**3. Results**

### **3.1 Habits and time on the web**

Only two out of 643 people (0.3%) did not have their own smartphones. What habits did the participants highlight? **Table 1** shows mean frequencies of males and females related to some typical behaviors with this device assessed by specific items of the smartphone-usage questionnaire.

Gender differences (m *vs.* f) were tested through a multivariate analysis of variance (MANOVA) with the 10 behavior frequencies as dependent variables. MANOVA revealed a significant multivariate test (Pillai's trace = 0.239, p < 0.001, < 0.001, η<sup>p</sup> <sup>2</sup> = 0.24) and several significant effect tests (**Table 2**).

Overall, messaging, social networking, listening to music, and browsing were the preferred activities. Males play games and watch streaming videos significantly more than females; females attend social networks, send messages, record photos, and videos, and listen to music significantly more than males.


**Table 1.**

*Estimated frequencies were rated through a Likert scale: 1 =* never*,2=* sometimes*,3=* often*, and 4 =* always*.*


*Adolescents Suspended in the Space-Time: Problematic Use of Smartphone… DOI: http://dx.doi.org/10.5772/intechopen.101632*

SS *= sum of squares;* df *= degrees of freedom; and* MS *= mean of squares.*

*Significant results are in boldface.*

#### **Table 2.**

*Statistics of between-subjects effect tests from the MANOVA males* vs. *females with behavior frequencies as dependent variables (*N *= 620).*

On average, women always rated that they were more active than men in all other measures of the smartphone usage questionnaire, except gaming by a console. Some of these differences were highly significant (**Table 3**).

Males and females differed also for *Smart\_Q-R* scores: *M*<sup>m</sup> = 29.21, *SD*<sup>m</sup> = 6.24, *vs. M*<sup>f</sup> = 31.53, *SD*<sup>f</sup> = 6.81, *MS*<sup>e</sup> = 42.394, *F*(1, 629) = 19.941, *p* < 0.0001, η<sup>p</sup> <sup>2</sup> = 0.031. With a range of 14–56, women revealed greater involvement than men in smartphone use.

#### **3.2 Dissociative phenomena**

Some differences related to dissociative phenomena between men and women emerged too.

In relation to the DisUADI scale, over a range of points from 15 to 75, the group of participants averaged 32.98 (*SD* = 9.76, *N* = 625). Women scored significantly higher (**Tables 4** and **5**).

Differently with the A-DES – Total, which is a measure developed for adolescents (average score ranging between 1 and 10), this group of participants settled on an average score of 2.09 (*SD* = 1.59, *N* = 628), with no significant difference between males and females. Indeed, differences emerged for the AII and DD subscales, but not for DA and PI subscales (**Tables 4** and **5**).


df *= degrees of freedom; and* MS*<sup>e</sup> = error mean of squares. Significant results are in boldface.*

#### **Table 3.**

*Descriptive (means and standard deviations) and inferential statistics (univariate ANOVAs – Males* vs. *females) of other smartphone usage measures estimated by participants: <sup>1</sup> frequencies were expressed through four points (1 =* never*,2=* sometimes*,3=* often*,4=* always*); <sup>2</sup> time was estimated through five points (5 =* more than 5 h*,4=* between 3 and 5 h*,3=* between 1 and 3 h*,2=* less than an hour*, and 1 =* never*); <sup>3</sup> duration was estimated through three points (3 =* increased*,2=* same*,1=* decreased*).*


*DisUADI = dissociation scale of internet use, abuse, addiction questionnaire; A-DES = adolescent dissociative experience scale; DA = dissociative amnesia; AII = absorption and imaginative involvement; DD = depersonalization and derealization; and PI = passive influence.*

#### **Table 4.**

*Means and standard deviations of dissociative measures (males = 332 for DisUADI, 334 for A-DES; females = 293 for DisUADI, 294 for A-DES).*


*Adolescents Suspended in the Space-Time: Problematic Use of Smartphone… DOI: http://dx.doi.org/10.5772/intechopen.101632*

*DisUADI = dissociation scale of internet use, abuse, addiction questionnaire; A-DES = adolescent dissociative experience scale; DA = dissociative amnesia; AII = absorption and imaginative involvement; DD = depersonalization and derealization; PI = passive influence;* SS *= sum of squares;* df *= degrees of freedom; and* MS *= mean of squares. Significant results are in boldface.*

#### **Table 5.**

*Statistics of between-subjects effect tests (males* vs. *females) from ANOVAs for DisUADI (*N *= 625) and A-DES Total (N = 628) measures, and from the MANOVA for A-DES subscales (multivariate test: Pillai's trace = 0.031,* p *= 0.001, η<sup>p</sup> <sup>2</sup> = 0.03).*

If the group means scores are relatively low, the large variability around the means reveals that several dissociative phenomena occurred. The A-DES standards state that a score of 4 can be considered the cut-off value for a presence of dissociative phenomena out the normality [17]. In the A-DES total score, 48 men (14.37%) and 59 women (20.02%) achieved scores of 4 or higher; the highest score was 9 from a single male participant. By dichotomizing the groups into participants who have A-DES scores less than 4 or equal/greater than 4, a two-by-two contingency table revealed the non-independence of two factors: χ<sup>2</sup> (1, *N* = 628) = 4.01, *p* = 0.045, two-ways.

#### **3.3 Regression analysis**

The next step of the analysis was the estimate of the associations between all the measures, differentiating males from females, since the two groups showed significantly different percentages of dissociative experiences.

The analysis of the associations revealed numerous and interesting correlations between smartphone behavioral habits, the *Smart\_Q-R* scores, and the dissociation scales. These results are reported in **Tables 6**–**10**.

Two separate stepwise linear regressions (for male and female groups), with DisUADI measures as dependent variables and smartphone usage behaviors, *Smart-Q-R* indexes, and A-DES subscale and total scores as predictors were performed. The analysis revealed that the strongest predictors were A-DES total score for men and *Smart\_Q-R* index for women, respectively (**Table 11**).

#### **4. Discussion**

Analysis revealed several differences in smartphone preferred activities as a function of users' gender. Some of these differences were expected: women more


#### **Table 6.**

*Pearson's* r *coefficients between typical smartphone habits and* Smart\_Q-R *and dissociation measures of male group. Significance (*p*) levels and* N*s are reported too.*

attended socials and were more engaged in relational behaviors than men; instead, men resulted more engaged in playing games and watching videos by streaming than women. These results are literature confirmations [20].

However, more interesting were the gender differences related to the measures of smartphone overuse and dissociative phenomena. Indeed, women estimated more frequent smartphone usage than men. Women also reported more dissociative phenomena. This gender difference results from both when the mean group scores on the DisUADI are considered, and when percentages of scores equal to/above the


#### *Adolescents Suspended in the Space-Time: Problematic Use of Smartphone… DOI: http://dx.doi.org/10.5772/intechopen.101632*

#### **Table 7.**

*Pearson's* r *coefficients between smartphone habits and* Smart\_Q-R *and dissociation measures of female group. Significance (*p*) levels and* N*s are reported too.*

4-point cutoff in A-DES are compared. Women showed higher scores than men in *absorption and imaginative involvement* and *depersonalization and derealization* subscales of A-DES too.

These differences suggested to analyze separately women and men associations between study variables. Numerous significant associations were found for both groups. Several associations resulted weak (*r* indices less than 0.30): both genders highlighted dissociative measures correlating with perceived daily time spent with the smartphone, in messaging, and in front of a computer, with the feeling that


#### **Table 8.**

*Pearson's* r *coefficients between other smartphone habits and* Smart\_Q-R *and dissociation measures of male group. Significance (*p*) levels and* N*s are reported too.*

annual time spent on-screen increased, and with more frequent use of smartphone before falling asleep.

However, stronger indices ((*r* > 0.30) emerged between DisUADI scores and the estimates of two specific behaviors: *overthinking* (i.e., constantly thinking about online activities even when he/she was not connected and was busy doing other things) and *lying* (i.e., if in the past he/she sometimes lied about the time he/she had spent online). Similarly, *Smart\_Q-R* scores resulted strongly associated with all dissociative scales in both groups, particularly to DisUADI scores.

In both genders DisUADI scale resulted strongly associated also with the A-DES scale and subscales: this is a proof of concurrent validity.

Therefore, at this point, we wondered which was the best predictor of the DisUADI index and if predictors would have been different for men and women. Some differences emerged again. In both male and female groups, A-DES total score and *Smart\_Q-R* emerged as the strongest predictors, but in reverse order: for men, A-DES total was the strongest one, for women the *Smart\_Q-R*. These two measures


#### *Adolescents Suspended in the Space-Time: Problematic Use of Smartphone… DOI: http://dx.doi.org/10.5772/intechopen.101632*

#### **Table 9.**

*Pearson's* r *coefficients between other smartphone habits and* Smart\_Q-R *and dissociation measures of the female group. Significance (*p*) levels and* N*s are reported too.*

alone accounted for 41% and 53% of the variance by male and female group, respectively. The two measures together accounted for 51% and 63% of the variance by male and female group, respectively.

If we look at the other variables entered the models, in the male group three variables emerged that explained another 0.04% of the variance; in the female group, five variables emerged that explained another 0.03% of the variance: a negligible contribution for both groups, even if some of these variables (such as overthinking) had shown a strong positive correlation index.

These results suggest taking into consideration the *Smart\_Q-R* index above all to explain the dissociative phenomena measured with the DisUADI. The *Smart\_Q-R* index summarizes an estimate of the intensity of 14 behaviors (e.g., frequency of connections, positive mood and facilitation of social relationships, and so on) foreshadowing an unhealthy overuse of the smartphone if it is high [21]. Some of the *Smart\_Q-R* behaviors are typical behaviors referred to flow (e.g., lack of perception of passing time) or to dissociative experiences (e.g., sense of alienation when connected). Therefore, the strict associations that emerged between


**Table 10.**

*Pearson's* r *coefficients between* Smart\_Q-R *and dissociation measures of male (below the diagonal) and female (above the diagonal) groups. Significance (*p*) levels and* N*s are reported too.*

*Smart\_Q-R*, DisUADI and A-DES scores in both regressions supported the idea that smartphone overuse can induce flow and dissociative experiences, especially in the female gender.

Why did women seem more vulnerable than men? The results of this study say that female participants were above all more intense smartphone users than men. An aim for future research is to find out which model of smartphone using is more likely to activate dissociative phenomena: this study suggests various potential behaviors (e.g., overthinking, streaming, playing games, etc.) but without one more strongly emerging.

#### **5. Conclusions**

Currently, the demand for the use of mobile devices to communicate, have fun and relax, read and study, search for information, etc., is so intense that it is impossible to escape it. Particularly, adolescents need to stay connected through their devices to be updated on the activities of the group and peers and to extend the


*Adolescents Suspended in the Space-Time: Problematic Use of Smartphone… DOI: http://dx.doi.org/10.5772/intechopen.101632*

#### **Table 11.**

*Stepwise-linear regression analysis for the male and female groups: Dependent variable DisUADI.*

school time of interactions. The time to devote to all these societal demands is increasing, so they are needed to always remain connected.

In this digital cultural context, the time that teenagers have to dedicate to viewing their smartphone backlit screens is enormously dilated. In this context, the outcome of compulsive and problematic smartphone use becomes highly probable [22, 23]. If this happens, it is not uncommon to experience a complete absorption in the activity that is taking place with the smartphone, encountering flow experiences [24, 25].

The study presented in this chapter finds precisely the prolonged use of the smartphone as an important precursor of the dissociative experiences declared by a convenience sample of adolescents. Experiencing complete absorption in the activity that is taking place can reinforce the activity itself and thus initiate a circular causality loop that reinforces the problematic use of the device and leads to dissociative experiences.

The study has some limitations: the individual characteristics (e.g., extroversion, sensation seeking, or sensitivity to rewards) were not investigated. Some personal characteristics could shed light on different dispositions/risk factors regarding problematic smartphone use [26] and therefore the predisposition to dissociation. Furthermore, the data do not show a clear direction of causality between problematic smartphone use and levels of dissociation, but an evident concomitance that represents a start for the study of dissociative phenomena connected to the overuse of backlit screens. This research line could serve to redefine the concept of VDU dissociative trance in terms of cognition and flow experiences. Understanding the nature of these processes will help to understand the "suspensive" and dissociated risk of the digital mind and to prevent psychopathological problems through the correct use of digital technology while respecting human neurodevelopment.

#### **Acknowledgements**

The authors acknowledge the high school participants made these analyzes possible with their responses. They also acknowledge teachers, managers, and auxiliary school staff with patience and courtesy made it possible to collect the data.

Our thanks go to all of them.

### **Author details**

Massimo Ingrassia\*, Gioele Cedro, Sharon Puccio and Loredana Benedetto Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy

\*Address all correspondence to: massimo.ingrassia@unime.it

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Adolescents Suspended in the Space-Time: Problematic Use of Smartphone… DOI: http://dx.doi.org/10.5772/intechopen.101632*
