Personality Traits that Predispose or Protect in Smartphone Addiction and Their Implications for Intervention: A Narrative Review

Abstract

Technological advancement has transformed nearly every aspect of modern life. Nonetheless excessive use of smartphone has become a matter of concern. Adolescents and young adults tend to be more attached or addictive to smartphones and their effects are seen either physically or psychologically. However, there are still no established diagnostic criteria for smartphone addiction in The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. The present review aimed at looking into the available evidence of personality factors in connection with smartphone use and thereby exploring the role of personality in interventions for smartphone addiction on the ground of theoretical foundation. Findings revealed that high neuroticism was significantly related to excessive use and even predicted problematic use of smartphones. Though studies revealed that extraversion may cause an individual to be inclined to increased smartphone usage, it would not lead to smartphone addiction. Similarly, openness to experience traits showed a slight negative association and were not significant predictors of smartphone addiction, whereas conscientiousness and high agreeableness appeared to be protective factors against its development. Research on the relationship between smartphone addiction and social–emotional distress is still in its early stages, requiring cautious generalization. Since eliminating smartphone use completely is impractical, possible interventions for smartphone addiction linked with personality are recommended.

Introduction

Advancement of technology has transformed nearly every aspect of modern life. Various electronic media devices such as smartphones or mobile phones, tablets, and computers are some typical examples. The newer version of the smartphone, with its numerous functions has made many individual devices redundant. Smartphones have thus become an integral part of our daily lives. As smartphones become more and more sophisticated and multifunctional, adolescents and young users are becoming increasingly dependent upon this technology not only for interpersonal communication but also as a tool for seeking online information, entertainment, relaxation, shooting pictures and videos, and other yet-to-be invented applications, as well as “doom scrolling.”^1,2 While these gadgets have become symbols of status and identity, they are also increasingly being used to deliver therapy and interventions through smartphone applications.³

Age and gender factors

While smartphones have become indispensable in modern life, their excessive use raises concerns due to their physical and psychological effects on individuals. Smartphone ownership is nearly ubiquitous among teens and young adults, who form a bulk of smartphone users.⁴ Growing up in the era of digital innovations makes them more reliant upon digital sources than the older generations. This also increases their proclivity to become more attached or addicted to smartphones.^4–8 Though no gender difference was found in smartphone addiction,⁹ the underlying reasons for smartphone use vary across genders. For example, female students preferred communicational functions while males preferred to explore their smartphones, seek information, browse the Internet or gaming.^10,11 Furthermore, problematic smartphone use for text messaging increased with age among the girls.¹²

Smartphone addiction

Addiction-like behavior to smartphones is a serious problem for an individual’s social life and work. According to the Pew Research Center, 64% of the American population use smartphones, and 46% of them are at risk for addiction.⁴ As a subset of behavioral addiction, Griffiths (1996) proposed the concept of technological addiction, which is operationally defined as human–machine interaction and is nonchemical in nature.¹³ Addictive smartphone use can be regarded as an impulse control disorder that does not involve an intoxicant and is similar to pathological gambling.³ Whether the excessive use of various technologies can be or should be called an “addiction,” scholars have argued that excessive use of technology can be considered problematic. In other words, a certain behavior might be addictive when it is associated with negative consequences and physical and psychological reinforcements.¹⁴ The terms such as “excessive use of smartphones,” “problematic smartphone use,” or “smartphone addiction” have been used interchangeably in recent research.¹⁵ While only such a classification is provided, there are, however, no clearly recognized criteria till date for smartphone addiction as a diagnostic category in The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5).¹⁶ Indeed, to date, the only support for smartphone addiction as a real entity comes from exploratory studies relying on self-reports or clinical case studies.⁶

Scales

There is a lot of variation in measures used to assess smartphone addiction across studies.² The greatest challenges occur in the form of diversity of terms, criteria, and constructs.¹⁷ Many studies have developed self-reported questionnaires to identify problematic smartphone or mobile phone use or to diagnose individuals with smartphone addiction, overuse, dependence, or attachment and validated them. This has been used later by other researchers. Many scale developers used either the DSM-IV or DSM-5 criteria for substance use to examine criterion-related validity during development. Others chose to use Griffiths’ (2005) component-based descriptive model of addiction, which includes the following core components: salience, mood modification, tolerance, withdrawal, conflict, and relapse.¹⁸ Some scales that have been developed include Smartphone Addiction Scale (SAS) (Cronbach’s = 0.97), SAS-Short Version (SAS-SV) (Cronbach’s = 0.91), Smartphone Addiction Proneness Scale (SAPS) (Cronbach’s = 0.81), Mobile Phone Problem Use Scale (MPPUS) (Cronbach’s = 0.93), and Problematic Mobile Phone Use Questionnaire (Cronbach’s = 0.65–0.85) scale. A recent systematic review examined 78 existing validated scales that have been developed over the past 13 years to measure, identify, or characterize excessive or problematic smartphone use by evaluating their theoretical foundations and their psychometric properties. The findings reported that although criterion validity and internal consistency were mentioned for most of the scales, temporal stability (test–retest reliability) was not documented in most of the scales, questioning the reliability. Analysis of the psychometric properties of the various scales revealed lack of temporal stability in some of the most frequently used scales (e.g., SAS).¹⁸

Risk factors

Use of smartphone can be viewed as a coping response to emotional or social difficulties. When smartphone is used excessively to cope with negative affective states (e.g., depression or anxiety), and alternate means of coping responses are diminished, individuals may find themselves relying on online activities to avoid negative feelings. Increased accessibility and easy availability of smartphones among college students has shown negative impact on academic performance, mental well-being, sleep quality, and particular association with depression and psychological morbidity.^19,20 Addictive symptoms have also been reported due to excessive use of smartphones. An earlier study using an exploratory factor analysis identified four addiction symptoms: “losing control and receiving complaints,” “anxiety and craving,” “withdrawal/escape,” and “productivity loss.” Findings revealed that adolescents who scored higher on leisure boredom and sensation seeking were more likely to be addicted to smartphones. Conversely, subjects who scored high on self-esteem demonstrated less of such tendency.³

There have been some studies which have indicated that personality or specific personality traits may influence the development of smartphone addiction.^6,15,21 The Big Five personality traits consist of five dimensions: extraversion (versus introversion), agreeableness (versus antagonism), conscientiousness (or constraint), emotional instability (or neuroticism), and openness to experience (or intellect).^22,23 Kuss et al. identified increased neuroticism and low agreeableness as risk factors for smartphone use,²⁴ whereas clinical traits such as depression, attention deficit/hyperactivity disorder (ADHD) and aggression were associated with addiction.^2,15 Furthermore, Billieux et al. (2015) proposed an integrative pathway model to provide a theoretical framework for problematic mobile phone use, as seen in Figure 1.²⁵ In the model, neuroticism traits, where the individual is prone to anxiety or emotional instability is mainly present in the first pathway of excessive reassurance pathway. Relationships between various facets of impulsivity and specific psychological mechanisms are seen at the second pathway.²⁶ Agreeableness and conscientiousness traits are mainly present in the impulsive pathway. The third or extraversion pathway contains the final two personality traits from the Big Five personality traits, i.e., extraversion and openness to experience.

FIG. 1.

Integrative pathway model for problematic mobile phone use (adapted from Billieux et al., 2015).

While using smartphones, addiction-like symptoms can be the consequences of a need for reassurance promoted by factors such as increased anxiety, poor self-esteem, insecure attachment, or increased emotional instability. Though recent research evidence has documented the association of personality with smartphone use, there is dearth of evidence on implications of personality for interventions for mobile or smartphone addiction. In this context, the present review aimed at looking into the available evidence of personality factors in connection with smartphone use and thereby exploring the role of personality in interventions for smartphone addiction on the ground of theoretical foundation.

Method

A comprehensive systematic search was conducted and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines²⁷ was followed for designing, conducting, and reporting the review (Figure 2).

FIG. 2.

PRISMA flow diagram showing the selection of articles included in the review.

Data source and search strategy

Electronic databases including PubMed, APA PsycINFO, and Ovid were searched using a series of keyword combinations related to smartphone addiction and personality. The publication date of the articles was from 2000 to 2023. The keywords and Medical Subject Headings used for electronic search included “Personality,” “Big Five Personality Trait,” “Traits,” “Neuroticism,” “Extraversion,” “Openness,” “Agreeableness,” and “Conscientiousness.” The key words were combined with the search terms “Smartphone use,” OR “Smartphone Addiction,” OR “Problematic smartphone use” using Boolean operator “AND” to restrict the search results to the subject area of investigation. The reference lists of the initially retrieved articles were examined to identify any relevant studies that might have been overlooked in the database search.

Study selection—Inclusion and exclusion criteria

Studies that are relevant for the review were identified based on the occurrence of the terms “Personality” or “Smartphone use” in the title, abstract, or body of the article. The main inclusion criteria were articles that concentrated either solely or adjunct with smartphone addiction, the personality factors focused on any single or combined existing traits of the Big Five personality traits with the peer-reviewed studies written in English and published between 2000 and 2023. Studies were included regardless of the study design (quantitative/qualitative/mixed methods), study setting (online/offline), and country of origin. Studies focusing more on behavioral outcomes rather than personality traits, book chapters, editorials, studies with a sample size of <100 (to minimize the influence of selection bias), case series, case reports, review articles, meta-analyses, letters, conference abstracts or posters, credible blog posts, and preprints were all excluded. Gray literature and unpublished data (policy papers, institutional reports, dissertations, and theses) were also excluded because their quality could not be adequately assessed.

Two independent authors (N.J. and M.S.) conducted the literature search. They screened the titles, abstracts, and keywords based on the review objectives. Duplications of the selected articles were removed by exporting their citations to Zotero reference management software.²⁸ The title and abstracts were screened for relevance, followed by retrieval of the relevant full-text articles for further screening. The retrieved full-text articles were further reviewed for final inclusion based on the eligibility criteria. Consensual agreement between the reviewing authors was used to make decisions concerning study inclusion and data interpretation.

Data extraction and synthesis

Data from the selected studies were extracted and entered into the relevant data field of the review matrix using the following headings: study title, author, year of publication, research setting, study design, study participants and sample size, measurement tools, and key findings. The full-text articles of final inclusion were distributed among the team members for data extraction after thorough reading. The data extracted from the team members were collated into the review matrix and reviewed to ensure consistency and completeness. A descriptive–analytical framework was used to synthesize the extracted data to provide a coherent and meaningful narrative account relevant to the study topic.

Results

The initial database search yielded a total of 260 studies. After deleting duplicates and following the exclusion of articles that did not meet the inclusion criteria, a total of 19 studies were included for the review (Figure 2). The studies included in the review were conducted in South Korea (21%), followed by Australia, Iran, Japan, Turkey, and others countries from all over the world. The characteristics of the studies included in the review are summarized in Table 1.

Table 1.

Study Characteristics of the Studies Included in the Review (n = 19)

S. No.	Author/s (year) andjournal	Country	Study design	Study participants with sample size	Study instruments	Major findings
1.	Arpaci and Kocadag Unver (2020)²⁹ Psychiatric Quarterly	Turkey	Quantitative cross-sectional study	320 undergraduate students (age: M = 20.36, SD = 2.35)	Big-Five-Inventory Smartphone Addiction Inventory	Neuroticism was positively correlated and conscientiousness was negatively related with smartphone addiction for women. There was a negatively significant relationship between agreeableness and smartphone addiction for both genders.
2.	Choi et al. (2015)³⁰ Journal of Behavioral Addictions	South Korea	Quantitative cross-sectional study	448 college students (178 males and 270 females; age: M = 20.89, SD = 3.09)	The Smartphone Addiction Scale The Young’s Internet Addiction Test The Alcohol Use Disorders Identification Test The Beck Depression Inventory I, The State–Trait Anxiety Inventory (Trait Version) The Character Strengths Test The Connor–Davidson Resilience Scale	The risk factors for smartphone addiction were female gender, Internet use, alcohol use, and anxiety, while the protective factors were depression and temperance.
3.	Cocoradă et al. (2018)¹⁰ Children and Youth Services Review	Romania	Quantitative cross-sectional study	717 participants: high school (40%) and university students	The Smartphone Addiction Scale—The Short version The International Personality Item Pool (IPIP 50) Scales The Media and Technology Usage and Attitudes Scale	Number of hours spent using the smartphone, general use, positive attitudes toward this and anxiety without technology mediate the relation between personality traits and risk for addiction. Neuroticism, openness, and conscientiousness predict negatively the risk addiction to the smartphone.
4.	Ehrenberg et al. (2008)³¹ Cyberpsychology and Behavior	Australia	Quantitative cross-sectional study	200 university students (146 females, 54 males; age: M = 19.06, SD = 1.80)	The Neuroticism-Extraversion-Openness Five-Factor Inventory (NEO FFI) Personality Inventory Coopersmith Self-Esteem Inventory Adult Form	More neurotic individuals reported stronger mobile phone addictive tendencies, while more disagreeable individuals and those with lower self-esteem reported stronger instant messaging addictive tendencies.
5.	Eichenberg et al. (2021)²¹ Frontiers in Psychiatry	Austria	Quantitative cross-sectional study	497 university students (120 men and 377 women; age: M =19.38, SD = 16.50	The Smartphone Addiction Scale The 10-Item Big Five Inventory The Brief Symptom Inventory Short version of the Social Support Questionnaire (F-SozU-K-14)	Respondents with problematic smartphone use showed significantly higher values for extraversion and neuroticism compared than nonaddicted users. Students with problematic smartphone use showed significantly higher levels in terms of depression and anxiety.
6.	Erdem and Uzun (2020)⁷ Technology, Knowledge and Learning	Turkey	Quantitative cross-sectional survey design	485 university students (314 males, 171 females	Turkish adaptations of The Smartphone Addiction Scale The Big Five Inventory	Agreeableness and conscientiousness were shown to be negative predictors, neuroticism was a positive predictor, and extraversion and openness to experience were not found to be significant predictors.
7.	Horwood and Anglim (2018)³² Computers in Human Behavior	Australia	Quantitative cross-sectional study	393 adults (79% female; mean age = 24.4, SD = 7.1)	Mobile Phone Problem Use Scale Smartphone Use Scale The 300 item IPIP-NEO Inventory The 200-item version of the HEXACO PI-R	Problematic smartphone use was positively correlated with neuroticism and negatively correlated with conscientiousness. In the HEXACO framework, honesty-humility, agreeableness, conscientiousness, and openness all showed moderate negative correlations with problematic smartphone use.
8.	Jeong et al. (2020)¹⁵ Addictive Behaviors	South Korea	Quantitative cross-sectional study	714 middle-school students (389 boys, 325 girls)	Young’s Internet Addiction Test Smartphone Addiction Scale Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) Scales Conners–Wells’ Adolescent Self-Report Scale short version (CASS) Aggression Questionnaire The Korean version of the State–Trait Anger Expression Inventory Beck Depression Inventory-II and Beck Anxiety Inventory	Both clinical characteristics, including ADHD symptoms, aggression, expression of anger, depression, and anxiety, and personality characteristics, represented by BIS/BAS, played important roles in the severity of excessive Internet use and excessive smartphone use.
9.	Jo et al. (2018) Addiction Research & Theory	South Korea	Quantitative cross-sectional study	7003 participants (3,115 males and 3,888 females)	The Smartphone Addiction Proneness Scale Dickman’s Impulsivity Inventory (DII)	The level of SAP was significantly different between age groups, with the adolescent group having the highest percentage of SAP. Dysfunctional DII score was highest in the adolescent group, and there was a significant difference between the adolescent group and the other two age groups. Moreover, the higher the level of SAP, the greater the dysfunctional impulsivity score.
10.	Kheradmand et al. (2023)³³ Frontiers in Psychiatry	Iran	Quantitative cross-sectional study	382 university students (148 males and 234 females)	The smartphone addiction scale The Persian version of the Cloninger temperament and character inventory	Mean scores of people with smartphone addiction were higher in novelty-seeking, harm avoidance, and self-transcendence than the nonaddicts. In persistence and self-directedness, the mean scores of the smartphone addiction group were lower than the non addicts.
11.	Kim et al. (2016)⁶ Plos One	Republic of Korea	Quantitative cross-sectional study	4,854 participants (2,573 men and 2,281 women) aged 20–49 years	The Korean Smartphone Addiction Proneness Scale for adults BIS/BAS questionnaire The Dickman Dysfunctional Impulsivity Instrument The Brief Self-Control Scale	SAP predictors were female gender, weekend average usage hours, personality traits of pursuit for desired appetitive goals, positive responses to anticipated rewards, dysfunctional impulsivity, and low self-control.
12.	Kita and Luria (2018)³⁴	Israel	Quantitative cross-sectional study	221 young drivers (64.7% male) aged 17–22 years	The Smartphone Addiction Scale questionnaire The International Personality Item Pool scale	There was a negative relationship between participants’ openness to experience and smartphone use while driving, and a positive relationship between both extraversion and neuroticism and smartphone use while driving.
13.	Lee et al. (2014)³⁵ Social Indicators Research	Malaysia	Quantitative cross-sectional study	187 participants (89 men, 98 women; aged between 19 and 59 years)	Mobile Phone Usage Questionnaire Big Five Inventory, Interaction Anxiousness Scale UCLA Loneliness Scale version 3	Personality, social anxiety (SA), and loneliness broadly predicted preferences for voice calling or text messaging. The predictability of time spent on voice calls/messaging and number of people called/exchanged text messages also revealed significant findings in regards to the openness-to-experience personality dimension, loneliness and SA.
14.	Mosalanejad et al. (2019)³⁶ Journal of Research in Medical and Dental Science	Iran	Quantitative analytical, cross-sectional study	233 students	NEO Personality Inventory-Revised Smartphone Addiction Questionnaire Loneliness Scale Stress Inventory	Loneliness and daily stress are higher among the addicted students. According to multivariate regression, conscientiousness, which is a dimension of personality traits, is found to have a significant relationship with the smartphone addiction.
15.	Peterka-Bonetta et al. (2019)³⁷ Frontiers in Public Health	Germany (respondents were from 59 different countries)	Quantitative cross-sectional study (online platform used)	773 participants (males = 303, females = 470; age: M 23.11, SD = 7.32)	The Trait Self-Description Inventory-short form The Internet Addiction Test-short form The Barratt Impulsiveness Scale 15 The Interaction Anxiousness Scale The Smartphone Addiction Inventory	Low conscientiousness and high neuroticism were most robustly associated with higher Internet and smartphone use disorder (IUD/SUD). Furthermore, social anxiety and impulsivity showed positive correlations with IUD and SUD.
16.	Remondi et al. (2020)³⁸ Cyberpsychology, Behavior, and Social Networking	Italy	Quantitative cross-sectional study	539 young adults (female = 380)	The Kessler Psychological Distress Scale (K10) The Internet Addiction Test The Smartphone Addiction Scale—Short Version The Generalized Problematic Internet Use Scale 2 The Experiences in Close Relationships-12 The Barratt Impulsivity Scale The Toronto Alexithymia Scale-20	Attachment anxiety had no direct effect on technology addiction, whereas attachment avoidance had a small negative direct effect, but only among women. Insecure attachment loneliness dimensions were significantly associated with psychological risk factors, whereas the latter had a significant, direct association with technology addiction.
17.	Takao et al. (2009)³⁹ Cyberpsychology and Behavior	Japan	Quantitative cross-sectional study	488 participants (age: M = 20.77, SD = 1.66)	The Mobile Phone Problem Usage Scale The Self-Monitoring Scale The Martin-Larsen Approval Motivation Scale The UCLA Loneliness Scale	Problematic mobile phone use was a function of gender, self-monitoring, and approval motivation but not of loneliness.
18.	Toda et al. (2016)⁴⁰ Open Journal of Preventive Medicine	Japan	Quantitative cross-sectional study	184 medical university students	Wakayama Smartphone-Dependence Scale (WSDS) General Health Questionnaire Ten-Item Personality Inventory	For males, smartphone dependence positively correlated with somatic symptoms, anxiety, insomnia and emotional instability, and negatively correlated with agreeableness. For females, smartphone dependence positively correlated with somatic symptoms, severe depression, and extraversion.
19.	Volungis et al. (2020)⁴¹ International Journal of Mental Health and Addiction	USA	Quantitative cross-sectional study	150 undergraduate college students (mean age = 19.28)	Smartphone Frequency and Health Questionnaire The Smartphone Addiction Scale NEO-FFI-3	Neuroticism had a positive relationship with smartphone addiction, while extraversion, openness, agreeableness, and conscientious all had a negative relationship with smartphone addiction.

The findings are organized under the following headings:

Personality traits and smartphone addiction

By coding the content of each study in alignment with the stated objectives, the review demonstrated associations between smartphone use and the five major personality traits: neuroticism, extraversion, agreeableness, openness, and conscientiousness. High neuroticism was found to be significantly related to excessive use of smartphones,^{25,26,40–43} and even predicted problematic use of smartphones.³² Individuals high in neuroticism experienced helplessness, insecurity, depressive symptoms, were emotionally labile and felt shameful.²² Agreeableness and conscientiousness showed a heightened inverse association with smartphone addiction among the older samples (individuals >26 years of age),⁴⁴ young to middle aged adults^7,32,35,37, and even high school and college students.^10,41 Low agreeableness was found to be a risk factor for smartphone addiction⁴⁵ though it had a weak association with both genders.²⁹ While no relationship between agreeableness and consciousness was found with smartphone addiction,^21,41,46 conscientiousness in young people did not explain smartphone use while driving either directly or indirectly, beyond the variance explained by other traits.³⁴ Problematic smartphone use was found to be positively related to extraversion^10,21,47 but not neuroticism⁴⁸ while other studies showed that the pathways of these two personality dimensions are distinct.⁴⁹ Extraversion was associated with higher smartphone use related to text messaging.^31,35 With regards to driving, a negative relationship was found with openness to experience while a positive relationship was seen with extraversion and smartphone use.³⁴

Emotional and clinical factors

Depression, anxiety, social anxiety, impulsivity, low self-control, and ADHD symptoms were all positively associated with smartphone addiction. Interestingly, depression was also identified as a protective factor in one study,³⁰ possibly reflecting differing methodologies or subtypes of use.

Behavioral mediators

Positive attitudes, daily smartphone hours, general usage patterns, and anxiety without technology mediated the relationship between personality and smartphone addiction risk. Driving behavior was found to be linked with personality traits: neuroticism and extraversion increased smartphone use while driving; but there was negative relationship between participants’ openness to experience and smartphone use while driving.³⁴

Other psychological constructs

Attachment anxiety and avoidance were indirectly associated with smartphone addiction through psychological risk pathways.³⁸ Loneliness and daily stress were higher among addicted students and linked to personality traits like low conscientiousness.^35,36 Motivational factors (e.g., self-monitoring and approval-seeking) influenced problematic phone use more than loneliness in some contexts.³⁹

Discussion

The findings are interpreted in the context of previous research to explore the association between pathways for problematic smartphone use and the Five-Factor Model of personality proposed by Costa and McCrae.⁵⁰

It has been reported that individuals high in neuroticism may attempt to control their relationship status via compulsively checking social media and instant messaging apps⁵¹ owing to their need to seek reaffirmation of being part of a social group⁴⁴ in addition to their underlying traits of social anxiety, fear of failure, or them having a firm superego.⁵²

Impulsivity is a personality factor wherein an individual chooses a smaller reward that could be immediately received over a larger reward that may be obtained after a delay.^6,53 It was found that impulsiveness over smartphone use is more likely to occur during both positive and negative intense distressing emotions.^54,55 Existing literature supports the association between impulsivity as a psychological construct and the development of problematic smartphone use.^26,56–59

In line with these trait descriptions, research suggests that individuals with high agreeableness are found to be gullible, submissive, and selfless while those low on these traits are deceitful, manipulative, suspicious, callous, and grandiose. Individuals high on conscientiousness tend to be more of a perfectionist and workaholic and those low on this trait were more irresponsible, rash, and distractible.²²

These contrasting traits may underlie different motivations and vulnerabilities in smartphone use. For instance, high extraversion entails attention seeking and excitement seeking behavior while low extraversion is represented by detached coldness, social withdrawal, and anhedonia. High openness is seen by magical thinking, perceptual dysregulation, and eccentricity and low openness by the individual being close minded and inflexible.²² Though studies revealed that extraversion may cause an individual to be inclined to increased smartphone usage, it would not lead to smartphone addiction.^{7,25,26,41,44} Similarly, openness to experience traits had an overall small negative association and was not a significant predictor for smartphone addiction^{7,10,21,31,41,44} but was related to higher voice calling frequencies and tendency to send more text messages.³⁵ The nonsignificant predictor of extraversion and openness to experience on smartphone addiction could be due to individuals’ ability to differentiate between social interactions in face-to-face settings and those occurring in online environments.⁷

Some of the common limitations reported across studies were that the research design of many of the studies were cross-sectional^{6,7,15,51,56,57} and were nonclinical samples.^6,15,30,33 The samples mainly comprised of females who were late adolescents or college-going individuals.^{6,10,30,31,33,36,41} The subjects were assessed predominantly with self-report questionnaires^{6,7,10,15,31,41,56,57,60} and online surveys.^56,57

The other limitations that were picked out from literature were that other psychological factors were not measured⁶ or only one domain such as impulsivity was measured ignoring the other associated psychological factors.^6,11,52 Some questionnaires did not assess information such as hours of use, purpose of use, or contents most used.³⁰ The Smartphone Addiction Proneness Scale (SAPS) has been used across several research studies which has a cutoff score though no studies are available regarding the sensitivity or specificity of the scale.⁶⁰ A psychometric tool used as an ad hoc was Korean Smartphone Addiction Proneness Scale (K-SAPS) to detect smartphone addiction predisposition though it can be administered only on clinical samples to uphold that a certain behavior is pathological.⁶¹

Recommendations for Interventions

Based on the discussion, the following interventions are suggested to prevent and address smartphone addiction:

Development of a more comprehensive tool for diagnosing addiction to communication technologies rather than only using a measurement to rely on the indicators.³¹

Assessment of smartphone use and problematic use in mental and perhaps physical health examinations is necessary.⁴¹

In educational settings, the personality traits should be looked at and their relations to smartphone addiction in students and they should be psycho-educated of their predisposition to help prevent them from becoming addicted to smartphones at an early stage^9,50 and to understand the importance of self-control.⁵²

Interventions should aim to provide at-risk individuals with ways to cope with daily stressors, and consequently negative emotions, as an alternative to using their smartphone.⁵¹

Being similar to other behavioral addictions, the regulation of impulsivity and improvement of self-control is an important intervention technique.^39,62

Individuals who are addicted to smartphones have low levels of persistence and self-directedness and high levels of novelty seeking, harm avoidance, and self-transcendence. These personality traits may be linked to mood disorders, depression, ADHD, and narcissism. It would be quite beneficial to prevent smartphone addiction if health or school professionals are aware of the personality traits of children and adolescents who are prone to it.³³

Greater attachment security may reduce the distress levels in impulsivity, and difficulties faced in experiencing, verbalizing, and regulating emotions which may lead to a reduced intensity of technology addiction.³⁸

Attention should be paid to personality and affective symptoms when treating individuals with Internet and/or excessive smartphone use disorders.⁶³

Limitations

Till date, smartphone addiction is not a confirmed diagnosis, and there is no established diagnostic criteria for smartphone addiction.⁶⁰ The lack of representative samples in few studies is an important limitation, thereby restricting the generalizability of findings to the wider population.⁵¹ Consequently, a cautious approach should be adopted in categorizing excessive smartphone use under the umbrella of addiction.⁶ Furthermore, as this is a narrative review, the results were not confined solely to preselected articles; rather, additional studies with supportive findings were included to enrich the discussion, which may introduce a degree of selection bias.

Conclusions

Despite the diagnostic criteria for smartphone addiction being proposed,⁶¹ further research is required to establish smartphone addiction and the diagnostic measurement.⁶³ This is because studies to date have used inconsistent operational definitions and measurements for smartphone addiction. Therefore, any generalization or comparison of findings across studies should be done judiciously.⁴¹

From the perspective of personality domains, high neuroticism was significantly associated with excessive smartphone use^26,40–43 and, in some studies, it was the only trait that significantly predicted problematic smartphone use.³² Anxiety levels and frequency of neurotic personality traits have been shown to increase the severity of smartphone addiction.⁴³ Conversely, extraversion may not necessarily lead to addiction but may contribute to higher levels of smartphone usage.^{7,25,26,41,44} Impulsiveness was more likely to occur during both positive and negative intense distressing emotions.^54,55 Openness to experience traits had an overall small negative association and was not a significant predictor for smartphone addiction.^{7,10,21,31,41,44} Conscientiousness is potentially a protective factor in developing smartphone addiction, since it is the ability to control impulses. However, personality may not always serve as a moderating factor in the relationship between smartphone addiction and mental health.⁴¹ High agreeableness scores may be a protective factor with these individuals being likable and pleasant and emphasize harmony in relationships.⁶⁴ Individuals with high conscientiousness and agreeableness are more likely to use problem-focused and social-support coping strategies when faced with daily stressors²³ and may be less likely to use technology as a form of avoidance strategy.⁶⁵ During adulthood, these traits may play a key role to help individuals deal more effectively with stress,⁶⁶ and ultimately protect themselves from developing smartphone addiction.⁵¹It is necessary for future studies to differentiate between factors such as social media use and gaming from general smartphone use,⁶⁰ as these domains are predominantly operated through Internet-enabled smartphones, making their boundaries intertwined.³⁸ In addition, research assessing the relationship of smartphone addiction and social–emotional distress is still in its infancy.⁴¹ It would be pertinent to examine mediation and the possible causality between particular variables using a longitudinal design.^51,57

Finally, eliminating smartphone use completely is not a practical option, since they also comprise of many applications which can be effective tools for coping with social–emotional distress. But it is a matter of finding the right balance of use for everyone. It is therapeutically prudent to consider which features are most beneficial including the applications that improve the emotional well-being of individuals and to increase their adaptive behaviors.⁴¹

Footnotes

Acknowledgments

The authors thank Mr. K. John Zachariah, Mr. Tarun George, Ms. Sarah John, and Mr. Vivek Misra who helped in the guidance for the review article.

Authors’ Contributions

M.K.S. conceptualized the article. N.J. and M.S. drafted the first version of the article. M.K.S. edited the first draft and provided new ideas to be incorporated in the final article. All the authors read and approved the final article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

The researchers did not receive any specific grant from funding agencies in the public, commercial, or the profit sectors.

References

Anand

, Sharma

, Chakraborty Hakur

, et al. Doomsurfing and doomscrolling mediate psychological distress in COVID‐19 lockdown: Implications for awareness of cognitive biases. Perspect Psychiatr Care, 2022; 58(1):170–172; doi: 10.1111/ppc.12803

Sahu

, Gandhi

, Sharma

. Mobile phone addiction among children and adolescents: A systematic review. J Addict Nurs, 2019; 30(4):261–268; doi: 10.1097/JAN.0000000000000309

Leung

. Linking psychological attributes to addiction and improper use of the mobile phone among adolescents in hong kong. J Child Media, 2008; 2(2):93–113; doi: 10.1080/17482790802078565

Lenhart

, Purcell

, Smith

, et al. Social Media & Mobile Internet Use among Teens and Young Adults. Washington, D.C.; 2010.

Lee

, Busiol

. A review of research on phone addiction amongst children and adolescents in Hong Kong. Int J Child Adolesc Heal, 2016; 9(4):433–442.

Kim

, Jeong

, Cho

, et al. Personality factors predicting smartphone addiction predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control. PLoS One, 2016; 11(8):e0159788; doi: 10.1371/journal.pone.0159788

Erdem

, Uzun

. Smartphone addiction among undergraduates: Roles of personality traits and demographic factors. Tech Know Learn, 2022; 27(2):579–597; doi: 10.1007/s10758-020-09467-1

Orben

, Przybylski

. The Association between Adolescent Well-Being and Digital Technology Use. University of Oxford; 2019.

Bhardwaj

, Ashok

. Mobile Phone addiction and loneliness among teenagers. Int J Indian Psychol, 2015; 2(3):27–34.

10.

Cocoradă

, Maican

, Cazan

, et al. Assessing the smartphone addiction risk and its associations with personality traits among adolescents. Child Youth Serv Rev, 2018; 93:345–354; doi: 10.1016/J.CHILDYOUTH.2018.08.006

11.

Mothar

NMM

, Hassan

MBA

, Hassan

MSBH

, et al. The importance of smartphone’s usage among malaysian undergraduates. IOSR J Humanit Soc Sci, 2013; 14(3):112–118.

12.

Gallimberti

, Buja

, Chindamo

, et al. Problematic cell phone use for text messaging and substance abuse in early adolescence (11- to 13-year-olds). Eur J Pediatr, 2016; 175(3):355–364; doi: 10.1007/s00431-015-2645-y

13.

Griffiths

. Does internet and computer “Addiction” Exist? some case study evidence. CyberPsychology Behav, 2000; 3(2):211–218; doi: 10.1089/109493100316067

14.

Griffiths

. A “components” model of addiction within a biopsychosocial framework. J Subst Use, 2005; 10(4):191–197; doi: 10.1080/14659890500114359

15.

Jeong

, Lee

, Kim

, et al. Associations of personality and clinical characteristics with excessive Internet and smartphone use in adolescents: A structural equation modeling approach. Addict Behav, 2020; 110(May):106485; doi: 10.1016/j.addbeh.2020.106485

16.

Frances

, Widiger

. Psychiatric diagnosis: Lessons from the DSM-IV past and cautions for the DSM-5 future. Annu Rev Clin Psychol, 2012; 8:109–130; doi: 10.1146/ANNUREV-CLINPSY-032511-143102

17.

De-Sola Gutiérrez

, Rodríguez de Fonseca

, Rubio

. Cell-Phone addiction: A review. Front Psychiatry, 2016; 7:175; doi: 10.3389/fpsyt.2016.00175

18.

Harris

, Regan

, Schueler

, et al. Problematic mobile phone and smartphone use scales: A systematic review. Front Psychol, 2020; 11(May):672; doi: 10.3389/fpsyg.2020.00672

19.

Khalil

, Alharbi

, Alhawasawi

, et al. Prevalence of internet addiction among nursing students and the association with their academic performance and mental health. Athens J Heal, 2016; 3(4):291–306.

20.

Alhajjar

. Internet addiction and psychological morbidity among nursing students in Gaza-Palestine. AJAP, 2014; 3(4):99–103; doi: 10.11648/j.ajap.20140304.13

21.

Eichenberg

, Schott

, Schroiff

. Problematic Smartphone Use—Comparison of Students With and Without Problematic Smartphone Use in Light of Personality. Front Psychiatry, 2020; 11:599241.

22.

Trull

, Widiger

. Dimensional models of personality: The five-factor model and the DSM-5. Dialogues Clin Neurosci, 2013; 15(2):135–146; doi: 10.31887/DCNS.2013.15.2/TTRULL

23.

Penley

, Tomaka

. Associations among the Big Five, emotional responses, and coping with acute stress. Pers Individ Dif, 2002; 32(7):1215–1228; doi: 10.1016/S0191-8869(01)00087-3

24.

Kuss

, van Rooij

, Shorter

, et al. Internet addiction in adolescents: Prevalence and risk factors. Comput Human Behav, 2013; 29(5):1987–1996; doi: 10.1016/j.chb.2013.04.002

25.

Billieux

, Maurage

, Lopez-Fernandez

, et al. Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Curr Addict Rep, 2015; 2(2):156–162; doi: 10.1007/s40429-015-0054-y

26.

Billieux

. Problematic use of the mobile phone: A literature review and a pathways model. Cpsr, 2012; 8(4):299–307; doi: 10.2174/157340012803520522

27.

Moher

, Liberati

, Tetzlaff

, et al.; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med, 2009; 6(7):e1000097; doi: 10.1371/journal.pmed.1000097

28.

Corporation for Digital Scholarship. Zotero: 6.0. 2024. Available from: https://www.zotero.org/ [Last accessed: April 7, 2025].

29.

Arpaci

, Kocadag Unver

. Moderating role of gender in the relationship between big five personality traits and smartphone addiction. Psychiatr Q, 2020; 91(2):577–585; doi: 10.1007/S11126-020-09718-5

30.

Choi

, Kim

, Choi

, et al. Comparison of risk and protective factors associated with smartphone addiction and Internet addiction. J Behav Addict, 2015; 4(4):308–314; doi: 10.1556/2006.4.2015.043

31.

Ehrenberg

, Juckes

, White

, et al. Personality and self-esteem as predictors of young people’s technology use. Cyberpsychol Behav, 2008; 11(6):739–741; doi: 10.1089/cpb.2008.0030

32.

Horwood

, Anglim

. Personality and problematic smartphone use: A facet-level analysis using the Five Factor Model and HEXACO frameworks. Comput Human Behav, 2018; 85:349–359; doi: 10.1016/J.CHB.2018.04.013

33.

Kheradmand

, Amirlatifi

, Rahbar

. Personality traits of university students with smartphone addiction. Front Psychiatry, 2023; 14:1083214; doi: 10.3389/FPSYT.2023.1083214/BIBTEX

34.

Kita

, Luria

. The mediating role of smartphone addiction on the relationship between personality and young drivers’ smartphone use while driving. Transp Res Part F Traffic Psychol Behav, 2018; 59:203–211; doi: 10.1016/J.TRF.2018.09.001

35.

Lee

, Tam

, Chie

. Mobile phone usage preferences: The contributing factors of personality, social anxiety and loneliness. Soc Indic Res, 2014; 118(3):1205–1228; doi: 10.1007/S11205-013-0460-2

36.

Mosalanejad

, Nikbakht

, Abdollahifrad

, et al. the prevalence of smartphone addiction and its relationship with personality traits, loneliness and daily stress of students in jahrom university of medical sciences in 2014: A cross-sectional analytical study. J Res Med Dent Sci, 2019; 7(2):131–136.

37.

Peterka-Bonetta

, Sindermann

, Elhai

, et al. Personality associations with smartphone and internet use disorder: A comparison study including links to impulsivity and social anxiety. Front Public Heal, 2019; 7(JUN); doi: 10.3389/FPUBH.2019.00127

38.

Remondi

, Compare

, Tasca

, et al. Insecure attachment and technology addiction among young adults: The mediating role of impulsivity, alexithymia, and general psychological distress. Cyberpsychol Behav Soc Netw, 2020; 23(11):761–767; doi: 10.1089/cyber.2019.0747

39.

Takao

, Takahashi

, Kitamura

. Addictive personality and problematic mobile phone use. Cyberpsychol Behav, 2009; 12(5):501–507; doi: 10.1089/cpb.2009.0022

40.

Toda

, Ezoe

, Mure

, et al. Relationship of smartphone dependence to general health status and personality traits among university students. Ojpm, 2016; 06(10):215–221; doi: 10.4236/OJPM.2016.610020

41.

Volungis

, Kalpidou

, Popores

, et al. Smartphone addiction and its relationship with indices of social-emotional distress and personality. Int J Ment Health Addiction, 2020; 18(5):1209–1225; doi: 10.1007/s11469-019-00119-9

42.

Kwon

, Lee

, Won

, et al. Development and validation of a smartphone addiction scale (SAS). PLoS One, 2013; 8(2):e56936; doi: 10.1371/JOURNAL.PONE.0056936

43.

Mok

, Choi

, Kim

, et al. Latent class analysis on internet and smartphone addiction in college students. Neuropsychiatr Dis Treat, 2014; 10:817–828; doi: 10.2147/NDT.S59293

44.

Marengo

, Poletti

, Settanni

. The interplay between neuroticism, extraversion, and social media addiction in young adult Facebook users: Testing the mediating role of online activity using objective data. Addict Behav, 2020; 102:106150; doi: 10.1016/J.ADDBEH.2019.106150

45.

Kuss

, Griffiths

, Karila

, et al. Internet addiction: A systematic review of epidemiological research for the last decade. Curr Pharm Des, 2014; 20(25):4026–4052.

46.

Trub

, Barbot

. Great Escape or path to self-expression?: Development and validation of a scale of motivations for text messaging. Meas Eval Couns Dev, 2020; 53(1):44–61; doi: 10.1080/07481756.2019.1667244

47.

, Katoh

, Chen

, et al. Text messaging: Are dependency and Excessive Use discretely different for Japanese university students? Psychiatry Res, 2014; 216(2):255–262; doi: 10.1016/J.PSYCHRES.2013.12.024

48.

Bianchi

, Phillips

. Psychological predictors of problem mobile phone use. Cyberpsychol Behav, 2005; 8(1):39–51; doi: 10.1089/cpb.2005.8.39

49.

Igarashi

, Motoyoshi

, Takai

, et al. No mobile, no life: Self-perception and text-message dependency among Japanese high school students. Comput Human Behav, 2008; 24(5):2311–2324; doi: 10.1016/J.CHB.2007.12.001

50.

McCrae

, Costa

PT.

JR , Personality in Adulthood. Taylor & Francis; 2003; doi: 10.4324/9780203428412

51.

Marengo

, Sindermann

, Hackel

, et al. The association between the Big Five personality traits and smartphone use disorder: A meta-analysis. J Behav Addict, 2020; 9(3):534–550; doi: 10.1556/2006.2020.00069

52.

de Vries

MFRK

. The dangers of feeling like a fake. Harv Bus Rev, 2005; 83(9):108–116. 159.

53.

Dawe

, Loxton

, Gullo

, et al. The role of impulsive personality traits in the initiation, development, and treatment of substance misuse problems. Transl Addict Sci Into Pract, 2007:321–339; doi: 10.1016/B978-008044927-2/50064-X

54.

Cyders

, Smith

. Emotion-based dispositions to rash action: Positive and negative urgency. Psychol Bull, 2008; 134(6):807–828; doi: 10.1037/A0013341

55.

Cyders

, Smith

. Mood-based rash action and its components: Positive and negative urgency. Pers Individ Dif, 2007; 43(4):839–850; doi: 10.1016/J.PAID.2007.02.008

56.

Contractor

, Weiss

, Tull

, et al. PTSD’s relation with problematic smartphone use: Mediating role of impulsivity. Comput Human Behav, 2017; 75:177–183; doi: 10.1016/j.chb.2017.05.018

57.

Grant

, Lust

, Chamberlain

. Problematic smartphone use associated with greater alcohol consumption,mental health issues, poorer academic performance, and impulsivity. J Behav Addict, 2019; 8(2):335–342; doi: 10.1556/2006.8.2019.32

58.

Cao

, Su

, Liu

, et al. The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents. Eur Psychiatry, 2007; 22(7):466–471; doi: 10.1016/J.EURPSY.2007.05.004

59.

Billieux

, Van Der Linden

, Rochat

. The role of impulsivity in actual and problematic use of the mobile phone. Appl Cogn Psychol, 2008; 22(9):1195–1210; doi: 10.1002/ACP.1429

60.

H S

, Na

, Kim

. The relationship between smartphone addiction predisposition and impulsivity among Korean smartphone users. Addict Res Theory, 2018; 26(1):77–84; doi: 10.1080/16066359.2017.1312356

61.

Kardefelt-Winther

. Commentary on: Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research: Problems with atheoretical and confirmatory research approaches in the study of behavioral addictions. J Behav Addict, 2015; 4(3):126–129; doi: 10.1556/2006.4.2015.019

62.

Mottram

, Fleming

. Extraversion, impulsivity, and online group membership as predictors of problematic internet use. Cyberpsychol Behav, 2009; 12(3):319–321; doi: 10.1089/CPB.2007.0170

63.

Sha

, Sariyska

, Riedl

, et al. Linking internet communication and smartphone use disorder by taking a closer look at the Facebook and WhatsApp applications. Addict Behav Rep, 2019; 9:100148; doi: 10.1016/J.ABREP.2018.100148

64.

Weinstein

, Lejoyeux

. Internet addiction or excessive internet use. Am J Drug Alcohol Abuse, 2010; 36(5):277–283; doi: 10.3109/00952990.2010.491880

65.

Busch

, McCarthy

. Antecedents and consequences of problematic smartphone use: A systematic literature review of an emerging research area. Comput Human Behav, 2021; 114:106414; doi: 10.1016/J.CHB.2020.106414

66.

Bleidorn

, Hopwood

, Lucas

. Life events and personality trait change. J Pers, 2018; 86(1):83–96; doi: 10.1111/JOPY.12286