales perhaps are better conceptualized as a measuring maladaptive smartphone use, rather than addictive use, as endorsements such behaviors perhaps do not rise to the severity levels of addiction ( Panova and Carbonell, 2018 ).

In a similar vein, some scales appear to measure the degree to which individuals report salient emotional connections to their smartphone. The Young Adult Attachment to Phone Scale ( Trub and Barbot, 2016 ) and the Adolescent Preoccupations with Screens Scale ( Hunter et al., 2017 ) share item content related to feelings of safety with and feelings of anxiety when without one’s phone. Such scales measure attachment styles, in that an individual’s mood state can shift depending on the smartphone device’s proximity. The relative convenience of smartphone functions in daily life can mean that feelings of irritation or concern are likely to present when one does not have immediate access to it. Relatedly, scales like the Mobile Attachment Scale ( Konok et al., 2016 ) and the Mobile Phone Affinity Scale ( Bock et al., 2016 ) have items which measure a preference for mobile communication, resulting in strong preferences for having one’s smartphone device instantly accessible. This emotional attachment resulting in dysphoria can mimic addiction withdrawal symptoms in this way. Problematic smartphone use often co-occurs with depression and anxiety as a means of experiential avoidance ( Elhai et al., 2017 ). But, these scales and criteria may simply be reflecting a strong “liking” for the ease of communication to others via calling/texting, can result in different emotional reactions depending on whether the device is accessible or not. Future research should examine how endorsement of particular problematic smartphone use behaviors perhaps better explained by general psychopathology like depression and anxiety, rather than addiction.

Numerous researchers have published scales purportedly assessing “smartphone addiction” or “phone addiction.” However, some researchers feel as though we do not currently have the necessary evidence supported by research to accurately conceptualize smartphone use as having the capability of developing into an addictive behavior. Griffiths (2013) argues that “we are not yet in a position to confirm the existence of a serious and persistent psychopathological addictive disorder related to mobile phone addiction on the basis of population survey data alone” (p. 77). Perhaps this is the reason that a standard cut-off point to determine when smartphone use becomes problematic has yet to be established. Similarly, the Internet addiction framework was frequently used by the developers of several of the reviewed problematic smartphone use scales to establish criterion-related validity. However, Internet addiction is not currently recognized by the DSM-5 as a non-substance related addictive disorder due to the lack of research indicating similarity in manifestation or dysfunction with addictive disorders recognized by the DSM-5.

Additionally, there is a lack of sufficient research investigating how to effectively characterize problematic smartphone use, and it is currently unclear whether “problematic” ought to be defined by the quantity of use, patterns of use, or by the negative consequences or marked distress as a result of usage. If researchers intend to define problematic smartphone use as an “addiction” similar to a substance-use disorder, all three of those criteria, among others (e.g., “recurrent use in situations in which it is physically hazardous” or “continued use despite having persistent or recurrent social or interpersonal problems caused by or exacerbated by use”), would need to be present in order to diagnose dysfunctional or problematic smartphone use as an addiction ( American Psychiatric Association, 2013 ). This should be reflected in the self-report scales researchers are developing, testing, and validating.

Panova and Carbonell (2018) support Griffiths’ (2013) previously described argument in that they similarly suggest moving away from the addiction framework when considering problematic behaviors such as the problematic use of smartphone or other technological devices. They reference a pattern of weak study designs in the smartphone literature, such as full reliance on correlational studies, or a lack of longitudinal and experimental studies that examine associated cognitive, psychological or behavior dysfunction. They also strongly advocate for the use of terms such as “problematic use” over “addiction” when describing these behaviors. They noted that it is imperative that a research-supported criterion for problematic smartphone use be identified before using officially recognized addictive disorders to establish criterion-related validity.

Limitations of Reviewed Scales

In addition, there were many fundamental limitations to the development and intended uses of the reviewed scales. For instance, all of the reviewed scales that assessed phone use were self-report, and, therefore, cannot reliably measure actual phone usage. This is a limitation in this particular field of research that needs to be addressed. Further, when developing these new scales, many of the researchers’ hypotheses for creating these scales were that problematic phone use would correlate not with actual use, but, instead, with associated personality traits including self-esteem and impulsivity (e.g., Bianchi and Phillips, 2005 ; Billieux et al., 2008 ; Leung, 2008 ). Future research may aim to develop or modify an existing scale or consider running an experimental study in which they actively measure phone usage among individuals that includes a method to separate “normal” and “problematic” use. Interestingly, global researchers ( Monge Roffarello and De Russis, 2019 ), as well as Google (2019) , have recently created smartphone applications (e.g., “Socialize” and “Digital Wellbeing”) that can track phone usage among other features and even provide an intervention for excessive use (e.g., allowing users to set limits for amount of time allotted for specific application usage per week or per day). These applications are excellent examples for researchers to consider using as an alternative to self-report scales in measuring smartphone usage.

In a research setting, these applications would provide investigators with the opportunity to gather objective data on smartphone use from smartphone-using participants following the instruction of having the application downloaded on participants’ phones for a specific period of time. Future research ought to also investigate the effectiveness of these applications as intervention tools for problematic smartphone use. However, to reiterate, due to the heterogeneity of smartphone use frequency (i.e., functional versus dysfunctional) described by Elhai et al. (2018) , researchers should recognize that objective smartphone use data collected through use of these applications or other methods is a measure of smartphone use frequency, not necessarily problematic smartphone use. Additionally, necessary steps ought to be taken to safeguard ethical considerations and minimize risks associated with instructing participants to download applications on their smartphone devices with the inherent function of tracking their activity (e.g., protection of privacy).

Secondly, the majority of the development articles for the reviewed scales reported only internal consistency as a means of establishing reliability for their scale. Internal consistency is widely used in scale development, and the coefficient is based off of the interrelatedness of the items within the scale. However, this does not mean that the items as a whole are necessarily related to the intended construct or possess established validity. If we were going to rank scales as the most reliable based solely off of their internal consistency coefficients, the PMUM, MTUAS, and SAS (α = 0.97) would have been at the top of the list. However, researchers such as Thompson (2003) have called the use of internal consistency as the sole measure of reliability “sloppy” and not representative of the quality of the scale. While a higher Cronbach’s alpha may demonstrate the consistency of the items in the measure, the items may not be accurately capturing problematic phone usage. If there had been other reliability and validity statistics offered in the development articles of the reviewed scales, perhaps specific scales could have been recommended with confidence in this review for future use.

Thirdly, there was a large amount of variability in the types of samples studied in these development articles, which makes it difficult to compare the utility of each scale. The following should be interpreted not as limitations, but, instead, as interesting findings about the diverse origin of subjects studied in each scale’s development. For instance, a small percentage (16/84) of the studies were conducted with participants in the United States, with many of the scales having been written in languages besides English. For example, as many as six scales were developed in Chinese (WEUS, PSUMS, MPAI, SAS-C, SQAPMPU, and MPATS), four in Turkish (MAS, PMPUS, PS, and PMPUS), and seven in Korean (ECPUS, CPAS, SAPS, SAI, SAS, SAS-SV, and SOS-Q). Based on research conducted by the Pew Research Center (2018), South Korea has the largest percentage of smartphone owners. Therefore, the large number of scales that have been developed for and within that population is understandable. Yet, there were also several scales that were developed in English-speaking areas outside of the United States, such as in Australia (MMPUS, IMAT, APSS, and MPIQ) and the United Kingdom (PMPUQ-R). All of this information can be viewed in Tables 1 – 3 and Appendix A .

Lastly, the intended use of the reviewed scales varied depending on the theoretical models or criteria upon which they were based. Most of the scales were intended to measure problematic use, addiction, dependence, and excessive use of mobile phones. For instance, Leung (2008) , one of the earliest developers of a mobile phone index used to measure “addiction” symptoms demonstrated by mobile users, based her construction of the MPAI off of the idea that adolescents had started excessively using mobile phones during their leisure time as a way of counteracting boredom due to too much time with not enough to do; further, this type of activity, labeled as “leisure boredom,” had been shown to be associated with deviant activity and negative affect. Interestingly, there was a large percentage (38%) of scales purportedly assessing smartphone or mobile phone “addiction,” which is surprising given the aforementioned literature that has been opposed to labeling problematic smartphone use as an “addiction” ( Griffiths, 2013 ; Panova and Carbonell, 2018 ). Additionally, while several of these scales were developed with the hopes of being used in the future for clinical purposes (e.g., diagnosis of problematic smartphone use), since there is no mention of problematic smartphone use as a disorder an addiction in the DSM-IV or DSM-5 or ICD-11, it seems as though authors must become content with their scales being confined for research purposes only. This further indicates a need for additional research on the conceptualization and demonstrated severity of problematic smartphone use, and whether it should be given consideration for a place in the next edition of the DSM or ICD. Until then, we are unable to recommend the use of a specific scale or specific scales to assess this behavior due to a lack of sufficient research on the construct.

Limitations of the Current Study

This review is not without limitations. First, the only databases used in the systematic search were PsycINFO (EBSOhost) and MEDLINE Complete (EBSCOhost). PsycINFO was utilized due to being a specialized database that can provide unique search results specific to topics of psychology; additionally, it has been used in several largely cited systematic reviews ( Bramer et al., 2017 ; Elhai et al., 2017 ). We also utilized the MEDLINE Complete database rather than the PubMed interface due to the convenience of access through EBSOhost. Secondly, many reliability coefficients were not able to be listed due to many of the articles being published solely in a foreign language and, therefore, we were unable to identify and/or interpret the coefficients; the articles being inaccessible; or simply because the coefficients were not reported in the articles. That last point may also be expressed as a limitation of the scales themselves.

Future Directions

Future research must be conducted in order to further identify potential cognitive, neurological, physical, behavioral or social dysfunction related to smartphone use. Currently, no causal relationships between smartphone use and dysfunction in these previously listed areas have been established. Until then, conceptualizing smartphone use in such a way as to assert that the behavior can become problematic or clinical in nature should be done with caution. Additionally, a standard cut-off point at which smartphone use becomes dysfunctional ought to be investigated. With more evidence of causal relationships between smartphone use and dysfunction as well as a more formulated and standardized conceptualization of the behavior, researchers will be able to construct more accurate and specific scales for identifying problematic use.

This review serves as an opportunity to compare and contrast the numerous scales that have been published in the past 13 years and to analyze the psychometric properties of each of the individual scales in order to determine which, if any, of the included scales should be considered to be adequate tools for assessing problematic smartphone use or smartphone addiction. However, it is recommended that further research be conducted to sufficiently conceptualize the behavior and its development, manifestation, and associated dysfunction. In order to best develop tools to assess the behavior, we must first understand smartphone use with an increased focus on contexts, functions, and motivations for use, rather than simply borrowing item criteria from assessment scales of more established substance or behavioral addictions. Currently, there is still much to learn about smartphone use and at what point and for whom the use becomes problematic.

Author Contributions

BH and SF conceived the study, interpreted the data, and participated in drafting the manuscript. TR and JS participated in drafting the manuscript. All authors read and approved the final manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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EBSCOhost Search Strategy

(smartphone[tiab] OR smart phone[tiab] OR cellular phone[tiab] OR cell phone[tiab] OR cell-phone[tiab] OR mobile device[tiab] OR mobile phone[tiab])

(problematic[tiab] OR problem[tiab] OR dependence[tiab] OR dependency[tiab] OR overuse[tiab] OR addiction[tiab] OR nomophobia[tiab] OR attachment[tiab] OR excessive[tiab] OR compulsive[tiab])

(questionnaire[tiab] OR scale[tiab] OR index[tiab] OR test[tiab] OR inventory[tiab] OR index[tiab] OR instrument[tiab] OR assessment[tiab] OR measurement[tiab] OR survey[tiab] OR psychometric ∗ [tiab] OR validation[tiab] OR development[tiab])

(“1990”[PDAT]: “2019”[PDAT]).

Keywords : mobile phone, smartphone, problematic use, addiction, assessment

Citation: Harris B, Regan T, Schueler J and Fields SA (2020) Problematic Mobile Phone and Smartphone Use Scales: A Systematic Review. Front. Psychol. 11:672. doi: 10.3389/fpsyg.2020.00672

Received: 24 October 2019; Accepted: 19 March 2020; Published: 05 May 2020.

Reviewed by:

Copyright © 2020 Harris, Regan, Schueler and Fields. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sherecce A. Fields, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Effects of Mobile Use on Subjective Sleep Quality

Nazish rafique.

1 Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

Lubna Ibrahim Al-Asoom

Ahmed abdulrahman alsunni, farhat nadeem saudagar, latifah almulhim.

2 College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

Gaeda Alkaltham

Associated data.

  • Patel N. Cell phone radiations and its effects in public health - Comparative review study. MOJ Public Health . 2018;7(2):14–17. doi: 10.15406/Mojph.2018.07.00197 [ CrossRef ]

The objective of this study was to find out the association between mobile use and physiological parameters of poor sleep quality. It also aimed to find out the prevalence of mobile-related sleep risk factors (MRSRF) and their effects on sleep in mobile users.

Materials and Methods

This cross-sectional study was conducted on 1925 students (aged 17–23yrs) from multiple Colleges of Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. The study tools used were Pittsburgh sleep quality index (PSQI) and MRSRF online questionnaires.

The mean age (±SD) of participants was 19.91 ± 2.55 years. Average mobile screen usage time was 8.57±4.59/24 hours, whereas average mobile screen usage time in the bed after the lights have been turned off was 38.17±11.7 minutes. Only 19.7% of subjects used airplane mode, while 70% kept the mobile near the pillow while sleeping. The blue light filter feature was used by only 4.2% of the participants. “Screen usage time of ≥8 hours” was positively correlated with sleep disturbances and decrease in the length of actual sleeping time (p =0.023 and 0.022). “Using the mobile for at least 30 minutes (without blue light filter) after the lights have been turned off” showed positive correlation with poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency (p= 0.003, 0.004 and 0.001). “Keeping the mobile near the pillow while sleeping” was also positively correlated with daytime sleepiness, sleep disturbances and increased sleep latency (p =0.003, 0.004 and 0.001).

This study concludes that using mobile screen ≥8 hours/24 hours, using the mobile for at least 30 minutes before sleeping after the lights have been turned off and keeping the mobile near the pillow are positively associated with poor sleep quality. Moreover, we observed that MRSRF were highly prevalent amongst the mobile users.

Introduction

Sleep is a physiological state of unawareness which is regulated homeostatically. 1 Almost one-third of our lives are spent while sleeping. 2 Sleep plays an important role in cognitive and physical functions, cellular toxin removal, disease prevention and restoration of both mind and body. 3 – 5 A major decline in the sleep hours and its strong correlation with obesity, diabetes, and other chronic debilitating diseases have been documented in the past 20–30 years. 5 , 6

Proper sleep is especially important for children and adolescents. 6 Lack of sleep in adolescents is becoming an important health issue worldwide. 7 Many factors can affect sleep hygiene 8 but the role of mobile use in causing sleep problems in adolescence has gained huge attention in the past few years. 9 A recent review by Sohn et al reported that one in every four children and young people are suffering from Problematic cell phone use (PSU), which is linked to depression, anxiety and poor sleep quality. 10 Current metaanalysis by Carter et al showed that bedtime use of media devices was positively associated with poor sleep quality and excessive daytime sleepiness. 11

Mobile use at bedtime (after the lights have been turned off), can cause poor sleep quality (PSQ) by various mechanisms. 12 Due to technology revolution, most of the mobile phone users now have smartphones which enable them to access internet and social networks, watching videos, online chatting and playing games. 13 This results in exposure to stimulating content, mobile phone overuse and phone addiction thus contributing to hyper arousal in pre bedtime period and poor sleep quality. 14

A major factor which can contribute to PSQ is the blue light emitted by screens of mobile phones. 15 This blue light can decrease the production of melatonin, the hormone which controls the sleep/wake cycle or circadian rhythm. Reduction in melatonin makes it difficult to fall and stay asleep. 16 Some studies have found that exposure to blue light increases brain alertness 17 and can stimulate cognitive functions, which in turn can lead to PSQ. 18

Moreover, the mobile phones receive and transmit the signals through radiofrequency electromagnetic fields (RF-EMFs). 19 It is well documented that RF-EMFs can pass through the skull, and reach the brain. 20 Therefore, this technology may pose dangers for human health, of particular interest are its effects on sleep parameters and sleep electroencephalogram (EEG). 21 Some studies have reported that RF-EMFs exposure can result in changes in EEG during rapid eye movement (REM) sleep, non-REM sleep, and sleep latency. 22 – 24 All these findings further strengthen the role of mobile in causing PSQ.

Limited availability of the data regarding the “Prevalence of mobile use and its association with sleep quality in the Saudi population” compelled us to design this project. To our knowledge, the current study recruited the largest number of samples of young Saudi population for investigating the link between mobile phone use and sleep quality. We hypothesize that a positive association exists between mobile use and poor sleep quality. We also aimed to find out the prevalence of mobile-related sleep risk factors (MRSRF) in mobile users and their effects on sleep, ie, using mobile before sleeping after the lights have been turned off, not enabling airplane mode on mobiles, putting the mobiles near or below the pillows and bedside while sleeping. As no previous studies are available to highlight these important findings.

This cross-sectional study was conducted from January 2018 till August 2019 on 1925 students (aged 17–23yrs) from multiple colleges of Imam AbdulRahman Bin Faisal University, Dammam (IAU).

Sample size calculation was done by using open source epidemiologic statistics for public health tools software (accessed at: http://epitools.ausvet.com.au/content.php?page=1Proportion&Proportion ). The calculation was based on estimated prevalence of poor sleep quality in mobile users in a target population of 5000 students and desired precision as 0.02 (2%), confidence interval as 0.95 (95%). The calculated sample size was 2017.

The study tool included two questionnaires: mobile-related sleep risk factors Questionnaire (MRSRF) and Pittsburg sleep quality index (PSQI) ( Supplementary material ).

Identification of Mobile-Related Sleep Risk Factors (MRSRF)

This online questionnaire (generated by using Google forms) was designed by the authors based on relevant required information, extracted from few previous studies. 8 , 13 , 14 , 21 The face validity of the questionnaire was confirmed by professors of physiology and respiratory therapy at IAU, whereas test retest technique was used to verify the reliability (interval of three weeks) with a group of 30 students (P = 0.002; r = 0.84).

MRSRF Questionnaire includes seven items which focus on the following areas: Total duration of mobile use/day, using mobile while in the bed when the lights have been turned off, using blue light filters on mobile, keeping the mobile under pillow, keeping the mobile 2 meters away from the bed and putting the mobile on airplane mode while sleeping.

Identification of Sleep Quality

The pittsburgh sleep quality index (psqi).

Various questionnaires are used to identify the sleep quality. But PSQI has been found to be most effective in terms of reliability and validity. It includes 19 self-rated items, which focus on seven main areas including: subjective sleep quality, sleep latency (time taken to fall asleep), sleep duration, habitual sleep efficiency (the ratio of total sleep time to time in bed), sleep disturbances, the use of sleep-inducing medicines and daytime dysfunction. 25

PSQI Scoring

The PSQI includes a scoring key for calculating a patient’s seven subscores, each of which ranges from 0 to 3.

  • A score of 0 indicates no difficulty.
  • A score of 3 indicates severe difficulty.

The 7 component scores are then added to make a global score with a range of 0–21.

  • A score of 0 means no difficulty.
  • A score of 5 or more indicates poor sleep quality.
  • A score of 21 means severe difficulties in all areas.
  • (The higher the score, the worse the quality).

Data Collection

Data were collected by convenience sampling technique, and response rate was 38.5%, as 1925 out of 5000 students volunteered and completed the questionnaire. A five minutes briefing session was given in the class to explain the rational of study and terminologies used in the questionnaire (Average strength of students/class was 50). The online questionnaire was shared with each class on their WhatsApp groups, and a time of 8 minutes was provided to the students to fill the questionnaire. The students were assured about the confidentiality of their personal information.

Inclusion Criteria

  • The students between 17 and 26 years who were willing to participate in the study.
  • The students who use mobile phone daily, even if they use it for a brief moment.

Exclusion Criteria

The students suffering from

  • Any diagnosed sleep disorder.
  • Any diagnosed chronic respiratory problem (including nasal congestion, chest infections, asthma, adenoids, allergic rhinitis)
  • Any chronic physical or mental illness, affecting their sleep.
  • Using any prescription medication for at least last 3 months.

Finally, 156 students were excluded, and 1925 were selected.

Ethical approval of the study was taken by Deanship of Scientific Research College of Medicine (IAU).

Statistical Analysis

The data were analyzed using Statistical Package for Social Sciences (SPSS) for Windows, Version 20.0. Descriptive statistics were used to determine the demographic data.

Comparison of sleep quality and sleep parameters in the participants with various “Mobile-related sleep risk factors” (MRSRF) was done by using Cross tab Chi square test for nominal variables, and independent t test for qualitative data. A p value of <0.05 was considered statistically significant.

Correlation of Poor sleep quality and various sleep parameters with MRSRF was done by using Pearson and Spearman tests.

Binary regression analysis test was run using the quality of sleep (poor PSQI>5 versus good PSQI<5) as the dependent variable and the following variables were the independent factors: age, gender, screen usage time >8 hours, using the mobile phone during two hours before sleep, using mobile in bed after lights are turned off, duration of cell phone use after the lights are turned off (minutes), keeping the mobile phone near the pillow while sleeping.

A shapiro-Wilk’s test (p>0.05) and visual inspection of histogram showed that the data were approximately normally distributed.

The mean age (±SD) of participants was 19.91 ± 2.55 years. Number of female participants was 1502 (77%), whereas the number of male participants was 423 (21%). 98% of the participants owned smart phones. Average screen usage time was 8.57±4.59/24 hours, and 38% of the participants reported of using mobile for more than 8/24 hours. Almost 88.7%of the subjects were using the mobile after the lights have been turned off with an average screen usage time of 38.17±11.7 minutes. Out of these 88.7% subjects, 84.5% were not using the blue light filters on their mobiles. Average time spent on watching videos was 1.8±1.74 hours. Poor sleep quality was seen in 33% of males and 37% of females. Comparison of these parameters between male and female subjects is given in Table 1 .

Comparison of Screen and Sleep-Related Parameters, Between Male and Female Subjects

Note: P value <0.05 is considered statistically significant.

Prevalence of mobile usage, and various MRSRF are shown in Table 2 . Only 19.7% of subjects used airplane mode, while 70% kept the mobile near the pillow while sleeping. The blue light filter feature was used by only 4.2% of the participants. It was observed that the greater number of females as compared to males keep the mobiles near their pillow while sleeping (p = 0.029). Whereas the number of females who use airplane mode on their mobiles while sleeping was greater than the number of males using this function (p = 0.021).

Prevalence of Mobile Usage and “Mobile-Related Sleep Risk Factors” in Study Participants

Abbreviation: MRSRF, mobile-related sleep risk factors.

Comparison of sleep quality in the participants with various MRSRF is shown in Table 3 . Data indicated that the subjects who use the mobile after the lights have been turned off for at least 30 minutes (without a blue light filter in mobile), and who put the mobile near their pillow while sleeping have a statistically significant poor sleep quality (P=0.001, 0.001) respectively.

Comparison of Sleep Quality in the Participants with Various “Mobile-Related Sleep Risk Factors”

Further analysis revealed a strong positive correlation of poor sleep quality with Using Mobile for at least 30 minutes after the lights are turned off (without a blue light filter in mobile) (p=0.018) Table 4 .

Correlation of Poor Sleep Quality with Various “Mobile-Related Sleep Risk Factors”

Correlation of various sleep parameters with MRSRF is highlighted in Table 5 . Screen usage time of >8 hours was positively but weakly correlated with sleep disturbances and decrease in the length of actual sleeping time (P value 0.023 and 0.022, respectively). Using the mobile after the lights have been turned off for at least 30 minutes (without a blue light filter in mobile) showed positive but weak correlation with daytime sleepiness, sleep disturbances and increased sleep latency (p= 0.003, 0.004 and 0.001). Keeping the mobile near the pillow while sleeping was also positively but weakly correlated with daytime sleepiness, sleep disturbances and increased sleep latency (p =0.003, 0.004 and 0.001).

Correlation of Various Sleep Parameters with “Mobile-Related Sleep Risk Factors”

Our study showed a high prevalence and prolonged duration of mobile use in young adults. Average mobile screen usage time was 8.57±4.59/24 hours, and 38% of the participants reported of using mobile for more than 8/24 hours. Almost similar results have been demonstrated by other authors, Rideout et al and Strasburger found that school-aged children and adolescents spend almost a 7 hours/day in front of a screen. 26 , 27 Moreover, a recent review of literature reported that one in every four children and young people are suffering from (PSU). 28

Using Mobile after the lights have been turned off was reported by 88.7% of the subjects (average duration 38 minutes). About 75% of study participants of Munezawa et al reported of using mobile after lights out. Whereas a large study conducted on 90,000 young participants showed that only 17% of their subject use mobile after the lights have been turned off. This difference may be due to the reason that their study population included younger subjects from grades 7 to 12 only, who are under strict supervision of their parents as compared to the older adults. 29

One supreme finding of this study was that using mobile for at least 30 minutes after the lights have been turned off (without a blue light filter in mobile) correlates with poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency. A study on 844 Flemish subjects (18–94 years old) also revealed that using mobile after lights out negatively affects PSQI scores, sleep latency, sleep efficiency and causes more sleep disturbance and daytime dysfunction. 30 Almost similar results were reported by a Japanese study on younger subjects (aged 13 to 19 years). 29 But these studies did not mention the exact duration of mobile use; moreover, they have not specified that either or not their subjects were using a blue light filter mode on their mobiles. The data regarding the use of blue light filers on mobile screens are scarce, but our study indicated that only 4.4% of the subjects are using this filter. So we can assume that most of the participants in the above-mentioned studies were also using their mobiles without a blue light filter.

Some recent studies have indicated that the blue light emitted by the mobile screens is the major culprit behind the PSQ in late night mobile users. As most of the mobile screens emit blue light in wave length between 400−495 nm and blue light in the range of 460–480 nm can cause a phase-shifting in human circadian clock by decreasing the production of melatonin. 31 , 32 Reduced melatonin levels have been linked to prolonged sleep latency and sleep disturbances. 16 Moreover, exposure to blue light increases brain alertness and stimulates cognitive functions, resulting in PSQ. 17 , 18 Our study findings were also supportive of the above-mentioned facts, we observed that participants who “used the mobile for at least 30 minutes after the lights have been turned off (without a blue light filter),” showed strong positive correlation with poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency. Moreover, a comparative study of Mortazavi et al found that using amber blue light filter in the mobiles, significantly improves the sleep quality, but this study used a small sample size of 43 participants only. 33 We therefore recommend further case control and experimental studies with larger sample size to confirm these findings.

In addition to the blue light effects of mobile screen, using mobile in pre bed time, ie, surfing the web, playing a game, seeing something exciting on facebook, or reading a negative email can also cause physical and psychological hyperexcitability contributing to hyper arousal state and PSQ. 13 , 14

Another important finding of this study was that putting the mobile near pillow while sleeping caused increased sleep latency, sleep disturbances and daytime sleepiness. These effects may be caused by; a continuous urge to see notifications and updates on the nearly placed phone, 14 disturbance created by the vibrations from receiving notifications and messages, heat generated by charging phones and RF-EMF exposure from the mobile phone. RF-EMF exposure can cause changes in EEG during REM and non-REM sleep. 21 – 24 During the sleep time, when the mobile phones are not in use, they still emit RF-EMFs; however, the levels are much lower than that of a phone call. Moreover, the smart phones are constantly scanning for signals, text updates, emails, and software updates. Even notifications that we receive through apps require a certain level of radiation to be released. 34 , 35 These RF-EMFs can cross the skull and reach the brain 20 causing neuronal hyper-excitability resulting in various sleep problems. 36 The above-mentioned findings may be the underlying reason of the sleep problems (daytime sleepiness, sleep disturbances and increased sleep latency) seen in our study subjects who placed their mobiles near their pillows while sleeping. But further experimental and case control studies are required to confirm this causal relationship.

To our knowledge, this is the first study which also aimed to find out the prevalence of MRSRF in mobile users. To our surprise, there was limited awareness about the possible hazardous effects of the mobile phone on human health, especially on sleep. As 88.7% of the subjects mentioned that they used mobile for at least 30 minutes after the lights are turned off, blue light filter feature was used by only 4.2% of the participants. Only 19.7% of the subjects used airplane mode, whereas 70% kept the mobile near their pillow while sleeping. Although we consider smartphone use to be a source of PSQ, many adolescents mistakenly believe that these media facilitate them to sleep. 37 It is therefore strongly recommended that health authorities should conduct seminars and awareness sessions in schools, colleges and universities. And students should be educated about the “hazardous effects of mobile phone use on sleep” and should be encouraged to implement the safety practices to prevent these effects.

This study incorporated a large sample size and showed a positive association between bedtime mobile use and poor sleep quality. It also provided an insight into the causal relationship between mobile use and poor sleep quality, ie, hazardous effects of RF-EMFs and blue light emitted from the mobiles phones on sleep. But due to study limitations, and lack of objective measures we were not able to measure these effects directly. So we recommend further experimental and case control studies to probe the role of these causal factors, especially (RF-EMF and Blue light emitted from mobile screens), in causing poor sleep quality.

Conclusions

This study concludes that

  • “Using the mobile for at least 30 minutes (without blue light filter) after the lights have been turned off” results in poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency.
  • “Keeping the mobile near the pillow while sleeping” positively correlates with daytime sleepiness, sleep disturbances and increased sleep latency.
  • Mobile-related sleep risk factors (MRSRF), ie, “using mobile before sleeping after the lights have been turned off, not using blue light filter, not using airplane mode, putting the mobile near the pillow while sleeping” were highly prevalent amongst the mobile users.

Acknowledgment

The authors are thankful to Dr. Afzal Haq Asif, Dr. Faisal Fahad Essa Alousi, Dr. Hina Khan, Saira Saeed, and Dr. Samina Bashir for their help in data collection.

The authors report no conflicts of interest in this work.

IMAGES

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  2. (PDF) Smart Phone usage Pattern: A Study of College Students

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COMMENTS

  1. Smartphone use and academic performance: A literature review

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  2. (PDF) A Literature Review on the Effects of the Smartphone Use from

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  7. PDF DICION PAPER ERIE

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