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Nomophobia, which is a neologism derived from the combination of “no mobile,” “phone,” and “phobia” is considered to be a modern situational phobia and indicates a fear of feeling disconnected.
No psychometric scales are available in Italian for investigating such a construct. We therefore planned a translation and validation study of the Nomophobia Questionnaire (NMP-Q), which is an instrument developed by Yildirim and Correia. Subjects were recruited via an online survey using a snowball approach.
The NMP-Q was translated from English into Italian using a classical “backwards and forwards” procedure. In order to explore the underlying factor structure of the translated questionnaire, an exploratory factor analysis was carried out. A principal component analysis approach with varimax rotation was performed. Multivariate regression analyses were computed to shed light on the psychological predictors of nomophobia.
A sample of 403 subjects volunteered to take part in the study. The average age of participants was 27.91 years (standard deviation 8.63) and the sample was comprised of 160 males (160/403, 39.7%) and 243 females (243/403, 60.3%). Forty-five subjects spent less than 1 hour on their mobile phone per day (45/403, 11.2%), 94 spent between 1 and 2 hours (94/403, 23.3%), 69 spent between 2 and 3 hours (69/403, 17.1%), 58 spent between 3 and 4 hours (58/403, 14.4%), 48 spent between 4 and 5 hours (48/403, 11.9%), 29 spent between 5 and 7 hours (29/403, 7.2%), 36 spent between 7 and 9 hours (36/403, 8.9%), and 24 spent more than 10 hours (24/403, 6.0%). The eigenvalues and scree plot supported a 3-factorial nature of the translated questionnaire. The NMP-Q showed an overall Cronbach alpha coefficient of 0.95 (0.94, 0.89, and 0.88 for the three factors). The first factor explained up to 23.32% of the total variance, while the second and third factors explained up to 23.91% and 18.67% of the variance, respectively. The total NMP-Q score correlated with the number of hours spent on a mobile phone.
The Italian version of the NMP-Q proved to be reliable.
New information and communication technologies (ICTs) have become particularly widespread and are increasingly utilized in modernized cultures. Due to their frequent use and their ubiquitous and ever-present nature, ICTs are often perceived as an irreplaceable part of a highly dynamic and interconnected society [
However, although mobile devices enable users to perform a variety of tasks in an unprecedentedly rapid, easy, and effective way, they can also lead to serious medical problems. These problems include exposure to radiation, “screen dermatitis,” tumors, and infertility [
Nomophobia, a neologism that is derived from the combination of “no mobile,” “phone,” and “phobia” has recently emerged as a modern problem, denoting the fear of feeling disconnected. Nomophobia is currently considered a situational phobia [
The fear of not being able to use a smartphone or a mobile phone and/or the services it offers...the fear of not being able to communicate, losing the connectedness that smartphones allow, not being able to access information through smartphones, and giving up the convenience that smartphones provide [
Symptoms that characterize nomophobia include an excessive use of a mobile phone, which is kept permanently switched on, with the subsequent feeling of anxiety at the thought of a lack of network coverage. Other symptoms include the habit of continuously looking at the mobile screen in order to check for messages or missed calls (
The NMP-Q was translated from English into Italian using a classical “backwards and forwards” procedure. The questionnaire was then administered, along with a general questionnaire comprising basic sociodemographic information (age, gender, schooling level) and average smartphone use.
This investigation was designed as a cross-sectional study. Individuals were recruited via an online survey using a snowball approach that exploited Google Forms, which is an open-source tool for developing online questionnaires [
Continuous data were represented as means and standard deviations (SDs), while categorical data were expressed as percentages. Skewness and kurtosis were computed for each item score. Acceptable values for asymmetry/skewness and kurtosis are in the range from -2 to +2 in case of normal univariate data distribution. To investigate the factor structure of the translated questionnaire, an exploratory factor analysis (EFA) was carried out, using the principal component analysis (PCA) approach with
The Kaiser-Meyer-Olkin (KMO) measure was calculated to assess the sampling adequacy. Ideally, the KMO should be >60. The likely number of factors was determined by (1) the number of factors with eigenvalues greater than 1 [
A total of 403 subjects (average age 27.91 years, SD 8.63; 160 males and 243 females, representing 39.7% and 60.3% of the sample, respectively) volunteered to take part in the study and were administered the Italian version of the NMP-Q (
Regarding the schooling level, 2 subjects (2/403, 0.5%) had completed only elementary school, while 39 and 173 had completed middle school (39/403, 9.7%) and high school (173/403, 42.9%), respectively. All other subjects (189/403, 46.9%) had received higher education.
Concerning average smartphone use, 45 subjects usually spent less than 1 hour on their mobile phone (45/403, 11.2%), 94 spent between 1 and 2 hours (94/403, 23.3%), 69 spent between 2 and 3 hours (69/403, 17.1%), 58 spent between 3 and 4 hours (58/403, 14.4%), 48 spent between 4 and 5 hours (48/403, 11.9%), 29 spent between 5 and 7 hours (29/403, 7.2%), 36 spent between 7 and 9 hours (36/403, 8.9%) and 24 spent more than 10 hours (24/403, 6.0%). The means, SDs, and skewness and kurtosis figures are reported in
Since 403 subjects constituted a sample size large enough to compute reliable estimations of correlations among variables, we proceeded with the EFA. The N/k ratio (number of subjects/number of items) was 20.15:1, thus satisfying the requirements for commencing a factor analysis. Barlett’s test of sphericity was significant (Chi-square=5,796.275, degrees of freedom=19,
1. Mi sento a disagio senza poter accedere costantemente alle informazioni tramite il mio smartphone
2. Sono infastidito/a se non riesco a cercare informazioni sul mio smartphone quando voglio farlo
3. Non essere in grado di ricevere le notizie (ad esempio gli ultimi aggiornamenti su eventi, meteo, ecc) sul mio smartphone mi rende nervoso/a
4. Sono seccato/a se non posso usare il mio smartphone e/o le sue applicazioni quando voglio farlo
5. L'idea di rimanere a corto di batteria nel mio smartphone mi spaventa
6. Se sono a corto di credito o se ho esaurito il mio limite di giga mensile, mi prende il panico
7. Se non c'è campo o non posso connettermi al Wi-Fi, rimango sempre a controllare per vedere se c'è segnale o se riesco a connettermi a una rete Wi-Fi
8. Se non posso usare il mio smartphone, ho paura di rimanere bloccato/a da qualche parte
9. Se non ho potuto controllare il mio smartphone per un po' di tempo, avverto il desiderio di controllarlo
Se non ho il mio smartphone con me,
10. Mi sento in ansia perché non riesco a comunicare istantaneamente con la mia famiglia e/o con gli amici
11. Sono preoccupato/a perché la mia famiglia e/o gli amici non possono raggiungermi
12. Mi sento nervoso/a perché non sono in grado di ricevere messaggi di testo e chiamate
13. Sono in ansia perché non riesco a rimanere in contatto con la mia famiglia e/o con gli amici
14. Sono nervoso/a perché non riesco a sapere se qualcuno mi ha cercato
15. Mi sento in ansia perché la mia connessione costante con la mia famiglia e gli amici è come se fosse rotta
16. Sono nervoso/a perché mi sento disconnesso/a dalla mia identità online
17. Sono a disagio perché non posso rimanere aggiornato/a con gli ultimi sviluppi dei social media e dei siti on-line
18. Mi sento a disagio perché non riesco a controllare le notifiche per gli aggiornamenti dei miei collegamenti e reti online
19. Mi sento in ansia perché non riesco a controllare i miei messaggi e-mail
20. Mi sento strano/a perché non saprei cosa fare
Mean scores with standard deviation, skewness, and kurtosis.
Item Number | Mean | Standard deviation | Skewness | Kurtosis |
1 | 3.610 | 1.720 | 0.122 | –0.997 |
2 | 4.241 | 1.759 | –0.263 | –0.970 |
3 | 2.744 | 1.582 | 0.668 | –0.426 |
4 | 3.945 | 1.775 | –0.024 | –1.019 |
5 | 3.663 | 1.790 | 0.179 | –0.938 |
6 | 2.519 | 1.621 | 0.780 | –0.484 |
7 | 2.732 | 1.694 | 0.938 | 0.063 |
8 | 2.390 | 1.631 | 1.126 | 0.428 |
9 | 3.975 | 1.793 | –0.025 | –0.972 |
10 | 3.591 | 1.835 | 0.132 | –1.065 |
11 | 3.846 | 1.858 | –0.024 | –1.103 |
12 | 3.417 | 1.786 | 0.271 | –1.016 |
13 | 3.588 | 1.825 | 0.172 | –1.065 |
14 | 3.434 | 1.806 | 0.308 | –0.944 |
15 | 2.675 | 1.716 | 0.762 | –0.466 |
16 | 1.921 | 1.371 | 1.594 | 1.913 |
17 | 2.141 | 1.518 | 1.297 | 0.796 |
18 | 2.184 | 1.563 | 1.326 | 0.876 |
19 | 2.600 | 1.725 | 0.907 | –0.189 |
20 | 2.285 | 1.609 | 1.158 | 0.406 |
As a result of the initial solution, four factors explaining up to 65.90% of the total variance were extracted. Before
As seen in
The Italian version of the NMP-Q showed an overall Cronbach alpha coefficient of .95 (.94, .89, and .88 for the three factors). As such, the internal consistency of the questionnaire can be considered good to excellent. The effect of dropping each variable per time is shown in
The NMP-Q total score correlated with the number of hours spent using a mobile phone (standardized beta-coefficient=.385,
In summary, the number of hours spent on a mobile phone turned out to be a predictor of all subscales, while gender was associated with D1 (although borderline significant) and D3. Finally, the schooling level correlated with D1. Further details are reported in
Factor loading of the Nomophobia Questionnaire. Salient factor loadings are indicated in italics.
Item Number | D1 | D2 | D3 |
1 | 0.255 | 0.307 | |
2 | 0.235 | 0.110 | |
3 | 0.201 | 0.301 | |
4 | 0.257 | 0.186 | |
5 | 0.452 | 0.292 | |
6 | 0.273 | 0.368 | |
7 | 0.143 | 0.359 | |
8 | 0.366 | 0.105 | |
9 | 0.334 | 0.394 | |
10 | 0.255 | 0.244 | |
11 | 0.078 | 0.255 | |
12 | 0.299 | 0.356 | |
13 | 0.159 | 0.272 | |
14 | 0.369 | 0.328 | |
15 | 0.438 | 0.137 | |
16 | 0.265 | 0.108 | |
17 | 0.163 | 0.198 | |
18 | 0.156 | 0.194 | |
19 | 0.235 | 0.253 | |
20 | 0.136 | 0.208 |
Cronbach alpha coefficient when one item is dropped.
Variable dropped | Raw alpha | Change in raw alpha | Standardized alpha | Change in standardized alpha |
1 | 0.9420 | -0.002916 | 0.9421 | -0.002891 |
2 | 0.9428 | -0.002099 | 0.9429 | -0.002036 |
3 | 0.9431 | -0.001823 | 0.9432 | -0.001779 |
4 | 0.9424 | -0.002526 | 0.9425 | -0.002475 |
5 | 0.9417 | -0.003142 | 0.9419 | -0.003078 |
6 | 0.9422 | -0.002689 | 0.9422 | -0.002763 |
7 | 0.9432 | -0.001728 | 0.9431 | -0.001808 |
8 | 0.9440 | -0.0009210 | 0.9440 | -0.0009085 |
9 | 0.9419 | -0.003009 | 0.9420 | -0.002976 |
10 | 0.9409 | -0.003969 | 0.9412 | -0.003782 |
11 | 0.9422 | -0.002717 | 0.9424 | -0.002575 |
12 | 0.9402 | -0.004725 | 0.9404 | -0.004517 |
13 | 0.9410 | -0.003918 | 0.9413 | -0.003683 |
14 | 0.9399 | -0.004983 | 0.9402 | -0.004768 |
15 | 0.9412 | -0.003729 | 0.9413 | -0.003698 |
16 | 0.9423 | -0.002553 | 0.9420 | -0.002922 |
17 | 0.9423 | -0.002538 | 0.9422 | -0.002754 |
18 | 0.9420 | -0.002886 | 0.9418 | -0.003143 |
19 | 0.9443 | -0.0006234 | 0.9443 | -0.0006999 |
20 | 0.9434 | -0.001453 | 0.9418 | -0.003143 |
Multivariate regression analysis investigating the impact of variables (age, gender, schooling level) on total and subscale scores.
Model | B | Standard deviation | Beta | |||
(Constant) | 31.819 | 7.413 | 4.292 | .000 | ||
Number of hours spent using a mobile phone | 4.450 | 0.590 | .385 | 7.541 | .000 | |
Age | 0.105 | 0.141 | .038 | 0.747 | .455 | |
Gender | 4.403 | 2.310 | .091 | 1.906 | .057 | |
Schooling level | 0.801 | 1.352 | .028 | 0.593 | .554 | |
(Constant) | 10.722 | 2.556 | 4.195 | .000 | ||
Number of hours spent using a mobile phone | 1.435 | 0.203 | .362 | 7.055 | .000 | |
Age | 8.142E-005 | 0.049 | .000 | 0.002 | .999 | |
Gender | 1.463 | 0.796 | .088 | 1.837 | .067 | |
Schooling level | 1.052 | 0.466 | .108 | 2.258 | .024 | |
(Constant) | 11.518 | 2.955 | 3.897 | .000 | ||
Number of hours spent using a mobile phone | 1.849 | 0.235 | .402 | 7.860 | .000 | |
Age | 0.044 | 0.056 | .040 | 0.781 | .436 | |
Gender | 0.196 | 0.921 | .010 | 0.213 | .832 | |
Schooling level | -0.375 | 0.539 | -.033 | -0.697 | .486 | |
(Constant) | 9.579 | 3.056 | 3.135 | .002 | ||
Number of hours spent using a mobile phone | 1.165 | 0.243 | .254 | 4.790 | .000 | |
Age | 0.061 | 0.058 | .056 | 1.057 | .291 | |
Gender | 2.745 | 0.952 | .142 | 2.882 | .004 | |
Schooling level | 0.125 | 0.557 | .011 | 0.223 | .823 |
The impact on each item of age, gender, schooling level, and number of hours spent using a mobile phone is shown in
The interaction between the number of hours spent on a mobile device and gender significantly affected only item 10 (
Based on the results of the reliability analysis, the internal consistency coefficient (Cronbach alpha) for the scale and all subscales of the NMP-Q was good, demonstrating that the NMP-Q is able to generate reliable scores. This finding is comparable with the alpha coefficient of the original instrument (Cronbach coefficient of .945, range of Cronbach coefficient for subscales from .819 to .939) [
These factors partially corresponded to the four factors found by Yildirim and Correia [
The large sample size and the findings obtained in terms of psychometric properties represent the major strengths of our study. However, our research suffers from a number of limitations that should be properly recognized. First, since the sample is not representative, caution should be taken when making generalizations about our results. Further research should seek to replicate the results of the present study using more representative samples. Moreover, as for any other self-reported questionnaire, the self-reported structure of the NMP-Q may be a limitation due to social desirability bias. Nevertheless, there is a dearth of studies on nomophobia and this study makes it possible for researchers to use a reliable and validated instrument.
Nomophobia is a modern, emerging, situational, mobile phone-related phobia. The Italian version of the NMP-Q was validated and its psychometric properties were examined, showing a 3-factor structure. The Italian NMP-Q proved to be reliable and can therefore be employed by researchers. Further studies are needed to assess the consistency of the NMP-Q in other samples (either general or clinical), and to investigate comorbidities and predictors of nomophobia using a confirmatory factorial approach to obtain more robust results. The relationship of nomophobia with other ICT-related psychological disorders (such as the Internet addiction) also warrants further investigations.
Impact of age, gender, number of hours spent using a mobile phone, and schooling level on items scores.
exploratory factor analysis
information and communication technology
Kaiser-Meyer-Olkin measure
Nomophobia Questionnaire
principal component analysis
None declared.