Validation of a Spanish Questionnaire on Mobile Phone Abuse

Abstract

Mobile phone addiction has attracted much attention recently and is showing similarity to other substance use disorders. Because no studies on mobile phone addiction had yet been conducted in Spain, we developed and validated a questionnaire (Cuestionario de Abuso del Teléfono Móvil, ATeMo) to measure mobile phone abuse among young adults in Spanish. The ATeMo questionnaire was designed based on relevant DSM-5 diagnostic criteria and included craving as a diagnostic symptom. Using stratified sampling, the ATeMo questionnaire was administered to 856 students (mean age 21, 62% women). The MULTICAGE questionnaire was administered to assess history of drug abuse and addiction. Using confirmatory factor analysis, we found evidence for the construct validity of the following factors: Craving, Loss of Control, Negative Life Consequences, and Withdrawal Syndrome, and their association with a second order factor related to mobile phone abuse. The four ATeMO factors were also associated with alcoholism, internet use, and compulsive buying. Important gender differences were found that should be considered when studying mobile phone addictions. The ATeMo is a valid and reliable instrument that can be used in further research on mobile phone abuse.

Full text

ORIGINAL RESEARCH
published: 30 April 2018
doi: 10.3389/fpsyg.2018.00621
Frontiers in Psychology | www.frontiersin.org 1April 2018 | Volume 9 | Article 621
Edited by:
Pietro Cipresso,
Istituto Auxologico Italiano (IRCCS),
Italy
Reviewed by:
Juan Jose Fernandez Muñoz,
Universidad Rey Juan Carlos, Spain
Stephane Rothen,
Université de Genève, Switzerland
Roser Granero,
Universidad Autónoma de Barcelona,
Spain
*Correspondence:
Francisca López-Torrecillas
fcalopez@ugr.es
Specialty section:
This article was submitted to
Quantitative Psychology and
Measurement,
a section of the journal
Frontiers in Psychology
Received: 28 November 2017
Accepted: 12 April 2018
Published: 30 April 2018
Citation:
Olivencia-Carrión MA,
Ramírez-Uclés I, Holgado-Tello P and
López-Torrecillas F (2018) Validation of
a Spanish Questionnaire on Mobile
Phone Abuse. Front. Psychol. 9:621.
doi: 10.3389/fpsyg.2018.00621
Validation of a Spanish Questionnaire
on Mobile Phone Abuse
María A. Olivencia-Carrión 1, Isabel Ramírez-Uclés 2, Pablo Holgado-Tello 3and
Francisca López-Torrecillas 1
*
1Center for Research into the Mind, Brain and Behavior, Granada University, Granada, Spain, 2Department of Personality,
Assessment and Psychological Treatment, Universidad Nacional de Educación a Distancia, Madrid, Spain, 3Department of
Behavioral Sciences Methodology, Universidad Nacional de Educación a Distancia, Madrid, Spain
Mobile phone addiction has attracted much attention recently and is showing similarity to
other substance use disorders. Because no studies on mobile phone addiction had yet
been conducted in Spain, we developed and validated a questionnaire (Cuestionario de
Abuso del Teléfono Móvil, ATeMo) to measure mobile phone abuse among young adults
in Spanish. The ATeMo questionnaire was designed based on relevant DSM-5 diagnostic
criteria and included craving as a diagnostic symptom. Using stratified sampling, the
ATeMo questionnaire was administered to 856 students (mean age 21, 62% women).
The MULTICAGE questionnaire was administered to assess history of drug abuse and
addiction. Using confirmatory factor analysis, we found evidence for the construct validity
of the following factors: Craving, Loss of Control, Negative Life Consequences, and
Withdrawal Syndrome, and their association with a second order factor related to mobile
phone abuse. The four ATeMO factors were also associated with alcoholism, internet
use, and compulsive buying. Important gender differences were found that should be
considered when studying mobile phone addictions. The ATeMo is a valid and reliable
instrument that can be used in further research on mobile phone abuse.
Keywords: mobile phone, DSM-5, validity, Spanish population, abuse
INTRODUCTION
The mobile phone has many characteristics that make it attractive to young adults. It is primarily
used to communicate but also has many other interesting applications, including camera, internet,
music reproduction, games, and social media. The International Telecommunication Union report
(The International Telecommunication Union,2016) finds that 98% of young adults own a mobile
phone in Europe and other studies indicate that young women in particular have more interest in
mobile phones than other groups do (Roberts et al., 2014). There is evidence that mobile phone
abuse in related to physical and mental wellbeing problems, including social and psychological
disturbances such as attention deficit and hyperactivity disorder, disruptive behavior disorders,
anxiety disorders, mood disorders, substance use disorders, sleep disorders, and eating disorders
(Billieux et al., 2014; Foerster et al., 2015). In recent years, a co-occurrence has been established
between mobile phone dependence and other behavioral disorders such as internet addiction (Chiu
et al., 2013), compulsive buying (Jiang and Shi, 2016) and alcohol use (De-Sola et al., 2017a) or
use of other substances (Gallimberti et al., 2016). However, it remains unclear if an individual
that develops one addictive behavior (i.e., mobile phone abuse) is more likely to develop another
addictive behavior or a substance use problem.

Olivencia-Carrión et al. Questionnaire Mobile Phone Abuse
Although a definition of mobile phone abuse has not yet been
agreed upon, some researchers define mobile phone dependence
as a constant use of the device with a poor capacity to control
daily activities, exhibiting extreme nervousness and aggressive
behavior when deprived of its use; this excessive use is also
accompanied by a progressive deterioration in school/work
performance and social and family functioning (Billieux et al.,
2014; Lin et al., 2015). These symptoms have a major negative
impact on the life of the affected person, reflected in impaired
health or deprived social functioning; they have also been shown
to be equivalent to substance dependence as understood by the
current nosological systems Diagnostic and Statistical Manual of
Mental Disorders, Fifth Edition (DSM-5, American Psychiatric
Association,2012).
Mobile phone addiction could in many ways be similar to
substance dependence disorders (Foerster et al., 2015; Roser et al.,
2016). For instance, the abuse of psychotropic drugs (heroin,
cocaine, cannabis, etc.) and alcohol is a complex social, biological,
and psychological phenomenon. Whether an individual ever
uses alcohol or another substance, and whether that initial
use progresses to a substance use disorder of any severity,
depends on a number of factors. These include: a person’s genetic
makeup and other individual biological factors; psychological
factors related to a person’s unique history and personality;
and environmental factors, such as the availability of drugs,
family and peer dynamics, coping with stress, and access to
social support. Chronic consumption of several drugs (cannabis,
stimulants, and opioids) has been associated with the presence of
neuropsychological impairments in a broad range of functions.
In recent years neuropsychological research on substance abuse
has focused on the study of impairments in executive functions
linked to the prefrontal cortex and their influence on the
personality, cognitions, and behaviors of the substance abusers
(López-Torrecillas et al., 2000; Verdejo-García et al., 2004).
To date, pathological gambling is the non-substance related
addiction which has received most attention and has been
examined extensively. The results reveal a number of substantial
similarities between pathological gambling and substance-
related addictions concerning phenomenology, epidemiology,
personality factors, genetics, neurobiological processes, recovery,
and treatment (Walther et al., 2012; Contreras-Rodríguez et al.,
2016; Navas et al., 2017). In DSM-5, pathological gambling is
classified as a non-substance-related addiction and is, therefore,
removed from the former category “Impulse-Control Disorders”
and included in the new “Substance Use and Addictive Disorders”
category. Other potential non-substance-related addictions are
internet addiction, compulsive buying, sex addiction, and mobile
phone addiction, although these are not yet officially defined as
disorders due to a lack of evidence. Despite a substantial overlap,
it is not yet clear why some people become vulnerable to these
behaviors. The co-occurrence of non-substance-related addiction
with different forms of substance abuse such as smoking,
drinking, use of cannabis, and other illegal drugs among young
people has been repeatedly discussed (Vanyukov et al., 2012;
De-Sola et al., 2017a).
The literature also reveals an association between multiple
substance use and other risk behaviors among young adults. For
example, binge drinking, cannabis use, and tobacco use appear
to be more prevalent in young people (Van Rooij et al., 2014;
Abebe et al., 2015). The use of both alcohol and cannabis predicts
use of common addictive substances (Osuch et al., 2013; Viola
et al., 2014; Vorspan et al., 2015) and tends to be accompanied
by gambling (Larsen et al., 2013; Míguez and Becoña, 2015). In
addition, a number of authors (Mudry et al., 2011; Yau et al., 2012;
Grant et al., 2013; Lee et al., 2013; Mattebo et al., 2013; Schuster
et al., 2013; Van Rooij et al., 2014; Biolcati, 2015) have pointed
out the relationship between the amount of time young adults
spend gambling, abusing their mobile phones, using the internet,
playing video games, buying compulsively, or having sex and
increases in alcohol, tobacco, cannabis, and drug consumption.
The acknowledgement of behavioral addictions as disorders
can be traced as far back as Marlatt et al. (1988), who reported a
repetitive habit pattern that increases the risk of disease and/or
associated personal and social problems. Addictive behaviors
are often experienced subjectively as a loss of control in which
the behavior continues to occur despite volitional attempts
to abstain or moderate use. Furthermore, in the last decade,
a growing number of studies have established psychological
and neurobiological similarities between the excessive practice
of addictive behaviors (e.g., mobile phone abuse, compulsive
buying, sex, internet, video gaming, and eating disorders; Billieux
et al., 2010; Mentzoni et al., 2011). Research on the neurobiology
of addiction has also found a common mechanism between
substance addictions and behavioral addictions (Leeman and
Potenza, 2013; Weinstein and Lejoyeux, 2015). However, at
this point we do not know whether having one addictive
behavior increases the likelihood of developing other addictive
behaviors or other dependencies such as substance use disorders.
In addition, alcohol, drugs, and pathological gambling may
not be the only crippling addictions that we should address.
Unfortunately, other addiction statistics are scarce because many
destructive habits are not yet officially recognized as addictions,
including mobile phone addiction, game addiction, eating,
shopping, and sex addiction, all of which are problematic for
many reasons. They all involve direct manipulation of pleasure
through the use of products, similar to drug use disorders and
food-related disorders.
The concept of non-substance-related (or “behavioral”)
addiction describes syndromes analogous to substance addiction,
but with a focus on a certain behavior which, similar to substance
consumption, produces short-term reward and may persist
despite harmful consequences due to diminished control over
the behavior. Given that addictive behavior is not necessarily
restricted to substance consumption, the DSM-5 broadens the
category “Substance-Related Disorders” to a “Substance Use and
Addictive Disorders” category including both substance and
non-substance-related addictions. The Diagnostic and Statistical
Manual of Mental Disorders—4th Edition (DSM-IV; American
Psychiatric Association ,2002) conceptualized two discrete
substance use disorders (SUD), abuse and dependence, defined
by mutually exclusive sets of diagnostic criteria. Abuse required
endorsement of one or more (1+) of four abuse criteria, and
dependence required endorsement of three or more (3+) of
seven dependence criteria. In contrast, the proposed Diagnostic
Frontiers in Psychology | www.frontiersin.org 2April 2018 | Volume 9 | Article 621

Olivencia-Carrión et al. Questionnaire Mobile Phone Abuse
and Statistical Manual of Mental Disorders—5th Edition (DSM-
5; American Psychiatric Association, 2012) conceptualizes a
unitary SUD construct, varying only in terms of severity. The
literature reviewed here includes studies on postulated behavioral
addictions related to the use of mobile phones, shopping, sex,
internet, video gaming, and food, along with other studies that
analyzed the co-occurrence of these addictions with substance
abuse (for instance tobacco, alcohol, and cannabis substances).
However, these are not included in the DSM-5 because of current
lack of evidence. In order to be able to obtain relevant evidence in
the first place, we need valid and reliable instruments that allow
us to measure addictive behaviors such as mobile phone abuse.
The study of mobile phone abuse started in 2004 with the
development of the Mobile Phone Dependency Questionnaire
(CPDQ; Toda et al., 2004) designed for use in university
populations and validated in a population of high school students
by Kawasaki et al. (2006). Another instrument available for use
in adult populations is the Mobile Phone Problem Use Scale
(MPPUS; Bianchi and Phillips, 2005); including a recent short
version (Foerster et al., 2015) and a version for teenagers [Mobile
Phone Addiction Scale (MPAS; Leung, 2008)]. This scale has been
translated into Japanese (Takao et al., 2009) and Spanish (López-
Fernández et al., 2012), with some items previously translated
for use in the Spanish university population (Ruiz-Olivares et al.,
2010). Although the Mobile Phone Problem Use Scale is one of
the most frequently used instruments to assess mobile phone
addiction, other instruments exist including the Mobile Phone
Usability Questionnaire (MPUQ; Ryu and Smith-Jackson, 2006)
and Problematic Mobile Phone Use Questionnaire (PMPUQ;
Billieux et al., 2008). In Eastern countries, three scales have been
developed: the Mobile Phone Dependence Inventory (MPDI; Xu
et al., 2008); the Excessive Cellular Phone Use Survey (ECPUS;
Ha et al., 2008), and the Smartphone Addiction Scale (SAS-SV;
Kwon et al., 2013). At present there are 5 instruments translated
into Spanish: the first one, already mentioned previously—
MPPUS, Bianchi and Phillips (2005)—has been adapted by
López-Fernández et al. (2012); the second one is the Cell-
phone Over-use Scale (COS; Jenaro et al., 2007) for university
populations; the third one is the Questionnaire of Mobile-Related
Experiences (CERM; Fargues et al., 2009) for adult populations;
the fourth one is the Test for Mobile Phone Dependence [TMD]
(Chóliz, 2012) for adolescents (including a new reduced version,
Chóliz et al., 2016), and finally, the fifth is a questionnaire that
focuses only on the dimension of Craving (De-Sola et al., 2017b).
However, no studies have been conducted in Spain to identify
mobile phone addiction in young adults using the DSM-5 criteria
(American Psychiatric Association, 2012). To this end, it is
necessary to develop a valid and reliable instrument measuring
mobile addiction, having in mind the modifications made in
the DSM-5 (American Psychiatric Association, 2012). These
modifications imply that mobile phone addiction should be
considered in relation to substance use disorders and behavioral
addictions.The diagnostic symptoms of substance use disorders
since recently include a new criterion, craving, featured in
the DSM-5 (American Psychiatric Association, 2012). One of
the most accepted definitions of craving is that of compulsive
craving—an irrational and intense desire or uncontrollable
compulsion to consume a particular psychoactive substance
and/or perform a certain behavior, which leads to compulsive
search rituals (Blasco et al., 2008; Igarashi et al., 2008; De-Sola
et al., 2017b). Hence, craving should be considered as a criterion
to establish a diagnosis and understand the different mediating
variables when developing treatments, analyzing relapses, and
designing prevention strategies. Therefore, the main purpose
of this study was to develop and validate a questionnaire
to measure mobile phone dependence among young Spanish
speaking adults.
METHODS
Participants
The sample comprised of community-dwelling young adults
between 17 and 45 years of age (mean 21.12 years old, standard
deviation=3.05, 62.38% women and 37.62% men). They were
recruited from the student population of the University of
Granada. Participants were recruited by university faculty during
class breaks and were selected using a probabilistic sampling
design. In particular, a cluster stratified sample design was
adopted. Strata were based on the different university faculties.
Cluster samples were extracted such that majors and years of
study were represented in proportion to the total number of
students in each faculty. Finally, all students of the cluster
sample were included in the final sample. There were 856
participants recruited between September 2013 and June 2014.
The participants were informed about the aims of the study
and provided signed informed consent prior to participation.
Inclusion criteria were having a mobile phone, wanting to
participate, and signing the informed consent form. Prior to
recruitment the study was approved by the Research Ethics
Committee from the University of Granada, Spain.
Measures
The Mobile Phone Abuse Questionnaire (ATeMo)
The Mobile Phone Abuse Questionnaire (ATeMo) was developed
to assess mobile phone dependence. It consists of 25 items
covering addictive symptoms, based on the diagnostic criteria
for behavioral addiction (gambling) and the DSM-5 (American
Psychiatric Association, 2012), and also taking into account
substance abuse disorders, and instruments that measure
addiction to mobile phones, internet, and social networks.
The addictive symptoms considered were craving, loss of
control, negative life consequences, and withdrawal syndrome.
Specifically, the questionnaire assessed the use of the mobile
phone, the disturbance of daily activities, the increase in time
spent to obtain the same satisfaction, loss of control, difficulties
in stopping using the phone and the irritability produced, and the
negative feelings experienced when the mobile phone cannot be
used. The 25 items were answered on a 5-point Likert scale that
ranged from 0 (strongly disagree) to 4 (strongly agree), resulting
in a final score between 0 and 100 (see Table 1).
The MULTICAGE CAD-4
The MULTICAGE CAD-4 was designed to screen for a history of
drug abuse and addiction behavior. It assesses alcoholism (items
Frontiers in Psychology | www.frontiersin.org 3April 2018 | Volume 9 | Article 621

Olivencia-Carrión et al. Questionnaire Mobile Phone Abuse
TABLE 1 | Mobile Phone Abuse Questionnaire (ATeMo).
1. When I forget my mobile phone I feel restless.
(Cuando me olvido del móvil me siento intranquilo)
2. I’d rather lose my wallet than my mobile phone.
(Prefiero perder la cartera que el móvil)
3. I don’t want to go to places where the mobile signal is weak.
(No quiero ir a lugares donde la señal de móvil sea débil)
4. When I travel I often touch my mobile phone.
(Cuando viajo suelo estar tocando el móvil)
5. I use whatsapp/line or similar more than 4 hours a day.
(Uso whatsapp/line o similar más de 4 horas al día)
6. I use whatsapp/line or similar while I study/work.
(Uso whatsapp/line o similar mientras estudio/trabajo)
7. I use whatsapp/line or similar when I’m with my friends or my family.
(Uso whatsapp/line o similar cuando estoy con amigos o familia)
8. I use whatsapp/line or similar at night, in bed before going to sleep.
(Uso whatsapp/line o similar por la noche, en la cama antes de dormir)
9. I unconsciously check whatsapp or the messages I have.
(Inconscientemente compruebo los whatsapp o mensajes que tengo)
10. I feel happy when I receive a message or whatsapp.
(Me siento feliz cuando recibo un mensaje o whatsapp)
11. I express my feelings better through whatsapp than talking.
(Expreso mis sentimientos mejor con los whatsapp que hablando)
12. I can never spend enough time on my mobile phone.
(Nunca puedo estar el tiempo que necesito utilizando mi móvil)
13. I have used my mobile phone to make myself feel better when I was
feeling down.
(Utilizo el móvil para sentirme mejor cuando estoy bajo de ánimo)
14. I lose sleep due to the time I spend on my mobile phone.
(Pierdo horas de sueño utilizando el móvil)
15. When out of range for some time, I become preoccupied about the
thought of missing a whatsapp or message.
(Cuando estoy sin cobertura me preocupo por los whatsapp o
mensajes que pueda perder)
16. I have attempted to spend less time on my mobile phone but I’m
unable to.
(He intentado gastar menos tiempo usando el móvil pero no lo consigo)
17. I have aches and pains that are associated with my mobile phone use.
(Tengo molestias físicas o dolores producidos por el uso del móvil)
18. I become irritable if I have to switch off my mobile phone for a meeting,
dinner engagements or when at the movies.
(Me irrita tener que apagar el móvil en situaciones como reuniones,
cenas, encuentros o en el cine)
19. I feel lost without my mobile phone.
(Me siento perdido sin mi teléfono móvil)
20. I feel angry if someone interrupts me when I’m using my mobile phone.
(Me enfado si alguien me interrumpe cuando estoy usando móvil)
21. If I had to spend 6 h without using my cell phone, I would feel restless
or nervous.
(Si tuviera que pasar 6 horas sin utilizar el móvil, me sentiría inquieto o
nervioso)
22. I feel bored when I’m not using my mobile phone.
(Me siento aburrido cuando no estoy utilizando el móvil)
23. I neglect my work or class assignments to use my mobile phone.
(Desatiendo mis tareas de trabajo o clase para usar el móvil)
(Continued)
TABLE 1 | Continued
24. I ignore my friends to use my mobile phone.
(Desatiendo a mis amigos para usar el móvil)
25. I ignore my family to use my mobile phone.
(Desatiendo a mi familia para usar el móvil)
Instructions: We are interested in how people use mobile phones to communicate. Please
indicate the degree to which you agree or disagree with each of the following statements
regarding your use of your mobile phone on the following scale:
0, strongly disagree; 1, disagree; 2, neutral; 3, agree; and 4, strongly agree.
Scoring and interpretation: Sum the items in parenthesis for subscale scoring: Craving (1,
2, 3, 4, 9, 10, 12, 16), Loss of Control (5, 8, 13, 14), Negative Life Consequences (6, 7,
11, 17, 18, 23, 24, 25) and Withdrawal Syndrome (15, 19, 20, 21, 22).
1–4), gambling disorders (items 5–8), drug addiction (items 9–
12), eating disorders (items 13–16), internet addiction (items 17–
20), video gaming addiction (items 21–24), compulsive buying
disorder (items 25–28) and sex addiction (items 29–32). The
psychometric properties have been well established in Spanish
adult populations. It demonstrates high >0.7 Cronbach’s alpha
coefficient. In the exploratory factor analysis, 8 components are
identified that identify the proposed structure the diagnostic
sensitivity for alcohol was 92.4%, and between 94 and 100% for
heroin, cocaine and cannabis (Pedrero-Pérez et al., 2007).
Procedure
The study consisted of two stages: in the first stage the instrument
was developed and in the second stage it was validated.
The construction of the Mobile Phone Abuse Questionnaire
(ATeMo) was based on the DSM-5 (American Psychiatric
Association, 2012) that does not recognize mobile addiction
as a disorder but makes reference to tobacco addiction and
gambling. Ideas were taken from instruments that measure
addictions to mobile phones, internet, and social networks and
items were created taking into account all the aforementioned.
For the construction of the items, criteria for constructing
items for Likert questionnaires were used (Jenaro et al., 2007;
Billieux et al., 2008; Fargues et al., 2009; Chóliz, 2012; Chóliz
et al., 2016; De-Sola et al., 2017a). This set of defined criteria
together with the items that evaluated them were reviewed by
three experts on clinical psychology, educational psychology, and
psychometrics. The experts collaborated in writing and ensuring
the understanding, clarity, and consistency in the definitions of
the criteria and the items. For the evaluation of the items a5-point
rating system was applied (from 0 to 4) taking into account the
frequency from never to always (Fishman and Galguera, 2003;
Schepers, 2009; Furr, 2011; DeVellis, 2012). Once the expert
evaluation was concluded, a pilot experiment was carried out on
a sample of 65 university students. They were asked to indicate
whether the items in the questionnaire were comprehensible
or not, encouraging them to raise any doubts that they had
regarding each item.
The instrument was then administered to the final sample
of participants in order to establish its validity. The data
were collected from students of the University of Granada
through stratified sampling by conglomerates, according to
majors and groups of the different degrees taught at the
Frontiers in Psychology | www.frontiersin.org 4April 2018 | Volume 9 | Article 621

Olivencia-Carrión et al. Questionnaire Mobile Phone Abuse
University of Granada (Psychology, Speech Therapy, Tourism,
English, History, Literature, GADE, Economy, Biology, Physics,
Optics, Primary, Infant, Pedagogy, Law, Medicine, Pharmacy,
Social Work, Policies, Sociology, Information Technology, Roads
and Telecommunications). Teachers responsible for the selected
groups were sent an email informing them of the objectives of
the study and requesting their help so that the students could
participate. They were asked to inform their students about the
study and the time during breaks was used to complete the
questionnaires. It was emphasized that the participation was
voluntary, that is, the students were free not to participate if they
preferred it.The teachers also emphasized the need for honesty
when filling out the survey and guaranteed the confidentiality
of the responses. The survey started with short demographic
questions (sex and age) followed by the ATeMo questionnaire.
Data Analysis
To obtain empirical evidence about the construct validity
of the questionnaire and given the ordinal nature of the
data, we conducted Confirmatory Factor Analysis (CFA) using
polychoric correlations and Unweighted Least Squares (ULS)
as estimation method (Hernández et al., 2000; Yang-Wallentin
et al., 2010; Morata-Ramírez et al., 2015). We also tested the
basic psychometric properties of the dimensions obtained (mean,
standard deviation, reliability and discrimination). For criterion
validity, correlation analysis was performed to determine the
relationship of the ATeMo factors and the sub-dimensions
of the MULTICAGE CAD-4. Gender differences between the
different factors of the questionnaire were also examined
through a MANOVA. Finally, to achieve an initial approximate
interpretation of the scores, we calculated the percentiles in the
total sample and split them by gender. The statistical programs
used were SPSS 15.0 for Windows and LISREL 8.71 (Jöreskog and
Sörbom, 1996).
RESULTS
Confirmatory Factor Analysis (CFA)
In order to obtain empirical evidence about the adequateness
of the postulated structure ofthe ATeMo questionnaire, a CFA
was conducted. In line with the theoretical background, the
dimensional structure considered implied a general second-order
factor referring to mobile phone dependence and four first order
factors. The four first order factors were the following: eight
items contributed to the first factor of Craving (1, 2, 3, 4, 9,
10, 12, 16), four items to the second factor of Loss of Control
(5, 8, 13, 14), eight items to the third factor of Negative Life
Consequences (6, 7, 11, 17, 18, 23, 24, 25) and five items to
the fourth factor Withdrawal Syndrome (15, 19, 20, 21, 22).
For the model examined (Figure 1), the Global fit Indices were:
χ²=274.18; d.f.=265; p=0.34. The value of the Root Mean
Square Error of Approximation (RMSEA) was 0.021, with a 90%
interval between 0.0 and 0.050. The Goodness of Fit Index (GFI)
was 0.97, the Adjusted Goodness of Fit Index (AGFI) was 0.97,
the comparative Fit Index (CFI) was 1, the Normed Fit Index
(NFI) was 1 and the Standardized Root Mean Square Residual
(SRMR) was 0.06. These data show that the fit values of the model
are appropriate. All the lambdas and gammas parameters were
statistically significant.
Reliability
The reliability of ATeMo was assessed using Cronbach’s alpha
coefficients (Table 3) and the resulting values were: Total score
0.91; Craving factor 0.74; the Loss of Control factor 0.70;
Negative Life Consequences factor 0.77; and for the Withdrawal
Syndrome factor 0.77. In addition, we calculated descriptors for
the ATeMo from the CFA (mean, standard deviation and mean
discrimination of the items of each dimension: Table 2).
Criterion Validity
To determine the criterion validity, we calculated the Pearson
bivariate correlation index between the total score and each of
the ATeMo factors, as well as with the MULTICAGE CAD-4
subscales (see Table 3). There was a positive correlation between
the ATeMo total score and Alcoholism, Gambling disorders,
Internet addiction, and Compulsive buying in the MULTICAGE
CAD-4 subscales. Furthermore, there was a positive correlation
between the Negative Life Consequences factor of ATeMo
and Drug addiction in the MULTICAGE CAD-4 subscale;
the Craving and Loss of Control ATeMo factors and Video
game addiction in the MULTICAGE CAD-4 subscale; and the
Negative Life Consequences and Withdrawal Syndrome factors
of ATeMo and Sex addiction in the MULTICAGE CAD-4
subscale.
In general terms, the direction of the correlations is consistent
with what was expected, however, given the large sample size,
correlations of 0.073 (Craving-Video games addiction), for
example, result statistically significant. For this reason, according
to Rosnow and Rosenthal (1996) none of the correlations
presents a large effect size (|r|>0.37). The effect size is
medium (|r|>0.24) for the correlations between the factors of
ATeMO with Internet Addiction, and Compulsive Buying. The
correlation between the factors of ATeMO and Alcoholism and
Eating Disorders have a low effect size (|r|>0.10). All other
correlations have an irrelevant effect size.
Score Interpretation
In order to provide preliminary data to help interpret the scores
obtained, the 10th to 90th percentiles are presented for the total
sample, and for men and women separately (Table 4).
DISCUSSION
In the present study we have developed a new valid and
reliable scale to measure mobile phone abuse and dependence
in Spain (ATeMo). The ATeMo Questionnaire consists of 25
items covering addictive symptoms, based on the diagnostic
criteria of the DSM-5 (American Psychiatric Association, 2012).
It is evaluated on a 5-point Likert-type scale ranging from 0
(strongly disagree) to 4 (agree), resulting in a final score in
the range of 0–100. According to results from a confirmatory
factor analysis, the ATeMo represents a general second order
factor and four first order factors consistent with addiction
theory: Craving, Loss of Control, Negative Life Consequences,
Frontiers in Psychology | www.frontiersin.org 5April 2018 | Volume 9 | Article 621

Olivencia-Carrión et al. Questionnaire Mobile Phone Abuse
FIGURE 1 | χ²=274.18; d.f. =265; p=0.34; RMSEA =0.021, GFI =0.97; AGFI =0.97; CFI =1; NFI =1; and SRMR =0.06.
and Withdrawal Syndrome. These factors show considerable
overlap with the symptoms proposed previously (Bianchi and
Phillips, 2005; Rutland et al., 2007; Igarashi et al., 2008; Yen
et al., 2009; Walsh et al., 2010; Chóliz, 2012; Merlo et al.,
2013; Chóliz et al., 2016) and were developed according to the
criteria for the diagnostic symptoms of substance dependence
disorders in the DSM-IV-TR (American Psychiatric Association,
2002) and the DSM-5 (American Psychiatric Association, 2012),
the latter more recently including craving as a diagnostic
criterion.
Frontiers in Psychology | www.frontiersin.org 6April 2018 | Volume 9 | Article 621

Olivencia-Carrión et al. Questionnaire Mobile Phone Abuse
In assessing the reliability of the ATeMo questionnaire,
Cronbach’s alpha coefficients were calculated, demonstrating
it had excellent internal consistency as seen elsewhere in
similar studies in Spain (Chóliz, 2012; López-Fernández et al.,
2012; Vanyukov et al., 2012; Chóliz et al., 2016). These
coefficients were higher than those obtained in some previous
studies (Fargues et al., 2009), where measures were developed
according to the criteria for diagnosing symptoms of substance
dependence disorders in DSM-IV-TR (American Psychiatric
Association, 2002). The MULTICAGE CAD-4 subscales were
used to determine potential criterion validity of ATeMo,
identifying a positive correlation between the ATeMo total
score and Alcoholism, Drug addiction, Eating disorders, Internet
addiction, and Compulsive Buying subscales (Chiu et al., 2013;
Gallimberti et al., 2016; Jiang and Shi, 2016; De-Sola et al.,
2017a). Furthermore, there was a positive correlation between
the Craving ATeMo factor, Alcoholism, Eating disorders, and
Internet addiction, and a negative correlation with Video gaming
addiction in the MULTICAGE CAD-4 subscale. Similarly, there
was a positive correlation between the ATeMo factor Loss of
Control and Alcoholism, Eating disorders, Internet addiction,
and Compulsive buying, as well as a negative correlation with
Gambling Disorders in the MULTICAGE CAD-4 subscale.
This is consistent with the positive correlation between self-
control and addiction identified previously (Jiang and Shi,
2016). Again, there was a positive correlation with Negative
Life Consequences as an ATeMo factor and Alcoholism, Drug
addiction, Eating disorders, Internet addiction, Compulsive
buying, and Sex addiction in the MULTICAGE CAD-4
subscale, and there was a similarly positive correlation between
Withdrawal Syndrome as an ATeMo factor and Alcoholism,
TABLE 2 | Cronbach’s alpha coefficients and the mean, standard deviation, and
mean discrimination for the AteMo questionnaire derived from the CFA.
F1 (Cv) F2 (LC) F3 (NLC) F4 (WS) ATeMo (TS)
Mean 11.85 6.71 7.48 4.02 30.07
Standard deviation 5.39 3.66 4.72 3.53 15.00
Cronbach’s α0.74 0.70 0.77 0.77 0.91
Mean discrimination 0.45 0.49 0.48 0.55 0.52
Cv, Craving; LC, Loss of Control; NLC, Negative Life Consequences; WS, Withdrawal
Syndrome; and TS, Total Scores.
Eating disorder, Internet addiction, Video gaming addiction,
and Compulsive buying. Indeed, loss of control, negative life
consequences and withdrawal syndrome were already considered
as diagnostic criteria for addiction disorders prior to DSM-5
(American Psychiatric Association, 2012).
The relationships described above are consistent with
previous considerations that alcohol consumption may predict
problematic mobile phone use (De-Sola et al., 2017a). They are
also consistent with previous results on the relationship between
Internet and mobile phone addiction (Chiu et al., 2013) and with
previous results suggesting common impulsive aspects between
compulsive buying and mobile phone addiction (Jiang and Shi,
2016).
Furthermore, the survey conducted indicated a common
continuum of substance abuse and behavioral addictions, as
identified previously in surveys that focused on such co-
morbidity (Chiu et al., 2013; Jiang and Shi, 2016; De-Sola et al.,
2017a; although an association between eating disorders and
mobile phone abuse is yet to be found). These results suggest
that alcohol, drugs, and pathological gambling may not be the
only crippling addictions. Addiction statistics are scarce because
many destructive habits (such as gaming, shopping, sex, etc.) are
not yet officially recognized as addictions, although they could be
problematic for many reasons. Some of these involve the direct
manipulation of pleasure through the consumption of products
like in the case of drug use disorders and food-related disorders.
The results obtained with ATeMo indicate that there are
gender differences between males and females regarding mobile
phone abuse, with scores ≥8 for the former and ≥10 for the latter
potentially indicating mobile phone addiction. These results are
consistent with previous findings indicating that females send
more and longer texts, they talk for longer than men on the
phone, and tend to regard mobile phones as a social tool (Roberts
et al., 2014).
Our findings demonstrate that the ATeMo is a valid and
reliable instrument that can be administered to different groups
of university students. In addition, while this instrument was
developed for university students, renewed construct validity and
reliability analyses could convert it into a version suitable for
adolescents.
Our results should be evaluated in view of several important
limitations. First, the sample used in this study was relatively
homogeneous with respect to age and educational level. Second,
TABLE 3 | Correlations between the total score, the factors of ATeMo, and the MULTICAGE CAD-4 subscales.
MULTICAGE CAD-4
1 2 3 4 5 6 7 8
F1(Cv) 0.127** −0.001 0.013 0.210** 0.252** −0.073* 0.287** 0.032
F2 (LC) 0.139** −0.036 0.025 0.193** 0.218** −0.075* 0.260** 012
F3(NLC) 0.196** 0.062 0.100* 0.172** 0.300** 0.037 0.305** 093**
F4 (WS) 0.163** 0.022 0.032 0.161** 0.293** −0.047 0.278** 0.073*
ATeMO (TS) 0.180** 0.015 0.050 0.214** 0.307** −0.044 0.328** 0.061
1, Alcoholism; 2, Gambling disorders; 3, Drug addiction; 4, Eating disorders; 5, Internet addiction; 6, Video games addiction; 7, Compulsive buying; 8, Sex addiction; F (Cv), Craving;
F2 (LC), Loss of Control; F3 (NLC), Negative Life Consequences; F4 (WS), Withdrawal Syndrome; and ATeMO (TS), Total Scores. *p<0.05, **p<0.01, ***p<0.001.
Frontiers in Psychology | www.frontiersin.org 7April 2018 | Volume 9 | Article 621

Olivencia-Carrión et al. Questionnaire Mobile Phone Abuse
TABLE 4 | Percentiles, raw scores in ATeMo, mean and standard deviation for
men and women.
Pc Raw Scores
Men Woman Total
10 1 2 2
20 2 4 3
30 3 5 5
40 4 7 6
50 5 8 7
60 6 9 8
70 7 10 9
80 8 11 10
90 10 12 12
Mean 5.37 7.51 6.71
Sd. 3.46 3.54 3.66
Pc, percentile; Sd, standard deviation.
mobile phone addiction should be investigated in relation to
a number of variables, such as demographic, personality, and
clinical characteristics. This could advance our understanding
of the interaction of humans with technology, as well as our
understanding of the nature and causes of technology-related
addictions. Overall, taking into account the lack of a valid and
reliable questionnaire to measure the addiction to the mobile
phone, ATeMo could be an adequate instrument to measure the
mobile phone addiction in future investigations.
Regarding clinical implications, the development of the
ATeMo questionnaire to detect mobile phone abuse is an
important step in the development of diagnostic and treatment
procedures and in the design of prevention and intervention
strategies.
In future studies, it would be of interest to examine the
problems associated with mobile phone use in relation to
variables such as solitude, depression, self-esteem, well-being,
academic success, and other demographic variables. Further
studies into the problematic use of mobile phones will not only
allow us to better understand this problem but they should
provide information to aid the committees determining future
DSM criteria, especially in relation to addictions associated
with new technologies. Moreover, a more profound analysis
thorough ROC curves of the cut-off thresholds should be
performed to help interpret the scores obtained and to classify
the subjects. Moreover, other construct validity evidences should
be investigated. In this sense, invariance analysis by gender,
of age group, for example, is necessary to obtain empirical
evidences about the equivalence in the constructs and items
operatized in ATeMO. Once guarantee this issue, Differential
Item Functioning and a deep comparative analysis by the sorting
variables considered will be necessary to ensure that the decisions
made based on the test scores are valid.
In summary, we have developed a scale to measure Mobile
Phone Abuse, ATeMo, that takes into account the criteria
for the diagnosis of substance use or addiction described in
DSM-5 (American Psychiatric Association, 2002). The evaluation
of craving was an important aspect of this questionnaire, as
previously no measures existed that were consistent with the
DSM-5 (American Psychiatric Association, 2012) criteria. The
majority of measures had been developed based on the literature
on substance use and addiction (Toda et al., 2004; Bianchi
and Phillips, 2005; Rutland et al., 2007; Igarashi et al., 2008;
Yen et al., 2009; Walsh et al., 2010; Chóliz, 2012; López-
Fernández et al., 2012; Merlo et al., 2013; Chóliz et al.,
2016), and the items in most of the previous instruments
reflect the diagnostic criteria for substance use or addiction
described in DSM-IV-TR (American Psychiatric Association,
2012). Based on the current findings we can conclude
that the ATeMo questionnaire has satisfactory reliability and
validity, having included craving as a diagnostic criteria for
dependence.
AVAILABILITY OF DATA AND MATERIALS
R code and data are available from the authors under request.
ETHICS STATEMENT
This study was approved by the Research Ethics Committee
from the Granada University. All procedures performed in our
study involving human participants were in accordance with the
ethical standards of the institutional research committee and
with the 1964 Helsinki declaration and its later amendments
or comparable ethical standards. Informed consent was
obtained from all individual participants included in the
study.
AUTHOR CONTRIBUTIONS
All the authors participated in the conception and design of the
work, specifically MO-C and FL-T, conceived the original idea
for the study, obtained funding and wrote the study protocol.
MO-C manages the day to day running of the study, including
all participant follow-up and IR-U and PH-T undertaked all data
analyses. This study paper was written by FL-T, IR-U, and PH-T
with input from all co-authors. All authors read and approved the
final manuscript and believe that the manuscript represents valid
work; carefully read and fully approve of it.
ACKNOWLEDGMENTS
This research was supported by the Occupational Medicine Area
(Prevention Service) of the University of Granada. We would like
to thank all of the participant in this study.
Frontiers in Psychology | www.frontiersin.org 8April 2018 | Volume 9 | Article 621

Olivencia-Carrión et al. Questionnaire Mobile Phone Abuse
REFERENCES
Abebe, D. S., Hafstad, G. S., Brunborg, G. S., Kumar, B. N., and Lien, L. (2015).
Binge drinking, cannabis and tobacco use among ethnic Norwegian and ethnic
minority adolescents in Oslo, Norway. J. Immigr. Minor. Health 17, 992–1001.
doi: 10.1007/s10903-014-0077-9
American Psychiatric Association (2012). DSM-5: The Future of Psychiatric
Diagnosis. Available inline at: http://www.dsm5.org/Pages/Default.aspx
American Psychiatric Association (2002). Diagnostic and Statistical Manual of
Mental Disorders IV-TR. Washington, DC: American Psychiatric Association.
Bianchi, A., and Phillips, J. G. (2005). Psychological predictors of problem mobile
phone use. Cyberpsychol. Behav. 8, 39–51. doi: 10.1089/cpb.2005.8.39
Billieux, J., Gay, P., Rochat, L., and Van der Linden, M. (2010). The role of urgency
and its underlying psychological mechanisms in problematic behaviours.
Behav. Res. Ther. 48, 1085–1096. doi: 10.1016/j.brat.2010.07.008
Billieux, J., Philippot, P., Schmid, C., Maurage, P., De Mol, J., and Van, D. L. (2014).
Is dysfunctional use of the mobile phone a behavioural addiction? Confronting
symptom based versus process based approaches. Clin. Psychol. Psychother. 22,
460–468. doi: 10.1002/cpp.1910
Billieux, J., Van Der Linden, M., and Rochat, L. (2008). The role of impulsivity
in actual and problematic use of the mobile phone. Appl. Cogn. Psychol. 22,
1195–1210. doi: 10.1002/acp.1429
Biolcati, R. (2015). Women between normality and dependence: a study of
problematic consumption and dependence in women aged 30-50 years. J. Subst.
Use 20, 143–150. doi: 10.3109/14659891.2013.878759
Blasco, J., Martínez-Raga, J., Carrasco, E., and Didia-Attas, J. (2008). Atención y
craving o ganas compulsivas. Avances ensu conceptualización y su implicación
en la prevención de recaídas. Adicciones 20, 365–376.
Chiu, S. I., Hong, F. Y., and Chiu, S. L. (2013). An analysis on the correlation
and gender difference between college students’ internet addiction
and mobile phone addiction in Taiwan. ISRN Addict. 2013:360607.
doi: 10.1155/2013/360607
Chóliz, M. (2012). Mobile-phone addiction in adolescence: the test of mobile
phone dependence (TMD). Prog. Health Sci. 2, 33–44.
Chóliz, M., Pinto, L., Phansalkar, S. S., Corr, E., Mujjahid, A., Flores, C.,
et al. (2016). Development of a Brief Multicultural Version of the Test of
Mobile Phone Dependence (TMD brief) Questionnaire. Front. Psychol. 7:650.
doi: 10.3389/fpsyg.2016.00650
Contreras-Rodríguez, O., Albein-Urios, N., Vilar-López, R., Perales, J. C.,
Martínez-González, J. M., Fernández-Serrano, M. J., et al. (2016). Increased
corticolimbic connectivity in cocaine dependence versus pathological gambling
is associated with drug severity and emotion-related impulsivity. Addict. Biol.
21, 709–718. doi: 10.1111/adb.12242
De-Sola, J., Talledo, H., Rubio, G., and de Fonseca, F. R. (2017a). Psychological
factors and alcohol use in problematic mobile phone use in the
Spanish population. Front. Psychiatry 8:11. doi: 10.3389/fpsyt.2017.
00011
De-Sola, J., Talledo, H., Rubio, G., and de Fonseca, F. R. (2017b). Development
of a mobile phone addiction craving scale and its validation in a
spanish adult population. Front. Psychiatry 8:90. doi: 10.3389/fpsyt.
2017.00090
DeVellis, R. F. (2012). Scale Development, Theory and Applications. Los Angeles,
CA: SAGE.
Fargues, M. B., Lusar, A. C., Jordania, C. G., and Sánchez, X. C. (2009). Validación
de dos escalas breves para evaluar la adicción a internet y el abuso de móvil.
Psicothema 21, 480–485.
Fishman, J. A., and Galguera, T. (2003). Introduction to Test Construction
in the Social and Behavioral Sciences. Lanham: Rowman And Littlefield
Publishers, Inc.
Foerster, M., Roser, K., Schoeni, A., and Röösli, M. (2015). Problematic mobile
phone use in adolescents: derivation of a short scale MPPUS-10. Int. J. Public
60, 277–286. doi: 10.1007/s00038-015-0660-4
Furr, M. (2011). Scale Construction and Psychometrics for Social and Personality
Psychology. Los Angeles, CA: SAGE Publications.
Gallimberti, L., Buja, A., Chindamo, S., Terraneo, A., Marini, E., Rabensteiner,
A., et al. (2016). Problematic cell phone use for text messaging and substance
abuse in early adolescence (11- to 13-year-olds). Eur. J. Pediatr. 175, 355–364.
doi: 10.1007/s00431-015-2645-y
Grant, J. E., Schreiber, L. R., and Odlaug, B. L. (2013). Phenomenology
and treatment of behavioural addictions. Can. J. Psychiatry 58, 252–259.
doi: 10.1177/070674371305800502
Ha, J. H., Chin, B., Park, D. H, Ryu, S. H., and Yu, J. (2008). Characteristics of
excessive cellular phone use in korean adolescents. Cyber. Psychol. Behav. 11,
783–784. doi: 10.1089/cpb.2008.0096
Hernández, A., Muñiz, J., and García, E. (2000). Comportamiento del modelo
de respuesta graduada en función del número de categorías de la escala.
Psicothema 12, 288–291.
Igarashi, T., Motoyoshy, T., Takai, J., and Yoshida, T. (2008). No mobile, no
life: Self-perception and text-message dependency among Japanese high school
students. Comput. Hum. Behav. 24, 2311–2324. doi: 10.1016/j.chb.2007.12.001
Jenaro, C., Flores, N., Gómez-Vela, M., González-Gil, F., and Caballo, C. (2007).
Problematic internet and cell-phone use: psychological behavioral, and health
correlates. Addict. Res. Theory 15, 309–320. doi: 10.1080/16066350701350247
Jiang, Z., and Shi, M. (2016). Prevalence and co-occurrence of compulsive
buying, problematic internet and mobile phone use in college students
in Yantai, China: relevance of self-traits. BMC Public Health 16:1211.
doi: 10.1186/s12889-016-3884-1
Jöreskog, K. G., and Sörbom, D. (1996). LISREL 8 User’s reference guide. Chicago,
IL: Scientific Software.
Kawasaki, N., Tanei, S., Ogata, F., Burapadaja, S., Loetkham, C., Nakamura, T.,
et al. (2006). Survey on cellular phone usage on students in thailand. J. Physiol.
Anthropol. 25, 377–382. doi: 10.2114/jpa2.25.377
Kwon, M., Kim, D. J., Cho, H., and Yang, S. (2013). The smartphone addiction
scale: development and validation of a short version for adolescents. PLoS ONE
8:83558. doi: 10.1371/journal.pone.0083558
Larsen, C. V. L., Curtis, T., and Bjerregaard, P. (2013). Harmful alcohol use
and frequent use of marijuana among lifetime problem gamblers and the
prevalence of cross-addictive behaviour among green land inuit: Evidence from
the cross-sectional inuit health in transition green land survey 2006-2010. Int.
J. Circumpolar Health 72, 1–9. doi: 10.3402/ijch.v72i0.19551
Lee, K., Lee, H. K., Gyeong, H., Yu, B., Song, Y. M., and Kim, D. (2013). Reliability
and validity of the Korean version of the Internet Addiction Test among college
students. J. Korean Med. Sci. 28, 763–768. doi: 10.3346/jkms.2013.28.5.763
Leeman, R. F., and Potenza, M. N. (2013). A targeted review of the neurobiology
and genetics of behavioural addictions: an emerging area of research. Can. J.
Psychiatry 58, 260–273. doi: 10.11772F070674371305800503
Leung, L. (2008). Linking psychological attributes to addiction and improper use of
the mobile phone among adolescents in Hong Kong. J. Child Media 2, 93–113.
doi: 10.1080/17482790802078565
Lin, T. T. C., Chiang, Y., and Jiang, Q. (2015). Sociable people beware?
Investigating smartphone versus no smartphone dependency symptoms
among young Singaporeans. Soc. Behav. Personal 43, 1209–1216.
doi: 10.2224/sbp.2015.43.7.1209
López-Fernández, O., Honrubia-Serrano, M., and Freixa-Blanxart, M. (2012).
Adaptación española del “Mobile Phone Problem Use Scale” para población
adolescente. Adicciones 24, 123–130. doi: 10.20882/adicciones.104
López-Torrecillas, F., García, J., Godoy, J. F., García, M. P., Izquierdo, D. G., and
Sánchez-Barrera, M. B. (2000). Variables modulating stress and coping that
discriminate drug consumers from low or nondrug consumers. Addict. Behav.
25, 161–165. doi: 10.1016/S0306-4603(98)00121-X
Marlatt, G. A., Baer, J. S., Donovan, D. M., and Kivlahan, D. R. (1988). Addictive
behaviors: etiology and treatment. Annu. Rev. Psychol. 39, 223–252.
Mattebo, M., Tydén, T., Häggström-Nordin, E., Nilsson, K. W., and Larsson, M.
(2013). Pornography consumption, sexual experiences, lifestyles, and self-rated
health among male adolescents in Sweden. J. Dev. Behav. Pediatr. 34, 460–468.
doi: 10.1097/DBP.0b013e31829c44a2
Mentzoni, R. A., Brunborg, G. S., Molde, H., Myrseth, H., Skouverøe, K. J. M.,
Hetland, J., et al. (2011). Problematic video game use: estimated prevalence and
associations with mental and physical health. Cyberpsychol. Behav. Soc. Netw.
14, 591–596. doi: 10.1089/cyber.2010.0260
Merlo, L. J., Stone, A. M., and Bibbey, A. (2013). Measuring problematic mobile
phone use: Development and preliminary psychometric properties of the
PUMP scale. J Addict. 2013:912807. doi: 10.1155/2013/912807
Míguez, C. M., and Becoña, E. (2015). Do cigarette smoking and alcohol
consumption associate with cannabis use problem gambling among Spanish
adolescents? Adicciones 27, 8–16.
Frontiers in Psychology | www.frontiersin.org 9April 2018 | Volume 9 | Article 621

Olivencia-Carrión et al. Questionnaire Mobile Phone Abuse
Morata-Ramírez, M., Holgado-Tello, F., P., Barbero-García, I., and Mendez, G.
(2015). Confirmatory factor analysis. Recommendations for unweighted least
squares method related to Chi-Square and RMSEA. Accion Psicol. 12, 79–90.
doi: 10.5944/ap.12.1.14362
Mudry, T. E., Hodgins, D. C., el-Guebaly, N., Cameron Wild, T., Colman,
I. B., Schopflocher, D. et al. (2011). Conceptualizing excessive behaviour
syndromes: a systematic review. Curr. Psychiatry Rev. 7, 138–151.
doi: 10.2174/157340011796391201
Navas, J. F., Billieux, J., Perandrés-Gómez, A., López-Torrecillas, F., Cándido, A.,
and Perales, J. C. (2017). Impulsivity traits and gambling cognitions associated
with gambling preferences and clinical status. Int. Gambl. Stud. 17, 102–124.
doi: 10.1080/14459795.2016.1275739
Osuch, E., Vingilis, E., Ross, E., Forster, C., and Summerhurst, C. (2013). Cannabis
use addiction risk and functional impairment in youth seeking treatment for
primary mood or anxiety concerns. Int. J. Adolesc. Med. Health 25, 309–314.
doi: 10.1515/ijamh-2013-0067
Pedrero-Pérez, E. J., Rodríguez-Monje, M. T., Gallardo- Alonso, F., Fernández-
Girón, M., Pérez- López, M., and Chicharro- Romero, J. (2007). Validación
de un instrumento para la detección de trastornos de control de
impulsos y adicciones: el MULTICAGE CAD-4. TrastorAdic 9, 269–278.
doi: 10.1016/S1575-0973(07)75656-8
Roberts, J. A., Petnji-Yaya, L. H., and Manolis, C. (2014). The invisible addiction:
Cell-phone activities and addiction among male and female college students. J.
Behav. Addict. 3, 254–265. doi: 10.1556/JBA.3.2014.015
Roser, K., Schoeni, A., Foerster, M., and Röösli, M. (2016). Problematic mobile
phone use of Swiss adolescents: is it linked with mental health or behaviour?
Int. J. Public Health 61, 307–315. doi: 10.1007/s00038-015-0751-2
Rosnow, R. L., and Rosenthal, R. (1996). Computing contrasts, effect sizes, and
counternulls on other people’s published data: general procedures for research
consumers. Psychol. Methods 1, 331–340. doi: 10.1037/1082-989X.1.4.331
Ruiz-Olivares, R., Lucena, V., Pino, M. J., and Herruzo, J. (2010). Análisis
de comportamientos relacionados con el uso/abuso de internet, teléfono
móvil, compras y juego en estudiantes universitarios. Adicciones 22, 301–310.
doi: 10.20882/adicciones.171
Rutland, J. B., Sheets, T., and Young, T. (2007). Development of a scale
to measure problem use of short message service: the SMS problem use
diagnostic questionnaire. Prog. Health Sci. 10, 841–843. doi: 10.1089/cpb.
2007.9943
Ryu, Y. S., and Smith-Jackson, T. L. (2006). Reliability and Validity of the Mobile
Phone Usability Questionnaire (MPUQ). J. Usab. Stud. 2, 39–53.
Schepers, J. (2009). Test Construction: Theory and practice.A Course for Beginners.
Saarbrucken: VDM, Verlag Dr. Muller.
Schuster, R. M., Mermelstein, R., and Wakschlag, L. (2013). Gender-specific
relationships between depressive symptoms, marijuana use, parental
communication and risky sexual behavior in adolescence. J. Youth Adolesc. 42,
1194–1209. doi: 10.1007/s10964-012-9809-0
Takao, M., Takahashi, S., and Kitamura, M. (2009). Addictive personality
and problematic mobile phone use. Cyberpsychol. Behav., 2, 501–507.
doi: 10.1089cpb.2009.0022
The International Telecommunication Union (2016). Available online at: http://
www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures’2016.pdf
Toda, M., Monden, K., Kubo, K., and Morimoto, K. (2004). Cellular phone
dependence tendency of female university students. JPN J. Hyg. 59, 383–386.
doi: 10.1265/jjh.59.383
Van Rooij, A. J., Kuss, D. J., Griffiths, M. D., Shorter, G. W., Schoenmakers, M. T.,
and Mheen, V. D. (2014). The (co-)occurrence of problematic video gaming,
substance use, and psychosocial problems in adolescents. J. Behav. Addict. 3,
157–165. doi: 10.1556/JBA.3.2014.013
Vanyukov, M. M., Tarter, R. E., Kirillova, G. P., Kirisci, L., Reynolds, M. D., Kreek,
M. J., et al. (2012). Common liability to addiction and “gateway hypothesis”:
theoretical, empirical and evolutionary perspective. Drug Alcohol Depend. 123,
3–17. doi: 10.1016/j.drugalcdep.2011.12.018
Verdejo-García, A., López-Torrecillas, F., Carmen, O. G., and Pérez-García, M.
(2004). Clinical implications and methodological challenges in the study of
the neuropsychological correlates of cannabis, stimulant, and opioid abuse.
Neuropsychol. Rev. 14, 1–41. doi: 10.1023/B:NERV.0000026647.71528.83
Viola, T. W., Tractenberg, S. G., Wearick-Silva, L., de, O. R., Pezzi, J., and
Grassi-Oliveira, R. (2014). Long-term cannabis abuse and early-onset cannabis
use increase the severity of cocaine withdrawal during detoxification and
rehospitalization rates due to cocaine dependence. Drug Alcohol Depend. 144,
153–159. doi: 10.1016/j.drugalcdep.2014.09.003
Vorspan, F., Mehtelli, W., Dupuy, G., Bloch, V., and Lépine, J. (2015). Anxiety and
substance use disorders: co-occurrence and clinical issues. Curr. Psychiatry Rep.
17:4. doi: 10.1007/s11920-014-0544-y
Walsh, S. P., White, K. M., McD., and Young, R. (2010). Needing to connect:
the effect of self and others on young people’s involvement with their
mobile phones. Aust. J. Psychol. 62, 194–203. doi: 10.1080/000495309035
67229
Walther, B., Morgenstern, M., and Hanewinkel, R. (2012). Co-occurrence of
addictive behaviours: personality factors related to substance use, gambling and
computer gaming. Eur. Addict. Res. 18, 167–174. doi: 10.1159/000335662
Weinstein, A., and Lejoyeux, M. (2015). New developments on the neurobiological
and pharmaco-genetic mechanisms underlying internet and videogame
addiction. Am. J. Addict. 24, 117–125. doi: 10.1111/ajad.12110
Xu, H., Wu, X., Lan, Y., and Chen, Y. (2008). Development of mobile phone
dependence inventory for college students. Chin. J. Clin. Psychol. 16, 26–27.
Yang-Wallentin, F., Jöreskog, K. G., and Luo, H. (2010). Confirmatory factor
analysis of ordinal variables with misspecified models. Struct. Equ. Model. 17,
392–423. doi: 10.1080/10705511.2010.489003
Yau, Y. H., Crowley, M. J., Mayes, L. C., and Potenza, M. N. (2012). Are Internet
use and videogame-playing addictive behaviors? Biological, clinical and public
health implications for youths and adults. Min. Psichiatr. 53, 153–170.
Yen, C. F., Tang, T. J., Yen, J. Y., Lin, H. C., Huang, C. F., Liu, S. C., et al. (2009).
Symptoms of problematic cellular phone use, functional impairment and its
association with depression among adolescents in southern Taiwan. ?J. Adolesc.
32, 863–873. doi: 10.1016/j.adolescence.2008.10.006
Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2018 Olivencia-Carrión, Ramírez-Uclés, Holgado-Tello and López-
Torrecillas. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (CC BY). The use, distribution or reproduction in
other forums is permitted, provided the original author(s) and the copyright owner
are credited and that the original publication in this journal is cited, in accordance
with accepted academic practice. No use, distribution or reproduction is permitted
which does not comply with these terms.
Frontiers in Psychology | www.frontiersin.org 10 April 2018 | Volume 9 | Article 621