cell phone addiction research paper

• Research article
• Open access
• Published: 11 September 2020

The relationship of smartphone addiction with psychological distress and neuroticism among university medical students

• Leonard Yik-Chuan Lei 1 ,
• Muhd Al-Aarifin Ismail   ORCID: orcid.org/0000-0001-5117-0489 1 ,
• Jamilah Al-Muhammady Mohammad 1 &
• Muhamad Saiful Bahri Yusoff 1  

BMC Psychology volume  8 , Article number:  97 ( 2020 ) Cite this article

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Smartphone plays a vital role in higher education as it serves as a device with multiple functions. Smartphone addiction was reported on the rise among college and university students. The addiction may result in unwanted consequences on their academic performance and psychological health. One factor that consistently relates to psychological distress and smartphone addiction is the neurotic personality trait. This study explored the relationship of smartphone addiction with psychological health and neuroticism among USM medical students.

A cross-sectional study was carried out on medical students in a public medical school. DASS-21, the neuroticism-subscale of USMaP-i and SAS-SV were administered to measure psychological distress, neuroticism, and smartphone addiction of the medical students. Spearman correlation was performed to examine the correlation between smartphone addiction with psychological distress and neuroticism. Simple linear regression was performed to investigate relationship factors of smartphone addiction.

A total of 574 medical students participated in this study. The prevalence of smartphone addiction was 40.6%. It was higher among male (49.2%) compared to female (36.6%) medical students. The result showed a fair positive correlation between smartphone addiction and psychological health (rdepression = 0.277, p -value < 0.001; ranxiety = 0.312, p-value < 0.001; rstress = 0.329, p-value < 0.001). However, there was a poor positive correlation between smartphone addiction and neuroticism (r = 0.173, p -value < 0.001). The simple linear regression showed a significant increase in the levels of depression, anxiety, stress and neuroticism upon one unit increase in smartphone addiction (bdepression = 0.101, p -value < 0.001; banxiety = 0.120, p-value < 0.001; bstress = 0.132, p-value < 0.001; bneuroticism = 0.404, p-value < 0.05). These results indicated significant relationships between smartphone addiction, psychological health and neuroticism.

This study suggested a high prevalence of smartphone addiction among medical students, particularly in male medical students. The smartphone addiction might lead to psychological problems and the most vulnerable group is the medical student with the neurotic personality trait.

Peer Review reports

Smartphones possess the means to enrich learning activities from medical education in undergraduate and postgraduate training [ 1 ]. For example, smartphones are used in the pursuit of finding solutions to patient care, improving lifelong medical education, and professional partners through the use of social media [ 2 ]. With the advent of smartphones, its uses in higher education cannot be ignored and need to be examined to explore the consequences of its application. Smartphones can be defined as a hand-held device built as a mobile computing platform with advanced computing ability and connectivity. It serves to combine the functions of portable media players, low-end compact digital cameras, pocket video cameras, and GPS navigation units [ 3 ]. Furthermore, smartphones are being used for more than just a phone but rather a device that provides multiple functions including surfing the internet, email, navigation, social networking, and games [ 4 ].

Smartphones are gaining increasing importance in health care and researchers and developers are enticed with their applications related to health [ 5 ]. These devices have multiple features that can be positively employed which include speedy access to information, enhanced organization, and instantaneous communication [ 6 ] and can with certainty be used to enhance education [ 7 ]. However, smartphone addiction is a vital issue in the global population with problems comprising physical difficulties like muscular pain and eye illnesses, and psychological difficulties such as auditory and tactile delusion [ 8 ].

The use of smartphones reached figures over 50% in the majority of developed countries [ 9 ]. Malaysian Communications and Multimedia Commission (MCMC) has reported that in Malaysia, 24.5 million users have access to the internet in 2016 [ 10 ]. Smartphone stays as the most popular gadget for users to enter the internet (89.4%) creating a mobile-oriented country [ 10 ]. Over the past several years, there has been an increasing amount of studies that explored smartphone addiction [ 11 , 12 , 13 , 14 ]. The bulk of these studies focused on smartphone addiction and its potential influences on individuals [ 15 ]. On top of that, among Malaysian medical schools, two studies showed the prevalence of at-risk cases of developing smartphone addiction: 46.9% in Universiti Putra Malaysia (UPM) [ 16 ] and 52.2% in Universiti Teknologi MARA (UiTM) [ 17 ]. Several studies have reported a high prevalence of smartphone addiction: the prevalence of smartphone addiction in a Malaysian medical school was 46.9% [ 16 ], Saudi Arabian university students was 48% [ 18 ], Saudi Arabian dental students was 71.9% [ 19 ] and Indian medical students was 85.4% [ 20 ]. Conversely, some studies have reported a low prevalence of smartphone addiction: the prevalence of smartphone addiction in Chinese medical college students was 29.8% [ 4 ], Saudi Arabian students was 33.2% [ 21 ] and Saudi Arabian medical students was 36.5% [ 22 ]. These results suggested more than a quarter of students in higher education experienced smartphone addiction that requires further exploration of possible factors contributing to it as well as its consequences on students’ wellbeing.

Previous studies have shown links between smartphone addiction and depression [ 23 , 24 , 25 ], smartphone addiction and anxiety [ 26 , 27 , 28 , 29 ], smartphone addiction and stress [ 11 , 30 ], smartphone addiction and neuroticism [ 27 , 31 ]. However, these studies are in the general population and university students. Our study is looking at the specific population of medical students in a public medical school. Also, to the best of our knowledge, there are no studies in medical students that links smartphone addiction, psychological distress, and neurotic traits. Medical students use smartphones to facilitate retrieving information resources [ 32 , 33 , 34 ]. There is almost universal ownership of smartphones among medical students [ 35 ]. The applications on smartphones in medical education have reported to increase student involvement, improve the feedback process, and enhance communication between student and teacher [ 36 , 37 , 38 , 39 ]. Instant messaging smartphone applications such as WhatsApp can be used as a method to facilitate communication and education among groups of medical students [ 40 ]. It is important to know the effect of the increased use of smartphones in relation to psychological distress among medical students.

The study of smartphone addiction among medical students become vital as it enlightens us on smartphone usage. In Malaysia, there have been numerous studies on smartphone addiction [ 41 , 42 ]. However, there are not many studies done on smartphone addiction and its effects on medical undergraduates. Individuals measured with the personality trait that is high in neuroticism may be predisposed to addiction and behavioural problems [ 43 ]. Thus, we also decided to include the personality trait neuroticism as it is linked to addiction [ 44 , 45 ], and we are interested in studying the relationship between neuroticism and smartphone addiction. Neuroticism is present among medical students [ 46 ]. Medical students with neurotic tendencies behave negatively to academic stress, and this becomes a contributing factor to low academic performance [ 46 ]. Students with neuroticism are more vulnerable to smartphone addiction which can lead to psychological distress. The current research may play a role in developing intervention measures such as behavioral therapies and counseling. It also may serve to help medical students improve their awareness of their emotionality and its effect on smartphone use. Therefore, the primary focus of this study is to investigate the relationship of smartphone addiction with psychological distress and neuroticism among medical students.

Research hypothesis

The prevalence of smartphone addiction among USM medical students is more than 40%.

There is a significant correlation between smartphone addiction and depression among USM medical students.

There is a significant correlation between smartphone addiction and anxiety among USM medical students.

There is a significant correlation between smartphone addiction and stress among USM medical students.

There is a significant correlation between smartphone addiction and neuroticism among USM medical students.

Depression, anxiety, stress, and neuroticism significantly predict the smartphone addiction level.

Study sample

A cross-sectional study was carried out on year 1 to year 5 medical students in a public medical school in Malaysia. Before handing out the questionnaires, all students were informed about the study and their participation was voluntary. An informed consent to participate and publication was obtained from all participants. Bias was not explored in this study.

Sampling method and calculation

The sample size was determined by the single proportion size formula. The initial sample size calculation was based on a pilot study that involved students and staff [ 17 ] and the largest sample size needed was 384. After taking into account, 30% drop-out rate, the total number of sample size was 384/ (1–0.3) = 548. All medical students were included, those who did not sign the consent form were excluded from the study.

Data collection tools

There were three psychometric instruments used in this study; 1) Depression Anxiety Stress Scales (DASS-21); 2) modified USM Personality Inventory (USMaP-i); and 3) Smartphone Addiction Scale – Short Version (SAS-SV).

The DASS instrument was first introduced by Lovibond and Lovibond (1995) and delivered a self-reporting measure, which was created to evaluate the features associated to anxiety, stress, and depression. For each DASS-21 subscale, the score must be multiplied by two to simulate the DASS-42 version: the range of score from 0 to 42. A high score in each subscale is equal to a high degree of symptoms [ 47 ]. In a validation study done, the internal consistency of each subscale was high ranging from 0.70 for the stress sub-scale to 0.88 for the overall scale. The scores on each of the three subscales and the combinations of two or three of them were able to identify mental disorders of depression and anxiety in women with a sensitivity of 79.1% and a specificity of 77% at the optimal cut off of > 33 [ 23 ].

Modified USMaP-i

The USM Personality Inventory was created to measure the Big-Five Personality traits and is identified to be a reliable and valid instrument to evaluate the personality traits of prospective medical students [ 48 ]. This inventory was created explicitly to identify personal traits of Malaysian candidates who seek to apply to the medical course in USM [ 48 ]. The 15-item version of USMaP-i showed an acceptable level of internal consistency with each personality domain ranged from 0.64 and 0.84 as reported on the International Personality Item Pool Website [ 49 ]. We selected the 3 items that represent neuroticism to assess the neuroticism personality trait. Neuroticism is usually associated with features like depression, distress, anxiety, moodiness, poor coping ability, and sadness [ 50 ]. The total Cronbach alpha for the neuroticism subscale for USMaP-I is 0.722 [ 26 , 49 ].

The smartphone addiction scale was first developed and validated by Kwon, Lee et al. (2013) as a way to evaluate smartphone addiction in teenagers. This scale has shown to be validated with high psychometric sound properties in various countries [ 3 , 12 , 16 , 27 , 51 ]. The Smartphone Addiction Scale - Short Version (SAS-SV) is a validated scale that consists of ten items in the questionnaire that are used to measure the levels of smartphone addiction [ 3 ]. The total score is from 10 to 60. The coefficient for Cronbach alpha correlation obtained is 0.91 for Smartphone Addiction Short Version [ 3 ]. The strength of SAS-SV is that it can be used to discern a potentially high-risk group for smartphone addiction, both in the educational field and community [ 3 ]. The cut-off point for significant smartphone addiction for male is 31 and female is 33 based on the recommendation by Kwon et al. (2013).

Data collection

Data collection was performed via a self-guided questionnaire. Individuals were screened for one inclusion criteria and one exclusion criteria. Individuals who were medical students were eligible to participate (inclusion criteria). Individuals who were not willing to participate were not included in the study (exclusion criteria). Participants who submitted incomplete responses were excluded from this study.

Ethical consideration

Ethical clearance was obtained from the Human Research Ethics Committee of USM (JEPeM) with study protocol code (USM/JEPeM/18070352). Signed consents were taken from medical students. Instructions and information about this study were given to them. Each medical student was given an ID for tracing and profiling purposes. They were informed that the results of this study will not affect their academic results in any way. The questionnaires were distributed to all medical students after lecture sessions.

Statistical analysis

The data was analyzed using Statistical Package for Social Sciences (SPSS) version 24. Spearman correlation and simple linear regression tests were performed to examine the relationships of smartphone addiction with psychological distress and neuroticism. To accurately represent the relationship of smartphone addiction and neuroticism, the items in the modified USMaP-i were recoded due to negative items present in the neuroticism subscale: (Question 6 + Question 10 + Question 14). In the regression analysis, depression, anxiety, stress and neuroticism are independent variables and smartphone addiction is the dependent variable. A single regression analysis is used to account for the effects of multicollinearity because the correlation coefficient values of stress, depression, anxiety and neurotic tendencies are large. This research was not designed to investigate the gender differences and its correlations with depression, anxiety, stress and neuroticism. Further gender issues are not within the scope of this study.

Response rate

The survey’s response rate was 83.9% (574 out of 674). There was a higher proportion of female medical students (68.5%) than male medical students (31.5%). Malay students were the majority (65.3%) followed by Chinese (16%), Indian (15.5%) and other races (3.1%). The majority of students were between 19 and 23 years old. The proportion of students in each year of study was more or less similar or equal in numbers. Medical students that did not participate in the survey for reasons of lack of interest, time constraints, and fatigue.

In this study, the prevalence of smartphone addiction found was 40.6%. There is a higher prevalence of male students addicted to smartphone (49.2%) compared to the female students (36.6%). The results of this analysis can be seen in Table  1 .

Correlation of smartphone addiction, psychological distress and neuroticism

The correlation analysis for smartphone addiction with psychological health and neuroticism is shown in Table  2 . The result showed a fair positive correlation between smartphone addiction and psychological health among USM medical students (rdepression = 0.277, p -value < 0.001; ranxiety = 0.312, p-value < 0.001; rstress = 0.329, p-value < 0.001). However, there was a poor positive correlation between smartphone addiction and neuroticism (r = 0.173, p -value < 0.001).

Assumption was not met as normality of distribution was violated.

Linear regression of smartphone addiction, psychological distress and neuroticism

The regression analysis for smartphone addiction with psychological health and neuroticism is shown in Table  3 . The simple linear regression study showed a significant increase in depression, anxiety, stress and neuroticism levels upon one unit increase in smartphone addiction (bdepression = 0.101, p -value < 0.001; banxiety = 0.120, p-value < 0.001; bstress = 0.132, p -value < 0.001; bneuroticism = 0.404, p-value < 0.05). These results indicated significant relationships between smartphone addiction, psychological health and neuroticism. Smartphone addiction is a significant relationship factor of depression, anxiety and stress, while neuroticism is a significant relationship factor of smartphone addiction.

Prevalence of smartphone addiction

The prevalence of smartphone addiction among USM medical students was 40.6%; hypothesis 1 assumes the prevalence of smartphone addiction among USM medical students is more than 40%. This study reported that there is a higher prevalence of smartphone addiction among male medical students compared to female medical students, which is similar to a few other studies [ 4 , 52 , 53 ]. Other studies found a higher prevalence of smartphone addiction in females compared to males [ 3 , 12 , 22 , 54 , 55 ]. Interestingly, previous studies [ 24 , 25 ] did not report that gender is associated with smartphone addiction. The high prevalence of smartphone addiction (40.6%) in this study may be explained by smartphones becoming the main communication device among Malaysians and elsewhere. The percentage of smartphone consumers gradually rose from 68.7% in 2016 to 75.9% in 2017 [ 10 ]. Another observation is that medical students are using smartphones for social media messaging services such as WhatsApp and WeChat for communication purposes as well as for their studies, hence smartphones are becoming a vital tool in personal and professional life [ 56 ]. A study reported that WhatsApp assisted in easy learning and provided a way for clear communication of knowledge in shorter periods [ 57 ]. The higher prevalence of smartphone addiction in male medical students may be due to male medical students using their smartphone more for their entertainment such as online games while females use their smartphones for social interactions [ 58 , 59 , 60 ].

Relationship between smartphone addiction and depression

This study found a significant and fair positive correlation (r = 0.277) between smartphone addiction and depression, as in research hypothesis 2. Likewise, previous studies among adults reported a strong positive correlation between smartphone addiction and depression [ 28 ]. Other findings further support the fact that high levels of smartphone addiction were correlated with depression [ 29 ]. In the Malaysian context, a study among university students showed students who had high scores of smartphone addiction reported high scores of depression [ 61 ] that suggests a relationship between smartphone addiction and depression. Another study found that the group with high smartphone use showed greater levels of depression compared to the low smartphone use group among university students in Turkey [ 12 ]. However, other studies have found no relationship between smartphone addiction and depression [ 62 ]. These facts consistently suggested a positive correlation between smartphone addiction and depression.

The regression analysis showed the increase of smartphone addiction scores leads to the increase of depression scores, indicating it is a relationship factor. These results are similar to previous findings, in which smartphone addiction was reported to be found as a predictor of depression for undergraduates in a local Malaysian university [ 61 ]. Another study also supports this finding, in which it reported the severity of smartphone use predicted depression [ 12 ]. Conversely, previous studies reported vice-versa whereby depression predicted smartphone addiction among university students [ 25 , 30 , 63 ]. These facts suggested that smartphone uses among university students should be considered as high-risk behaviour that negatively affects their psychological health. There are several possible explanations for our results. Individuals with mood disorders are more prone to become a smartphone addict [ 64 ]. Lemola et.al (2015) reported that using electronic media at night is associated with sleep disturbances and depressive symptoms. One study stated that individuals with lower levels of self-perceived health conditions and emotions tended to display an excessive use of smartphones [ 65 ]. This suggested that individuals were in a constant cycle of attempting to compensate for their perceived health status, without being fully aware that smartphone addiction has undesirable implications to their physical, emotional, and social well-being.

The relationship between smartphone addiction and depression is evident in this study and shows that medical students that have smartphone addiction are at risk of having depression. Medical students displaying high levels of smartphone addiction and depression should be observed and given help if necessary. This can be done by promoting the responsible usage of smartphone use among medical students in activities. Sensible usage of smartphones is suggested, especially on younger adults who could be at greater risk of depression [ 28 ].

Relationship between smartphone addiction and anxiety

The results of our study indicate that there was a fair positive correlation between (r = 0.312) smartphone addiction and anxiety, as in research hypothesis 3. Demirci et, al. (2015) has found that smartphone use severity was positively correlated with anxiety and that corresponds with the findings in our study. Several other studies describe smartphone addictions are reported to increase with anxiety levels [ 54 , 55 , 66 , 67 ].

Our regression analysis revealed that increased smartphone addiction scores are a significant relationship factor in increased anxiety scores. Demirci et, al. (2015) reported that smartphone use severity predicted anxiety and it is consistent with our findings. A study reported that smartphone addiction was reported to be found as a predictor of anxiety in Malaysian undergraduate students [ 61 ]. In contrast, previous studies reported that anxiety significantly predicted smartphone addiction [ 25 , 31 , 63 ].

A possible explanation for our results is medical students may habitually check their smartphones in the likelihood of reducing their anxiety by receiving assurance through messages from their friends. The pattern of an individual checking his or her phone and receiving notifications also function in getting social reassurance behaviour from friends [ 68 ]. This behaviour of seeking reassurance can generally include symptoms of loneliness, depression, and anxiety that is the driving factor for reassurance seeking [ 68 ].

Relationship between smartphone addiction and stress

The results of this study indicate that there was a fair positive correlation between (r = 0.329) smartphone addiction and stress, as in research hypothesis 4. In another important finding, Samaha et, al. (2016) show the results between the risk of smartphone and perceived stress, reporting a slight positive correlation with an elevated risk of smartphone addiction associated with elevated levels of perceived stress which supports our study. Previous studies reported that stress leads to smartphone use [ 56 , 69 ], while another study proposes that smartphone use may cause stress [ 70 ]. In our regression analysis, increased smartphone addiction scores are a significant relationship factor in increased stress scores. Conversely, in a sample of Taiwanese university students reported a positive predictive effect of family and emotional stresses on smartphone addiction [ 11 ].

There are several explanations for the study results. Medical students are under stressful medical training [ 71 ], therefore they are prone to being under stress which in turn lowers self-control which may increase their chances of smartphone addiction. Smartphone addiction is influenced by self-control [ 72 ]. Self-control is defined as the capacity to alter one’s responses, such as overriding some impulses to bring behavior in line with goals and standards [ 73 ]. According to Cho et, al. (2017), an increase in stress degree results in a lowered self-control ability, and reduction in self-control further increases the chances of smartphone addiction.

Relationship between smartphone addiction and neuroticism

In this study, the results indicate that there is a poor positive correlation between (r = 0.173) smartphone addiction and neuroticism, as in research hypothesis 5. Neuroticism was reported to be significantly related to excessive use of smartphones [ 55 ] and corresponded with the findings of our study. These results are similar to other study findings reported that individuals who possessed high levels of neuroticism also report a high level of smartphone addiction [ 74 ]. In another study, neuroticism predicts problematic smartphone use [ 75 ]. However, the study [ 76 ] did not report a significant relationship between neuroticism and problematic phone use. In our regression analysis, increased neuroticism scores are a significant relationship factor in increased smartphone addiction scores. It was found neurotic personality increased the degree of smartphone addiction [ 55 ]. Problematic mobile use is positively associated with neuroticism [ 77 ]. There are several explanations for this result, medical students may be more vulnerable to smartphone addiction. The neuroticism trait has been linked to smartphone addiction [ 78 , 79 ]. A study stated that neuroticism is associated with a chain-mediating effect with smartphone addiction and depression, all vital variables that deteriorate the quality of life [ 80 ]. Apart from that, another study showed that there is a positive relationship between neuroticism and smartphone use while driving [ 81 ]. Another possible explanation is that medical students with neuroticism may depend on their smartphones to get reassurance from their friends. Individuals with high neuroticism tend to use their smartphones to get emotional and social reassurance from their relationships [ 82 ].

Implications and future research

Depression, anxiety, stress and neuroticism significantly predict smartphone addiction level, as in research hypothesis 6. The results show that there is a high prevalence of smartphone addiction among medical students. This means that a large proportion of students are affected by smartphone over usage and suggests a widespread occurrence that needs to be addressed by all relevant parties. It raises a deep concern because academic performances may be affected by a large number of medical students with smartphone addiction. As a consequence of smartphone addiction, individuals with smartphone addiction might meet with difficulties such as interpersonal adjustments, managing time, and academic performance [ 83 ]. This might affect the performance of the medical school as a whole in terms of academic results. The high prevalence of smartphone addiction in this study shows that they are at risk to have problems. Medical students displaying high levels of smartphone addiction should be monitored and given further help. Prevention is better than cure, thus smartphone addiction among medical students is recommended for early detection so that appropriate interventions can be planned accordingly. We also have to take into account the high prevalence of male medical students. Several approaches can be suggested to medical students who require further help for smartphone addiction; namely cognitive-behavioral approach, motivational interviewing, and behavioural cognitive treatment [ 84 ]. The implications for the intervention from the results of the study are to provide a baseline for research incorporating approaches tailored for medical students with smartphone addiction. This should address the most vulnerable group of students with the neuroticism personality trait.

Limitations and future research

Considering limited undergraduate smartphone addiction studies in the local setting, the results reported in this study provide insights into the professional health care team. It should be noted that smartphone usage is culturally bound experience and will contrast across countries with varying degrees of technology availability and advances in that region. This study does not report cause and effect relationships. Confounding variables were not studied. For example, in the curriculum at the medical school, each student has to go through e-learning (teaching and learning activities) and assessment that requires smartphone use. This suggests that in reality the medical students may be tasked with activities that require them to use their smartphones for education purposes, i.e. hours spent on smartphones for assignments and lectures. Further research can build upon our findings and investigate screening and interventions for smartphone addiction among medical students.

Conclusions

This study found the prevalence of smartphone addiction among medical students was high, particularly in male medical students. The smartphone addiction might lead to psychological problems and the most vulnerable group was students with the neuroticism personality trait. Thus, there is a need to create and implement programs to promote healthy smartphone usage to minimize the impact of smartphone addiction on psychological health. By doing so, one may implement effective intervention and prevention strategies to groups of students with smartphone addiction. We believe that with a proper guidance; students may be able to use their smartphones more responsibly.

Availability of data and materials

Data generated and analysed during the current study are not publicly available as individual privacy could be comprised; however, may be available from the corresponding author on reasonable request and with the permission of the medical school.

Abbreviations

Depression Anxiety Stress Scale

Human Research Ethics Committee of University Sains Malaysia

Malaysian Communication and Multimedia Commission

Smartphone Addiction Scale Short Version

School of Medical Sciences

Universiti Teknologi MARA

Universiti Putra Malaysia

Universiti Sains Malaysia

iUniversiti Sains Malaysia Personality Inventory

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Leonard Yik-Chuan Lei, Muhd Al-Aarifin Ismail, Jamilah Al-Muhammady Mohammad & Muhamad Saiful Bahri Yusoff

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LLYC, the first author, made significant contributions to the research design, data collection and interpretation, and writing of the manuscript. MAAI, JAMM and MSBY who have expertise in quantitative research, were involved in the research design and the collection and interpretation of the data. MSBY contributed to the collection and interpretation of the data and the writing of the manuscript. MAAI, JAMM also contributed significantly to the writing of the manuscript. All the authors have critically reviewed and approved the final draft and are responsible for the content of the manuscript.

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Ismail MA-A is a medical doctor and currently works as a Medical Educationist at the Medical Education Department, Universiti Sains Malaysia. He is coordinator for E-learning programme of the medical school. His research interest is in technology-enhanced teaching and learning and teaching evaluation.

Mohammad JA-M is a medical doctor and currently works as a Medical Educationist and Master in Science (Medical Education) Programme Coordinator at the Department of Medical Education, Universiti Sains Malaysia. Her research interests include mentoring, student assessment including formative assessment in teaching and learning.

Yusoff MSB is an Associate Professor and Head, Department of Medical Education, School of Medical Sciences USM. His research interest includes medical student wellbeing, assessment, validity, reliability, development of psychological measurement, stress management, medical student admission, feedback, emotional intelligence, and personality.

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Lei, L.YC., Ismail, M.AA., Mohammad, J.AM. et al. The relationship of smartphone addiction with psychological distress and neuroticism among university medical students. BMC Psychol 8 , 97 (2020). https://doi.org/10.1186/s40359-020-00466-6

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• Smartphone addiction
• Medical students
• Psychological distress
• Neuroticism

BMC Psychology

ISSN: 2050-7283

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Is smartphone addiction really an addiction?

Affiliation.

• 1 1 The Department of Psychology, Ramon Llull University , Barcelona, Spain.
• PMID: 29895183
• PMCID: PMC6174603
• DOI: 10.1556/2006.7.2018.49

Aims In light of the rise in research on technological addictions and smartphone addiction in particular, the aim of this paper was to review the relevant literature on the topic of smartphone addiction and determine whether this disorder exists or if it does not adequately satisfy the criteria for addiction. Methods We reviewed quantitative and qualitative studies on smartphone addiction and analyzed their methods and conclusions to make a determination on the suitability of the diagnosis "addiction" to excessive and problematic smartphone use. Results Although the majority of research in the field declares that smartphones are addictive or takes the existence of smartphone addiction as granted, we did not find sufficient support from the addiction perspective to confirm the existence of smartphone addiction at this time. The behaviors observed in the research could be better labeled as problematic or maladaptive smartphone use and their consequences do not meet the severity levels of those caused by addiction. Discussion and conclusions Addiction is a disorder with severe effects on physical and psychological health. A behavior may have a similar presentation as addiction in terms of excessive use, impulse control problems, and negative consequences, but that does not mean that it should be considered an addiction. We propose moving away from the addiction framework when studying technological behaviors and using other terms such as "problematic use" to describe them. We recommend that problematic technology use is to be studied in its sociocultural context with an increased focus on its compensatory functions, motivations, and gratifications.

Keywords: Internet; addiction; mobile phones; problematic use; smartphones; technology.

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• Behavior, Addictive / classification*
• Smartphone*

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ORIGINAL RESEARCH article

The association between smartphone addiction and sleep: a uk cross-sectional study of young adults.

• 1 Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
• 2 Population Policy Practice, National Institute of Health Research, Great Ormond Street Hospital Biomedical Research Centre Institute of Child Health, University College London, London, United Kingdom
• 3 Addictions Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
• 4 South London and Maudsley NHS Foundation Trust, Beckenham, United Kingdom

Background: In a large UK study we investigated the relationship between smartphone addiction and sleep quality in a young adult population.

Methods: We undertook a large UK cross-sectional observational study of 1,043 participants aged 18 to 30 between January 21st and February 30th 2019. Participants completed the Smartphone Addiction Scale Short Version, an adapted Pittsburgh Sleep Quality Score Index and reported smartphone use reduction strategies using both in-person ( n = 968) and online ( n = 75) questionnaires. A crude and adjusted logistic regression was fitted to assess risk factors for smartphone addiction, and the association between smartphone addiction and poor sleep.

Results: One thousand seventy one questionnaires were returned, of which 1,043 participants were included, with median age 21.1 [interquartile range (IQR) 19–22]. Seven hundred and sixty three (73.2%) were female, and 406 reported smartphone addiction (38.9%). A large proportion of participants disclosed poor sleep (61.6%), and in those with smartphone addiction, 68.7% had poor sleep quality, compared to 57.1% of those without. Smartphone addiction was associated with poor sleep (aOR = 1.41, 95%CI: 1.06–1.87, p = 0.018).

Conclusions: Using a validated instrument, 39% young adults reported smartphone addiction. Smartphone addiction was associated with poor sleep, independent of duration of usage, indicating that length of time should not be used as a proxy for harmful usage.

Introduction

Smartphone use has become ubiquitous over the past decade. This has been accompanied by growing concerns around excessive and potentially harmful use ( 1 ). There are emerging reports of problematic behavior patterns in relation to smartphone use which mirror those of addiction ( 2 ). Although smartphone addiction is not formally recognized as a clinical diagnosis, it is a subject of active research. Validated instruments have been developed to characterize problematic smartphone use in terms of recognized dimensions of behavioral addiction with scores above which the research subject is considered to report smartphone addiction ( 3 , 4 ). Research subjects reporting smartphone addiction describe a decreased interest in face-to-face relationships, use despite knowledge of the negative consequences, impaired control over and pre-occupation with their devices, and anxiety when their phones are inaccessible; these are not unlike the symptom domains of substance use disorders or other behavioral addictions ( 2 , 5 – 7 ). Studies have highlighted associations of smartphone addiction with reduced productivity and with lower academic attainment ( 8 , 9 ), demonstrating the negative functional impact on young people's lives and future prospects. Indirect harms resulting from smartphone addiction include propensity for accidents, for example through use whilst driving, and potential contribution to the obesity crisis by facilitating sedentary lifestyles ( 10 , 11 ). Although at an early stage, there is also some neuroimaging evidence of volume and activity parallels between smartphone addiction and other addictions ( 12 ).

Studies to date have used the length of smartphone usage (measured as the total daily length of smartphone use) as an exposure indicative of problematic usage ( 13 ). However, while it is true that heavy use is seen in people with any addiction, it is also true that this is not sufficient for an addiction to be present, reflected in the ICD-11 criteria for gaming and gambling disorders ( 6 ) and in proposed diagnostic criteria for smartphone addiction ( 7 ). For an addiction to be present, subjective distress and functional impairment must also be present – in the case of smartphone addiction, neglect of other meaningful activities, pre-occupation with the phone, distress when access to the phone is not possible, and continued use despite evidence of harm. Measuring duration of use is an inexact proxy for addiction, as some people may experience the features of addiction with lower duration of use while others may use their phone in an adaptive way for long periods of time (for example, answering work emails during a long commute) but be able to put the phone down without distress and attend to appropriate activities such as communicating with family members, or going to bed on time ( 14 ).

It is important to note that smartphone addiction has several terms of reference including “nomophobia,” “problematic smartphone use,” and “smartphone dependence.” There is also a lack of consensus around whether putative “smartphone addiction” represents a distinct clinical identity and meets the criteria to be formally considered a behavioral addiction ( 15 , 16 ). Furthermore, it remains unclear whether this dependence is on the smartphone itself or on the apps available through the device; whether the phone itself is like a substance of abuse or more like the needle through which addictive apps are delivered ( 14 ). There are similar patterns of behavior associated with specific applications (e.g., Facebook addiction, Instagram addiction) that are being investigated in their own right, and it is possible that certain types of phone use (e.g., social media use) may have more addictive implications than others (e.g., calling, texting), as the former involves display and expectation of approval through the creation, sharing, and viewing of content, while the latter replicates face-to-face relationships in terms of one-to-one communication ( 17 , 18 ). Nevertheless, there is evidence of the existence of a behavioral phenotype that resembles addiction. The physical harms highlighted above, as well as emerging associations with psychiatric symptoms such anxiety and depressed mood indicate a pressing need to further investigate this growing phenomenon ( 19 ).

While the negative effects of screen time on sleep have been previously reported, smartphones are portable, hand-held devices that have much higher potential of interrupting sleep quality or quantity ( 20 ). Problematic smartphone use has been consistently linked to poor sleep in previous studies ( 4 , 21 ), and smartphone overuse has been associated with daytime tiredness, longer sleep latency, and reduced sleep duration ( 22 – 24 ). In particular, smartphone use close to sleep initiation has been shown to delay circadian rhythm and found associated with total sleep time, where longer usage was associated with poor sleep ( 25 ). Furthermore, poor sleep outcomes may mediate the relationships between smartphone addiction and psychopathological symptoms ( 26 ). However, despite consistent advice from health bodies concerning the negative impacts of smartphone use on sleep, the majority of adults in the UK use their phones during the night and close to bed time ( 27 ).

A recent international systematic review found that the prevalence of smartphone addiction was around 25% in teenagers and young people ( 4 ). The weight of this evidence was from South and East Asia, and it has been noted that levels of smartphone addiction are often higher in Asian samples than in Western populations, possibly reflecting cultural practices around internet and smartphone use ( 28 , 29 ). This study includes the largest UK sample to date to investigate the prevalence of smartphone addiction, and to clarify the association between smartphone addiction and sleep outcomes, in this population.

Study Design and Study Population

Participants were recruited opportunistically across multiple campuses at King's College London, England, between January 21st and February 4th, 2019. Participants were approached by researchers to describe the study, and invited to complete a paper-based case report form (CRF) based at four separate locations during the stated data collection period. Additionally, participants were invited to complete an identical online version of the CRF through an internal research recruitment process. Eligibility criteria included students at King's College London aged between 18 and 30 who owned a smartphone. Participants were excluded if they did not adequately complete the Smartphone Addiction Scale – Short version [SAS-SV [3]] or the adapted Pittsburgh Sleep Quality Index [PSQI ( 30 )]. The study was undertaken in accordance with the Declaration of Helsinki. Ethical approval was received from the King's College Research Ethics Office (Study ID: 9138; MRS-18/19-9138) and the full protocol is available on request. All face to face participants provided informed verbal consent prior to involvement and those submitting online gave consent by responding to the questionnaire.

The case report form (CRF) was co-developed amongst researchers, teenagers and young people with experience of smartphone use ( Supplementary Table 1 ). The CRF included demographic information, smartphone use characteristics, a validated scale for smartphone addiction [SAS-SV ( 3 )], an adapted sleep score based on the Pittsburgh Sleep Quality Instrument [PSQI ( 30 )], and a range of reduction strategies. To reduce perception bias, the CRF included neutral non-directive phrasing about smartphone use collecting both positive and negative aspects.

Smartphone Use Characteristics

Participants were asked about the quantity of smartphone use (the average length of daily time) and the timing of use.

Smartphone Addiction Scale – Short Version (SAS-SV)

The SAS-SV is a 10-question validated scale that was developed to assess smartphone addiction in children (mean age of 14.5) ( 3 ). Participants are asked to rate statements related to their smartphone use, such as “Using smartphone longer than intended” on a 6-point Likert scale, from “strongly disagree” (1) to “strongly agree” (6). The resulting total score is between 10 and 60, with higher totals indicating higher risk of smartphone addiction. Total scores of 31 and 33 were used as diagnostic thresholds for males and females respectively, in accordance with the original study which found strong internal consistency (Cronbach's alpha = 0.91, AUC = 0.96 for boys, AUC = 0.89 for girls). This scale has been widely used internationally and has been found to have similarly strong internal consistencies using the same thresholds for this study's age group ( 31 , 32 ).

Participants were asked to rate their subjective sleep quality on an average weeknight on a Likert scale of 1–10 and the number of hours they slept on an average weeknight on a Likert scale of <4 to 12, taking into account the expected average number of hours of sleep for adults, in order to assess sleep quality and duration. Participants were additionally asked the number of days a week they felt noticeably tired or fatigued during the day (0–7) and the number of nights a week where they felt it difficult to fall asleep (0–7) to measure daytime tiredness and sleep latency. Based on these responses, scores for each component were calculated, which were then combined to calculate a global sleep score, adapted from the Pittsburgh Sleep Quality Index [PSQI ( 30 )], where a score of ≤ 5 was considered good sleep ( 33 ).

Reduction Strategies

Commonly used strategies to reduce smartphone use included within the CRF were identified from the literature and through consultation with subject matter experts and young people ( Supplementary Table 2 ). Participants were asked to rate the effectiveness of any strategies employed from ineffective to very effective.

Sample Size Justification

Estimates of the prevalence of poor sleep prevalence are wide-ranging. At study conception it was estimated that 42% of participants without problematic smartphone usage would exhibit poor sleep ( 34 ), and this would increase to 55% in those exhibiting problematic smartphone usage ( 4 ). In order to detect this difference using an independent chi-squared test of proportions with 90% power and 5% significance, we would need to include 650 participants. Building on this, to account for 15% missing data, we would need to include at least 780 participants in total.

The primary outcome was the association between sleep quality and smartphone addiction. Secondary outcomes were to determine: the association of smartphone addiction with demographics and usage characteristics; and the impact of reduction strategies on mitigating the effect of smartphone addiction on sleep.

Statistical Analysis

Demographic and smartphone usage characteristics of the sample were summarized, comparing participants with good, and poor sleep. Crude logistic regression models were included, to assess poor sleep and demographic (site, sex, and age) and smartphone usage characteristics. An adjusted multivariable logistic regression was carried out fitting the demographic with important characteristics found from the crude univariable analyses.

Both crude odds ratio[s] (OR) and adjusted OR (aOR) were presented alongside their respective 95% confidence intervals (95%CI), and P -values (<0.05 considered statistically significant). SPSS Versions 25 and 26 (IBM Corp., Armonk, N.Y., USA) were used to input and analyse data.

To determine the association between smartphone addiction and the demographic and usage characteristics, a multivariable logistic model adjusting for the same covariates as for the primary outcome was created. Due to multi co-linearity total usage, latest time of use and usage cessation prior to sleep were not fitted within the same analysis models.

Missing Data and Population Under Investigation

Individuals with missing item data of no more than 30% of the SAS-SV, or the adapted sleep score were proportionally mean imputed ( 35 ). Due to the completeness of the data collected, a complete case analysis was used.

Role of the Funding Source

There was no funding source for this study. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Characteristics of the Participants

We received 1,071 completed CRFs, of which 1,043 participants were eligible and included (completion rate 97.8%). The 28 excluded participants were ineligible due to age, or non-completion of the SAS-SV or items from the PSQI score. Of those included, 38 of the SAS-SV score, and 85 of the adapted PSQI had a single item of each domain missing and were (within participant) domain mean-imputed.

The mean age of the included participants was 21.1 (IQR 19–22, range 18–30), where 92.1% ( n = 961) were aged under 26, and 73.2% ( n = 763) of the participants were female ( Table 1 ). In terms of smartphone usage, 23.7% ( n = 247) used their phones for 3 h per day, while 18.5% ( n = 193) used their phones for more than 5 h daily. A large proportion of the young adult population exhibited poor sleep quality (61.6%, n = 643). Of those exhibiting smartphone addiction, 68.7% ( n = 279) had poor sleep quality compared to 57.1% ( n = 364) of those not exhibiting smartphone addiction. Of those participants that ceased smartphone use with 30 min of initiating sleep, 61.4% ( n = 478) had poor sleep, compared to 53.6% ( n = 45) of those who ceased use more than 1 h prior to initiating sleep.

Table 1 . The included population sociodemographic and phone use characteristics.

Prevalence of Smartphone Addiction, Sociodemographic Characteristics and Smartphone Usage

The overall prevalence of smartphone addiction was 38.9% (95%CI: 35.9–41.9%; n = 406 / 1,043). This includes 35.7% of males who were addicted and 40.1% of females ( Table 3 ). For participants aged under 21 years, 42.2% exhibited smartphone addiction, compared to 34.2 and 28.0% of participants aged 22–25 years, and over 26 years, respectively. Of participants who used their smartphone for 2 or less hours per day, 20.3% were addicted, compared to 53.9% of those who used it for more than 5 h. Of those that stopped using their device more than an hour before bedtime, 23.8% exhibited addiction, compared to 42.0% of those stopping <30 min before bedtime ( Table 3 ).

Primary Outcome of Poor Sleep Associated With Smartphone Usage

We assessed demographic factors', phone usage characteristics', and reduction strategies' associations with poor sleep. Age, sex or site were not associated with poor sleep ( Table 2 ). There was an association between poor sleep and those addicted (compared to not addicted, OR = 1.65, 95%CI:1.27–2.14, p < 0.001); and screen time (compared to 3, 2 h OR = 0.59, 95%CI 0.41–0.86, p = 0.007).

Table 2 . The association between poor sleep and smartphone addiction, using crude and multivariable logistic regression.

In the multivariable analysis after adjustment for age, gender, site, screen time, and location of phone at night, those addicted exhibited a 41% increased odds of poor sleep (aOR = 1.41, 95%CI: 1.06–1.87, p = 0.018) ( Table 2 ). Age, sex or site was not significantly associated with poor sleep. Total daily use of 2 or less hours reduced odds of poor sleep by 38% (aOR = 0.62, 95%CI: 0.42–0.92, p = 0.018).

Secondary Outcome of Demographics Associated With Smartphone Addiction

In a crude analysis, age, site and ethnicity were associated with smartphone addiction ( Table 3 ). There was a decreased odds of smartphone addiction in older groups, with 22–25 year olds having a 29% decreased odds compared with those 21 and younger (OR = 0.71, 95%CI: 0.53–0.95, p = 0.015), and participants 26 or older having a 47% decreased odds (OR = 0.53, 95%CI:0.32–0.89, p = 0.015). Those of Asian ethnicity had increased odds of addiction (OR = 1.75, 95%CI: 1.32–2.32, p < 0.001) when compared to a White reference population.

Table 3 . The association between sociodemographic factors and smartphone addiction, using crude and multivariable logistic regression.

The total number of hours spent on smartphones per day was significantly and positively associated with the SAS-SV score. After adjustment, age, ethnicity, site and screen time were associated with addiction.

Secondary Outcome of Smartphone Usage Characteristics and Addiction

Use for 2 h or less per day showed significantly decreased odds of smartphone addiction, compared with a reference of 3 h (OR = 0.55, 95%CI 0.36–0.85, p = 0.007, Table 4 ). Use for 5 or more hours per day showed a 2.5 times increase in odds (OR = 2.53, 95%CI: 1.71–3.74, p < 0.001). After adjustment for confounding factors, a consistent pattern of association was found between usage characteristics and addiction. There was a 39% reduction in odds of addiction for those using their phones for 2 h or less compared with typical usage of 3 h (aOR = 0.61; 95%CI 0.39–0.96; p = 0.031).

Table 4 . The association between smartphone use characteristics and smartphone addiction, using crude and multivariable logistic regression.

Later time of usage was also significantly associated with smartphone addiction in a crude logistic regression analysis ( Table 3 ). Use at 1 a.m. or later resulted in a four times increased risk of smartphone addiction, compared to those whose latest time of phone use was before 11 p.m. (OR = 4.06, 95%CI:2.48–6.65, p < 0.001). After adjustment, this finding remained consistent (aOR = 3.91, 95%CI:2.32–6.61, p < 0.001). Use within 30 min of initiating sleep resulted in a two times increased risk of smartphone addiction, which remained significant and consistent after adjustment (aOR = 2.17, 95%CI:1.27–3.70, p = 0.004).

Smartphone Usage Reduction Strategies

92.1% of participants attempted at least one reduction strategy ( Supplementary Tables 2 , 3 ). The most popular strategies were putting your phone on “do not disturb” or in “airplane mode” at night (67.7%); turning off notifications (68.4%); and putting your phone on silent (85.1%). Those who reported smartphone addiction used more strategies than those who did not (mean difference = 0.28, 95%CI: 0.021–0.54, p = 0.034).

This study included 1,043 young adults at a UK university and examined the phenomenon of smartphone addiction. The prevalence of smartphone addiction was 38.9%. Smartphone addiction had associations with both ethnicity and age. Smartphone addiction was associated with poorer sleep.

Our estimated prevalence is consistent with other reported studies in young adult populations globally, which are in the range of 30–45%, and with Yang et al. ( 29 ) who studied a similar university population in the UK ( 4 , 36 – 39 ). Noe et al. ( 40 ). estimate a UK prevalence of 19% using the SAS-SV with the same thresholds; however this study included an older population (up to 46 years) with a smaller sample size ( n = 64). The inverse association between age and smartphone addiction highlighted in our study may explain this variation in prevalence estimates. It is likely that differences in prevalence across the field may be due to the varying criteria of instruments used, or different applications of cut-off scores, and we have previously outlined the differences between the most widely used instruments [Sohn et al. ( 4 )].

Smartphone addiction was more prevalent amongst younger participants. This may reflect increased willingness amongst younger generations to adopt newer uses for smartphones (e.g., gaming, social media), which may confer greater risk of addiction ( 41 ). This could also related to younger participants potentially having more time for such endeavors. Participants from Asian ethnic backgrounds were at greater risk for smartphone indication, which may be due to cultural differences, such as social norms and characteristics including individualism ( 29 , 42 ). There was no association between smartphone addiction and gender, at odds with other studies which have found that females are more at risk, but it should be noted that the SAS-SV applied a gender-based standardized threshold to determine addiction ( 43 ).

Longer use was significantly associated with smartphone addiction, which is consistent with other studies that have found that increased exposure is linked with increased dependency ( 44 ). Furthermore, later time of use was also significantly associated with smartphone addiction, with use after 1 a.m. conferring a 3-fold increased risk. This association may be indicative of impaired control and use despite harm, which are a characteristic of a behavioral addiction. Smartphone ownership has previously been linked with more electronic media use in the night and later bedtimes in a survey of adolescents ( 45 ).

Our study provides further support to the growing body evidence that smartphone addiction has a deleterious impact on sleep ( 16 , 20 , 23 ). However, this relationship remained significant after adjusting for daily screen time (which was not seen as predictive after adjustment for smartphone addiction). This finding suggests that although duration of exposure, as with any addiction, is a risk factor for smartphone addiction, it is not the only determining component, reflecting the ICD-11 criteria for gaming and gambling disorders, in which duration of use may be one component of diagnosis but is not the only indicator ( 6 , 14 ). Furthermore, this result indicates that the relationship between sleep quality and smartphone addiction is not simply due to the duration of exposure, as suggested by other studies ( 46 ). It highlights that studies reporting a lack of association between smartphones and clinical outcome when using screen time alone should be interpreted with caution, as they have perhaps overlooked smartphone addiction as the harmful exposure ( 47 ).

The results of this study indicate that self-reported smartphone addiction is prevalent amongst young adults attending university and that it is linked with use at later times of the day in addition to total duration of use. Public health bodies should take this evidence into account when developing guidelines around smartphone use and sleep hygiene. Furthermore, clinicians, parents, and educators should be aware of the pervasiveness of smartphone addiction, and be prepared to consider the potential wide-reaching impact of smartphones on sleep. Despite the cross-sectional nature of this study, the findings suggest that the amount of time spent on their phones, and latest time of use can be indicative of those at risk for an addicted pattern of smartphone use. Should smartphone addiction become firmly established as a focus of clinical concern, those using their phones after midnight or using their phones for 4 or more hours per day are likely to be at high risk, and should guide administration of the SAS-SV. However, it should be noted that duration of smartphone use alone does not indicate smartphone addiction; it is merely indicates increased risk for development of this pattern of behavior. Future studies should examine longitudinal associations between smartphone use patterns and smartphone addiction, and between smartphone addiction and health harms, as well as exploring strategies to reduce harms, particularly in relation to sleep. As there is continued debate concerning the possibility that smartphones may be a means to access addictive material, such as social media applications or games, rather than the addiction themselves, future research should also focus on identifying types of use associated with higher risk of smartphone addiction.

This study collected data from a large sample of 18–30 year olds in the United Kingdom using a validated and widely used scale. There were several limitations to this study. Namely, due to the cross-sectional nature of data collection, no causal relationships can be drawn, and we cannot ignore the possibility of reverse causality. In particular, it is possible that poor sleep may be a result of concurrent mental health disorders that were not assessed for in this study, which may result in or be independently associated with increased smartphone usage and smartphone addiction risk. In addition, the self-reported data collection method we used may introduce common-method and response biases. Caution should be taken over the estimate of prevalence since a convenience sampling method was used. Additionally, caution should be taken in generalizing the results of this study, as the sampled population is not representative of the UK-wide population of young adults. Finally, these data were collected before the global pandemic, which may have led to a shift in smartphone usage patterns.

Conclusions

Smartphone addiction is prevalent and occurs more frequently amongst younger adults. Proxy measures of screen time were not synonymous with addiction; a validated addiction instrument should be used to capture this phenomenon. Those exhibiting smartphone addiction experienced poorer sleep.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Ethical approval was received from the King's College Research Ethics Office (Study ID: 9138; MRS-18/19-9138). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

BC, NK, and SS: conceived the study, were responsible first draft of the manuscript, and approved the final draft of the manuscript. BC, NK, LK, and SS: generated the study material. LK and SS: collected the data. BC and SS: analyzed and interpreted the data. BC, NK, PR, and SS: edited the manuscript. BC was the study Guarantor. All authors contributed to the article and approved the submitted version.

Conflict of Interest

NK was employed by the NHS and was a trustee of the Gordon Moody Association. Her PhD (2010-2013) was joint-funded by the Wellcome Trust and GSK and during that time she received research materials from GSK and educational support from GSK and the Lundbeck Foundation. BC was partially supported by the Biomedical Research Centre at South London and Maudsley NHS Foundational Trust and King's College London.

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

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2021.629407/full#supplementary-material

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Keywords: smartphone addiction, sleep, smartphone harm reduction strategies, screen time, young adults, behavioral addiction

Citation: Sohn SY, Krasnoff L, Rees P, Kalk NJ and Carter B (2021) The Association Between Smartphone Addiction and Sleep: A UK Cross-Sectional Study of Young Adults. Front. Psychiatry 12:629407. doi: 10.3389/fpsyt.2021.629407

Received: 14 November 2020; Accepted: 01 February 2021; Published: 02 March 2021.

Reviewed by:

Copyright © 2021 Sohn, Krasnoff, Rees, Kalk and Carter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ben Carter, ben.carter@kcl.ac.uk

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

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• v.7(3); Jul-Sep 2017

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

Subramani parasuraman.

Unit of Pharmacology, AIMST University, Kedah, Malaysia

Aaseer Thamby Sam

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

Stephanie Wong Kah Yee

Bobby lau chik chuon.

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

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

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

Conclusion:

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

INTRODUCTION

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

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

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

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

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

Demographic details of the study participants

Statistical analysis

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

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

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

Habituation analysis of mobile phone usage

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

Analysis of awareness of mobile phone hazards

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

Addiction behavior analysis of mobile phone

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

Comparison of habituation of mobile phone usage between genders

Comparison of addiction behavior between genders

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

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

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

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

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

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

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

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

Limitations

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

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How to Have a Healthier Relationship With Your Phone

Unplugging is often impossible. Here’s a realistic guide to using your tech in a way that serves you.

By Eric Athas

A few years ago, a Google employee sent an email to thousands of her co-workers: What if for six weeks straight, you spent one night per week without technology?

The email was from Laura Mae Martin, Google’s executive productivity adviser, a role that, among other things, was created to help staff members foster healthier relationships with their gadgets and apps. After she sent the note, Ms. Martin was flooded with responses from co-workers eager for a respite from some of the very products they helped build. Thousands of employees have since participated in the annual “No-Tech Tuesday Night Challenge,” said Ms. Martin, author of the upcoming book “Uptime: A Practical Guide to Personal Productivity and Wellbeing.”

The problem she was trying to solve isn’t unique to Google workers. One survey found that most Americans say they spend too much time on their phones. But dramatic solutions — a digital detox , a phone downgrade or a complete exit from social media — may feel impractical.

Is it possible to have a healthy relationship with technology while still using it daily? Fortunately, according to experts, the answer is a resounding yes.

Start with one simple question.

You know that urge you get to reach for your phone without realizing it? And then, before you know it, you’re an hour into a social media binge?

If you want to peacefully coexist with technology, you need to get a handle on those impulses . Start by noticing when you have an urge to lift your phone or open social media on your browser window, said Richard J. Davidson, the founder and director of the Center for Healthy Minds at the University of Wisconsin-Madison.

By becoming conscious of what you’re about to do, you’re interrupting an automatic behavior and awakening the part of your brain that governs self-control, he said. As one research article suggests, awareness of your actions can help you rein in bad habits.

“When you become aware of the urge, simply ask yourself, ‘Do I really need to do this right now?’” Dr. Davidson said.

Asking that question may help you pause, reflect on and resist the temptation to check your device. And let’s be realistic: Sometimes you may decide to indulge in some screen time. But by being conscious of your urges, you can become more intentional about your habits, Dr. Davidson said.

Take the “mobile” out of your mobile devices.

Dr. Anna Lembke, a professor of psychiatry and addiction medicine at Stanford University School of Medicine, said one of the biggest problems with smartphones is what she calls “texting while running to catch a bus.” Using our devices while we’re on the move — walking from meeting to meeting, taking a child to school or catching a bus — prevents us from being more engaged in our lives, Dr. Lembke said.

“We’re missing out on a wealth of information and signaling in the world around us, and also depriving ourselves of the opportunity to process and interpret what we’ve experienced,” she said.

One way to create harmony with your technology is to limit your phone use when you’re on the move. Headed out for a walk? Turn off your notifications. Going to grab a coffee? Leave your phone on your desk. If you’re feeling brave, try powering down your phone while in transit, said Dr. Lembke, who wrote “Dopamine Nation: Finding Balance in the Age of Indulgence.” It won’t buzz with notifications, text messages or phone calls, which Dr. Lembke said could help you focus on the world around you.

Schedule tiny tech breaks.

Extended vacations from your gadgets may not be possible. But if you’re trying to spend less time staring at your screens, 10- or 15- minute breaks might be a more practical option, said Dr. Adam Gazzaley, a neuroscientist at the University of California, San Francisco and author of “The Distracted Mind: Ancient Brains in a High-Tech World.” You might take a quick walk, close your eyes, work on a puzzle or read a book.

Another trick: Put tech breaks in your calendar, Dr. Gazzaley said. It may feel odd to schedule something like “take a phone-free walk,” but it shouldn’t if it’s a priority, he said.

Control your environment.

Don’t rely on your willpower alone to keep your screen time down, said James A. Roberts, an expert on consumer behavior at Baylor University. Instead, tweak your surroundings.

“Anything you can do to create an environment that makes it as easy as possible to distance yourself from the phone will be helpful,” said Dr. Roberts, who wrote “Too Much of a Good Thing: Are You Addicted to Your Smartphone?”

Here are a few things you can try:

Get an alarm clock . A phone alarm forces you to pick up your device upon waking up, making it far too easy to start reading email and alerts, Dr. Roberts said. But a stand-alone alarm clock allows you to leave your phone untouched until you decide it’s time to dive in.

Appoint an accountability partner . Dr. Roberts suggested asking a family member or friend to remind you to put down your device when you’ve been on it for too long, when someone’s trying to have a conversation with you, or at other moments when it is disrupting life in the offline world.

Delete social media from your phone. To manage social media use without quitting it entirely, you’ll need to make it less accessible, Dr. Roberts said. One tip he suggested is to delete it from your phone but keep it on your computer so you can still use it for work or keeping in touch with family and friends.

Make the technology work for you.

One thing experts agreed on: To forge a healthy relationship with technology, you need to be in control of it and not the other way around. Think about your gadgets as tools that you decide how to use.

“Make it work for you, not against you,” said Ms. Martin, the productivity expert at Google. “Whether it’s an email program or your dishwasher, it’s the intention behind how you’re using it that really makes the big difference.”

Eric Athas is a deputy editor on the Newsroom Development & Support team at The Times, which trains reporters and editors on new skills and tools. More about Eric Athas

How to Make Your Smartphone Better

These days, smartphones include tools to help you more easily connect with the people you want to contact — and avoid those you don’t. Here are some tips .

Trying to spend less time on your phone? The “Do Not Disturb” mode can help you set boundaries and signal that it may take you a while to respond .

To comply with recent European regulations, Apple will make a switch to USB-C charging for its iPhones. Here is how to navigate the change .

Photo apps have been using A.I. for years to give you control over the look of your images. Here’s how to take advantage of that .

The loss of your smartphone can be disruptive and stressful. Taking a few simple steps ahead of time can make things easier if disaster strikes .

Many default settings make us share superfluous amounts of data with tech companies. Here’s how to shut those off .