Mobile phones have become essential tools of modern life [1
]; globally, mobile–cellular subscriptions are 103.5 per 100 inhabitants. In Australia, where this study takes place, subscriptions are 109.6 per 100 inhabitants [2
]. The ubiquity and technological evolution of the device has seen them enter and change the dynamic of environments, human behaviour and social cohesion [3
]. Mobile phones have developed into the exponentially more powerful “smartphone”, internet-enabled devices affording users more features and facilities such as digital cameras, GPS navigation, media players, applications facilitating social connection, access to unlimited information, games, gambling, video, music and much more [4
]. Above all, however, the smartphone is a social tool, allowing users to interact and network [5
] (Please note that the terms ‘smartphone’ and ‘mobile phone’ are used interchangeably).
In Australia, 84 percent of the population reported that they accessed the internet via a mobile phone, with 79 percent reporting that they used a phone to access the internet multiple times a day [6
]. This uptake has both positive and negative consequences. Communication and a sense of connectedness [7
] across vast distances occur at little cost [8
]. The device can facilitate a sense of belonging and social inclusion [9
]. Mobile applications assist in healthy eating, physical activity or chronic disease management [10
], while GPS navigation and applications that manage speed assist road users [11
]. There is also considerable research into the positive and even life-saving connections afforded by mobile phones in different communities, from Indigenous communities in Australia [12
] to LGBTIQ+ young people [13
] through to people suffering with serious mental illness [14
In contrast, negative consequences include unhealthy and/or harmful uses such as addictive patterns of use, anti-social use such as cyber-bullying or avoiding physical social interactions [15
] and dangerous and risky use such as using a mobile phone while driving [17
], engaging in applications to facilitate risky sexual behaviour [18
], or pre-existing addictions like gambling [19
In order to determine the psychological predictors of one’s cognitive and behavioural interaction with their mobile phones, Walsh and colleagues [9
] developed the Mobile Phone Involvement questionnaire (MPI-Q). By measuring the frequency of mobile phone use, mobile phone involvement, self-identity and validation from others, they revealed the importance of distinguishing between frequency of use and one’s psychological involvement with the smartphone, determining that, regardless of why one is using their mobile, “frequent” or “involved” use produces dependency. They also identified the need to belong, feel connected and develop a social identity as major social psychological drivers related to mobile phone use [9
Taken together, these findings are significant. If time is positively correlated with dependency, and social–psychological factors such as belonging, connectedness and social identification encourage attachment to mobile phones, being without a mobile phone should then cause feelings of disconnectedness, isolation, or psychological disquiet [22
Indeed, not being able to communicate and losing connectedness are part of a collection of symptoms that have come to be known as nomophobia [23
]. The term, an abbreviation of “no mobile phone phobia”, refers to the discomfort, nervousness or anxiety caused by being out of contact with a mobile phone [22
Predicated upon the MPI-Q, Yildirim and Correia [23
] developed a scale for measuring the “phobia”, the Nomophobia Questionnaire (NMP-Q) (Appendix A
). They used the NMP-Q to study 301 undergraduate students and revealed the existence of an “irrational” fear of not being able to communicate, losing the connectedness that mobile phones facilitate, not being able to access information and the loss of convenience. Additionally, the respondents avoided environments where their smartphones were inaccessible. This begs the questions, how would these individuals behave in environments where phone use is prohibited, and does the extent to which they avoid being without a mobile phone lead to problematic phone behaviours?
As a relatively new measurement, the classification of nomophobia is contested. Similarly, the concept of “nomophobia” is complex and worth unpacking. There exists an overlap between nomophobia and other types of phobias, behavioural addictions [5
], and mental illnesses [24
], leading to the terms phobia and addiction being used interchangeably to define the condition [23
]. Phobias are extreme or irrational fears, causing, at worst, a strong desire to avoid the situation or at best a dreaded endurance [27
]. A mobile phone can be a channel for connection, learning, belonging and inclusion. To describe the fear of being without this conduit as a phobia is reductive and pathologises mobile phone use as something to be overcome or treated; to score highly on the nomophobia scale may merely demonstrate the device’s usefulness than illustrate a pathology. However, earlier studies that sought to test this concept defined nomophobia as a “disorder of the modern world” [25
], and Bragazzi and Puente [22
] suggested it be added to the Diagnostic and Statistical Manual of Mental Disorders (DSM).
For instance, Lee and colleagues [24
] sought to examine how nomophobia’s psychometric properties might relate to existing disorders [22
]. Their study employed the NMP-Q to measure its relationship with obsessiveness among 400 college-aged participants. The results indicated higher scores of obsessiveness corresponded to higher levels of nomophobia. They also found that an obsessive individual is likely to exhibit more anxiety when their mobile phone is absent [24
]. This may mean nomophobia merely indicates the existence of other psychiatric disorders [22
]. Other studies attempted to identify the predictors of nomophobia [28
]. Olivencia-Carrión and colleagues [29
] surveyed 968 participants (average age = 23 years) to explore a possible relationship between temperament, personality and the development of nomophobia. Two statistically significant contributors were discovered: Cooperation (people who are socially tolerant, empathetic, helpful, and compassionate) was associated with low levels of nomophobia, while reward dependence (the tendency to respond constantly and intensely to signals of reward and show a sensitivity to threat cues) was associated with higher levels of nomophobia [29
]. Argumosa-Villar and colleagues [28
] researched the relationship between nomophobia and personality. They found nomophobia and extraversion to be positively correlated, while conscientiousness, emotional stability and self-esteem were negatively correlated [28
]. Tams et al. [30
] detailed the interdependencies between nomophobia, social threat, uncertainty and control in the prediction of stress. They revealed nomophobia leads to stress by generating feelings of being socially threatened. Such feelings manifest when individuals have low certainty about how long they must be without a phone, and low control over when they can choose to regain access.
In short, the fear of being without a mobile phone has a relationship with psychometric traits that can lead to problematic behaviour; however, there is no research detailing whether nomophobia is actually problematic. Additionally, a recent systematic literature review of over 42 studies focused on nomophobia [31
] found the condition exists globally; however, no studies have explored nomophobia is Australia.
1.2. Problematic Mobile Phone Use
How problematic use is defined, measured and contextualised is a matter of significant debate [32
]. Research suggests a number of problematic behaviours can arise from smartphone use. Users can become dependent resulting in an inability to turn off the phone, can feel a sense of loss without it, feel unable to live without it or they might be unable to resist the impulse to use the phone. Using certain functions on a phone in prohibited or forbidden areas such as libraries, the cinema, aeroplanes or spaces where silence is required has also been documented. In addition to anti-social use, dangerous use of smartphones has been extensively documented, such as distracted driving [33
] or crossing roads [34
]. A variety of scales exist to measure problematic use [32
]; however, this research uses the Problematic Mobile Phone Use Questionnaire (PMPUQ-R) [36
] given its ubiquity and reflection of contemporary developments in mobile technologies and society. The PMPUQ-R captures three pertinent problematic themes: dependent use, prohibited use and dangerous use (Appendix B
). Each factor within the scale is devised in consideration of specific criteria and rigorous research to distinguish problematic use from mere overuse or benign phone behaviours.
1.3. Aims and Hypothesis
The aim of the current study is to explore the relationship between nomophobia and problematic smartphone use to determine if the fear of being without one’s phone can predict problematic behaviour such as dangerous, prohibited and dependent use. This is important as studies avoid characterising smartphone overuse or dependency as an addiction due to the absence of physical health decrements. Similarly, problematic use may contribute to the same psychopathological conditions that beget nomophobia, such as feelings of isolation, low self-esteem, sensitivity to threat cues and stress. In determining whether problematic behaviours arise in the context of nomophobia, arguments can be made and interventions designed in support of reducing phone use to create better environments and improve the well-being of vulnerable phone users. It was hypothesised that individuals with higher levels of nomophobia would engage in more problematic mobile phone use.
Participants completed an online survey (SUDS) that took approximately 20 min to complete (described below). The survey was available online from June 2019 to August 2019. A total of 3806 participants commenced the online survey; however, 624 did not progress further than the eligibility questions and were excluded from the analyses. Three hundred and forty-four participants were also excluded because they had completed less than 51 percent of the survey, meaning they did not complete both the NMP-Q and PMPUQ-R questionnaires. The number of participants included in the current analyses was 2838. Participants comprised smartphone users over 18 with a valid Victorian driver licence and who reported that they were regular drivers (i.e., driven at least once per week during the previous month).
1.4.2. Data Collection
This study formed part of a larger project examining factors that relate to smartphone use while driving. The online survey named the Smartphone Use and Driving Survey (SUDS) consisted of seven sections administered in the following order: (a) socio-demographic characteristics (e.g., age, gender, income), (b) average time spent daily using a smartphone, (c) nomophobia severity (NMP-Q), (d) problematic mobile phone use (PMPUQ-R), (e) driving and smartphone use history, (f) formal and informal deterrence effects and (g) smartphone use while driving behaviours. This study, however, focussed on the first four measurements: socio-demographic characteristics, daily time spent using a smartphone, nomophobia severity and problematic mobile phone use.
Socio-Demographic Characteristics and Smartphone Use
Participants were asked to provide information about their age group, gender, combined household income (before tax) and highest degree or level of education completed. Participants were also asked about their smartphone (i.e., whether they were Android or iPhone users). Daily time spent using a smartphone use was measured by asking participants how many hours per day, on average, they spent using their smartphone. Instructions for acquiring a real-time reading were provided to participants for both Android and iPhone users by directing them to their phone’s inbuilt screen-time application.
Nomophobia severity, or participants’ psychological attachment to their mobile phone, was measured using the NMP-Q [23
] (Appendix A
). The NMP-Q is a 20-item questionnaire, where each item is rated on a 7-point Likert scale (where 1 = Strongly disagree, 7 = Strongly agree). The NMP-Q comprises four factors; (1) not being able to communicate, (2) losing connectedness, (3) not being able to access information and (4) giving up convenience [23
]. Nomophobia severity is calculated by summing item responses to produce a total score ranging from 20 to 140, where higher scores represent higher levels of nomophobia. As per Yildirim and Correia’s [23
] scoring instructions, an NMP-Q score less than or equal to 20 indicates the “absence” of nomophobia; an NMP-Q score greater than 20 and less than 60 corresponds to a “mild” level of nomophobia; an NMP-Q score greater than 60 and less than 100 corresponds to a moderate level of nomophobia; and an NMP-Q score greater than or equal to 100 corresponds to a severe level of nomophobia. The NMP-Q has excellent internal consistency for all the items (Cronbach’s alpha = 0.95) (23, p. 135).
Problematic Mobile Phone Use
The PMPUQ-R is a 16-item questionnaire divided into three factors: dependent use, prohibited use and dangerous use [36
]. Each item is rated on a 4-point Likert scale (where 1 = Strongly disagree, 4 = Strongly agree). Overall scores ranged from 16 to 64, where higher scores represent a greater likelihood of potential problems arising due to mobile phone use. The PMPUQ-R also has excellent internal consistency for all the items (Cronbach’s alpha = 0.86) [36
Ethics approval for this study was granted by the Monash University Human Research Ethics Committee. Participants were invited to complete the online survey on a voluntary basis through various online avenues. The survey was advertised on the VicRoads Facebook page, among the Community Road Safety Councils, the Royal Automotive Club of Victoria’s (RACV) newsletter, Monash University’s student Facebook page and a Facebook page created especially for the survey. All posts explained the purpose of the study and that participants who completed the survey could opt to go into a draw to win one of four $100 gift-card prizes.
1.6. Data Analysis
Descriptive analyses were conducted to reveal participant demographics (Table 1
) and daily smartphone use (Table 2
). Chi-square tests were conducted to explore the relationships between socio-demographic characteristics and daily time spent using a smartphone (Table 3
). Nomophobia severity was described (Table 4
), and chi-square tests explored the relationship between socio-demographic characteristics and nomophobia severity levels (Table 5
Three separate regression models were conducted to explore the impact of age, gender, average hours spent per day on smartphone and nomophobia severity as predictors of each problematic mobile phone use factor (dependent, prohibited, dangerous).
Scores for dependent use were normally distributed, and therefore, a multiple regression model was conducted. However, the scores for prohibited and dangerous use were not normally distributed (i.e., positively skewed by over 30 percent) and were therefore transformed into binary variables (where 0 = no problematic use, and 1 = problematic use), and logistic regression models were conducted. All statistical analyses were conducted using IBM SPSS v 25 (IBM SPSS, Inc., Chicago, IL, USA).
2.1. Socio-Demographic Characteristics
As shown in Table 1
, participants were most likely to be aged between 40 and 59 years (34.2 percent) and female (52.9 percent).
2.2. Smartphone Use
Participants were asked about their smartphone type and the number of hours per day, on average, that they spend using their smartphone. More than half of the participants (53.8 percent) reported that they used an iPhone, while the remaining participants (46.2 percent) reported using an Android. As shown in Table 2
, most participants (56.7 percent) reported that they used their smartphone up to three hours per day, and the remaining reported that they used it three hours or more per day.
Chi-square tests were conducted to explore the relationships between daily time spent using a smartphone (≤3 h vs. >3 h per day) and age group and gender. As shown in Table 3
, there was a significant relationship between daily time spent using a smartphone and age group. Participants aged 18–25 years were more likely to spend more than three hours on their phone per day compared to all other age groups, while participants aged 60 years and older were more likely to spend three hours or less per day on their smartphones. There was also a significant relationship between daily time spent using a smartphone per day and gender. Males were more likely to spend three hours or less per day on their smartphones compared to females.
Participants’ responses to the nomophobia scale were also assessed. The mean nomophobia score was 69.4 (SD = 25.1, Range = 20.0–140.0) and had excellent internal consistency (Cronbach’s Alpha = 0.955). The distribution was approximately symmetric with a skewness value of 0.26. Participants’ nomophobia scores were then classified into one of four nomophobia severity categories as described by Yildirim and Correia [23
]. As shown in Table 4
, most participants were classified as having a “moderate” level of nomophobia (48.7 percent). It was also interesting to note that less than one percent of participants had an “absence” level of nomophobia (0.8 percent), while 13.2 percent were classified as having a “severe” level of nomophobia.
Chi-square tests were conducted to explore the relationships between age, gender, average hours spent on a smartphone per day and levels of nomophobia severity (Table 5
). There were significant relationships between levels of nomophobia severity, age, gender and average hours per day spent on a smartphone. Older participants (i.e., aged 40–59 years and aged 60+ years) were less likely to experience moderate or severe levels of nomophobia, while younger participants (i.e., aged 18–25 years) were more likely to experience severe levels of nomophobia. Female participants were more likely than male participants to experience moderate or severe levels of nomophobia, while participants who spend more than three hours on their smartphone per day were more likely to experience severe levels of nomophobia compared to participants who spend three hours or less per day.
2.4. Relationship between Nomophobia and Problematic Mobile Phone Use
A multiple regression model was conducted to investigate the impact of age group, gender, level of nomophobia severity and hours spent using a smartphone per day on the likelihood that participants engage in dependent mobile phone use (Table 6
The multiple regression model was statistically significant, f(4) = 657.307, p < 0.001. The model explained 48.7 percent (R2) of the variance in dependent mobile phone use and had great internal consistency (Cronbach’s Alpha = 0.86). All degrees of nomophobia were significant predictors of dependent use. That is, the greater the nomophobia, the more likely problematic dependent behaviours occur. Those scoring severe levels of nomophobia were 11.7 times more likely than the absence of nomophobia cohort to be problematically dependent on their mobile phones. Age was strongly negatively correlated with participants’ problematic dependent mobile phone use, meaning younger phone users were more likely to be dependent. Spending over three hours a day on a smartphone was a significant predictor of greater dependent use. Gender did not significantly predict participants’ dependent mobile phone use.
Logistic regression models were conducted to investigate the impact of age group, gender, level of nomophobia severity and hours spent using a mobile phone per day on the likelihood that participants engage in prohibited mobile phone use (Yes, No) or dangerous mobile phone use (Yes, No) (Table 7
and Table 8
The logistic regression model was statistically significant, χ2(8) = 555.214, p < 0.001. The model explained 18.1 percent (Cox and Snell R2) to 25.4 percent (Nagelkerke R2) of the variance in prohibited mobile phone use and correctly classified 73.2 percent of cases. The prohibited factor had good internal consistency (Cronbach’s Alpha = 0.73). Younger participants were most likely to engage in prohibited mobile phone use. For example, compared to participants aged 18–25 years, participants aged 26–39 years were 54.1 percent less likely to engage in prohibited mobile phone use, while participants aged 60 years and older were 91 percent less likely to engage in prohibited mobile phone use. Male participants were 1.4 times more likely to engage in prohibited mobile phone use compared to female participants. Participants with “severe” nomophobia levels were 10.3 times more likely to engage in prohibited mobile phone use compared to participants with no nomophobia (i.e., “absence”). However, there was no significant difference in prohibited mobile phone use between participants with an absence of nomophobia or a “mild” level of nomophobia. Participants who spend over three hours per day on their mobile phones were 1.5 times more likely to engage in prohibited mobile phone use than participants who spend three hours or less per day on their mobile phones.
The logistic regression model was statistically significant, χ2(8) = 328.854, p < 0.001. The model explained 11.2 percent (Cox and Snell R2) to 16.0 percent (Nagelkerke R2) of the variance in dangerous mobile phone use and correctly classified 73.1 percent of cases and had good internal consistency (Cronbach’s Alpha = 0.71). Compared to participants aged 18–25 years, participants aged 26–39 years were 38 percent less likely to engage in dangerous mobile phone use, while participants aged 60 years and older were 70 percent less likely to engage in dangerous mobile phone use. Male participants were 1.9 times more likely to engage in dangerous mobile phone use compared to female participants. Participants with severe nomophobia were 14 times more likely to engage in dangerous mobile phone use compared to participants with no nomophobia. Participants that spend over three hours per day on their mobile phones were 1.6 times more likely to engage in dangerous use than participants that spend less than 3 h per day on their mobile phones.
Living in the modern world without a mobile phone can put one at a disadvantage; for many, it is not merely a matter of convenience. Sociologically speaking, the desire to seek connection in an increasingly individualised world [72
], to develop a sense of belonging among a fragmentized and global community [73
], to have access to unlimited information in a society that values and preferences knowledge [74
] and to enjoy the instant gratification associated with convenience [75
] are not, ostensibly, a collection of irrational motivations. Indeed, those most likely to be high in nomophobia embody some psychological traits—low self-esteem, emotional instability, low social tolerance—that have been shown to benefit from connection and belonging [76
]. The increase in problematic use among higher levels of nomophobia may merely be an indicator of environmental shortcomings, where one’s offline world is deficient in the benefits a mobile phone usually facilitates. Shifting the focus from an individual’s relationship with their phone toward the substance and dynamic of their offline realities may help to inform thoughtful countermeasures geared toward enriching the absence of mobile technology rather than integrating and encouraging more use. What this may look like is a matter for policy makers; however, by defining and amplifying the rewards of offline spaces, the desire for knowledge, belonging, convenience and connection may transmute into the distinctly analogue experience of wisdom, being, discovery and connecting. This would require further empirical exploration; however, reducing problematic use may first require reducing the drivers of nomophobia.
The contribution of these findings may inform interventions aimed at reducing dangerous, prohibited and problematic dependent use. From a practical sense, it is important to consider ways to deter or accommodate vulnerable phone users from entering environments where phone use is prohibited or dangerous, or from developing habits that lead to dependency. This study has contributed to understanding the prevalence and characteristics of nomophobia within a large Australian sample, and its relationship with problematic use, which may contribute to developing better solutions to safely integrate or mitigate problematic use. As we argue, exhibiting problematic behaviours may indicate an over-reliance on one’s mobile phone to facilitate or elicit that which is lacking offline. It is not merely sufficient to reproach the individual or their behaviour—interventions must consider the existence of and reasons for nomophobia and how it may be encouraging problematic use. This might translate into campaigns that, rather than moralising problematic phone use, acknowledge the device’s important social functions and offer mechanisms for reducing problematic behaviours without disconnecting people from their network. The rate at which younger people adopt smartphone technology makes it increasingly important for parents, educators, government policy makers and the community to be aware of problematic behaviours that attachment to the device can cause, while correctly identifying all vulnerable phone users and redressing their behaviour appropriately. These results also support the need for further research into the effects of nomophobia on specific problematic environments. For instance, although a plethora of research around mobile phone driver distraction exists [78
], no research has analysed dependency as a predictor for using a mobile phone illegally while driving. If people scoring high in nomophobia are more likely to use their phones illegally while driving, road safety policy could address phone habits alongside current risk-focused [83
] interventions. Similarly, pre-existing psychological conditions can exacerbate nomophobia and, as our research has shown, can lead to problematic use. Addressing these antecedent traits may have a flow on effect. For instance, mindful practices have been shown to reduce problematic dangerous mobile phone behaviours within the road safety arena [84
]. Such techniques may be effectively employed earlier to reduce problematic use in other environments.