Content uploaded by Francisca López-Torrecillas
Author content
All content in this area was uploaded by Francisca López-Torrecillas on Nov 15, 2018
Content may be subject to copyright.
Contents lists available at ScienceDirect
Psychiatry Research
journal homepage: www.elsevier.com/locate/psychres
Temperament and characteristics related to nomophobia
Maria Angustias Olivencia-Carrión
a,⁎
, Ramón Ferri-García
b
, María del Mar Rueda
b
,
Manuel Gabriel Jiménez-Torres
a
, Francisca López-Torrecillas
a,⁎
a
Center Research Mind Brain and Behaviour (CIMCYC), University of Granada, Spain
b
Department of Statistics and Operations Research and IEMath-GR, University of Granada, Spain
ARTICLE INFO
Keywords:
Nomophobia
Temperament
Character
Cooperation
Reward dependence
ABSTRACT
Nomophobia is defined as the fear of being out of mobile phone contact and is considered to be a phobia of the
modern age. The current study set out to establish the relationship between temperament and personality and
the development of nomophobia. The sample was composed of 968 participants selected from the Andalusian
population, of which there were 182 males and 785 females aged from 23.19 years. The instruments used were
the Questionnaire to Assess Nomophobia (QANIP; Olivencia-Carrión et al., 2018) and the Temperament and
Character Inventory Revised (TCI-R; Cloninger et al., 1993). We found that cooperation is a characteristic that
significantly reduces nomophobic levels, particularly for the two factors of Mobile Phone Addiction and Negative
Consequences. Furthermore, Reward Dependence appears to be positively related to two of the factors involved
in nomophobia, namely Mobile Phone Addiction and Loss of Control,suggesting a relationship between
Nomophobia and personality. These findings are discussed in terms of their usefulness for identifying the per-
sonality predictors of nomophobia in order to develop preventive and intervention strategies.
1. Introduction
Nomophobia is considered to be a disorder of the modern world,
derived from the technological developments and advances that have
been produced by virtual communication. It is defined as the fear of
being out of mobile phone contact and is considered a modern age
phobia that has been introduced to our lives as a product of the inter-
action between people and mobile information and communication
technologies (Nagpal and Kaur, 2016). Although Nomophobia has been
regarded as a controversial term, it is referred to as dependence on
mobile phones (Dixit et al., 2010) or an addiction to mobile phones
(Forgays et al., 2014). Wang et al. (2014) defined it as the feelings of
discomfort, anxiety, nervousness or distress that result from being out
of contact with a mobile phone, even causing suicidal ideation as well
as attempts. King et al. (2014) revised the definition of nomophobia in
order to increase its modern day relevance as a fear of being unable to
communicate through a MP. Nomophobia is a term that refers to a
collection of behaviours or symptoms related to MP use. Therefore, in
the case of nomophobia, people with nomophobia or nomophobes
would have an irrational fear of being out of mobile phone contact or
being unable to use it, and thus they attempt to eliminate the chances of
not being able to use their mobile phone. In the case of being unable to
use their mobile phone, they experience intense feelings of anxiety and
distress (Szyjkowska et al., 2014; Thomée et al., 2011). In this regard, it
remains unclear as to how much distress and impairment can be caused
by nomophobia or the personality variables that are involved, and thus
there is uncertainty with regard to which dimensions merit inclusion in
personality classification. It is therefore necessary to determine whether
harmfulness is likely to occur as a consequence of the personality traits
inherent in nomophobic individuals.
Numerous studies have explored how personality traits contribute
to the onset and maintenance of addiction disorders in young adults,
with high impulsivity and low self-control scores being key factors in
addiction (Lee et al., 2012; Reynolds et al., 2006). Earlier studies have
found that self-control is negatively correlated with the use of tobacco,
alcohol, and cannabis, along with problematic gambling and computer
gaming. Depression and extraversion have also been shown to be spe-
cific to substance users (Walther et al., 2012).
Mobile phone abuse is related to both extraversion (Bianchi and
Phillips, 2005) and neuroticism (Kuss et al., 2014) although anxiety
levels and the frequency of neurotic personality traits increase the se-
verity of the addiction (Mok et al., 2014). Recently, high impulsivity
has been identified as one of the risk factors for addiction to social
networking sites among individuals who suffer from mobile phone
abuse (Kim et al., 2016; Wu et al., 2013).
Cloninger's personality model (Cloninger et al., 1993) is a four-di-
mensional structure comprised of the temperament dimensions referred
to as Novelty-Seeking (NS), Harm Avoidance (HA), Reward Dependence
https://doi.org/10.1016/j.psychres.2018.04.056
Received 5 October 2017; Received in revised form 22 February 2018; Accepted 29 April 2018
⁎
Corresponding authors.
E-mail addresses: maolivencia@ugr.es (M.A. Olivencia-Carrión), fcalopez@ugr.es (F. López-Torrecillas).
Psychiatry Research 266 (2018) 5–10
Available online 06 May 2018
0165-1781/ © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
T
(RD), and Persistence (P) along with three additional character di-
mensions. These character dimensions are Self-Directedness (SD), Co-
operativeness (C) and Self-Transcendence (ST). NS is the tendency to
approach novel situations for rewards, and to experience relief from
non-punishment. High NS includes impulsivity, quick temper, and
proneness to breaking rules. HA is the tendency to inhibit or avoid
responses to aversive cues, such as punishment and non-reward. RD is
the tendency to maintain responses that have been previously condi-
tioned through rewards. High RD is associated with being sociable and
sensitive to social cues. P is the tendency to maintain responses, despite
frustration and fatigue. High P is associated with persevering and being
ambitious. SD reflects the ability to control, regulate, and adapt one's
behaviour to a situation in order to achieve one's goals and values. C
reflects identification with, and acceptance of, others. Finally, ST is
thought to reflect imaginativeness and spirituality. Cloninger's Psy-
chobiological Model provides a better fit for the purpose of our goals,
for three reasons. First, the Temperament and Character Inventory (TCI-
R; Cloninger et al., 1993) predicts certain functional and clinical out-
comes (Arnau et al., 2008). Second, the model was specifically devel-
oped for the purpose of analysing addiction (Gat-Lazer et al., 2017;
López-Torrecillas et al., 2014a,b; Pedrero-Pérez and Ruiz-Sánchez de
León, 2013; Pombo et al., 2017; Vitoratou et al., 2015). Third, research
has demonstrated that personality character profiles predict life sa-
tisfaction. For instance, Park et al. (2015) examined the relationship
between life satisfaction and personality traits and found that the ST
profile was associated with the highest levels of life satisfaction,
whereas the depressive profile was associated with the lowest levels of
life satisfaction. Additionally, high SD, ST, and C were associated with
high life satisfaction. The SD was the strongest in the assessment of
one's quality of life, followed by ST and C. Similarly,
Gutiérrez et al. (2016) indicated that temperament and character affect
mental health, and in general, P stood out as the most important di-
mension regarding career success. SD was the best predictor of social
functioning and HA was linked with clinical problems.
There has been a substantial body of research on the role of dis-
positional constructs (NS, HA, RD, P, SD, C and ST) in the risk of sub-
stance abuse (Lu et al., 2014; Gutierrez et al, 2016). Studies of Internet
addiction have found decreased RD and increased NS among Internet-
addicted participants (Ko et al., 2010) with the latter obtaining higher
scores for TCI-R in NS, HA, P and ST; whilst lower scores in C also
tended to predict the presence of behavioural addiction (Farré et al.,
2015). In a similar survey, Kuss et al. (2014) identified increased
neuroticism and low agreeableness as risk factors for Internet addiction.
However, relatively few studies have examined personality traits
with regard to problematic and addictive abuse or nomophobia.
Problematic mobile phone abuse is related to extraversion and neuro-
ticism (Olivencia-Carrión et al., 2016; Takao, 2014), although anxiety
levels and frequency of neurotic personality traits increase the severity
of such an addiction (Mok et al., 2014). With regard to nomophobia,
King et al. (2014) investigated the appearance of emotional alterations
related to mobile phone abuse and found that nomophobes showed
significant increases in anxiety, tachycardia, respiratory alterations,
trembling, perspiration, panic, fear and depression when they were
apart from or unable to use a mobile phone in comparison with healthy
volunteers. However, the relationship between nomophobia and other
psychological characteristics has received relatively little attention, and
it may be particularly important to examine the predictors of nomo-
phobia. Accordingly, Nagpal and Kaur (2016) studied the gender dif-
ferences in nomophobia and impulsiveness in college students between
the ages of 18 and 23 years and found that there were gender differ-
ences in nomophobia with male students exhibiting higher levels of
nomophobia in comparison with their female counterparts. However,
no gender differences were found in impulsiveness or any of its com-
ponents.
1.1. Aims and hypothesis
The current study is an attempt to understand the modern age
mobile phone addiction known as nomophobia and its relationship with
temperament and personality in the adult population of the Spanish
autonomous community of Andalusia.
We take as our starting point the hypothesis that there are person-
ality variables (temperament and character) that protect against the
appearance of nomophobia. The temperament variables would be re-
flected in low scores in the Search for Novelty, Avoidance of Harm,
Dependence on Reward, and Persistence, whilst the character variables
would be represented by high scores on Self-directedness, Cooperation
and Self-transcendence and vice versa for the risk of the development of
nomophobia.
2. Methods
2.1. Data collection
A sample of 968 respondents from the city of Granada (Spain) was
employed in this experiment. The sample size was calculated according
to the sampling design used, based on a sampling error of + 5 per-
centage points and a confidence level of 95%. Participants were mainly
recruited at their workplace, via recruitment stands, advertisements,
and emails. Their managers/teachers were sent e-mails in which they
were asked to help recruit their employees/students. It was their
managers/teachers who provided us with details of those employees/
students willing to participate in the study. They were recruited from a
range of types of workplace within Granada, including local authorities,
healthcare providers, and retail outlets as well as institutions of higher
and further education, and there was heterogeneity in their geo-
graphical settings, which spanned city center and urban fringe loca-
tions. Participants were informed about the aims of the study and
provided signed informed consent. Ethical approval was obtained from
the Research Ethics Committee from the University of Granada, Spain.
The participants had an average age of 23.19 years (SD 7.23),
ranging between 17 and 55 years old, and the majority (81.1%) being
women. Sociodemographic variables revealed that the majority of the
sample was unemployed (81.3%), which is most likely to be a con-
sequence of the large proportion of students in the sample (78.9%). Of
the respondents who were employed (18.7%), 46.4% were working in
manual jobs, 33.7% in the services and army sector, and 17.7% in the
technological and business sectors. The average number of years of
education for the respondents was 14.07 years (SD 4.12).
2.2. Data preprocessing
An initial search was conducted for missing values, but only one was
found across all the predictor variables (the seven dimensions of the
TCI-R) and thus no action was taken. The individual that presented the
missing value was later excluded from the analysis, as this happened to
be an outlier. Skewness statistics were calculated for all variables
(predictor and predicted) to detect variables with high levels of asym-
metry, in order to transform these according to the nature of the
skewness and its severity. Square-root and log transformations were
used (Tabachnick and Fidell, 2000). Negative skewed variables were
reflected before the transformations, and after completion of the
skewness correction they were reflected again to recover their original
value (Osborne, 2005).
Tukey's (1997) criterion for finding outliers using the interquartile
range was used to find extreme univariate outliers, which resulted in
the exclusion of 3 individuals. In the case of multivariate outliers,
Mahalanobis distance was used, given that it approximately follows a
Chi-square distribution (Afifi and Azen, 1972), although Sidak (1967)
correction had to be used due to the multiple comparisons that take
place in the hypothesis test. Thus, with a final value of 0.00014 for
M.A. Olivencia-Carrión et al. Psychiatry Research 266 (2018) 5–10
6
alpha, 5 individuals were excluded using this process.
Pearson's correlation coefficient matrix was calculated for the multi-
collinearity check in predictor variables. Every pair of correlations was
below the selection criteria of 0.99 (Tabachnick and Fidell, 2000),
meaning that there is no multi-collinearity in predictor variables.
2.3. Weight adjustment
The recruitment of respondents was not probabilistic and could lead
to biased estimates since certain groups are substantially under-re-
presented. Moreover, the sampling frame does not cover the entire
population to which survey results are to be extrapolated. These errors
can be overcome by the use of reweighting or calibration techniques.
Calibration was defined in Särndal (2007) as "the determining of
weights or expansion factors, incorporating auxiliary information to
calculate adjusting factors to the weights originally defined in the
sample design, the use of these weights to calculate population totals
and other parameters in finite population, and the seizing of the cali-
bration adjustments to reduce significantly the bias contribution in the
presence of non-response and other non-sampling errors". The usage of
calibration estimators ensures that survey estimates are coherent with
those already in the public domain, while simultaneously reducing
sampling error and non-coverage (see Cabrera-León et al., 2015, 2017).
For the calibration conducted in this article, population totals of
gender, age, and years of schooling were used as auxiliary variable
totals. These quantities were retrieved from the 2013 population figures
provided by the Spanish National Institute of Statistics (INE), in the case
of gender and age, and from the 2011 Population and Households
Census (also conducted by the INE) in the case of years of schooling.
The retrieval was made for the region of Andalusia. Given that the
sampling frame was located inside this territory, this approach is fea-
sible if the study is to be carried out with the least possible bias.
The new sampling weights obtained in the calibration will be ap-
plied to a regression model using Raking calibration weights with all of
the three auxiliary variables under consideration. The purpose of the
regression is to obtain some measure of the effect that each dimension
of the TCI-R has on the nomophobia questionnaire, with calibration
playing an important role as it provides a certain level of safety in terms
of being able to generalize the measured effects to the entire popula-
tion. To test the hypothesis of whether the effects are null or sig-
nificantly different from null, p-values from the Wald test (Wald, 1943)
and its correction known as the working likelihood ratio (Rao and
Scott, 1984) will be provided, as these are the recommended tests to
apply when the sampling design is complex (Lohr, 2010).
2.4. Questionnaire to assess nomophobia (QANIP; Olivencia-Carrión et al.,
2018)
This questionnaire was developed by Olivencia-Carrión et al. (2018)
and consists of 11 items related to text message abuse, high frequency
of use, spending more than 4 hours per day using the mobile phone
(using the mobile phone all of the time) to cope with negative emotions
or problems, to feel better, showing extreme nervousness and ag-
gressive behaviour when deprived or unable to use the mobile phone,
progressive deterioration in school/work and social and family func-
tioning, and impairments in self and social perception. Each item is
scored from one to five and they describe a four-factor structure ac-
cording to the Exploratory Factor Analysis (EFA) and Confirmatory
Factor Analysis (CFA) performed on the sample of participants de-
scribed in Section 2.1: Factor 1 (Mobile Phone Abuse) consists of four
items (1, 3, 7 and 8) that described 18% of the variance. Factor 2 (Loss
of Control) involves three items (2, 5, and 6) that explained 11% of the
variance. Factor 3 (Negative Consequences) contains three items (9, 10,
and 11) that explained 10% of the variance. Finally, Factor 4 (Sleep
Interference) consists of only one item (number 4) that explained 6% of
the variance. Goodness-of-fit indices for EFA were 0.02 for RMSR,
0.976 for Tucker-Lewis Index (TLI), and 0.033 for RMSEA [CI 90%
0–0.57], while for CFA these were 0.045 for SRMR, 0.969 for Goodness-
of-Fit Index (GFI), 0.941 for TLI and 0.053 for RMSEA [CI 90%
0.039–0.067]. The Cronbach's Alpha reliability coefficient value for the
sample of the present study was 0.80. Convergent validity was assessed
with item-total correlations, which were all significant, while dis-
criminant validity was assessed testing the null hypothesis of mean
equality between the upper and lower groups of each item, which was
rejected for all of the items. Further details on scale analysis and
questionnaire validity can be found in Olivencia-Carrión et al. (2018).
As noted previously, the sample of 968 participants was used for both
the scale and factor analysis and for the weighted regression analysis.
2.5. Temperament and character inventory revised (TCI-R; Cloninger et al.,
1993)
This questionnaire consists of 240 items (5 of these on validity),
with a 5-point Likert-type response scale, grouped into 4 temperament
dimensions (NS, HA, RD, and P) and 3 character dimensions (SD, C and
ST). This instrument has been validated in a general Spanish population
(Gutiérrez-Zotes et al., 2004) and has satisfactory psychometric prop-
erties (Pelissolo et al., 2005).
3. Results
In order to meet required normality assumptions, Factors 2, 3 and 4
were log-transformed to reduce their original skewness. After these
transformations, the residuals of every regression model presented in
this section are normally distributed. Regression models were computed
using R (R Core Team, 2017), and the packages “sampling”(Tillé and
Matei, 2015) and “survey”(Lumley, 2014; Lumley, 2004). Partial cor-
relations and R-squared coefficients were obtained using the SSE-based
approach (Efron, 1978) and computed in R using the package “rsq”
(Zhang, 2017). Linear regression models obtained for all of the factors
of the scale using calibration weighting on the Andalusia population
totals are displayed in Table 1.
The main outcomes to emerge from these regression analyses are
the following: a) Cooperativeness significantly reduces nomophobic
levels, particularly for Factor 1, and b) Reward Dependence appears to
increase nomophobic levels for all of the factors, but primarily for
Factors 1 and 2, where its effect is significantly non-null.
The role of the remaining personality characteristics present in TCI-
R is unclear according to the models. However, several results are
worth noting: First, Novelty-Seeking was important for Factor 3 as a
nomophobia-enhancing characteristic. Second, Harm Avoidance, Self-
Transcendence, and Persistence (of marginal significance) were im-
portant for the same factor. Based on the R-squared values, the model
for Factor 3 is the most explanatory (explaining 0.1460, i. e. 14.6% of
the variability). However, R-squared values for all models are generally
low, meaning that non-controlled variables could be having a great
impact on nomophobia.
The model used to explain the behaviour of the total scale revealed
that Reward Dependence and Cooperation are statistically significant
contributors, with the former being positively linked to nomophobia,
and the latter having a negative correlation with this pathology.
Persistence also emerged as a marginally significant (in statistical
terms) addiction enhancer.
4. Discussion
The main purpose of the present study was to examine the re-
lationship between temperament and personality in nomophobia. Our
study showed that Cooperation (C) significantly reduces Nomophobic
levels for two of the various factors measured (Mobile Phone Addiction
and Negative Consequences), whereas RD appears to increase nomo-
phobic levels for all factors. Other variables such as Novelty Seeking
M.A. Olivencia-Carrión et al. Psychiatry Research 266 (2018) 5–10
7
(NS), Harm Avoidance (HA) and Self-Transcendence (T) also show a
positive, albeit weaker, relationship with Nomophobia. Similar results
have been found in previous studies on behavioural addiction (Farré
et al., 2015). Our results, however, tend to partially refute other pre-
vious findings. In particular, in our study we failed to find significant
differences in terms of the Self Directedness (SD) dimension, although
the Persistence (P) character dimension emerged as a marginally sig-
nificant addiction enhancer.
The NS dimension increases the score on the negative consequences
factor. These results have been observed in other studies of diverse
substance and behavioural addictions (Farré et al., 2015; Gutiérrez
et al., 2016; Lee et al., 2012; Lu et al., 2014; Reynolds et al., 2006). NS
has been defined as the tendency to seek reward signals and strong new
sensations about unknown stimuli. Individuals with high NS scores tend
to be impulsive, enthusiastic, exploratory, and curious. Hence, in-
dividuals high on NS may be more likely to be involved in frequent
communication by mobile phone, which is directly related to nomo-
phobia.
Regarding HA, the present study confirmed that high scores tend to
be associated with an increase in the Negative Consequences factor. HA
is considered as the tendency to respond to aversive stimuli with in-
hibition in order to avoid suffering, punishment, and frustration. High
scorers are regarded as apprehensive worriers that have strong feelings
of anxiety during unpredictable situations (Cross et al., 2011). Only a
few studies have found an increase of HA in nomophobic individuals;
nonetheless, the current results are consistent with other studies that
have found a link between HA or feelings of anxiety with substance
abuse or behavorial addiction (Mok et al., 2014; Gutiérrez et al., 2016).
Thus, overall it appears that temperament and character can have a
substantial impact on career, relationships, and mental health.
It is important to note that in the current study the RD dimension
was higher in nomophobics, primarily in the mobile phone addiction
and loss of control factors. RD is defined as the tendency to respond
constantly and intensely to signals of reward and avoid punishment,
showing a sensitivity to threat cues. It has also been further classified as
a tendency towards pessimism and having feelings of anxiety in un-
predictable situations. There are too little data in the literature on this
dimension to determine if this finding could be linked to other studies.
To our knowledge, the only available study for comparison is the one
reported by Walther et al. (2012) that established lower levels of RD
among Internet addicts. However, Aluja and Blanch (2011) associate
RD with extraversion, and thus our results are consistent with the work
of other authors (Olivencia-Carrión et al., 2016; Takao, 2014; Walther
et al., 2012) who have found that extraversion predicts addictive be-
haviours.
The C character dimension emerges as a characteristic that sig-
nificantly reduces levels of nomophobia, particularly for the factors of
mobile phone addiction and negative consequences. In the present
study, non-dependent excessive users were characterized by high levels
of C, which suggests that this category includes people who are more
socially tolerant, empathic, helpful, and compassionate. Thus, they may
be more likely to have peers to communicate with Lu et al. (2014)
which has been suggested to be a protective factor for mental health
(Gutiérrez et al., 2016). Individuals high on C have been described as
socially tolerant, empathic, helpful, and compassionate, as opposed to
intolerant, callous, unhelpful, and vengeful. Cooperativeness has been
used to describe people who show unconditional acceptance of others,
empathy with others' feelings, and willingness to help without a desire
for selfish domination. Cloninger et al. (1993) regarded high coopera-
tiveness as a sign of psychological maturity and advanced moral de-
velopment. Cooperativeness is assessed using five subscales in the
Temperament and Character Inventory: 1) Social acceptance vs. intol-
erance (C1); 2) Empathy vs. social disinterest (C2); 3) Helpfulness vs.
unhelpfulness (C3); 4) Compassion vs. revengefulness (C4), and 5)
Principles vs. self-advantage (C5). It has been found that drug depen-
dence is associated with lower C scores (Evren et al., 2007). It has also
Table 1
Regression models weighted with ranking calibration on age group, gender, and education level for Andalusia.
Factor 1 Factor 2 Factor 3 Factor 4 Total scale
(Mobile phone abuse) (Loss of control) (Negative consequences) (Sleep interference)
(Intercept) β
0
11.55
⁎⁎⁎
1.89
⁎⁎⁎
1.57
⁎⁎⁎
1.20
⁎⁎⁎
24.10
⁎⁎⁎
Std. Err. (0.14) (0.02) (0.02) (0.02) (0.32)
Novelty-seeking β
1
0.24 0.00 0.05
⁎⁎
−0.05 0.40
Std. Err. (0.20) (0.04) (0.02) (0.03) (0.48)
Partial cor. 0.1852 0.0990 0.2080 −0.1239 0.1449
Harm avoidance β
2
0.20 0.01 0.05*−0.05 0.43
Std. Err. (0.22) (0.04) (0.02) (0.05) (0.57)
Partial cor. 0.1778 0.1003 0.1575 −0.0642 0.1423
Reward dependence β
3
0.44
⁎⁎
0.06*0.03
+
0.01 1.04
⁎⁎
Std. Err. (0.17) (0.02) (0.01) (0.02) (0.36)
Partial cor. 0.2561 0.1947 0.1529 0.0000 0.2388
Persistence β
4
0.20 0.04 0.04
+
−0.00 0.73
+
Std. Err. (0.18) (0.03) (0.02) (0.02) (0.41)
Partial cor. 0.1862 0.1529 0.1694 0.0000 0.1806
Self-directedness β
5
0.29 −0.02 0.01 0.01 0.26
Std. Err. (0.19) (0.03) (0.02) (0.03) (0.45)
Partial cor. 0.1950 −0.1023 0.1126 0.0000 0.1386
Cooperativeness β
6
−0.57
⁎⁎
−0.03 −0.04*−0.03 −1.12*
Std. Err. (0.20) (0.02) (0.02) (0.03) (0.44)
Partial cor. −0.2732 −0.1341 −0.1654 −0.0781 −0.2295
Self-transcendence β
7
0.05 −0.01 0.07
⁎⁎⁎
−0.03 0.24
Std. Err. (0.15) (0.02) (0.02) (0.03) (0.35)
Partial cor. 0.1689 −0.1026 0.2540 −0.0619 0.1366
SSE-based R-squared 0.0977 0.0474 0.1460 0.0530 0.0833
Model deviance 5047.79 110.36 54.75 97.54 27,106.17
Dispersion 5.26 0.12 0.06 0.10 28.27
Number of observations (n) 960 960 960 960 960
⁎⁎⁎
p< 0.001,
⁎⁎
p< 0.01,
⁎
p< 0.05,
+
p< 0.1
M.A. Olivencia-Carrión et al. Psychiatry Research 266 (2018) 5–10
8
been found that Schizophrenia patients have lower C scores than con-
trols (Calvo de Padilla et al., 2006; Glatt et al., 2006; Molina et al.,
2017). Similarly, most individuals with personality disorders (e.g.,
obsessive compulsive disorder) are low in C, show poor interpersonal
functioning, and are described as intolerant, narcissistic, hostile or
disagreeable, critical, unhelpful, or vengeful (Kim et al., 2009).
Finally, the ST character dimension appears increase the score on
the factor of Negative Consequences. ST can be defined as having
spiritual maturity and the desire for spiritual realization, along with the
capacity for meditation and non-materialistic thinking. Moreover, it has
been linked to high levels of life satisfaction, which was highlighted in
some studies (Cloninger et al., 1993) mentioned in the literature re-
view.
Nomophobia can be considered within the framework of non-sub-
stance behaviour addictions. It could be described as a syndrome ana-
logous to substance addiction, but with a focus on a certain behaviour
which, similar to substance consumption, produces short-term reward
and may persist despite harmful consequences (due to diminished
control over the behaviour). The DSM-5 (APA, 2013) broadens the
category of “Substance-Related Disorders”to “Substance Use and Ad-
dictive Disorders”including substance and non-substance-related ad-
dictions. However, non-substance behaviour addictions currently only
include pathological gambling.
There are no specific and agreed diagnostic criteria for non-sub-
stance behaviour addictions like nomophobia, although clinical ex-
perience shows that the excessive use of new technologies is a real
problem that seriously affects certain individuals. Once again, history
repeats itself: Gambling was recognized as a nosological entity in 1980,
when the APA introduced it under the name of "pathological gambling";
however, its existence was recognized by professionals much earlier.
Currently only pathological gambling is recognized as a non-substance
behaviour addiction, whereas the remaining addictions without sub-
stance use (such as the newly emerged internet and mobile phone use)
are still subject to controversy and confusion. However, from clinical
experience, it is clear that the abusive use of new technologies (mobile
or internet) is a real problem that seriously affects people who suffer
from it (Sánchez-Carbonell et al., 2008).
The acknowledgement of behavioural addictions can be traced as
far back as Marlatt et al. (1988) who referred to a repetitive habit
pattern that increases the risk of disease and/or associated personal and
social problems. Addictive behaviours are often experienced sub-
jectively as a loss of control and persistence of the behavour despite
volitional attempts to abstain or achieve moderate use. Furthermore, in
the last decade, a growing amount of research has established psy-
chological and neurobiological similarities between the excessive
practice of these behaviours (e.g., mobile phone abuse/nomophobia,
shopping, sex, internet, video gambling, and eating) and addictive
patterns of use (Billieux et al., 2010; Mentzoni et al., 2011). Research
on the neurobiology of addiction has revealed the existence of a
common mechanism between substance addictions and behavioural
addictions (Leeman and Potenza, 2013; Weinstein and Lejoyeux, 2015).
The problem is that the relationship between the substances that are
included within the diagnostic criteria and those behaviours that are
supposed to be addictive is unknown, because the latter are not in-
cluded in the DSM-5. However, there is now enough evidence to suggest
that alcohol, drugs, and pathological gambling are not the only crip-
pling addictions. Addiction statistics are scarce because many destruc-
tive habits are not yet officially recognized as addictions, these include
mobile phone addiction/nomophobia, gaming, eating, shopping, and
sex, all of which are problematic for a number of reasons. Some of them
involve direct manipulation of pleasure through the use of products that
are ingested into the body, such as drug use disorders and food-related
disorders. The difficulty we have is that we do not know to what extent
these behaviours are addictive because they are not included in the
DSM-5 (APA, 2013) or any other diagnostic tool. Nevertheless, the aim
of our study was to examine the relationship between temperament and
personality in nomophobia. This in turn commits us to advance along
the path of nomophobia research and treatment. A definition of no-
mophobia must take into account the following symptoms: text message
abuse; high frequency of use, spending more than 4 hours per day using
the mobile phone (using the mobile phone all of the time) to cope with
negative emotions or problems or to feel better; showing extreme ner-
vousness and aggressive behaviour when deprived of or unable to use
the mobile phone; progressive deterioration in school/work and social
and family functioning; and impairment in self and social perception.
Our results should be evaluated in the context of several limitations
including that the Questionnaire to Assess Nomophobia (QANIP;
Olivencia-Carrión et al., 2018) employed in the present study requires
further psychometric evaluation. Nevertheless, the scale has been found
to have excellent psychometric properties and offers a concise measure
of nomophobia for use in future studies. Third, even those individuals
who are interested in seeking therapeutic change and admit to negative
personality characteristics sometimes portray themselves in an overly
positive light. Thus, when nomophobes are rewarded for a positive
presentation of themselves, the possibility for a dishonest response style
increases. Therefore, one limitation of this study refers to the accuracy
of participants’responses, since all of our measures relied upon self-
report.
5. Conclusion
There is a relationship between nomophobia and personality. In
particular, the probability of presenting nomophobia increases when an
individual has high RD scores, and decreases when the person has high
C scores. Other variables such as NS, HA and ST also appear to show
positive, albeit weaker, relationships with several nomophobic factors.
Undoubtedly, prevention and/or intervention techniques should target
personality traits, since these appear to have an impact on the devel-
opment of nomophobia.
Funding
No source of funding.
Declaration of interest
None to declare.
References
Afifi, A.A., Azen, S.P., 1972. Statistical Analysis: A Computer Oriented Approach.
Academic Press, New York.
Aluja, A., Blanch, A., 2011. The five and seven factors personality models: differences and
similitude between the TCI-R, NEO-FFI-R and ZKPQ-50-CC. Span. J. Psychol. 2,
659–666.
American Psychiatric Association, 2013. The Diagnostic and Statistical Manual of Mental
Disorders: DSM-5. Bookpoint, US.
Arnau, M.M., Mondon, S., Santacreu, J.J., 2008. Using the temperament and character
inventory (TCI) to predict outcome after inpatient detoxification during 100 days of
outpatient treatment. Alcohol 43, 583–588.
Bianchi, A., Phillips, J.G., 2005. Psychological predictors of problem mobile phone use.
Cyberpsychol. Behav. 8, 39–51.
Billieux, J., Gay, P., Rochat, L., Van der Linden, M., 2010. The role of urgency and its
underlying psychological mechanisms in problematic behaviours. Behav. Res. Ther
48, 1085–1096. http://dx.doi.org/10.1016/j.brat.2010.07.008.
Cabrera-León, A., Lopez-Villaverde, V., Rueda, M., Moya-Garrido, M.N., 2015. Calibrated
prevalence of infertility in 30- to 49-year-old women according to different ap-
proaches: a cross-sectional population-based study. Hum. Reprod. 30 (11),
2677–2685.
Cabrera-León, A., Rueda, M., Cantero-Braojos, M., 2017. Calibrated prevalence of dis-
abling chronic pain according to different approaches: a face-to-face cross-sectional
population-based study in Southern Spain. BMJ Open 7 (1), e014033.
Calvo de Padilla, M., Padilla, E., Alemán, G.G., Bourdieu, M., Guerrero, G., Strejilevich, S.,
Escobar, J.I., Svrakic, N., Cloninger, C.R., de Erausquin, G.A., 2006. Temperament
traits associated with risk of schizophrenia in an indigenous population of Argentina.
Schizophr. Res. 83, 299–302.
Cloninger, C.R., Svrakic, D.M., Przybeck, T.R., 1993. A psychobiological model of tem-
perament and character. Arch. Gen. Psychiatry 50, 975–990.
M.A. Olivencia-Carrión et al. Psychiatry Research 266 (2018) 5–10
9
Cross, C.P., Copping, L.T., Campbell, A., 2011. Sex differences in impulsivity: a meta-
analysis. Psychol. Bull. 137, 97–130.
Dixit, S., Shukla, H., Bhagwat, A., Bindal, A., Goyal, A., Zaidi, A.K., Shrivastava, A., 2010.
A study to evaluate mobile phone dependence among students of a medical college
and associated hospital of central India. Indian J. Community Med. 35, 339–341.
Efron, B., 1978. Regression and ANOVA with zero-one data: measures of residual varia-
tion. J. Am. Stat. Assoc. 73, 113–121.
Evren, C., Evren, B., Yancar, C., Erkiran, M., 2007. Temperament and character model of
personality profile of alcohol- and drug-dependent inpatients. Compr. Psychiatry 48
(3), 283–288.
Farré, J., Fernández-Aranda, F., Granero, R., Aragay, N., Mallorquí-Bague, N., Ferrer, V,
et al., 2015. Sex addiction and gambling disorder: similarities and differences.
Compr. Psychiatry 56, 59–68.
Forgays, D.K., Hyman, I., Schreiber, J., 2014. Texting everywhere for everything: gender
and age differences in cell phone etiquette and use. Comput. Hum. Behav. 31,
314–321.
Gat-Lazer, S., Geva, R., Gur, E., Stein, D., 2017. Reward dependence and harm avoidance
among patients with Binge-Purge type eating disorders. Eur. Eat. Disord. Rev. 25,
205–213.
Glatt, S.J., Stone, W.S., Faraone, S.V., Seidman, L.J., Tsuang, M.T., 2006.
Psychopathology, personality traits and social development of young first-degree
relatives of patients with schizophrenia. Br. J. Psychiatry 189, 337–345.
Gutiérrez-Zotes, J.A., Bayón, C., Montserrat, C., Valero, J., Labad, A., et al., 2004.
Temperament and Character Inventory Revised (TCI-R). Standardization and nor-
mative data in a general population sample. Actas Esp. Psiquiatr. 32, 8–15.
Gutiérrez, F., Gárriz, M., Peri, J.M, Vall, G., Torrubia, R., 2016. How temperament and
character affect our career, relationships, and mental health. Compr. Psychiatry. 6,
181–189.
Kim, S.J., Kang, J.I., Kim, C.H., 2009. Temperament and character in subjects with ob-
sessive-compulsive disorder. Compr. Psychiatry 50, 567–572.
King, A.L., Valença, A.M., Silva, A.C., Sancassiani, F., Machado, S, Nardi, A.E., 2014.
Nomophobia": impact of cell phone use interfering with symptoms and emotions of
individuals with panic disorder compared with a control group. Clin. Pract.
Epidemiol. Ment. Health. 10, 28–35.
Kim, Y., Jeong, J., Cho, H., Jung, D., Kwak, M., Rho, M.J, et al., 2016. Personality factors
predicting smartphone addiction predisposition: behavioural inhibition and activa-
tion systems, impulsivity, and self-control. PLoS One 11 (8), e0159788.
Ko, C.H., Hsiao, S., Liu, G.C., Yen, J.Y., Yang, M.J., Yen, C.F., 2010. The characteristics of
decision making, potential to take risks, and personality of college students with
Internet addiction. Psychiatry Res 175, 121–125.
Kuss, D.J., Griffiths, M.D., Karila, L., Billieux, J., 2014. Internet addiction: a systematic
review of epidemiological research for the last decade. Curr. Pharm. Des. 20,
4026–4052.
Lee, H.W., Choi, J.S., Shin, Y.C., Lee, J.Y., Jung, H.Y., Kwon, J.S., 2012. Impulsivity in
internet addiction: a comparison with pathological gambling. Cyberpsychol. Behav.
Soc. Netw. 15, 373–377.
Leeman, R.F., Potenza, M.N., 2013. A targeted review of the neurobiology and genetics of
behavioural addictions: an emerging area of research. Can. J. Psychiatry 58 (5),
260–273.
López-Torrecillas, F., Perales, J.C., Nieto-Ruiz, A., Verdejo-García, A., 2014a.
Temperament and impulsivity predictors of smoking cessation outcomes. PLoS One 9
(12), e112440.
López-Torrecillas, F., Nieto-Ruiz, A., Velasco-Ortuño, S., Lara-Fernández, M., López-
Quirantes, E.M., Castillo-Fernández, E., 2014b. The role of impulsivity in dropout
from treatment for cigarette smoking. Compr. Psychiatry 55, 1609–1613.
Lohr, S.L., 2010. Sampling: Design and Analysis, 2nd ed. Brooks/Cole, Boston.
Lu, X., Katoh, T., Chen, Z., Nagata, T., Kitamura, T., 2014. Text messaging: are de-
pendency and excessive use discretely different for Japanese university students?
Psychiatry Res. 15, 255–262.
Lumley, T., 2004. Analysis of complex survey samples. J. Stat. Softw. 9, 1–19.
Lumley, T. 2014. Survey: analysis of complex survey samples. R package version 3.30.
Marlatt, G.A., Baer, J.S., Donovan, D.M., Kivlahan, D.R., 1988. Addictive behaviours:
etiology and treatment. Annu. Rev. Psychol. 223–252.
Mentzoni, R.A., Brunborg, G.S., Molde, H., Myrseth, H., Skouverøe, K.J.M., Hetland, J.,
Pallesen, S., 2011. Problematic video game use: estimated prevalence and associa-
tions with mental and physical health. Cyberpsychol. Behav. Soc. Netw. 14 (10),
591–596.
Mok, J.Y., Choi, S.W., Kim, D.J., Choi, J.S., Lee, J., Ahn, H., et al., 2014. Latent class
analysis on internet and smart- phone addiction in college students. Neuropsychiatr.
Dis. Treat. 10, 817–828.
Molina, J.L., Calvó, M., Padilla, E., Balda, M., Alemán, G.G., Florenzano, N.V., Guerrero,
G., Kamis, D., Rangeon, B.M., Bourdieu, M., Strejilevich, S.A., Conesa, H.A., Escobar,
J.I., Zwir, I., Cloninger, C.R., de Erausquin, G.A., 2017. Parkinson a motor impair-
ment predicts personality domains related to genetic risk and treatment outcomes in
schizophrenia. NPJ Schizophr. 11 (3), 16036.
Nagpal, S.S., Kaur, R., 2016. Nomophobia: the problem lies at our fingertips. Indian J.
Public Health W. 12, 1135–1139.
Olivencia-Carrión, M.A, Pérez-Marfil, M.A, Ramos-Revelles, B., Lopez-Torrecillas, F.,
2016. Relation personality to mobile phone use and abuse. Accion Psicol. 13,
109–118.
Olivencia- Carrión, M.A., Ferri-García, R., Rueda, M., López-Torrecillas, F., 2018.
Reliability and construct validity of a questionnaire to assess the nomophobic
(QANIP). BMC Public Health (In revision).
Osborne, J., 2005. Notes on the use of data transformations. PARE 5, 42–50.
Park, H., Suh, B.S., Kim, W.S., Lee, H., Park, S., Lee, K., 2015. Character profiles and life
satisfaction. Compr. Psychiatry 04, 172–177.
Pedrero-Pérez, E.J., Ruiz-Sánchez de León, J.M., 2013. Subjective memory complaints,
personality and prefrontal symptomatology in young adults. Rev. Neurol. 57,
289–296.
Pelissolo, A., Mallet, L., Baleyte, J.M., Michel, G., Cloninger, C.R, et al., 2005. The
Temperament and Character Inventory-Revised (TCI-R): psychometric characteristics
of the French version. Acta Psychiatr. Scand. 112, 126–133.
Pombo, S., Ferreira, J., Levy, P.Q., Bicho, M., 2017. Is there a genetic support for the
Cloninger (type I/II) clinical classification of alcohol addiction? Psychiatry Res. 12
pii: S0165.
Rao, J.N.K, Scott, A.J., 1984. On Chi-squared tests for multiway contingency tables with
proportions estimated from survey data. Ann. Stat. 12, 46–60.
Reynolds, B., Ortengren, A., Richards, J.B., de Wit, H., 2006. Dimensions of impulsive
behaviour: Personality and behavioural measures. Pers. Individ. Dif. 40, 305–315.
Sanchez-Carbonell, X., Beranuy, M., Castellana, M., Chamarro, A.y, Oberst, U., 2008.
Internet and cell phone addiction: passing fad or disorder? Adicciones 20, 149–159.
Särndal, C.E., 2007. The calibration approach in survey theory and practice. Surv.
Methodol. 33, 99–119.
Sidak, Z.K., 1967. Rectangular condence regions for the means of multivariate normal
distributions. J. Am. Stat. Assoc. 62, 626–633.
Szyjkowska, A., Gadzicka, E., Szymczak, W., Bortkiewicz, A., 2014. The risk of subjective
symptoms in mobile phone users in Poland: An epidemiological study. Int. J. Occup.
Med. Environ. Health 27 (2), 293–303 04.
Tabachnick, B.G., Fidell, LS., 2000. Using Multivariate Statistics, 4th ed. Allyn & Bacon,
Boston.
Thomée, S., Härenstam, A., Hagberg, M., 2011. Mobile phone use and stress, sleep dis-
turbances, and symptoms of depression among young adults–a prospective cohort
study. BMC Public Health 31, 11–66.
Takao, M., 2014. Problematic mobile phone use and big-five personalitydomains. Indian
J. Community Med. 39, 111–113.
Tillé, Y., Matei, A., 2015. sampling: Survey Sampling. R package version 2.7. http://
CRAN.R-project.org/package=sampling.
Vitoratou, S., Ntzoufras, I., Theleritis, C., Smyrnis, N., Stefanis, N.C., 2015. Temperament
and character dimensions assessed in general population, in individuals with psy-
choactive substance dependence and in young male conscripts. Eur. Psychiatry 30,
474–479.
Wald, A., 1943. Tests of statistical hypotheses concerning several parameters when the
number of observations is large. Trans. Am. Math. Soc. 54, 426–482.
Wang, P., Liu, T., Ko, C., Lin, H., Huang, M., Yeh, Y., et al., 2014. Association between
problematic cellular phone use and suicide: the moderating effect of family function
and depression. Compr. Psychiatry 55, 342–348.
Walther, B, Morgenstern, M., Hanewinkel, R., 2012. Co-occurrence of addictive beha-
viours: personality factors related to substance use, gambling and computer gaming.
Eur. Addict. Res. 18, 167–174.
Weinstein, A., Lejoyeux, M., 2015. New developments on the neurobiological and phar-
maco‐genetic mechanisms underlying internet and videogame addiction. Am. J.
Addict. 24 (2), 117–125.
Wu, A.M., Cheung, V.I., Ku, L., Hung, E.P., 2013. Psychological risk factors of addiction to
social networking sites among Chinese smartphone users. J. Behav. Addict. 2,
160–166.
Zhang, D. 2017. rsq: R-Squared and Related Measures. R package version 1.0. https://
CRAN.R-project.org/package=rsq.
M.A. Olivencia-Carrión et al. Psychiatry Research 266 (2018) 5–10
10
