Edited by: Md. Atiqur Rahman Ahad, University of Dhaka, Bangladesh
Reviewed by: Eduardo J Pedrero-Pérez, Ayuntamiento de Madrid, Spain; José De-Sola, Complutense University of Madrid, Spain
This article was submitted to Human-Media Interaction, a section of the journal Frontiers in Psychology
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Mobile phone use and its potential addiction has become a point of interest within the research community. The aim of the study was to translate and validate the Test of Mobile Dependence (TMD), and to investigate if there are any associations between mobile phone use and problem gambling. This was a cross-sectional study on a Swedish general population. A questionnaire consisting of a translated version of the TMD, three problem gambling questions (NODS-CLiP) together with two questions concerning previous addiction treatment was published online. Exploratory factor analysis based on polychoric correlations was performed on the TMD. Independent samples
In recent years, behavioral addictions as clinical diagnoses have been given more interest and validity, marked by gambling disorder (GD) being accepted in the latest edition of DSM-5 in the chapter of “substance-related and addictive disorders”, reflecting its resemblance to substance use disorder (American Psychiatric Association,
Along with GD being accepted in DSM-5, Internet Gaming Disorder (IGD) was mentioned as a condition warranting more clinical research and experience before it might be considered for inclusion in the main book as a formal disorder (American Psychiatric Association,
The increased attention being directed toward behavioral addictions has during recent years also brought interest to the use of mobile phones. According to a survey made in 2017, 98% of the Swedish population owned a mobile phone. The majority of these were smartphones, facilitating Internet access, the use of applications of social networks, gaming and gambling (Davidsson and Thoresson,
The fact that almost everyone today owns a mobile phone, and that Internet access is possible virtually everywhere, has sparked an interest within the research field in how this might affect patterns of use. Some studies have examined potential benefits in using mobile phone services to support healthy behaviors, and in providing health interventions to different patient groups (Head et al.,
The World Health Organization has now raised attention toward potential health implications of excessive mobile phone use, and some researchers have even suggested a possible new form of behavioral addiction (Chóliz,
One of the questionnaires developed is the Test of Mobile Phone Dependence (TMD), which has been tested and validated on Spanish and Persian adolescents (Chóliz,
However, the concept of mobile phone dependence has not been without controversy within the research field. Some researchers have questioned the presence of a psychopathological concept such as mobile phone dependence, as no established theoretical framework or defined symptomatology exist (Billieux et al.,
To the best of the authors' knowledge, no research has evaluated whether a potential mobile phone dependence—or an addiction-like mobile phone behavior—may be associated with problem gambling, i.e., with the only addictive behavioral patterns so far established as a diagnosis. Since addictive disorders have shown to be associated with one another, there is reason to investigate whether such an association also exists between addiction-like mobile phone behavior and GD. Also, there is need to test potential evaluation instruments for mobile phone dependence in different settings, and to further examine their potential validity and usefulness, not only in the youngest individuals, but in the general population. Therefore, in the present study, the primary aim was to translate and validate an instrument for mobile phone dependence in the Swedish general population, through a web survey distributed through social media and on the Internet, and a secondary aim of the study was to investigate whether there is an association between mobile phone use, problem gambling and an experience of seeking treatment for a gambling problem.
The first part of the questionnaire was based on descriptive data collection. Age category and sex of responding subjects was collected, together with basic usage patterns of mobile phone use, such as total calls per day, total text messages per day, and subjective dependence (See Table
The questionnaire (Translated from the Spanish version of the TMD, for purpose of the reader).
While relaxing at home | ||||
While at work or in school | ||||
While with friends | ||||
While in bed | ||||
While eating lunch |
First, the TMD, a 22-item questionnaire originally developed and validated in Spain (Chóliz,
Furthermore, the NODS-CLiP, a validated three-question screening instrument for adult pathological and problem gambling was translated from English to Swedish through oblique translation technique and included in the final questionnaire (Toce-Gerstein et al.,
Finally, two non-compulsory dichotomous (yes/no) questions were added to the final questionnaire, to study if the subject had ever received professional treatment for gambling-, alcohol-, or drug-related problems. The two non-compulsory questions differed from the other questions in the questionnaire in that the subject could choose to leave them unanswered.
The final version of the Swedish questionnaire, consisting of descriptive data, the TMD, the NODS-CLiP and addiction treatment questions, was subsequently published on a webpage that was accessible online (see Table
The study sample was a convenience sample, and the questionnaire was open to any subject with access to the Internet. The questionnaire was accessible online between February 18th and April 6th, 2016. The questionnaire was predominantly distributed through Facebook, Twitter, Google, and E-mail.
Facebook—between the 18th and 20th of February, 2016, a link to the questionnaire was published by the authors on their private Facebook profile pages with an invitation to followers to share the link unconditionally.
Google and E-mail—a list of key words was purchased, related to mobile phone use, gambling and gaming. Among the words purchased were “best mobile phone,” “cheapest mobile phone subscription,” “betting,” “casino,” and “lottery.” E-mails were sent out to the principals of high schools and upper secondary schools in the cities of Landskrona, Lund and Malmö, with an appeal of spreading the questionnaire to their students. A convenience sample was used and principals of 65 schools were e-mailed between the 5th and 10th of March 2016.
Twitter—a twitter account was created, spreading the survey link on twitter pages of gambling companies, using hashtags related to gambling.
The data collection was made by PIB, a Swedish company experienced in online surveys. The sample was not nationally representative. Participants choosing to display the link to the survey had to provide informed consent electronically (but without any identifying personal information) in order to enter the questionnaires. No identifying information was obtained from the survey, and geographical location was not asked for, and age was reported only in 5-year categories, such that both direct and indirect identification would be impossible. The present web survey was entirely self-selective, and presented as a self-test for mobile phone dependence. At the end of the survey, the participants received an automated message indicating whether they scored above cut-off on mobile phone dependence, and including a brief advice to seek professional therapeutic assistance if unable to cut down on a potentially problematic pattern of mobile phone use. The study was approved by Lund regional ethics committee, Sweden (file number 2016/53).
Statistical analyses were performed using IBM SPSS Statistics version 23.0 and FACTOR version 10.8.01. Parallel Analysis was conducted through Monte Carlo simulation in SPSS. Polychoric correlation matrix was computed in FACTOR. A
The adequacy of the data for factor analysis was investigated with the Kaiser-Meyer-Olkin measure of sampling adequacy (KMO) and Bartlett's test of sphericity (Tabachnick and Fidell,
Reliability analysis of the TMD was performed through internal consistency computation of Cronbach's alpha coefficient and considered acceptable if it exceeded 0.7 (Tabachnick and Fidell,
Upon closing the online questionnaire, 1,769 people had entered the start page. Out of these, 28 people left instantly without proceeding to the questionnaire. Another 8 people were under 15 years old, and were denied to fill out the questionnaire. Out of the 1,769 people who entered the start page of the questionnaire, a total of 1,515 people completed it (men
Age distribution of the subjects.
15–18 years | 459 | 30.3 |
19–24 years | 137 | 9.0 |
25–29 years | 194 | 12.8 |
30–39 years | 339 | 22.4 |
40–49 years | 186 | 12.3 |
50 years or older | 200 | 13.2 |
1,515 | 100 |
Average daily mobile phone use.
On an average day, how many phone calls do you make with your mobile phone? (n, %) | 1,125 (80.9) | 209 (13.8) | 51 (3.4) | 12 (0.8) | 18 (1.2) | 1,515 (100) |
On an average day, how many SMS do you send? (n, %) | 728 (48.1) | 347 (22.9) | 159 (10.5) | 76 (5.0) | 205 (13.5) | 1,515 (100) |
How many people do you usually communicate with in a day through social media, chat functions, WhatsApp, Skype, or other?
None | 90 | 5.9 |
1–10 people | 1,201 | 79.3 |
11–50 people | 203 | 13.4 |
51–100 people | 12 | 0.8 |
More than 100 people | 9 | 0.6 |
1,515 | 100 |
The dataset proved suitable for exploratory factor analysis with a KMO of 0.920 and statistical significance for Bartlett's test of sphericity (p < 0.001). Parallel analysis suggested that three factors should be retained in the subsequent PCA based on polychoric correlations (Table
Parallel analysis based on principal components analysis of the TMD.
1 |
9.220 | 1.306 | 1.360 | 41.91 % |
2 |
1.943 | 1.248 | 1.291 | 50.74 % |
3 |
1.590 | 1.206 | 1.239 | 57.96 % |
4 | 1.078 | 1.173 | 1.198 | |
5 | 0.989 | 1.144 | 1.166 | |
6 | 0.844 | 1.120 | 1.139 | |
7 | 0.734 | 1.094 | 1.115 | |
8 | 0.647 | 1.070 | 1.089 | |
9 | 0.588 | 1.048 | 1.066 | |
10 | 0.536 | 1.026 | 1.044 | |
11 | 0.510 | 1.004 | 1.023 | |
12 | 0.455 | 0.984 | 1.004 | |
13 | 0.409 | 0.964 | 0.980 | |
14 | 0.389 | 0.943 | 0.962 | |
15 | 0.376 | 0.921 | 0.939 | |
16 | 0.336 | 0.900 | 0.918 | |
17 | 0.287 | 0.877 | 0.897 | |
18 | 0.280 | 0.855 | 0.876 | |
19 | 0.234 | 0.831 | 0.853 | |
20 | 0.224 | 0.803 | 0.826 | |
21 | 0.181 | 0.768 | 0.798 | |
22 | 0.153 | 0.719 | 0.761 |
PCA based on polychoric correlations.
1 | 0.617 | ||
2 | 0.757 | ||
3 | 0.780 | ||
4 | 0.946 | ||
5 | 0.864 | ||
6 | 0.565 | ||
7 | 0.833 | ||
8 | 0.486 | ||
9 | 0.591 | ||
10 | 0.848 | ||
11 | 0.356 | 0.454 | |
12 | 0.364 | ||
13 | 0.879 | ||
14 | 0.624 | ||
15 | 0.821 | ||
16 | 0.554 | 0.330 | |
17 | 0.524 | ||
18 | 0.429 | 0.450 | |
19 | 0.760 | ||
20 | 0.879 | ||
21 | 0.565 | ||
22 | 0.583 |
PCA based on polychoric correlations.
1 | 0.689 | 0.453 | 0.421 |
2 | 0.693 | 0.441 | 0.261 |
3 | 0.461 | 0.787 | 0.223 |
4 | 0.857 | 0.371 | 0.419 |
5 | 0.835 | 0.383 | 0.470 |
6 | 0.683 | 0.459 | 0.472 |
7 | 0.358 | 0.818 | 0.368 |
8 | 0.564 | 0.236 | 0.454 |
9 | 0.694 | 0.307 | 0.558 |
10 | 0.494 | 0.875 | 0.353 |
11 | 0.655 | 0.436 | 0.691 |
12 | 0.581 | 0.530 | 0.615 |
13 | 0.406 | 0.304 | 0.812 |
14 | 0.408 | 0.491 | 0.686 |
15 | 0.477 | 0.321 | 0.814 |
16 | 0.697 | 0.308 | 0.611 |
17 | 0.600 | 0.516 | 0.723 |
18 | 0.593 | 0.197 | 0.615 |
19 | 0.281 | 0.754 | 0.450 |
20 | 0.285 | 0.215 | 0.735 |
21 | 0.512 | 0.257 | 0.658 |
22 | 0.560 | 0.439 | 0.726 |
Reliability analysis of the 22-item TMD questionnaire showed acceptable internal consistency, with a Cronbach's alpha of 0.905. Parallel analysis based on principal components analysis extracted three factors that together accounted for 57.96% of the variance. Factor 1 accounted for 41.91% of the variance and was named “Loss of control,” including items 1, 2, 4, 5, 6, 8, 9, 11, 16, and 18. Factor 2 accounted for 8.83% of the variance and was named “Financial problems,” including items 3, 7, 10 and 19. Factor 3 accounted for 7.22% of the variance and was named “Tolerance and withdrawal symptoms,” including items 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, and 22. Cross-loadings were found for item 11 (“When I haven't used my mobile phone for a while, I feel the need to call someone, send an sms or to contact someone through social media”), 16 (“When my mobile phone is in my hand, I can't stop using it”), and 18 (“The first thing I do when I get up in the morning is to see if someone has called me, sent me an SMS or written to me through social media”), and assigned to the single factor with the highest loading. As it was assumed that this was the latent nature of these items, the items were retained in the test.
The mean test total score in the present study was 27.72 for the whole group, with a statistically significant difference in mean score between men (22.48) and women (30.96,
In total, 47 people (3.1%) had a total test score of 58 or higher, and 16 people (1.1%) had a total test score of 66 or higher. Logistic regression revealed a statistically significant (
Eighty-four (5.5%), 29 (1.9%), and nine subjects (0.6%) endorsed one, two or three gambling items, respectively, such that a total of 122 subjects (8.1%) were categorized as problem gamblers. Furthermore, 11 subjects (0.7%) answered “yes” to the question “Have you ever received professional care, treatment or counseling for gambling problems or gambling disorder?”
Problem gambling was statistically significant associated with a younger age group (chi-square linear-by-linear
No statistically significant difference was found in relation to mean test total score for the subjects with a history of receiving professional treatment for gambling (42.55 vs. 27.61, NS) and alcohol or other drug use disorder (30.18 vs. 27.63, NS). The total TMD score did not differ between problem gamblers and non-problem gamblers (28.25 vs. 27.67, NS, df 1, OR 1.00 [0.99–1.02]), and in linear regression, the total test score was unrelated to the number of endorsed gambling items (
The main objective of this study was to validate the TMD in a new setting, and in the general population, not exclusively among young people. The study also intended to investigate potential relations between high mobile phone use and problem gambling, or with having a history of having received professional treatment for gambling problems. One main finding was the high internal consistency of the TMD and its correlation with subjective, self-reported level of feeling dependent on one's mobile phone. A second main finding is that problem gambling, which was significantly associated to younger age and male sex, was statistically significant and positively associated with the TMD score, but only after adjusting for age and sex.
The internal consistency of the TMD was acceptable (Cronbach's alpha = 0.905), which may indicate that the items in the questionnaire are measuring the same construct, such as mobile phone dependence. The Cronbach's alpha figure in the present study is similar to the one Chóliz found in his study, with a Cronbach's alpha of 0.94 (Chóliz,
In the present study, the two proposed cut-off scores for TMD revealed a prevalence of mobile phone dependence of 3.1 and 1.1% respectively. These figures might be comparable to the prevalence figures found in some previous studies (Götestam and Johansson,
A statistically significant difference in mobile phone use in relation to sex was found, with women scoring higher than men. If future research provides a clinical picture of mobile phone dependence to be a clinical diagnosis, it might differ from other substance-use- and addictive disorders, as they so far have been found more commonly in men (Ladd and Petry,
The concept of an addictive disorder related to excessive mobile phone use is however far from controversial. Billieux and colleagues questions the framework of mobile phone dependence studies, accusing these for assuming a priori that a mobile phone dependence syndrome exists, and that tests were created in order to verify its existence. Furthermore, the same authors question to what extent already established substance use disorder criteria can be directly translated and used within a potential mobile phone dependence context (Billieux et al.,
Moreover, it is essential to consider the possibility of cohort effects and that some behaviors seem to be transient and sometimes episodic rather than chronic, as Thege and co-workers showed in their longitudinal study (Thege et al.,
Another main finding was the association between total TMD score and problem gambler status, although this association appeared only when controlling for sex and age, as the actual values in TMD score were virtually the same across gambling groups. An individual scoring one out of three on the NODS-CLiP questions was considered a “problem gambler,” yielding a lifetime 8-% prevalence of problem gambling. Although measured with a different instrument, previous data have reported a lifetime prevalence of problem gambling of around 2.5% in Sweden, but with markedly higher number in younger age groups (Abbott et al.,
The relationship between lifetime problem gambling and mobile phone behavior was markedly different when not adjusting for age and sex, such that problem gamblers and non-problem gamblers reached virtually the same total score on the TMD. In contrast, while female sex was associated with TMD score and male sex was associated with problem gambling, and while younger age predicted both TMD and problem gambling, the adjusted analysis revealed a statistical association between TMD score and increased likelihood of reporting a lifetime gambling problem. In addition to these overall measures, several other items describing mobile phone behavior (apart from the TMD) separated problem gamblers from non-problem gamblers, although this picture is not unanimous and harder to interpret. For example, lifetime problem gamblers reported more mobile phone calls per day and reported communicating with more people daily, whereas they did not report sending more text messages per day. The association with the frequency of using games in the mobile phone (and where games for money were included in the question, along with different types of videogames), may not seem surprising. For other associations seen here, more research will be needed in order to establish how different aspects of mobile phone use may differ between problem gamblers and the rest of the population. To the best of our knowledge, the present study is the first one to evaluate the potential association between a structured measure of mobile phone dependence, or an addiction-like mobile phone behavior, and problem gambling. Clearly, more research is needed in this area, in order to replicate the present findings of separate items being related to gambling behavior, as it cannot be excluded that certain components of a pattern of mobile phone use may be more associated with gambling behavior. Likewise, the direction and causality of such an association cannot be established from the present study, where problem gambling may have occurred at any time during a person's life, whereas the mobile phone pattern referred to the current situation. Clearly, gambling for money has been available for several decades more than has the use of mobile phone devices, and longitudinal studies, addressing gambling behavior and mobile phone behavior simultaneously, are needed. However, the findings of the present study do indicate a potential link between a more intense or exaggerated mobile phone use and the most clearly established non-substance addictive behavior, gambling, lending some support to potentially shared characteristics between problem gamblers and individuals with a more intense mobile phone use.
A strength of the present study is that subjects were able to fill in the questionnaire online, through their smartphone, tablet or computer. This allowed a large number of people to answer the questionnaire anonymously. As with all questionnaires, there is the possibility of bias. In the case of the TMD and the other questions, it is plausible to assume that social desirability bias as well as cultural normativity might have influenced some of the answers, as addiction have since long held a social stigma. The sampling method used in the study did not allow us to control the correct and honest completion of the questionnaire. However, use of Mahalanobis distance, the elimination of incompletely filled out questionnaires (
A limitation of the present study is that the majority of the subjects filling out the questionnaire were women (61.7%). One explanation to this might be the common finding that women in general seem to show a higher response rates in surveys (Smith,
In conclusion, the results of this study show that the TMD has acceptable internal consistency and that the test correlates with subjective dependence. The test may be useful in future screening for mobile phone use behavior, but a confirmatory factor analysis to further evaluate the instrument is recommended. However, more research is needed to further investigate the individual reasons for using the mobile phone in ways that may be considered an addiction. Moreover, in the present study it was noted that many people experience a subjective dependence, and that they assume they would experience withdrawal symptoms if unable to use their mobile phone. Finally, problem gambling was shown to be associated with mobile phone behavior, including the total TMD score, and although this link needs to be interpreted with great caution, it cannot be excluded that individuals with problematic gambling and with an addiction-like mobile phone behavior may share similar characteristics. More research is needed in order to examine the role of excessive mobile phone behavior in society, and its potential associations with other behavioral addictive disorders.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained by all individual participants included in the study. As none of the study participants were younger than 15 years old, parental approval was not needed. The project was approved by the regional ethics committee Lund, Sweden (file number 2016/53).
AF: Corresponding author, main author; MC: Founder of the TMD. Provision of feedback on the statistics. AH: Academic supervisor, providing feedback on the manuscript.
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.