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Open Access

Peer-reviewed

Research Article

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

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

Affiliation Department of Psychology, Sunway University, Selangor, Malaysia

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

* E-mail: [email protected]

• Clarissa Theodora Tanil, 
• Min Hooi Yong

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

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

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

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

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

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

Data Availability: All relevant data are within the manuscript.

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

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

Introduction

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

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

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

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

Present study

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

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

Materials and methods

Participants.

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

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

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

Ethical approval and informed consent

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

Study design

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

Working memory span test.

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

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

Positive and Negative Affect Scale (PANAS).

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

Smartphone Addiction Scale (SAS)

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

Phone conscious thought and perceived effect on learning

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

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

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

Statistical analysis

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

Preliminary analyses

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

Smartphone presence and memory recall accuracy

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

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

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

Sas and memory recal..

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

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

Phone conscious thought and memory accuracy.

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

Affect/mood changes after being separated from their phone

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

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

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

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

PA/NA difference on memory accuracy.

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

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

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

Perception between phone usage and learning

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

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

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

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

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

Implications

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

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

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

Acknowledgments

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

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From “brick” to smartphone: the evolution of the mobile phone

• FEATURES POSTERMINARIES
• Published: 05 March 2021
• Volume 46 , pages 287–288, ( 2021 )

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• Steve Moss 1  

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Telephony began in the 1870s with the invention of the telephone. Alexander Graham Bell filed a patent for his version of the telephone at the US Patent Office in Washington, DC, on February 14, 1876, just a few hours before his competitor Elisha Gray filed his patent based upon independent work. 1 Since then, materials research has pushed this field of technology involving the development, application, and deployment of telecommunication services, particularly in recent years.

The proliferation of telephones did not make a great overnight leap. In fact, as late as 1950, only 62% of American households contained a telephone, 2 and that number had been significantly smaller before World War II. Most communications were by telegram, letter, or through face-to-face discussions. One significant hindrance to communication was natural disasters, which often led to long periods of no information between family and friends.

On the evening of January 3, 1949, a devastating tornado struck the small town of Warren, Ark. 3 The tornado left more than 50 dead and more than 300 injured. My dad’s parents, brother, sister-in-law, niece, and nephew, as well as many friends, lived in Warren at that time and were affected. Telephone lines were destroyed. My parents and I lived far away and had no way to get in contact with them. More than a week passed before we received word that they were okay.

My father later accepted a faculty position at Arkansas A&M College in Monticello, 16 miles from Warren, to be closer to his family. When we moved to a farm near the campus, our first telephone line was a party line, which consisted of a single channel shared by many people. This offered little in the way of privacy, as others outside of your household could listen to conversations. We eventually upgraded to a private landline. The phone was located in a central part of the house, and the cord was only a few feet long, which meant that you were essentially tethered to that spot when making or answering a call, not ideal for any teenager craving privacy.

People then didn’t have the luxury of cell phones, and instead often used pay phones by inserting money or calling collect. Jim Croce has a wonderful song, “Operator,” 4 about an unsuccessful attempt to connect with some old friends. I often wonder if younger people understand the significance of the lyrics, including the phrase “You can keep the dime.”

Today, humanity is more connected than ever through the use of cell phones. However, mobile phones didn’t start in their current, sleek style. The first mobile phone by Motorola in 1983 5 was so big and heavy that it was nicknamed “the brick.” Current phones are significantly more lightweight and compact and have the capability to text, email, access social media, access the Internet, and much more.

According to recent surveys, 75% of the world’s population owns a cell phone. 6 , 7 Surveys in 2019 indicated that there were 5.11 billion unique mobile phone users, and that 2.71 billion of them used smartphones. People from China (> 782 million users) and India (> 386 million users) are the largest consumers of smartphones, followed by the United States (> 235 million users).

If you search for technological advances that facilitated progress to the current state of cell phone technology, you will find lists that include the Internet, global positioning systems, touch screens, cameras, high-speed modems, displays, batteries, and a host of other materials and technologies. 8 , 9

The computers that drive recent smartphones have 64-bit architectures. 10 , 11 They are usually fabricated as a system-on-a-chip and include multiple cores and extra features, such as neural engines and embedded motion coprocessors. They contain cameras with more than 10 megapixels and multi-element lens systems and include zoom capabilities and two-axis stabilization. The phones support a wide variety of standard communication protocols, including accessibility features for those who wear hearing aids. Recent smartphone microprocessors have been built with fin field-effect transistors (finFETs) 12 manufactured at the 10 nm, 7 nm, and 5 nm processing scales. They also include a range of sensors, including for facial identification, a barometer, a three-axis gyro, an accelerometer, a proximity sensor, an ambient light sensor, a Hall sensor, and a RGB light sensor. 10 , 11

These systems are also designed to take advantage of fifth-generation (5G) cell phone networks with advantages in bandwidth and data rates (eventually up to 10 Gbps). 13

Integrating even a fraction of these capabilities into the early Motorola mobile phone would have likely expanded the size, weight, and power requirements well beyond what one person could have easily carried. (As I write this, an image of a famous body-builder, Arnold Schwarzenegger, struggling to lift this enhanced “brick” popped into my head, as he was trailed by a large generator on wheels to power the phone.) This does not factor in the fact that many of these technologies did not exist at the time.

Microelectronics has evolved through a range of technologies and materials developments over the years 14 , 15 that have affected transistors (bipolar junction transistors, various metal–oxide–semiconductor field-effect transistors, including finFETs), dielectrics (thermal oxides, high- k dielectrics), metallization (aluminum, polysilicon, copper, tungsten vias), high levels of integration, including multilayer metallization, and integration of billions of transistors per chip. Fabrication of modern microchips involves many hundreds of process steps that have to be performed within narrow tolerances. It is remarkable that these fabrication lines yield in numbers high enough to be economically viable. If any step falls outside of the tolerances, then yield can fall catastrophically. This would kick off an investigation to determine the root cause(s) of the problem and can shut down fabrication lines for long periods of time—an expensive proposition. Developing these technologies and the processes that allow them to be inserted into high-yield fabrication lines have occupied hordes of materials researchers for decades.

I could write similar discussions of materials advances in batteries, displays, touch screens, and camera systems that have relied on similar hordes of materials researchers. However, I’m out of space for this article, so those stories will have to wait until another time.

The features described, the ease of carrying modern cell phones, and their economic affordability are driving the surge in worldwide usage. Access to information is only as good as the information. We are constantly bombarded with inaccurate information as well as disinformation. Filtering all of that can be difficult and time consuming. Instantaneous access to information using cell phone and other electronic technologies provides the unwary with an opportunity to make huge mistakes quickly.

The use of landline phones reached a peak in the 2000s. Now they are down to around 40% of American households and declining. 16 I am one of those neo-Luddites who has chosen to keep my landline. I find that, for now, it gives me some comfort to have it available.

Warts and all, the proliferation of cell phone systems is good. Widespread outages due to local events are unlikely to destroy all cell towers in a local community. Therefore, people are likely to maintain some capability for communication, even if impacted by tornados such as the one on January 3, 1949, in Warren, Ark.

The invention of the telephone - Ericsson

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Moss, S. From “brick” to smartphone: the evolution of the mobile phone. MRS Bulletin 46 , 287–288 (2021). https://doi.org/10.1557/s43577-021-00067-7

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• Published: 16 March 2021

5G mobile networks and health—a state-of-the-science review of the research into low-level RF fields above 6 GHz

• Ken Karipidis   ORCID: orcid.org/0000-0001-7538-7447 1 ,
• Rohan Mate 1 ,
• David Urban 1 ,
• Rick Tinker 1 &
• Andrew Wood 2  

Journal of Exposure Science & Environmental Epidemiology volume  31 ,  pages 585–605 ( 2021 ) Cite this article

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The increased use of radiofrequency (RF) fields above 6 GHz, particularly for the 5 G mobile phone network, has given rise to public concern about any possible adverse effects to human health. Public exposure to RF fields from 5 G and other sources is below the human exposure limits specified by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). This state-of-the science review examined the research into the biological and health effects of RF fields above 6 GHz at exposure levels below the ICNIRP occupational limits. The review included 107 experimental studies that investigated various bioeffects including genotoxicity, cell proliferation, gene expression, cell signalling, membrane function and other effects. Reported bioeffects were generally not independently replicated and the majority of the studies employed low quality methods of exposure assessment and control. Effects due to heating from high RF energy deposition cannot be excluded from many of the results. The review also included 31 epidemiological studies that investigated exposure to radar, which uses RF fields above 6 GHz similar to 5 G. The epidemiological studies showed little evidence of health effects including cancer at different sites, effects on reproduction and other diseases. This review showed no confirmed evidence that low-level RF fields above 6 GHz such as those used by the 5 G network are hazardous to human health. Future experimental studies should improve the experimental design with particular attention to dosimetry and temperature control. Future epidemiological studies should continue to monitor long-term health effects in the population related to wireless telecommunications.

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Introduction.

There are continually emerging technologies that use radiofrequency (RF) electromagnetic fields particularly in telecommunications. Most telecommunication sources currently operate at frequencies below 6 GHz, including radio and TV broadcasting and wireless sources such as local area networks and mobile telephony. With the increasing demand for higher data rates, better quality of service and lower latency to users, future wireless telecommunication sources are planned to operate at frequencies above 6 GHz and into the ‘millimetre wave’ range (30–300 GHz) [ 1 ]. Frequencies above 6 GHz have been in use for many years in various applications such as radar, microwave links, airport security screening and in medicine for therapeutic applications. However, the planned use of millimetre waves by future wireless telecommunications, particularly the 5th generation (5 G) of mobile networks, has given rise to public concern about any possible adverse effects to human health.

The interaction mechanisms of RF fields with the human body have been extensively described and tissue heating is the main effect for RF fields above 100 kHz (e.g. HPA; SCENHIR) [ 2 , 3 ]. RF fields become less penetrating into body tissue with increasing frequency and for frequencies above 6 GHz the depth of penetration is relatively short with surface heating being the predominant effect [ 4 ].

International exposure guidelines for RF fields have been developed on the basis of current scientific knowledge to ensure that RF exposure is not harmful to human health [ 5 , 6 ]. The guidelines developed by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) in particular form the basis for regulations in the majority of countries worldwide [ 7 ]. In the frequency range above 6 GHz and up to 300 GHz the ICNIRP guidelines prevent excessive heating at the surface of the skin and in the eye.

Although not as extensively studied as RF fields at lower frequencies, a number of studies have investigated the effects of RF fields at frequencies above 6 GHz. Previous reviews have reported studies investigating frequencies above 6 GHz that show effects although many of the reported effects occurred at levels greater than the ICNIRP guidelines [ 1 , 8 ]. Given the public concern over the planned roll-out of 5 G using millimetre waves, it is important to determine whether there are any related adverse health consequences at levels encountered in the environment. The aim of this paper is to present a state-of-the-science review of the bioeffects research into RF fields above 6 GHz at low levels of exposure (exposure below the occupational limits of the ICNIRP guidelines). A meta-analysis of in vitro and in vivo studies, providing quantitative effect estimates for each study, is presented separately in a companion paper [ 9 ].

The state-of-the-science review included a comprehensive search of all available literature and examined the extent, range and nature of evidence into the bioeffects of RF fields above 6 GHz, at levels below the ICNIRP occupational limits. The review consisted of biomedical studies on low-level RF electromagnetic fields from 6 GHz to 300 GHz published at any starting date up to December 2019. Studies were initially found by searching the databases PubMed, EMF-Portal, Google Scholar, Embase and Web of Science using the search terms “millimeter wave”, “millimetre wave”, “gigahertz”, “GHz” and “radar”. We further searched major reviews published by health authorities on RF and health [ 2 , 3 , 10 , 11 ]. Finally, we searched the reference list of all the studies included. Studies were only included if the full paper was available in English.

Although over 300 studies were considered, this review was limited to experimental studies (in vitro, in vivo, human) where the stated RF exposure level was at or below the occupational whole-body limits specified by the ICNIRP (2020) guidelines: power density (PD) reference level of 50 W/m 2 or specific absorption rate (SAR) basic restriction of 0.4 W/kg. Since the PD occupational limits for local exposure are more relevant to in vitro studies, and since these limits are higher, we have included those studies with PD up to 100–200 W/m 2 , depending on frequency. The review included studies below the ICNIRP general public limits that are lower than the occupational limits.

The review also included epidemiological studies (cohort, case-control, cross-sectional) investigating exposure to radar but excluded studies where the stated radar frequencies were below 6 GHz. Epidemiological studies on radar were included as they represent occupational exposure below the ICNIRP guidelines. Case reports or case series were excluded. Studies investigating therapeutical outcomes were also excluded unless they reported specific bio-effects.

The state-of-the-science review appraised the quality of the included studies, but unlike a systematic review it did not exclude any studies based on quality. The review also identified gaps in knowledge for future investigation and research. The reporting of results in this paper is narrative with tabular accompaniment showing study characteristics. In this paper, the acronym “MMWs” (or millimetre waves) is used to denote RF fields above 6 GHz.

The review included 107 experimental studies (91 in vitro, 15 in vivo, and 1 human) that investigated various bioeffects, including genotoxicity, cell proliferation, gene expression, cell signalling, membrane function and other effects. The exposure characteristics and biological system investigated in experimental studies for the various bioeffects are shown in Tables  1 – 6 . The results of the meta-analysis of the in vitro and in vivo studies are presented separately in Wood et al. [ 9 ].

Genotoxicity

Studies have examined the effects of exposing whole human or mouse blood samples or lymphocytes and leucocytes to low-level MMWs to determine possible genotoxicity. Some of the genotoxicity studies have looked at the possible effects of MMWs on chromosome aberrations [ 12 , 13 , 14 ]. At exposure levels below the ICNIRP limits, the results have been inconsistent, with either a statistically significant increase [ 14 ] or no significant increase [ 12 , 13 ] in chromosome aberrations.

MMWs do not penetrate past the skin therefore epithelial and skin cells have been a common model of examination for possible genotoxic effects. DNA damage in a number of epithelial and skin cell types and at varied exposure parameters both below and above the ICNIRP limits have been examined using comet assays [ 15 , 16 , 17 , 18 , 19 ]. Despite the varied exposure models and methods used, no statistically significant evidence of DNA damage was identified in these studies. Evidence of genotoxic damage was further assessed in skin cells by the occurrence of micro-nucleation. De Amicis et al. [ 18 ] and Franchini et al. [ 19 ] reported a statistically significant increase in micro-nucleation, however, Hintzsche et al. [ 15 ] and Koyama et al. [ 16 , 17 ] did not find an effect. Two of the studies also examined telomere length and found no statistically significant difference between exposed and unexposed cells [ 15 , 19 ]. Last, a Ukrainian research group examined different skin cell types in three studies and reported an increase in chromosome condensation in the nucleus [ 20 , 21 , 22 ]; these results have not been independently verified. Overall, there was no confirmed evidence of MMWs causing genotoxic damage in epithelial and skin cells.

Three studies from an Indian research group have examined indicators of DNA damage and reactive oxygen species (ROS) production in rats exposed in vivo to MMWs. The studies reported DNA strand breaks based on evidence from comet assays [ 23 , 24 ] and changes in enzymes that control the build-up of ROS [ 24 ]. Kumar et al. also reported an increase in ROS production [ 25 ]. All the studies from this research group had low animal numbers (six animals exposed) and their results have not been independently replicated. An in vitro study that investigated ROS production in yeast cultures reported an increase in free radicals exposed to high-level but not low-level MMWs [ 26 ].

Other studies have looked at the effect of low-level MMWs on DNA in a range of different ways. Two studies reported that MMWs induce colicin synthesis and prophage induction in bacterial cells, both of which are suggested as indicative of DNA damage [ 27 , 28 ]. Another study suggested that DNA exposed to MMWs undergoes polymerase chain reaction synthesis differently than unexposed DNA [ 29 ], although no statistical analysis was presented. Hintzsche et al. reported statistically significant occurrence of spindle disturbance in hybrid cells exposed to MMWs [ 30 ]. Zeni et al. found no evidence of DNA damage or alteration of cell cycle kinetics in blood cells exposed to MMWs [ 31 ]. Last, two studies from a Russian research group examined the protective effects of MMWs where mouse blood leukocytes were pre-exposed to low-level MMWs and then to X-rays [ 32 , 33 ]. The studies reported that there was statistically significant less DNA damage in the leucocytes that were pre-exposed to MMWs than those exposed to X-rays alone. Overall, these studies had no independent replication.

Cell proliferation

A number of studies have examined the effects of low-level MMWs on cell proliferation and they have used a variety of cellular models and methods of investigation. Studies have exposed bacterial cells to low-level MMWs alone or in conjunction with other agents. Two early studies reported changes in the growth rate of E. coli cultures exposed to low-level MMWs; however, both of these studies were preliminary in nature without appropriate dosimetry or statistical analysis [ 34 , 35 ]. Two studies exposed E. coli cultures and one study exposed yeast cell cultures to MMWs alone, and before and after UVC exposure [ 36 , 37 , 38 ]. All three studies reported that MMWs alone had no significant effect on bacterial cell proliferation or survival. Rojavin et al., however, did report that when E. coli bacteria were exposed to MMWs after UVC sterilisation treatment, there was an increase in their survival rate [ 36 ]. The authors suggested this could be due to the MMW activation of bacterial DNA repair mechanisms. Other studies by an Armenian research group reported a reduction in E. coli cell growth when exposed to MMWs [ 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. These studies reported that when E.coli cultures were exposed to MMWs in the presence of antibiotics, there was a greater reduction in the bacterial growth rate and an increase in the time between bacterial cell division compared with antibiotics exposure alone. Two of these studies investigated if these effects could be due to a reduction in the activity of the E. coli ATPase when exposed to MMWs. The studies reported exposure to MMWs in combination with particular antibiotics changed the concentration of H + and K + ions in the E.coli cells, which the authors linked to changes in ATPase activity [ 43 , 44 ]. Overall, the results from studies on cell proliferation of bacterial cells have been inconsistent with different research groups reporting conflicting results.

Studies have also examined how exposure to low-level MMWs could affect cell proliferation in yeast. Two early studies by a German research group reported changes in yeast cell growth [ 46 , 47 ]. However, another two independent studies did not report any changes in the growth rate of exposed yeast [ 48 , 49 ]. Furia et al. [ 48 ] noted that the Grundler and Keilmann studies [ 46 , 47 ] had a number of methodical issues, which may have skewed their results, such as poor exposure control and analysis of results. Another study exposed yeast to MMWs before and after UVC exposure and reported that MMWs did not change the rates of cell survival [ 37 ].

Studies have also examined the possible effect of low-level MMWs on tumour cells with some studies reporting a possible anti-proliferative effect. Chidichimo et al. reported a reduction in the growth of a variety of tumour cells exposed to MMWs; however, the results of the study did not support this conclusion [ 50 ]. An Italian research group published a number of studies investigating proliferation effects on human melanoma cell lines with conflicting results. Two of the studies reported reduced growth rate [ 51 , 52 ] and a third study showed no change in proliferation or in the cell cycle [ 53 ]. Beneduci et al. also reported changes in the morphology of MMW exposed cells; however, the authors did not present quantitative data for these reported changes [ 51 , 52 ]. In another study by the same Italian group, Beneduci et al. reported that exposure to low-level MMWs had a greater than 40% reduction in the number of viable erythromyeloid leukaemia cells compared with controls; however, there was no significant change in the number of dead cells [ 54 ]. More recently, Yaekashiwa et al. reported no statistically significant effect in proliferation or cellular activity in glioblastoma cells exposed to low-level MMWs [ 55 ].

Other studies did not report statistically significant effects on proliferation in chicken embryo cell cultures, rat nerve cells or human skin fibroblasts exposed to low-level MMWs [ 55 , 56 , 57 ].

Gene expression

Some studies have investigated whether low-level MMWs can influence gene expression. Le Queument et al. examined a multitude of genes using microarray analyses and reported transient expression changes in five of them. However, the authors concluded that these results were extremely minor, especially when compared with studies using microarrays to study known pollutants [ 58 ]. Studies by a French research group have examined the effect of MMWs on stress sensitive genes, stress sensitive gene promotors and chaperone proteins in human glial cell lines. In two studies, glial cells were exposed to low-level MMWs and there was no observed modification in the expression of stress sensitive gene promotors when compared with sham exposed cells [ 59 , 60 , 61 ]. Further, glial cells were examined for the expression of the chaperone protein clusterin (CLU) and heat shock protein HSP70. These proteins are activated in times of cellular stress to maintain protein functions and help with the repair process [ 60 ]. There was no observed modification in gene expression of the chaperone proteins. Other studies have examined the endoplasmic reticulum of glial cells exposed to MMWs [ 62 , 63 ]. The endoplasmic reticulum is the site of synthesis and folding of secreted proteins and has been shown to be sensitive to environmental insults [ 62 ]. The authors reported that there was no elevation in mRNA expression levels of endoplasmic reticulum specific chaperone proteins. Studies of stress sensitive genes in glial cells have consistently shown no modification due to low-level MMW exposure [ 59 , 60 , 61 , 62 , 63 ].

Belyaev and co-authors have studied a possible resonance effect of low-level MMWs primarily on Escherichia Coli (E. coli) cells and cultures. The Belyaev research group reported that the resonance effect of MMWs can change the conformation state of chromosomal DNA complexes [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ]; however, most of these experiments were not temperature controlled. This resonance effect was not supported by earlier experiments on a number of different cell types conducted by Gandhi et al. and Bush et al. [ 75 , 76 ].

The results of Belyaev and co-workers have primarily been based on evidence from the anomalous viscosity time dependence (AVTD) method [ 77 ]. The research group argued that changes in the AVTD curve can indicate changes to the DNA conformation state and DNA-protein bonds. Belyaev and co-workers have reported in a number of studies that differences in the AVTD curve were dependent on several parameter including MMW characteristics (frequency, exposure level, and polarisation), cellular concentration and cell growth rate [ 69 , 71 , 72 , 73 , 74 ]. In some of the Belyaev studies E. coli were pre-exposed to X-rays, which was reported to change the AVTD curve; however, if the cells were then exposed to MMWs there was no longer a change in the AVTD curve [ 64 , 65 , 66 , 67 ]. The authors suggested that exposure to MMWs increased the rate of recovery in bacterial cells previously exposed to ionising radiation. The Belyaev group also used rat thymocytes in another study and they concluded that the results closely paralleled those found in E. coli cells [ 67 ]. The studies on the DNA conformation state change relied heavily on the AVTD method that has only been used by the Balyaev group and has not been independently validated [ 78 ].

Cell signalling and electrical activity

Studies examining effects of low-level MMWs on cell signalling have mainly involved MMW exposure to nervous system tissue of various animals. An in vivo study on rats recorded extracellular background electrical spike activity from neurons in the supraoptic nucleus of the hypothalamus after MMW exposure [ 79 ]. The study reported that there were changes in inter-spike interval and spike activity in the cells of exposed animals when compared with controls. There was also a mixture of significant shifts in neuron population proportions and spike frequency. The effect on the regularity of neuron spike activity was greater at higher frequencies. An in vitro study on rat cortical tissue slices reported that neuron firing rates decreased in half of the samples exposed to low-level MMWs [ 80 ]. The width of the signals was also decreased but all effects were short lived. The observed changes were not consistent between the two studies, but this could be a consequence of different brain regions being studied.

In vitro experiments by a Japanese research group conducted on crayfish exposed the dissected optical components and brain to MMWs [ 81 , 82 ]. Munemori and Ikeda reported that there was no significant change in the inter-spike intervals or amplitude of spontaneous discharges [ 81 ]. However, there was a change in the distribution of inter-spike intervals where the initial standard deviation decreased and then restored in a short time to a rhythm comparable to the control. A follow-up study on the same tissues and a wide range of exposure levels (many above the ICNIRP limits) reported similar results with the distribution of spike intervals decreasing with increasing exposure level [ 82 ]. These results on action potentials in crayfish tissue have not been independently investigated.

Mixed results were reported in experiments conducted by a US research group on sciatic frog nerve preparations. These studies applied electrical stimulation to the nerve and examined the effect of MMWs on the compound action potentials (CAPs) conductivity through the neurological tissue fibre. Pakhomov et al. found a reduction in CAP latency accompanied by an amplitude increase for MMWs above the ICNIRP limits but not for low-level MMWs [ 83 ]. However, in two follow-up studies, Pakhomov et al. reported that the attenuation in amplitude of test CAPs caused by high-rate stimulus was significantly reduced to the same magnitude at various MMW exposure levels [ 84 , 85 ]. In all of these studies, the observed effect on the CAPs was temporal and reversible, but there were implications of a frequency specific resonance interaction with the nervous tissue. These results on action potentials in frog sciatic nerves have not been investigated by others.

Other common experimental systems involved low-level MMW exposure to isolated ganglia of leeches. Pikov and Siegel reported that there was a decrease in the firing rate in one of the tested neurons and, through the measurement of input resistance in an inserted electrode, there was a transient dose-dependent change in membrane permeability [ 86 ]. However, Romanenko et al. found that low-level MMWs did not cause suppression of neuron firing rate [ 87 ]. Further experiments by Romanenko et al. reported that MMWs at the ICNIRP public exposure limit and above reported similar action potential firing rate suppression [ 88 ]. Significant differences were reported between MMW effects and effects due to an equivalent rise in temperature caused by heating the bathing solution by conventional means.

Membrane effects

Studies examining membrane interactions with low-level MMWs have all been conducted at frequencies above 40 GHz in in vitro experiments. A number of studies investigated membrane phase transitions involving exposure to a range of phospholipid vesicles prepared to mimic biological cell membranes. One group of studies by an Italian research group reported effects on membrane hydration dynamics and phase transition [ 89 , 90 , 91 ]. Observations included transition delays from the gel to liquid phase or vice versa when compared with sham exposures maintained at the same temperature; the effect was reversed after exposure. These reported changes remain unconfirmed by independent groups.

A number of studies investigated membrane permeability. One study focussed on Ca 2+ activated K + channels on the membrane surface of cultured kidney cells of African Green Marmosets [ 92 ]. The study reported modifications to the Hill coefficient and apparent affinity of the Ca 2+ by the K + channels. Another study reported that the effectiveness of a chemical to supress membrane permeability in the gap junction was transiently reduced when the cells were exposed to MMWs [ 93 , 94 ]. Two studies by one research group reported increases in the movement of molecules into skin cells during MMW exposure and suggested this indicates increased cell membrane permeability [ 21 , 91 ]. Permeability changes based on membrane pressure differences were also investigated in relation to phospholipid organisation [ 95 ]. Although there was no evidence of effects on phospholipid organisation on exposed model membranes, the authors reported a measurable difference in membrane pressure at low exposure levels. Another study reported neuron shrinkage and dehydration of brain tissues [ 96 ]. The study reported this was due to influences of low-level MMWs on the cellular bathing medium and intracellular water. Further, the authors suggested this influence of MMWs may have led to formation of unknown messengers, which are able to modulate brain cell hydration. A study using an artificial axon system consisting of a network of cells containing aqueous phospholipid vesicles reported permeability changes with exposure to MMWs by measuring K + efflux [ 97 ]. In this case, the authors emphasised limitations in applying this model to processes within a living organism. The varied effects of low-level MMWs on membrane permeability lack replication.

Other studies have examined the shape or size of vesicles to determine possible effects on membrane permeability. Ramundo-Orlando et al., reported effects on the shape of giant unilamellar vesicles (GUVs), specifically elongation, attributed to permeability changes [ 98 ]. However, another study reported that only smaller diameter vesicles demonstrated a statistically significant change when exposed to MMWs [ 99 ]. A study by Cosentino et al. examined the effect of MMWs on the size distributions of both large unilamellar vesicles (LUVs) and GUVs in in vitro preparations [ 100 ]. It was reported that size distribution was only affected when the vesicles were under osmotic stress, resulting in a statistically significant reduction in their size. In this case, the effect was attributed to dehydration as a result of membrane permeability changes. There is, generally, lack of replication on physical changes to phospholipid vesicles due to low-level MMWs.

Studies on E. coli and E. hirae cultures have reported resonance effects on membrane proteins and phospholipid constituents or within the media suspension [ 39 , 40 , 41 , 42 ]. These studies observed cell proliferation effects such as changes to cell growth rate, viability and lag phase duration. These effects were reported to be more pronounced at specific MMW frequencies. The authors suggested this could be due to a resonance effect on the cell membrane or the suspension medium. Torgomyan et al. and Hovnanyan et al. reported similar changes to proliferation that they attributed to changes in membrane permeability from MMW exposure [ 43 , 45 ]. These experiments were all conducted by an Armenian research group and have not been replicated by others.

Other effects

A number of studies have reported on the experimental results of other effects. Reproductive effects were examined in three studies on mice, rats and human spermatozoa. An in vivo study on mice exposed to low-level MMWs reported that spermatogonial cells had significantly more metaphase translocation disturbances than controls and an increased number of cells with unpaired chromosomes [ 101 ]. Another in vivo study on rats reported increased morphological abnormalities to spermatozoa following exposure, however, there was no statistical analysis presented [ 102 ]. Conversely, an in vitro study on human spermatozoa reported that there was an increase in motility after a short time of exposure to MMWs with no changes in membrane integrity and no generation of apoptosis [ 103 ]. All three of these studies looked at different effects on spermatozoa making it difficult to make an overall conclusion. A further two studies exposed rats to MMWs and examined their sperm for indicators of ROS production. One study reported both increases and decreases in enzymes that control the build-up of ROS [ 104 ]. The other study reported a decrease in the activity of histone kinase and an increase in ROS [ 105 ]. Both studies had low animal numbers (six animals exposed) and these results have not been independently replicated.

Immune function was also examined in a limited number of studies focussing on the effects of low-level MMWs on antigens and antibody systems. Three studies by a Russian research group that exposed neutrophils to MMWs reported frequency dependant changes in ROS production [ 106 , 107 , 108 ]. Another study reported a statistically significant decrease in antigen binding to antibodies when exposed to MMWs [ 109 ]; the study also reported that exposure decreased the stability of previously formed antigen–antibody complexes.

The effect on fatty acid composition in mice exposed to MMWs has been examined by a Russian research group using a number of experimental methods [ 110 , 111 , 112 ]. One study that exposed mice afflicted with an inflammatory condition to low-level MMWs reported no change in the fatty acid concentrations in the blood plasma. However, there was a significant increase in the omega-3 and omega-6 polyunsaturated fatty acid content of the thymus [ 110 ]. Another study exposed tumour-bearing mice and reported that monounsaturated fatty acids decreased and polyunsaturated fatty acids increased in both the thymus and tumour tissue. These changes resulted in fatty acid composition of the thymus tissue more closely resembling that of the healthy control animals [ 111 ]. The authors also examined the effect of exposure to X-rays of healthy mice, which was reported to reduce the total weight of the thymus. However, when the thymus was exposed to MMWs before or after exposure to X-rays, the fatty acid content was restored and was no longer significantly different from controls [ 112 ]. Overall, the authors reported a potential protective effect of MMWs on the recovery of fatty acids, however, all the results came from the same research group with a lack of replication from others.

Physiological effects were examined by a study conducted on mice exposed to WWMs to assess the safety of police radar [ 113 ]. The authors reported no statistically significant changes in the physiological parameters tested, which included body mass and temperature, peripheral blood and the mass and cellular composition, and number of cells in several important organs. Another study exposing human volunteers to low-level MMWs specifically examined cardiovascular function of exposed and sham exposed groups by electrocardiogram (ECG) and atrioventricular conduction velocity derivation [ 114 ]. This study reported that there were no significant differences in the physiological indicators assessed in test subjects.

Other individual studies have looked at various other effects. An early study reported differences in the attenuation of MMWs at specific frequencies in healthy and tumour cells [ 115 ]. Another early study reported no effect in the morphology of BHK-21/C13 cell cultures when exposed to low-level MMWs; the study did report morphological changes at higher levels, which were related to heating [ 116 ]. One study examined whether low-level MMWs induced cancer promotion in leukaemia and Lewis tumour cell grafted mice. The study reported no statistically significant growth promotion in either of the grafted cancer cell types [ 117 ]. Another study looked at the activity of gamma-glutamyl transpeptidase enzyme in rats after treatment with hydrocortisone and exposure to MMWs [ 118 ]. The study reported no effects at exposures below the ICNIRP limit, however, at levels above authors reported a range of effects. Another study exposed saline liquid solutions to continuous low and high level MMWs and reported temperature oscillations within the liquid medium but lacked a statistical analysis [ 119 ]. Another study reported that low-level MMWs decrease the mobility of the protozoa S. ambiguum offspring [ 120 ]. None of the reported effects in all of these other studies have been investigated elsewhere.

Epidemiological studies

There are no epidemiological studies that have directly investigated 5 G and potential health effects. There are however epidemiological studies that have looked at occupational exposure to radar, which could potentially include the frequency range from 6 to 300 GHz. Epidemiological studies on radar were included as they represent occupational exposure below the ICNIRP guidelines. The review included 31 epidemiological studies (8 cohort, 13 case-control, 9 cross-sectional and 1 meta-analysis) that investigated exposure to radar and various health outcomes including cancer at different sites, effects on reproduction and other diseases. The risk estimates as well as limitations of the epidemiological studies are shown in Table  7 .

Three large cohort studies investigated mortality in military personnel with potential exposure to MMWs from radar. Studies reporting on over 40-year follow-up of US navy veterans of the Korean War found that radar exposure had little effect on all-cause or cancer mortality with the second study reporting risk estimates below unity [ 121 , 122 ]. Similarly, in a 40-year follow-up of Belgian military radar operators, there was no statistically significant increase in all-cause mortality [ 123 , 124 ]; the study did, however, find a small increase in cancer mortality. More recently in a 25-year follow-up of military personnel who served in the French Navy, there was no increase in all-cause or cancer mortality for personnel exposed to radar [ 125 ]. The main limitation in the cohort studies was the lack of individual levels of RF exposure with most studies based on job-title. Comparisons were made between occupations with presumed high exposure to RF fields and other occupations with presumed lower exposure. This type of non-differential misclassification in dichotomous exposure assessment is associated mostly with an effect measure biased towards a null effect if there is a true effect of RF fields. If there is no true effect of RF fields, non-differential exposure misclassification will not bias the effect estimate (which will be close to the null value, but may vary because of random error). The military personnel in these studies were compared with the general population and this ‘healthy worker effect’ presents possible bias since military personnel are on average in better health than the general population; the healthy worker effect tends to underestimate the risk. The cohort studies also lacked information on possible confounding factors including other occupational exposures such as chemicals and lifestyle factors such as smoking.

Several epidemiological studies have specifically investigated radar exposure and testicular cancer. In a case-control study where most of the subjects were selected from military hospitals in Washington DC, USA, Hayes et al. found no increased risk between exposure to radar and testicular cancer [ 126 ]; exposure to radar was self-reported and thus subject to misclassification. In this study, the misclassification was likely non-differential, biasing the result towards the null. Davis and Mostofi reported a cluster of testicular cancer within a small cohort of 340 police officers in Washington State (USA) where the cases routinely used handheld traffic radar guns [ 127 ]; however, exposure was not assessed for the full cohort, which may have overestimated the risk. In a population-based case-control study conducted in Sweden, Hardell et al. did not find a statistically significant association between radar work and testicular cancer; however, the result was based on only five radar workers questioning the validity of this result [ 128 ]. In a larger population-based case control study in Germany, Baumgardt-Elms et al. also reported no association between working near radar units (both self-reported and expert assessed) and testicular cancer [ 129 ]; a limitation of this study was the low participation of identified controls (57%), however, there was no difference compared with the characteristics of the cases so selection bias was unlikely. In the cohort study of US navy veterans previously mentioned exposure to radar was not associated with testicular cancer [ 122 ]; the limitations of this cohort study mentioned earlier may have underestimated the risk. Finally, in a hospital-based case-control study in France, radar workers were also not associated with risk of testicular cancer [ 130 ]; a limitation was the low participation of controls (37%) with a difference in education level between participating and non-participating controls, which may have underestimated this result.

A limited number of studies have investigated radar exposure and brain cancer. In a nested case-control study within a cohort of male US Air Force personnel, Grayson reported a small association between brain cancer and RF exposure, which included radar [ 131 ]; no potential confounders were included in the analysis, which may have overestimated the result. However, in a case-control study of personnel in the Brazilian Navy, Santana et al. reported no association between naval occupations likely to be exposed to radar and brain cancer [ 132 ]; the small number of cases and lack of diagnosis confirmation may have biased the results towards the null. All of the cohort studies on military personnel previously mentioned also examined brain cancer mortality and found no association with exposure to radar [ 122 , 124 , 125 ].

A limited number of studies have investigated radar exposure and ocular cancer. Holly et al. in a population-based case-control study in the US reported an association between self-reported exposure to radar or microwaves and uveal melanoma [ 133 ]; the study investigated many different exposures and the result is prone to multiple testing. In another case-control study, which used both hospital and population controls, Stang et al. did not find an association between self-reported exposure to radar and uveal melanoma [ 134 ]; a high non-response in the population controls (52%) and exposure misclassification may have underestimated this result. The cohort studies of the Belgian military and French navy also found no association between exposure to radar and ocular cancer [ 124 , 125 ].

A few other studies have examined the potential association between radar and other cancers. In a hospital-based case-control study in Italy, La Vecchia investigated 14 occupational agents and risk of bladder cancer and found no association with radar, although no risk estimate was reported [ 135 ]; non-differential self-reporting of exposure may have underestimated this finding if there is a true effect. Finkelstein found an increased risk for melanoma in a large cohort of Ontario police officers exposed to traffic radar and followed for 31 years [ 136 ]; there was significant loss to follow up which may have biased this result in either direction. Finkelstein found no statistically significant associations with other types of cancer and the study reported a statistically significant risk estimate just below unity for all cancers, which is reflective of the healthy worker effect [ 136 ]. In a large population-based case-control study in France, Fabbro-Peray et al. investigated a large number of occupational and environmental risk factors in relation to non-Hodgkin lymphoma and found no association with radar operators based on job-title; however, the result was based on a small number of radar operators [ 137 ]. The cohort studies on military personnel did not find statistically significant associations between exposure to radar and other cancers [ 122 , 124 , 125 ].

Variani et al. conducted a recent systematic review and meta-analysis investigating occupational exposure to radar and cancer risk [ 138 ]. The meta-analysis included three cohort studies [ 122 , 124 , 125 ] and three case-control studies [ 129 , 130 , 131 ] for a total sample size of 53,000 subjects. The meta-analysis reported a decrease in cancer risk for workers exposed to radar but noted the small number of studies included with significant heterogeneity between the studies.

Apart from cancer, a number of epidemiological studies have investigated radar exposure and reproductive outcomes. Two early studies on military personnel in the US [ 139 ] and Denmark [ 140 ] reported differences in semen parameters between personnel using radar and personnel on other duty assignments; these studies included only volunteers with potential fertility concerns and are prone to bias. A further volunteer study on US military personnel did not find a difference in semen parameters in a similar comparison [ 141 ]; in general these type of cross-sectional investigations on volunteers provide limited evidence on possible risk. In a case-control study of personnel in the French military, Velez de la Calle et al. reported no association between exposure to radar and male infertility [ 142 ]; non-differential self-reporting of exposure may have underestimated this finding if there is a true effect. In two separate cross-sectional studies of personnel in the Norwegian navy, Baste et al. and Møllerløkken et al. reported an association between exposure to radar and male infertility, but there has been no follow up cohort or case control studies to confirm these results [ 143 , 144 ].

Again considering reproduction, a number of studies investigated pregnancy and offspring outcomes. In a population-based case-control study conducted in the US and Canada, De Roos et al. found no statistically significant association between parental occupational exposure to radar and neuroblastoma in offspring; however, the result was based on a small number of cases and controls exposed to radar [ 145 ]. In another cross-sectional study of the Norwegian navy, Mageroy et al. reported a higher risk of congenital anomalies in the offspring of personnel who were exposed to radar; the study found positive associations with a large number of other chemical and physical exposures, but the study involved multiple comparisons so is prone to over-interpretation [ 146 ]. Finally, a number of pregnancy outcomes were investigated in a cohort study of Norwegian navy personnel enlisted between 1950 and 2004 [ 147 ]. The study reported an increase in perinatal mortality for parental service aboard fast patrol boats during a short period (3 months); exposure to radar was one of many possible exposures when serving on fast patrol boats and the result is prone to multiple testing. No associations were found between long-term exposure and any pregnancy outcomes.

There is limited research investigating exposure to radar and other diseases. In a large case-control study of US military veterans investigating a range of risk factors and amyotrophic lateral sclerosis, Beard et al. did not find a statistically significant association with radar [ 148 ]; the study reported a likely under-ascertainment of non-exposed cases, which may have biased the result away from the null. The cohort studies on military personnel did not find statistically significant associations between exposure to radar and other diseases [ 122 , 124 , 125 ].

A number of observational studies have investigated outcomes measured on volunteers in the laboratory. They are categorised as epidemiological studies because exposure to radar was not based on provocation. These studies investigated genotoxicity [ 149 ], oxidative stress [ 149 ], cognitive effects [ 150 ] and endocrine function [ 151 ]; the studies generally reported positive associations with radar. These volunteer studies did not sample from a defined population and are prone to bias [ 152 ].

The experimental studies investigating exposure to MMWs at levels below the ICNIRP occupational limits have looked at a variety of biological effects. Genotoxicity was mainly examined by using comet assays of exposed cells. This approach has consistently found no evidence of DNA damage in skin cells in well-designed studies. However, animal studies conducted by one research group reported DNA strand breaks and changes in enzymes that control the build-up of ROS, noting that these studies had low animal numbers (six animals exposed); these results have not been independently replicated. Studies have also investigated other indications of genotoxicity including chromosome aberrations, micro-nucleation and spindle disturbances. The methods used to investigate these indicators have generally been rigorous; however, the studies have reported contradictory results. Two studies by a Russian research group have also reported indicators of DNA damage in bacteria, however, these results have not been verified by other investigators.

The studies of the effect of MMWs on cell proliferation primarily focused on bacteria, yeast cells and tumour cells. Studies of bacteria were mainly from an Armenian research group that reported a reduction in the bacterial growth rate of exposed E. coli cells at different MMW frequencies; however, the studies suffered from inadequate dosimetry and temperature control and heating due to high RF energy deposition may have contributed to the results. Other authors have reported no effect of MMWs on E. coli cell growth rate. The results on cell proliferation of yeast exposed to MMWs were also contradictory. An Italian research group that has conducted the majority of the studies on tumour cells reported either a reduction or no change in the proliferation of exposed cells; however, these studies also suffered from inadequate dosimetry and temperature control.

The studies on gene expression mainly examined two different indicators, expression of stress sensitive genes and chaperone proteins and the occurrence of a resonance effect in cells to explain DNA conformation state changes. Most studies reported no effect of low-level MMWs on the expression of stress sensitive genes or chaperone proteins using a range of experimental methods to confirm these results; noting that these studies did not use blinding so experimental bias cannot be excluded from the results. A number of studies from a Russian research group reported a resonance effect of MMWs, which they propose can change the conformation state of chromosomal DNA complexes. Their results relied heavily on the AVTD method for testing changes in the DNA conformation state, however, the biological relevance of results obtained through the AVTD method has not been independently validated.

Studies on cell signalling and electrical activity reported a range of different outcomes including increases or decreases in signal amplitude and changes in signal rhythm, with no consistent effect noting the lack of blinding in most of the studies. Further, temperature contributions could not be eliminated from the studies and in some cases thermal interactions by conventional heating were studied and found to differ from the MMW effects. The results from some studies were based on small sample sizes, some being confined to a single specimen, or by observed effects only occurring in a small number of the samples tested. Overall, the reported electrical activity effects could not be dismissed as being within normal variability. This is indicated by studies reporting the restoration of normal function within a short time during ongoing exposure. In this case there is no implication of an expected negative health outcome.

Studies on membrane effects examined changes in membrane properties and permeability. Some studies observed changes in transitions from liquid to gel phase or vice versa and the authors implied that MMWs influenced cell hydration, however the statistical methods used in these studies were not described so it is difficult to examine the validity of these results. Other studies observing membrane properties in artificial cell suspensions and dissected tissue reported changes in vesicle shape, reduced cell volume and morphological changes although most of these studies suffered from various methodological problems including poor temperature control and no blinding. Experiments on bacteria and yeast were conducted by the same research group reporting changes in membrane permeability, which was attributed to cell proliferation effects, however, the studies suffered from inadequate dosimetry and temperature control. Overall, although there were a variety of membrane bioeffects reported, these have not been independently replicated.

The limited number of studies on a number of other effects from exposure to MMWs below the ICNIRP limits generally reported little to no consistent effects. The single in vivo study on cancer promotion did not find an effect although the study did not include sham controls. Effects on reproduction were contradictory that may have been influenced by opposing objectives of examining adverse health effects or infertility treatment. Further, the only study on human sperm found no effects of low-level MMWs. The studies on reproduction suffered from inadequate dosimetry and temperature control, and since sperm is sensitive to temperature, the effect of heating due to high RF energy deposition may have contributed to the studies showing an effect. A number of studies from two research groups reported effects on ROS production in relation to reproduction and immune function; the in vivo studies had low animal numbers (six animals per exposure) and the in vitro studies generally had inadequate dosimetry and temperature control. Studies on fatty acid composition and physiological indicators did not generally show any effects; poor temperature control was also a problem in the majority of these studies. A number of other studies investigating various other biological effects reported mixed results.

Although a range of bioeffects have been reported in many of the experimental studies, the results were generally not independently reproduced. Approximately half of the studies were from just five laboratories and several studies represented a collaboration between one or more laboratories. The exposure characteristics varied considerably among the different studies with studies showing the highest effect size clustered around a PD of approximately 1 W/m 2 . The meta-analysis of the experimental studies in our companion paper [ 9 ] showed that there was no dose-response relationship between the exposure (either PD or SAR) and the effect size. In fact, studies with a higher exposure tended to show a lower effect size, which is counterfactual. Most of the studies showing a large effect size were conducted in the frequency range around 40–55 GHz, representing investigations into the use of MMWs for therapeutic purposes, rather than deleterious health consequences. Future experimental research would benefit from investigating bioeffects at the specific frequency range of the next stage of the 5 G network roll-out in the range 26–28 GHz. Mobile communications beyond the 5 G network plan to use frequencies higher than 30 GHz so research across the MMW band is relevant.

An investigation into the methods of the experimental studies showed that the majority of studies were lacking in a number of quality criteria including proper attention to dosimetry, incorporating positive controls, using blind evaluation or accurately measuring or controlling the temperature of the biological system being tested. Our meta-analysis showed that the bulk of the studies had a quality score lower than 2 out of a possible 5, with only one study achieving a maximum quality score of 5 [ 9 ]. The meta-analysis further showed that studies with a low quality score were more likely to show a greater effect. Future research should pay careful attention to the experimental design to reduce possible sources of artefact.

The experimental studies included in this review reported PDs below the ICNIRP exposure limits. Many of the authors suggested that the resulting biological effects may be related to non-thermal mechanisms. However, as is shown in our meta-analysis, data from these studies should be treated with caution because the estimated SAR values in many of the studies were much higher than the ICNIRP SAR limits [ 9 ]. SAR values much higher than the ICNIRP guidelines are certainly capable of producing significant temperature rise and are far beyond the levels expected for 5 G telecommunication devices [ 1 ]. Future research into the low-level effects of MMWs should pay particular attention to appropriate temperature control in order to avoid possible heating effects.

Although a systematic review of experimental studies was not conducted, this paper presents a critical appraisal of study design and quality of all available studies into the bioeffects of low level MMWs. The conclusions from the review of experimental studies are supported by a meta-analysis in our companion paper [ 9 ]. Given the low-quality methods of the majority of the experimental studies we infer that a systematic review of different bioeffects is not possible at present. Our review includes recommendations for future experimental research. A search of the available literature showed a further 44 non-English papers that were not included in our review. Although the non-English papers may have some important results it is noted that the majority are from research groups that have published English papers that are included in our review.

The epidemiological studies on MMW exposure from radar that has a similar frequency range to that of 5 G and exposure levels below the ICNIRP occupational limits in most situations, provided little evidence of an association with any adverse health effects. Only a small number of studies reported positive associations with various methodological issues such as risk of bias, confounding and multiple testing questioning the result. The three large cohort studies of military personnel exposed to radar in particular did not generally show an association with cancer or other diseases. A key concern across all the epidemiological studies was the quality of exposure assessment. Various challenges such as variability in complex occupational environments that also include other co-exposures, retrospective estimation of exposure and an appropriate exposure metric remain central in studies of this nature [ 153 ]. Exposure in most of the epidemiological studies was self-reported or based on job-title, which may not necessarily be an adequate proxy for exposure to RF fields above 6 GHz. Some studies improved on exposure assessment by using expert assessment and job-exposure matrices, however, the possibility of exposure misclassification is not eliminated. Another limitation in many of the studies was the poor assessment of possible confounding including other occupational exposures and lifestyle factors. It should also be noted that close proximity to certain very powerful radar units could have exceeded the ICNIRP occupational limits, therefore the reported effects especially related to reproductive outcomes could potentially be related to heating.

Given that wireless communications have only recently started to use RF frequencies above 6 GHz there are no epidemiological studies investigating 5 G directly as yet. Some previous epidemiological studies have reported a possible weak association between mobile phone use (from older networks using frequencies below 6 GHz) and brain cancer [ 11 ]. However, methodological limitations in these studies prevent conclusions of causality being drawn from the observations [ 152 ]. Recent investigations have not shown an increase in the incidence of brain cancer in the population that can be attributed to mobile phone use [ 154 , 155 ]. Future epidemiological research should continue to monitor long-term health effects in the population related to wireless telecommunications.

The review of experimental studies provided no confirmed evidence that low-level MMWs are associated with biological effects relevant to human health. Many of the studies reporting effects came from the same research groups and the results have not been independently reproduced. The majority of the studies employed low quality methods of exposure assessment and control so the possibility of experimental artefact cannot be excluded. Further, many of the effects reported may have been related to heating from high RF energy deposition so the assertion of a ‘low-level’ effect is questionable in many of the studies. Future studies into the low-level effects of MMWs should improve the experimental design with particular attention to dosimetry and temperature control. The results from epidemiological studies presented little evidence of an association between low-level MMWs and any adverse health effects. Future epidemiological research would benefit from specific investigation on the impact of 5 G and future telecommunication technologies.

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This work was supported by the Australian Government’s Electromagnetic Energy Program. This work was also partly supported by National Health and Medical Research Council grant no. 1042464. 

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Karipidis, K., Mate, R., Urban, D. et al. 5G mobile networks and health—a state-of-the-science review of the research into low-level RF fields above 6 GHz. J Expo Sci Environ Epidemiol 31 , 585–605 (2021). https://doi.org/10.1038/s41370-021-00297-6

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DOI : https://doi.org/10.1038/s41370-021-00297-6

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20 Vital Smartphone Usage Statistics [2023]: Facts, Data, and Trends On Mobile Use In The U.S.

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Research Summary. Each and every day, most of us rely on our phones to communicate, research, and entertain ourselves. We even need our phones to navigate in the car or on foot. With that in mind, it’s unsurprising that cell phone usage has only grown over time.

To find out more about smartphone users in U.S., we’ve gathered all of the most interesting statistics. According to our extensive research:

81.6% of Americans, totaling 270 million people, own a smartphone as of 2023.

The average American spends 5 hours and 24 minutes on their mobile device each day.

Americans check their phones on average 96 times per day , or once every ten minutes.

There are roughly 6.92 billion smartphone users across the world. That’s 86.29% of the global population, as of 2023.

59.16% of website traffic comes from mobile devices, as of 2022.

General Smartphone Usage Statistics

Given that 81.6% of Americans own a smartphone, it’s not surprising that they play a pivotal role in everyday life. From briefly checking the weather to perusing Facebook for two hours, our research shows that:

As of 2021, 90% of mobile time is spent using apps.

That’s up by at least 25% when compared to 2019 numbers. In fact, smartphone users spent a record 3.8 trillion hours using apps in 2021.

Over the past five years (2017-2022), the mobile share of internet usage has increased from 51.1% to 59.1%.

In fact, in the first quarter of 2015, the mobile share of internet usage was only a third of computers and other devices (31.16%). While, as of the start of 2022, the majority of internet usage now comes from mobile devices (59.1%).

17% of Americans still use feature phones.

Remember blackberries and flip phones? Well, it turns out that 17% of Americans still use more archaic mobile devices. Of course, this number is skewed toward particular demographics, with 25% of those making less than $30,000 a year owning a feature phone and 40% of those aged 65 and older owning one.

Smartphones account for 70% of U.S. digital media time.

Some of the forms of digital media that are mostly consumed on smartphones include Games (94% of digital time), Social Media (92%), and Entertainment (83%). Meanwhile, the only two forms of digital media still reliant on desktops are Education (77% share) and Government (72%).

The average American spends at least five hours on their phone per day.

More specifically, just under half of all Americans spend between five to six hours on their phones per day, while a further 22% spend three to four hours on average on their phone daily. Believe it or not, less than 5% of Americans spend less than an hour on their phone per day.

College graduates are 20% more likely to have a smartphone than those who didn’t graduate High School.

Overall, 93% of college graduates have a smartphone, compared to only 75% of individuals with some High School experience. It’s also worth noting that the number jumps up to 89% for those who have some college experience.

Smartphone Use Statistics by Reliance and Addictiveness

Of course, the integration of smartphones in our everyday lives does come with some negative side effects. Today, we are more reliant on and addicted to mobile devices than ever before. Here are the facts:

The average American touches their phone 2,617 times per day.

For heavy users, that number can reach over 5,000. Sounds crazy, but it also counts all those times you’ve fumbled around your pockets in a panic because you thought you lost your phone. Overall, the average smartphone owner will unlock their phone at least 150 times a day.

The average American will check their phone once every ten to 12 minutes.

That means the average American checks their phone at least 96 times per day. Though, 66% of Americans actually check their phones 160 times every day.

27% of Americans living in households earning less than $30,000 a year rely on their smartphones for internet access.

While this is by far the largest demographic, wealthier Americans are also beginning to rely on their phones for the internet as well. 11% of those making between $30,000 and $99,999 are smartphone-reliant for internet access, while only 6% of those making $100,000+ are.

28% of those between the ages of 18-29 do not have broadband but do have a smartphone.

That’s a stark contrast compared to every other generation, who overwhelmingly have broadband. Only 11% of those between 30-49 have a smartphone and no broadband, while that percentage is 13% for those between 50-64 and 12% for those 65+.

Smartphone Usage Statistics and Mobile Commerce

No doubt, the growth of smartphones has had a positive effect on mobile commerce. Especially in the U.S., the mobile commerce industry continues to boom year after year. According to our research:

As of 2021, the m-commerce market comprises nearly 73% of total e-commerce.

What’s more, this is a 39.1% increase from 2016, when m-commerce was only 52.4% of the e-commerce market. In all likelihood, the increased reliance on mobile devices will only see this percentage increase further over time.

As of 2021, 79% of smartphone users have used their mobile devices to make a purchase.

Plus, when considering the fact that the average mobile phone purchase is $94.85, it’s unsurprising to see so many companies focus on apps and other forms of m-commerce advertising.

U.S. m-commerce is expected to experience a massive CAGR of 34.9% between 2020–2026.

This is unsurprising, given the growth of smartphone users within the past five years and the fact that a growing minority of Americans rely on their phones for internet access.

As of 2020, U.S. m-commerce annual revenue reached $339.03 billion.

That means that the U.S. market is responsible for around 11-12% of the world’s total m-commerce revenues.

Smartphone Usage Statistics: Trends and Projections

While it might seem obvious that smartphone usage will continue to increase, there are multiple important factors to consider. When it comes to smartphone usage trends and predictions, here’s what we found:

Year-over-Year (YoY) growth for cell phone usage is expected to be at a stable 2-3% by 2025.

While this might not seem like a lot, it equates to just under a billion new users. For instance, as of 2023, there are 6.92 billion smartphone users globally, and that number is expected to reach 7.34 billion by 2025.

The amount of time spent using smartphone apps in the U.S. has increased by 25% since 2019.

Globally, that number is as high as 30%, with countries like India (80%), Russia (50%), Indonesia (45%), and Turkey (45%) all seeing the highest increases.

By 2025, an estimated 72.6% of global smartphone owners will access the internet solely using their smartphones.

That means that by 2025 there will be over 1.3 billion people who will rely on their smartphones to access the internet worldwide.

There are roughly 10.37 billion mobile connections worldwide.

Believe it or not, that’s 2.46 billion more mobile connections than people worldwide (or 24% more mobile connections in the world than there are people).

There are projected to be 7.52 billion smartphone users by 2026.

That’s 12% more users than there are currently, which is healthy growth, considering the fact that 86% of the global adult population already owns a phone.

Since 2016, the number of smartphone users worldwide has grown by 50%.

In 2016, the number of global smartphone users was only 3.67 billion, or 45% of the total population at the time. However, as of 2022, the number of smartphone users around the globe is now 86% of the total population.

Cell Phone Usage Statistics FAQ

What is the average cell phone usage per day in 2022?

The average American uses their phone for at least five hours and 24 minutes per day. More specifically, just under half of all Americans spend between five to six hours on their phones per day, while a further 22% spend three to four hours on average on their phone daily. Believe it or not, less than 5% of Americans spend less than an hour on their phone per day.

What percentage of the world has a cell phone in 2022?

A considerable 86% of the global population has a smartphone in 2022. That’s a huge percentage, considering the fact that in 2016 the number of global smartphone users was only 3.67 billion, or 45% of the total population at the time.

Further, the prevalence of cell phones is still expected to become more pronounced, projected to be 7.52 billion smartphone users by 2026. That’s 12% more than the current 6.92 billion.

How many times does someone check their phone a day in 2022?

The average American checks their phone 96 times per day, or once every ten to 12 minutes. Though, we actually touch our phones up to 2,617 times per day and unlock our phones 150 times on average. That’s a lot.

How many smartphones are sold each year?

Over 1.5 billion smartphones are sold around the world each year. The current global leader is Samsung, which sold 272 million smartphones in 2021 alone. Other top players include Apple, Xiaomi, OPPO^, and Vivo which all reached their highest-ever annual shipments in the same year.

How many Americans have smartphones?

81.6% of Americans own smartphones, which is equivalent to over 270 million people. This percentage will likely continue to increase, as 53% of American children now have a smartphone before age 11.

What percent of people are addicted to their phones?

47% of people believe they’re addicted to our phones. Several symptoms of this addiction include the fact that 74% of Americans feel uneasy when they leave their home without their phone, 71% check their phone within the first 10 minutes of waking up. and 70% check their phone within the first 5 minutes of receiving a notification.

All together, these trends show that people rely on their phones a great deal, and are often unable to spend time apart from their devices.

It seems like everyone and their mother has a smartphone nowadays, and that’s because almost everyone does. In fact, 81.6% of Americans own smartphones, and 86.29% of the global population does.

And we rely on these devices too, checking them every ten to 12 minutes and spending over five hours using them on average. Believe it or not, over a quarter of those who make $30,000 or less and the same percentage of those between the ages of 18-29 rely on their smartphones for internet access.

And this trend is only expected to continue, as the U.S. e-commerce market is expected to experience a massive CAGR of 34.9% between 2020–2026. Not to mention the simple fact that the amount of time spent using smartphone apps in the U.S. has increased by 25% since 2019.

All of that, combined with the projection that the number of global smartphone users will grow by 12% through 2026, and it’s not hard to see how dominating smartphones really are.

Forbes. “Record 3.8 Trillion Hours Spent On Mobile Apps During 2021 In Another Blockbuster Year For Digital Economy.” Accessed on January 24th, 2021.

The Motley Fool. “A Foolish Take: 17% of Americans Use Feature Phones.” Accessed on January 24th, 2021.

Marketing Charts. “Smartphones Now Account for 70% of US Digital Media Time.” Accessed on January 24th, 2021.

Statista. “How much time on average do you spend on your phone on a daily basis?” Accessed on January 24th, 2021.

Dscout. “Putting a Finger on Our Phone Obsession.” Accessed on January 24th, 2021.

Fox 13. “Americans check their smartphones 96 times a day, survey says.” Accessed on January 24th, 2021.

Pew Research Center. “Digital divide persists even as Americans with lower incomes make gains in tech adoption.” Accessed on January 24th, 2021.

Pew Research Center. “Mobile Fact Sheet.” Accessed on January 24th, 2021.

Oberlo. “Mobile Commerce Sales in 2021.” Accessed on January 24th, 2021.

Appinventiv. “Future of Mobile Commerce: Stats & Trends to Know in 2021-2025.” Accessed on January 24th, 2021.

Statista. “Number of smartphone users from 2016 to 2021.” Accessed on January 24th, 2021.

TechCrunch. “Consumers now average 4.2 hours per day in apps, up 30% from 2019.” Accessed on January 24th, 2021.

CNBC. “Nearly three quarters of the world will use just their smartphones to access the internet by 2025.” Accessed on January 24th, 2021.

Statista. “Percentage of mobile device website traffic worldwide from 1st quarter 2015 to 4th quarter 2022.” Accessed on April 3, 2023.

BankmyCell. “How Many Smartphones Are in the World?” Accessed on April 3, 2023.

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Jack Flynn is a writer for Zippia. In his professional career he’s written over 100 research papers, articles and blog posts. Some of his most popular published works include his writing about economic terms and research into job classifications. Jack received his BS from Hampshire College.

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Smartphone Addiction and Associated Health Outcomes in Adult Populations: A Systematic Review

Zubair ahmed ratan.

1 School of Health and Society, Faculty of the Arts, Social Sciences and Humanities, University of Wollongong, Northfields Ave., Wollongong, NSW 2522, Australia; ua.ude.liamwou@142raz (Z.A.R.); ua.ude.liamwou@890am (M.S.A.)

2 Department of Biomedical Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh

Anne-Maree Parrish

Sojib bin zaman.

3 Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC 3800, Australia; [email protected]

Mohammad Saud Alotaibi

4 Department of Social Work, College of Social Sciences, Umm Al-Qura University, Mecca 24382, Saudi Arabia

Hassan Hosseinzadeh

Associated data.

Not applicable.

Background: Smartphones play a critical role in increasing human–machine interactions, with many advantages. However, the growing popularity of smartphone use has led to smartphone overuse and addiction. This review aims to systematically investigate the impact of smartphone addiction on health outcomes. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to carry out the systematic review. Five electronic databases including Medline, Web of Science, PsycINFO, PubMed, and Scopus were searched to identify eligible studies. Eligible studies were screened against predetermined inclusion criteria and data were extracted according to the review questions. This review is registered in PROSPERO (CRD42020181404). The quality of the articles was assessed using the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: A total of 27 of 2550 articles met the inclusion criteria. All of the studies were cross-sectional and focused on physical, mental, and neurological health outcomes. The majority of the studies focused on mental health outcomes and consistent associations were observed between smartphone addiction and several mental health outcomes. Anxiety and depression were commonly found to mediate mental health problems. A wide range of physical health sequelae was also associated with smartphone addiction. Furthermore, there was an association between smartphone addiction and neurological disorders. Conclusions: Our findings suggest that there are consistent associations between smartphone addiction and physical and mental health, especially mental health. Social awareness campaigns about smartphone addiction and its impact on physical and mental health are needed. Further studies, especially randomized controlled trials, are warranted to validate the impacts of smartphone addiction.

1. Introduction

The 21st century is known as the age of information technology. Wireless communication and the internet are remarkable entities resulting in revolutionary changes in the field of communication [ 1 ]. In 2007, computer-based phones (smartphones) were introduced [ 2 ]. Since then, smartphones have become an indispensable part of daily life in all communities and countries. As such, smartphones have become one of the fastest-growing sectors in the technology industry [ 3 ]. Over the past decade, smartphone ownership and use have been exponentially increased globally. For instance, there were about 2.1 billion smartphone users in 2017 and the number was projected to exceed 2.8 billion by 2020 worldwide [ 4 ].

A number of novel problematic behaviors have emerged in the information technology era, such as gambling, internet gaming, and sexual behaviors, which may lead to compulsive engagement [ 5 ]. Extreme instances may lead to individuals feeling unable to control these behaviors without external influence, and these behaviors may be considered non-substance or behavioral addictions [ 6 ]. Internet addiction is one of the earliest examined forms of information technology addiction [ 7 ]. The relatively newer concept of “smartphone addiction” (SA) has also been studied based on previous internet addiction research [ 8 ]. Smartphones distinguish their use from traditional Internet use on computers or laptops because smartphones allow users to access the internet continuously regardless of time and space. Smartphone addiction is fueled by an Internet overuse problem or Internet addiction disorder [ 9 ]. The increased use of smartphones has resulted in most in people communicating daily online, as a result of interactive texts and social media, instead of face-to-face human contact. Smartphones fetch a limitless range of cognitive activities for users; smartphones forge opportunities for individuals to engage in a range of online activities such as participating in social network sites, playing video games, and “surfing the web” [ 10 ]. However, the smartphone poses a negative impact on our ability to think, remember, pay attention, and regulate emotion [ 11 ]. The increase in popularity and frequency of smartphone use has led to the emergence of clinical cases of people presenting with abuse symptoms [ 12 ].

The concept of addiction is not easy to define, and the usage of the term addiction has been considered controversial; however, central to its definition is the dependence on a substance or activity [ 13 ].

Smartphone addiction (SA) is generally conceptualized as a behavioral addiction including mood tolerance, salience, withdrawal, modification, conflict, and relapse [ 14 ]. Literature suggests that there are associations between SA and mental health [ 15 ], physical health [ 16 ], and neurological problems [ 17 ]. Furthermore, tolerance, salience, withdrawal, and cravings [ 8 , 18 ] have been associated with excessive smartphone use. However, the evidence is not conclusive [ 19 ]. Still, there is debate in the literature about the positive or negative relationship between the amount of screen time or smartphone use and health outcomes. Existing studies have provided useful data; however, it is difficult to draw consensus without a systematic review.

This systematic review is an attempt to collate empirical evidence about the health impacts of smartphone addiction among the adult population. This study aims to provide evidence to inform policy or recommendations to control and prevent smartphone addiction.

The protocol of this systematic review is registered in PROSPERO (CRD42020181404). It was carried out using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines ( Figure 1 ). Literature searches were conducted in the five databases including Scopus, Medline, PubMed, Web of Science, and psycINFO databases. The search strategy for this review was initially developed by a series of consultations with the investigators and some preliminary searches (Z.A.R., A.M.P., S.B.Z., M.S.A., and H.H.). Expert librarians from the University of Wollongong were also consulted to refine and finalize the search strategy. All studies including controlled trials, case-control, cross-sectional, and cohort studies were included. Eligibility criteria included studies which explored smartphone exposure focusing on the adult population (aged over 18), published in the English language. This review excluded case reports, ideas, editorials, meta-analysis, review articles and opinions. Search terms included “smartphone”, “addiction”, “overuse”, “problematic use”, “excessive use”, and “adults”. Details of search strategies are provided in Supplementary Table S1 . Since the smartphone gained popularity in 2011 (after the debut of the smartphone), the literature was searched from January 2011 until July 2021. The reference lists of the selected papers were also searched for any eligible papers however no papers were found.

Preferred Reporting Item for Systematic Review (template taken from PRISMA flow diagram).

Three authors (Z.A.R., S.B.Z., and M.S.A.) independently reviewed all the retrieved abstracts and selected eligible papers. Any disagreements were resolved by discussion with senior researchers (A.M.P. and H.H.). The quality of each included study was assessed by using the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies and were given a rating of either “good”, “fair” or “poor” and the results of the quality assessment are presented in Supplementary Table S2 . The NIH quality assessment is a valid and reliable tool for the assessment of the methodological quality of cross-sectional studies [ 20 ].

3.1. Overall Search Findings

A total of 2550 potential studies were identified. After screening and removing duplicates, twenty-seven (27) studies were eligible for this review. A detailed study selection process based on the PRISMA flow chart is presented in Figure 1 . Sample sizes ranged from 30 to 5372 adults ( Table 1 ). Seven were conducted in South Korea [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ], three in Saudi Arabia [ 28 , 29 , 30 ], four in China [ 31 , 32 , 33 , 34 ], four in Turkey [ 35 , 36 , 37 , 38 ], one in India [ 39 ] one in Taiwan [ 40 ], one in Switzerland [ 41 ], one in the USA [ 42 ], one in Italy [ 43 ], one in Thailand [ 44 ], and three were international studies [ 45 , 46 , 47 ] ( Figure 2 ). Smartphone addiction was measured in the study sample using different scales, however, the Smartphone Addiction Scale, Short Version (SAS-SV; n = 8) was the most common measure ( Table 1 ). Among the selected studies, nine studies were considered to be “good”, seventeen articles were considered to be “fair”, and the remaining one was considered “poor” ( Table 2 ).

Global map indicating country of selected articles.

Smartphone addiction and associated health outcomes.

Summary of outcomes.

3.2. Main Findings

3.2.1. mental health.

As outlined in Table 2 , mental health was associated with SA in fourteen studies [ 22 , 25 , 27 , 28 , 30 , 31 , 33 , 36 , 40 , 41 , 42 , 45 , 46 , 47 ]. Depression and anxiety were the most common mental health conditions associated with SA [ 22 , 25 , 28 , 30 , 31 , 33 , 36 , 41 , 45 , 47 ]. Several depression measures were used; however, the Beck Depression Inventory was the most common measure used [ 28 , 30 , 36 , 40 ]. Alhassan et al. (2018) revealed that less-educated people and young adult users of the smartphone were at high risk of depression. Another study [ 28 ] found that the groups who were classified as smartphone-addicted had an increased risk of depression (relative risk 1.337; p < 0.001) and anxiety (relative risk 1.402; p < 0.001) [ 28 ]. Miles Richardson et al. (2018) found that problematic smartphone use (PSU) was positively related to anxiety [ 46 ].

Social anxiety was also associated with SA [ 41 ]. For instance, a study conducted in China during COVID-19 reported that COVID-19 anxiety was associated with the severity of problematic smartphone use [ 33 ].

Interestingly, female participants were more susceptible to SA [ 36 ] and showed significantly higher dependence on smartphones than men [ 25 ]. Further, a study conducted among university students in Thailand demonstrated that not only were female students more likely to be smartphone addicted, but smartphone addiction among female participants was likely to be negatively associated with psychological well-being [ 44 ].

3.2.2. Physical Health

Musculoskeletal problems.

The effect of SA on the musculoskeletal system was identified in four studies [ 24 , 26 , 34 , 43 ] ( Table 2 ). Among those studies, two studies reported cervical problems [ 24 , 34 ], one study demonstrated nerve thickness [ 26 ], and one study showed psoriatic arthritis [ 43 ]. Lee et al. (2014) compared cervical spine repositioning errors in different smartphone addiction groups and revealed that there were significant differences between non-addicted, moderately addicted, and severely addicted groups; the severe smartphone addict group showed the largest changes in posture, the cervical repositioning errors of flexion (3.2 ± 0.8), extension (4.9 ± 1.1), right lateral flexion (3.9 ± 1.0), and left lateral flexion (4.1 ± 0.7). [ 24 ]. A study conducted among 2438 young patients suffering from chronic neck pain found that cervical disc degeneration was more likely to be associated with SA [ 34 ]. Another study conducted among university students revealed that excess smartphone use can cause nerve injury [ 26 ]. Megna et al. (2018) found that SA was linked to higher signs of inflammation in the musculoskeletal structures of hand joints.

Sleep Quality and Sedentary Lifestyle

Five studies showed an association between smartphone addiction and sleep quality [ 29 , 35 , 38 , 39 , 40 ]. The Pittsburgh Sleep Quality Index (PSQI) was used in all five studies ( Table 1 ). A study conducted by Fahad et al. (2016) among 2367 university students reported 43% of the participants had decreased their sleeping hours due to SA, and 30% of the participants had an unhealthy lifestyle including weight gain, reduced exercise, and the consumption of more fast food when diagnosed with SA [ 29 ]. Another study conducted among migraine patients reported that SA can increase headache duration and decrease sleep quality [ 35 ].

One study conducted by Hye-Jin Kim et al. (2017) revealed that SA is associated with different types of accidents, such as traffic accidents; falls/slips; bumps/collisions; being trapped in the subway, impalement, cuts, and exit wounds; and burns or electric shocks [ 21 ]. The study found that self-reported experience of accidents was significantly associated with SA [ 21 ].

Neurological Problems

Two studies reported the neurological effect of SA [ 23 , 32 ]; one study found alterations in white matter integrity [ 32 ] and another study reported smaller grey matter volume [ 23 ]. Hu et al. (2017) used a high-resolution magnetic resonance imaging technique to identify white matter integrity in young adults with SA and found that smartphone-addicted participants had significantly lower white matter integrity [ 32 ]. Lee et al. (2019) found that smartphone-addicted participants had significantly smaller grey matter volume (GMV) in the right lateral orbitofrontal cortex (OFC) [ 23 ].

4. Discussion

In recent years, several articles have examined the role of smartphone addiction and associated health outcomes among the adult population, however, substantial gaps still remain. To the best of our knowledge, no previous systematic review has been conducted to summarize these findings among this cohort. Our review is the first systematic review that utilizes empirical evidence from the last decades that demonstrates the relationship between smartphone addiction and health outcomes among adults. Interestingly, studies conducted in different parts of the world showed similar effects on health outcomes as a result of smartphone addiction. Hence, the consistency across the studies strengthens the study findings, emphasizing the association between SA and health outcomes.

Our findings suggest that depression and anxiety are significantly linked with smartphone addiction. One national USA survey found that 46% of smartphone owners believed they could not live without their phones [ 48 ]. Overuse patterns of smartphones involves a tendency to check notifications all the time, and such behavior patterns can induce “reassurance seeking” which broadly includes symptoms such as depression and anxiety [ 49 ]. This “reassurance seeking” pathway corresponds to those individuals whose smartphone use is driven by the necessity to maintain relationships and obtain reassurance from others. Bilieux and colleagues explained this reassurance-seeking behavior with the theoretical model of “problematic mobile phone use” [ 50 ]. In addition, this checking behavior is related to the next pathway, the “fear of missing out” (FOMO). One study found that FOMO mediated relations between both depression and anxiety severity with SA [ 51 ].

From our results, it is evident that musculoskeletal pain and insomnia are the two most common physical problems related to SA. Fingers, cervical, back, and shoulder problems are most commonly linked to excessive smartphone usage. Prolonged use of smartphones can cause defective postures such as forwarding head posture, which can produce injuries to the cervical spine and cause cervical pain [ 52 ]. Numerous studies found De Quervain tenosynovitis (characterized by pain in the wrist over the radio styloid process—the thumb side of wrist) was associated with different electronic devices like gaming controllers, tablets, and smartphones [ 53 , 54 ]. Texting and chatting through smartphones have been considered a risk factor for De Quervain tenosynovitis [ 55 ].

Poor sleep quality and difficulty in falling asleep or maintaining sleep has been identified as one of the negative consequences of SA, which is similar to our results [ 56 , 57 ]. Moreover, in line with our finding, another systematic review revealed that SA is related to poorer sleep quality [ 58 ]. One study found that 75% of the young adults (age < 30 years) take their phones to bed, which may increase the likelihood of poor sleep quality [ 59 ]. Smartphone addicts are unsuccessful at controlling their smartphone use, even in bed. Again, fear of missing out could be the reason of taking phones in the beds as they do not want to miss any notification [ 60 , 61 ]. In addition, blue light emitted by smartphones can have a negative effect on circadian rhythms, leading to negative sleep consequences, such as going to sleep later than intended and thus reducing overall sleep time [ 62 ].

The neurological effect of SA is not clear yet from this review. However, currently neuroimaging studies play an important role in understanding the complexity of addictive behavior [ 63 ], as they can assess any pathological change in the brain. Two studies in this review reported the negative changes in grey matter and white matter integrity in the brain with the assistance of neuroimaging ( Table 2 ), which is similar to the neuropathy caused by substance abuse [ 64 , 65 ] and Internet addiction [ 66 , 67 ]. However, the modest sample size and the lack of a clinical evaluation are the potential limitations of these studies [ 23 , 32 ].

This review indicates that smartphone addiction shares similar features with substance abuse. A consistent relationship has been demonstrated between SA and physical and mental health symptoms, including depression, anxiety, musculoskeletal problems, and poor sleep. However, smartphones have become a part of daily life, facilitating work, education, or entertainment. Therefore, it is important not only to utilize the advantages of the smartphone but also to reduce the negative consequences. To address SA in a proper way, a validated definition and consistent diagnostic criteria of SA is required. The findings from this research suggest that healthcare providers and policymakers should recognize the problem and take necessary steps in raising community awareness about SA and its physical and mental impact.

5. Limitations

This systematic review has several limitations. First, all of the selected studies were cross-sectional ( Table 1 ), therefore drawing conclusions about causal directions of associations is not possible. Secondly, all the papers were excluded if not in the English language; however, SA has received attention in Asian and European countries, and findings may have been published in other languages. This may lead to exclusion of studies conducted in diverse cultures and may bias the results of the review. Thirdly, most of the studies that were qualified to be included in this review were performed in developed countries, which may question the generalizability our findings to developing countries. Finally, most of the outcomes were reported over less than one year of follow-up. No standard scale and cut-off scores were used for the determination of smartphone addiction.

6. Conclusions

The current review describes the effect of smartphones on health outcomes in the adult population. Although the diagnostic criteria and effect of smartphone addiction are yet to be fully established, this review provides invaluable findings about the health impact of smartphone addiction and has significant implications for policy and decision makers. There is a need for more longitudinal studies to validate and strengthen this review’s findings.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijerph182212257/s1 , Table S1. Electronic search strategy.

Author Contributions

Z.A.R. conceptualized and designed the study, conducted initial searches, assessed the eligibility of the retrieved papers in the titles/abstracts and full text. S.B.Z. and M.S.A. independently reviewed all the retrieved abstracts and selected eligible papers. Z.A.R., A.-M.P., S.B.Z., M.S.A. and H.H. critically assessed the eligible studies and extracted data. Z.A.R. analyzed and interpreted the data and drafted the manuscript. All authors critically reviewed the manuscript. A.-M.P. and H.H. reviewed and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

This research received no funding.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

Authors declared no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Which social media platforms are most common, who uses each social media platform, find out more, social media fact sheet.

Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.

Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.

Source: Surveys of U.S. adults conducted 2012-2023.

Usage of the major online platforms varies by factors such as age, gender and level of formal education.

% of U.S. adults who say they ever use __ by …

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Scientists identify ‘missing piece’ required for blood stem cell self-renewal

Key takeways/summary.

• Blood stem cells – key to transplants that are used as life-saving treatments for blood cancers and blood and immune disorders – have the capacity to self-renew, but quickly lose their ability to do so in a lab dish.
• UCLA scientists have identified a protein that not only enables blood stem cells to self-renew in a lab dish, but also allows these expanded cells to function effectively after being transplanted into mouse models.  
• The findings could help make blood stem cell transplants available to more people and improve the accessibility and safety of gene therapies that use these cells.

UCLA scientists have identified a protein that plays a critical role in regulating human blood stem cell self-renewal by helping them sense and interpret signals from their environment.

The study , published in Nature, brings researchers one step closer to developing methods to expand blood stem cells in a lab dish, which could make life-saving transplants of these cells more available and increase the safety of blood stem cell-based treatments, such as gene therapies.

Blood stem cells, also known as hematopoietic stem cells, have the ability to make copies of themselves via a process called self-renewal, and can differentiate to produce all the blood and immune cells found in the body. For decades, transplants of these cells have been used as life-saving treatments for blood cancers such as leukemia and various other blood and immune disorders.

However, blood stem cell transplants have significant limitations. Finding a compatible donor can be difficult, particularly for people of non-European ancestry, and the number of stem cells available for transplant can be too low to safely treat a person’s disease.   

These limitations persist because blood stem cells that have been removed from the body and placed in a lab dish quickly lose their ability to self-renew. After decades of research, scientists have come achingly close to solving this problem.

“We’ve figured out how to produce cells that look just like blood stem cells and have all of their hallmarks, but when these cells are used in transplants, many of them still don’t work; there’s something missing,” said  Dr. Hanna Mikkola , senior author of the new study and a member of the  Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA.

To pinpoint the missing piece that prevents these blood stem cell-like cells from being fully functional, Julia Aguade Gorgorio, the paper’s first and co-corresponding author, analyzed sequencing data to identify genes that are silenced when blood stem cells are placed in a lab dish. One such gene, MYCT1, which encodes a protein by the same name, stood out as being essential to these cells’ self-renewal capacity.

They found that MYCT1 regulates a process called endocytosis, which plays a key role in how blood stem cells take in the signals from their environment that tell them when to self-renew, when to differentiate and when to be quiet.

“When cells perceive a signal, they have to internalize it and process it; MYCT1 controls how fast and how efficiently blood stem cells perceive these signals,” said Aguade Gorgorio, an assistant project scientist in the Mikkola lab. “Without this protein, the signals from the cells’ environment turn from whispers into screams and the cells become stressed out and dysregulated.”

The researchers compare MYCT1 to the sensors in modern cars that monitor all nearby activity and selectively relay the most crucial information to drivers at the right time, aiding decisions like when to safely turn or change lanes. Without MYCT1, blood stem cells resemble anxious drivers who, used to relying on these sensors, suddenly find themselves lost without their guidance.

Next, the researchers used a viral vector to reintroduce MYCT1 to see if its presence could restore blood stem cell self-renewal in a lab dish. Restoration of MYCT1, they found, not only made the blood stem cells less stressed and enabled them to self-renew in culture but also allowed these expanded cells to function effectively after being transplanted into mouse models.

As a next step, the team will investigate why the silencing of the MYCT1 gene occurs, and then, how to prevent this silencing without the use of a viral vector, which would be safer for use in a clinical setting.  

“If we can find a way to maintain MYCT1 expression in blood stem cells in culture and after transplant, it will open the door to maximize all these other remarkable advances in the field,” said Mikkola, who is a professor of molecular, cell and developmental biology in the UCLA College and a member of the  UCLA Health Jonsson Comprehensive Cancer Center . “This would not only make blood stem cell transplants more accessible and effective but also improve the safety and affordability of gene therapies that utilize these cells.”

This work was supported by the National Institutes of Health, the Swiss National Science Foundation, the European Molecular Biology Organization, the UCLA Jonsson Cancer Center Foundation, the James B. Pendleton Charitable Trust, the McCarthy Family Foundation, the California Institute for Regenerative Medicine, the UCLA AIDS Institute, the Board of Governors Regenerative Medicine Institute at Cedars-Sinai Medical Center, the Royal Society, the Wellcome Trust and the UCLA Broad Stem Cell Research Center Stem Cell Training Program.

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“Rosetta Stone” of cell signaling could expedite precision cancer medicine

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A newly complete database of human protein kinases and their preferred binding sites provides a powerful new platform to investigate cell signaling pathways.

Culminating 25 years of research, MIT, Harvard University, and Yale University scientists and collaborators have unveiled a comprehensive atlas of human tyrosine kinases — enzymes that regulate a wide variety of cellular activities — and their binding sites.

The addition of tyrosine kinases to a previously published dataset from the same group now completes a free, publicly available atlas of all human kinases and their specific binding sites on proteins, which together orchestrate fundamental cell processes such as growth, cell division, and metabolism.

Now, researchers can use data from mass spectrometry, a common laboratory technique, to identify the kinases involved in normal and dysregulated cell signaling in human tissue, such as during inflammation or cancer progression.

“I am most excited about being able to apply this to individual patients’ tumors and learn about the signaling states of cancer and heterogeneity of that signaling,” says Michael Yaffe, who is the David H. Koch Professor of Science at MIT, the director of the MIT Center for Precision Cancer Medicine, a member of MIT’s Koch Institute for Integrative Cancer Research, and a senior author of the new study. “This could reveal new druggable targets or novel combination therapies.”

The study, published in Nature , is the product of a long-standing collaboration with senior authors Lewis Cantley at Harvard Medical School and Dana-Farber Cancer Institute, Benjamin Turk at Yale School of Medicine, and Jared Johnson at Weill Cornell Medical College.

The paper’s lead authors are Tomer Yaron-Barir at Columbia University Irving Medical Center, and MIT’s Brian Joughin, with contributions from Kontstantin Krismer, Mina Takegami, and Pau Creixell.

Kinase kingdom

Human cells are governed by a network of diverse protein kinases that alter the properties of other proteins by adding or removing chemical compounds called phosphate groups. Phosphate groups are small but powerful: When attached to proteins, they can turn proteins on or off, or even dramatically change their function. Identifying which of the almost 400 human kinases phosphorylate a specific protein at a particular site on the protein was traditionally a lengthy, laborious process.

Beginning in the mid 1990s, the Cantley laboratory developed a method using a library of small peptides to identify the optimal amino acid sequence — called a motif, similar to a scannable barcode — that a kinase targets on its substrate proteins for the addition of a phosphate group. Over the ensuing years, Yaffe, Turk, and Johnson, all of whom spent time as postdocs in the Cantley lab, made seminal advancements in the technique, increasing its throughput, accuracy, and utility.

Johnson led a massive experimental effort exposing batches of kinases to these peptide libraries and observed which kinases phosphorylated which subsets of peptides. In a corresponding Nature  paper published in January 2023, the team mapped more than 300 serine/threonine kinases, the other main type of protein kinase, to their motifs. In the current paper, they complete the human “kinome” by successfully mapping 93 tyrosine kinases to their corresponding motifs.

Next, by creating and using advanced computational tools, Yaron-Barir, Krismer, Joughin, Takegami, and Yaffe tested whether the results were predictive of real proteins, and whether the results might reveal unknown signaling events in normal and cancer cells. By analyzing phosphoproteomic data from mass spectrometry to reveal phosphorylation patterns in cells, their atlas accurately predicted tyrosine kinase activity in previously studied cell signaling pathways.

For example, using recently published phosphoproteomic data of human lung cancer cells treated with two targeted drugs, the atlas identified that treatment with erlotinib, a known inhibitor of the protein EGFR, downregulated sites matching a motif for EGFR. Treatment with afatinib, a known HER2 inhibitor, downregulated sites matching the HER2 motif. Unexpectedly, afatinib treatment also upregulated the motif for the tyrosine kinase MET, a finding that helps explain patient data linking MET activity to afatinib drug resistance.

Actionable results

There are two key ways researchers can use the new atlas. First, for a protein of interest that is being phosphorylated, the atlas can be used to narrow down hundreds of kinases to a short list of candidates likely to be involved. “The predictions that come from using this will still need to be validated experimentally, but it’s a huge step forward in making clear predictions that can be tested,” says Yaffe.

Second, the atlas makes phosphoproteomic data more useful and actionable. In the past, researchers might gather phosphoproteomic data from a tissue sample, but it was difficult to know what that data was saying or how to best use it to guide next steps in research. Now, that data can be used to predict which kinases are upregulated or downregulated and therefore which cellular signaling pathways are active or not.

“We now have a new tool now to interpret those large datasets, a Rosetta Stone for phosphoproteomics,” says Yaffe. “It is going to be particularly helpful for turning this type of disease data into actionable items.”

In the context of cancer, phosophoproteomic data from a patient’s tumor biopsy could be used to help doctors quickly identify which kinases and cell signaling pathways are involved in cancer expansion or drug resistance, then use that knowledge to target those pathways with appropriate drug therapy or combination therapy.

Yaffe’s lab and their colleagues at the National Institutes of Health are now using the atlas to seek out new insights into difficult cancers, including appendiceal cancer and neuroendocrine tumors. While many cancers have been shown to have a strong genetic component, such as the genes BRCA1 and BRCA2 in breast cancer, other cancers are not associated with any known genetic cause. “We’re using this atlas to interrogate these tumors that don’t seem to have a clear genetic driver to see if we can identify kinases that are driving cancer progression,” he says.

Biological insights

In addition to completing the human kinase atlas, the team made two biological discoveries in their recent study. First, they identified three main classes of phosphorylation motifs, or barcodes, for tyrosine kinases. The first class is motifs that map to multiple kinases, suggesting that numerous signaling pathways converge to phosphorylate a protein boasting that motif. The second class is motifs with a one-to-one match between motif and kinase, in which only a specific kinase will activate a protein with that motif. This came as a partial surprise, as tyrosine kinases have been thought to have minimal specificity by some in the field.

The final class includes motifs for which there is no clear match to one of the 78 classical tyrosine kinases. This class includes motifs that match to 15 atypical tyrosine kinases known to also phosphorylate serine or threonine residues. “This means that there’s a subset of kinases that we didn’t recognize that are actually playing an important role,” says Yaffe. It also indicates there may be other mechanisms besides motifs alone that affect how a kinase interacts with a protein.

The team also discovered that tyrosine kinase motifs are tightly conserved between humans and the worm species C. elegans,  despite the species being separated by more than 600 million years of evolution. In other words, a worm kinase and its human homologue are phosphorylating essentially the same motif. That sequence preservation suggests that tyrosine kinases are highly critical to signaling pathways in all multicellular organisms, and any small change would be harmful to an organism.

The research was funded by the Charles and Marjorie Holloway Foundation, the MIT Center for Precision Cancer Medicine, the Koch Institute Frontier Research Program via L. Scott Ritterbush, the Leukemia and Lymphoma Society, the National Institutes of Health, Cancer Research UK, the Brain Tumour Charity, and the Koch Institute Support (core) grant from the National Cancer Institute.

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