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Abstract

This study aimed to identify the variables (i.e., internalizing, and externalizing problems, self-control, emotion dysregulation, and alexithymia) relevant for Smartphone Addiction and non-suicidal self-injury (NSSI), conceptualized as emotion-regulation strategies, also assessing age and gender differences. Based on power analysis, N = 78 Italian adolescents (11–19 years; M_age = 14.24; SD = 1.56; 73.1% females) were considered. Step-wise multivariate linear regressions evidence a mutual association between NSSI and Smartphone Addiction, particularly relevant in pre-adolescence. Low self-control is significantly associated with the Smartphone Addiction, while emotion dysregulation and alexithymia with NSSI. This study supports NSSI and Smartphone Addiction conceptualization as emotion-regulation strategies and the importance of prevention interventions.

Keywords

Non-suicidal Self-Injury Smartphone Addiction Adolescence Emotion regulation Age differences Gender differences

Adolescence is a critical period characterized by changes in different areas of development related to physiological and psychosocial functioning (WHO, 2019). This developmental period is characterized by several vulnerabilities (Cicchetti and Rogosch, 2002) particularly associated with the still-developing neural structures. The period of adolescents’ neuro-development is considered a vulnerability toward the put in action of risk-taking behaviors (Ahmed et al., 2015) which might lay the bases for the development of mental health problems (Cicchetti and Rogosch, 2002). Indeed, half of lifetime mental health concerns occur by the age of 14 (Kessler et al., 2005) further accounting for a great prevalence of adult psychopathology, as often left untreated or unrecognized in adolescence (Cicchetti and Rogosch, 2002; Kessler et al., 2005).

Emotional development is one of the goals of this transition period together with identity formation (Kim et al., 2012), yet it is also characterized by low self-control and a strong desire for novelty-seeking (Herpertz-Dahlmann et al., 2013). Self-control can be defined as the capacity to control one’s impulses in favor of one’s priorities and greater goals (Milyavskaya and Inzlicht, 2017). As such, high self-control can be considered as protective (Kim et al., 2018) as it is associated with a greater capacity for both emotional and behavioral self-regulation (De Ridder et al., 2012), while low self-control is associated with risk-taking and addictive behaviors as well as with the development of emotional problems (Oliva et al., 2019).

In this regard, the importance of self-control is recognized referring to two spreading phenomena among adolescents, namely, Smartphone Addiction (Kim et al., 2018; Billieux et al., 2007; Cho et al., 2017; Jeong et al., 2019; Zhang et al., 2019; Pavia et al., 2016; Olson et al., 2020) and Non-Suicidal Self-Injury (NSSI) (Abi-Jaoude et al., 2020; Castaldo et al., 2020; Lockwood et al., 2016; Yusainy and Lawrence, 2014).

Smartphone Addiction

Recent prevalence studies show that, in Italy, between 85.5% (ISTAT, 2019) and 87% (Ipsos, 2017) of adolescents aged between 11 and 17 years have regular access to a smartphone, with over 72% of them accessing the internet through their smartphone. Accordingly, a 2015 study reports greater problematic smartphone use between the age of 15 and 16 years. Moreover, women (85.7%) present a greater use of the device compared to men (ISTAT, 2019) with female adolescents showing a greater risk of developing addictive tendencies toward the smartphone (Fischer-Grote et al., 2019; van Deursen et al., 2015). Smartphone Addiction refers to the potentially negative consequences associated with the excessive and maladaptive use of the smartphone, sharing aspects relevant for behavioral and substance addictions (Panova and Xavier, 2018), thereby considered by some as an impulse-control disorder (Pavia et al., 2016; Yau et al., 2012). It associates with a compulsive desire for smartphone use, craving, interpersonal problems, and an incapacity to recognize the behavior as problematic (Cha and Seo, 2018; Mahapatra, 2019; Panova and Xavier, 2018). Still, Smartphone Addiction has not been recognized as a diagnostic label since the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013) only recognizes Internet Addiction (IA) as a disorder. As regards gender difference, it is shown that women spend a greater amount of time using the smartphone compared to men (Roberts et al., 2014), which is relevant considering that the duration of smartphone use is reported by the literature as a factor predicting Smartphone Addiction.

Smartphone Addiction might be considered as an emotion-regulation strategy for mood enhancement requiring little cognitive effort (Mahapatra, 2019) and for which low self-control capacities play a relevant role (Jeong et al., 2019; Zhang et al., 2019; Kim et al., 2018; Cho et al., 2017; Billieux et al., 2007). Beyond the predictive role of low self-control (Kim et al., 2018), other relevant risk-factors are the increased use of Social Networks Sites (SNSs; Wu et al., 2013), internet use, female gender, and anxiety (Choi et al., 2015) with the latter also recognized as a consequence of Smartphone Addiction (Hussain et al., 2017; Jeong et al., 2019). Moreover, anxiety, together with depression and stress, associates with different levels of Smartphone Addiction severity (Elhaia et al., 2019), while anger particularly distinguishes those showing a more pervasive addiction (Elhaia et al., 2019). Another relevant variable shown to be predictive of Smartphone Addiction is alexithymia (Gao et al., 2018), a personality trait referred to as a difficulty in recognizing, comprehending, as well as verbalizing feelings and emotions (Sifneos, 1973; Taylor and Bagby, 2012). Alexithymia shows a direct as well as indirect association with Smartphone Addiction (Gao et al., 2018) accounting for the association between Smartphone Addiction and mental health (Mei et al., 2018), and depression, anxiety, and stress more specifically (Gao et al., 2018). Alexithymia can also be considered as an inflexible emotion-regulation strategy, thus referring to a maladaptive emotional-processing and leading to an inadequate reactivity to situations as well as more frequent stress (Taylor and Bagby, 2012; Panayiotou et al., 2019). Coherently, emotion regulation (ER; Gratz and Roemer, 2004) is defined as a multidimensional construct involving four aspects: the awareness, understanding, and acceptance of emotional experience; the ability to engage in goal-directed behaviors and to inhibit impulsive behaviors when experiencing negative emotions; the flexible use of situationally appropriate strategies to modulate the intensity and/or duration of emotional responses; and the willingness to experience negative emotions as part of pursuing meaningful activities in life. In this regard, emotion dysregulation refers to a deficit in any of these ER components (Gratz and Roemer, 2004). Conceptualizing Smartphone Addiction as an emotion-regulation strategy (Kardefelt-Winther, 2017; Mahapatra, 2019), studies show that difficulties in ER are positively associated with problematic smartphone use in adolescents, further underlying the mediating role of depression (Fu et al., 2019), with emotion dysregulation mediating the relation between psychological distress and Smartphone Addiction (Squires, Hollett, Hesson, and Harris, 2020).

Non-suicidal self-injury

NSSI has been included in Section III of the DSM-V among the disorders in need of future research (APA, 2013). Criteria A describes it as “intentional self-inflicted damage to the surface of his or her body of a sort likely to induce bleeding, bruising, or pain (e.g., cutting, burning, stabbing, hitting, excessive rubbing), with the expectation that the injury will lead to only minor or moderate physical harm (i.e., there is no suicidal intent)” (APA, 2013: 803). A 2017 systematic review of studies published between 1998 and 2016 (Cipriano et al., 2017) reports prevalence rates ranging between 7.5% and 46.5% in adolescence. Onset usually occurs during early adolescence, between 12 and 14 years old (Beckman et al., 2018; Cerrutti et al., 2011), albeit observed also in children under the age of 12 (Barrocas et al., 2012). Moreover, a 2015 systematic review focusing on longitudinal studies investigating the NSSI phenomenon (Plener et al., 2015) showed a peak in prevalence rates during mid-adolescence, around 15 or 16 years of age, and declining during late adolescence. Studies also report an association between NSSI and the female gender (Bresin and Schoenleber, 2015; Burton, 2014; Castaldo et al., 2020; Spears et al., 2014; Morey et al., 2017; Schwartz-Mette and Lawrence, 2019) particularly relevant in women showing an internalized symptomatology (Castaldo et al., 2020; Skegg et al., 2004). NSSI is indeed particularly associated with depression (Lee, 2016; Singhal et al., 2014; Latina and Statting, 2018) and anxiety (Moran et al., 2012; Armey et al., 2011; Nock, 2009; Moller et al., 2013; Hughes et al., 2019; Singhal et al., 2014). Moreover, a longitudinal study (McLaughlin et al., 2011) showed that internalizing symptoms significantly mediate the association between emotion dysregulation and NSSI (Kranzler et al., 2016). Relying on the above-mentioned conceptualization of ER (Gratz and Roemer, 2004), NSSI can thus be considered as an emotion-regulation strategy (Bentley et al., 2014; Muehlenkamp et al., 2009; Nock 2009; Chapman et al., 2006). A further association emerges between NSSI and alexithymia (Gatta et al., 2016; Garisch and Wilson, 2015). Alexithymia is associated with deficits related to a cognitive-experiential comprehension of emotions and an incapacity for proper interpersonal-regulation (Taylor and Bagby, 2012). As such, it could be hypothesized that those presenting NSSI behavior externally express their suffering as an attempt at containing their psychological sufferance.

The current study

In today’s time, a strong increase worldwide in smartphone use has occurred (Olson et al., 2020) and in Italy the forecasted smartphone users are expected to double by 2025 compared to 2017 (Statista, 2020). Concurrently, a significant increase in NSSI has also occurred favored by the greater reliance on the Internet, SNSs, and the smartphone (Abi-Jaoude et al., 2020; Arendt et al., 2019; Castaldo et al., 2020; Dyson et al., 2016; Lewis et al., 2014; Memon et al., 2018; Purington & Whitlock, 2010). As previously reported, the smartphone entails great possibility of use allowing to access multiple platforms and means of communication taking advantage of the continuous possibility to access the internet wherever and whenever. It was shown that around 42 million NSSI-related searches are made on Google per year (Lewis et al., 2014) while in a “Yahoo! Answers” database analysis, 33.6% of the questions regarding NSSI were asked to receive some form of validation for the behavior, which accounted for the reasons underlining the posting of NSSI-related contents (Lewis et al., 2012). Recent systematic reviews, which had focused on the role of social media in the context of NSSI (Dyson et al., 2016; Memon et al., 2018), support the notion for which NSSI adolescents actively share and search for such content as perceiving some form of social support and comradeship that foster a sense of belonging (Dyson et al., 2016) and that can lead to an increased frequency of NSSI behavior (Memon et al., 2018) to the point of antagonizing its interruption while favoring relapse (Dyson et al., 2016). Moreover, NSSI has also been considered in association with SNSs (Abi-Jaoude et al., 2020; Arendt et al., 2019; Memon et al., 2018) as the spreading of the NSSI phenomenon has been observed to associate with greater SNSs use (Memon et al., 2018; Whitlock et al., 2006, 2009) as well as with Internet Addiction (Abi-Jaoude et al., 2020). Moreover, NSSI was shown to associate with nocturnal smartphone use (Oshima et al., 2012) which in turn associated with both sleep problems and depressive symptoms (Dewi et al., 2018), the latter being strongly associated with NSSI behavior (Castaldo et al., 2020; Lee, 2016; Singhal et al., 2014; Latina and Statting, 2018). Yet to present knowledge, no study has considered NSSI in association with Smartphone Addiction specifically, instead potentially relevant among adolescents performing NSSI (Abi-Jaoude et al., 2020). As previously mentioned, because of their bio-psychosocial immaturity, adolescents already present difficulties in emotion regulation and then further compromised when faced with psychological disturbances (Ahmed et al., 2015). The joint investigation of NSSI and Smartphone Addiction within an already vulnerable sample of adolescents is thus expected to provide insights relevant to the domain of emotional regulation since the psychological consequences that might result from the presence of both NSSI and Smartphone Addiction might add up and further limit adolescents’ capacities to acquire more adaptive emotion-regulation capacities and strategies, instead important to improve and support mental health in adulthood (Ahmed et al., 2015).

Thereby, the purpose of the present study is to identify the variables that better account for NSSI behavior and Smartphone Addiction, both considered as ER strategies (Bentley et al., 2014; Chapman et al., 2006; Kardefelt-Winther, 2017; Mahapatra, 2019; Muehlenkamp et al., 2009; Nock, 2009), in a sample of adolescents showing NSSI behavior. Age and gender differences will be also assessed. Based on the reviewed literature, it is expected that (i) self-control will significantly associate with both NSSI and Smartphone Addiction and that (ii) internalizing symptoms versus externalizing symptoms will show a different relevance toward NSSI and Smartphone Addiction. Moreover, it is expected that (iii) being female will significantly associate with both NSSI and Smartphone Addiction as both behaviors are widely reported to be more prevalent in women (Abi-Jaoude et al., 2020; Bresin and Schoenleber, 2015; Burton, 2014; Spears et al., 2014; Morey et al., 2017; Schwartz-Mette and Lawrence, 2019; Skegg et al., 2004; Fischr-Grote et al., 2019; van Deursen et al., 2015; Roberts et al., 2014; Choi et al., 2015).

Method

Participants and procedure

The present Italian community sample is composed of N = 78 adolescents not yet subject to clinical attention and performing NSSI behavior. Mean age is 14.24 (SD = 1.56); 73.1% are women. Moreover, N = 27 (11–13 years; 34.6%) attend middle school and N = 51 (14–19 years; 65.4%) high school.

Participants’ data belong to multiple pre-test phases of the broader LOOK@ME research-intervention project (The Net-ONLUS, 2017). This project has started in 2017 and more than 1000 adolescents have taken part in it since then. It is aimed at supporting community samples of adolescents and to develop ad hoc interventions and focus groups to improve their self-control, emotion-regulation capacities, and well-being. The data considered in the present study were collected between 2019 and 2020 via an online battery of questionnaires administered through the schools’ computers during school hours by psychologists and trained psychology students. Parents of minors or adolescents over the age of 18 years signed informed consent that were collected before questionnaires administration. Subjects further provided oral consent before participation and were informed that they could retrieve from the study at any moment without any repercussion. Participants considered in the present study satisfied the following inclusion criteria: (1) being aged between 11 and 19 years and (2) having fully completed the self-report measures and both modules of the self-injurious self-report measure in particular. Data were collected in compliance with the Declaration of Helsinki (Italian law 196/2003) and protected under the UE GDPR 679/2016 law for the protection of personal data. This study was approved by the Ethical Committee of Psychology of the University of Padova (number 2322, June 6th, 2017).

Measurement tools

Brief Self-Control Questionnaire

The Brief Self-Control Questionnaire (BSCS) is a self-report measure developed by Tangney and colleagues (Tangney et al., 2004) and validated in Italy by Chiesi and colleagues (2020). It is composed of 13 items measured on a 5-point Likert scale (from 1 = “not at all like me” to 5 = “very much like me”) and is aimed at assessing the level of self-control capacities referred to the domains of achievement and task performance, impulse control, adjustment, interpersonal relationships, and moral emotions. BSCS provides a total final score with lower scores indexing lower self-control capacities. The internal consistency for the present study is α= .68.

Strength and Difficulties Questionnaire

The Strength and Difficulties Questionnaire (SDQ; Goodman et al., 2010) is a self-report questionnaire composed of 25 items measured on a 3-point Likert scale (1=“Not True”; 2= “Somewhat true”; 3= “Certainly true”) and aimed at identifying people at risk for psychological problems by collecting information related to their emotional, interpersonal, and behavioral profile with higher scores indexing the presence of more negative attributes, while lower scores greater positive attributes. The European validation (Marzocchi et al., 2004) was performed on a sample of healthy and low-risk adolescents age ranging between 11 and 17 years. The instrument provides a Total difficulty score composed of the sub-scales Internalizing problems and Externalizing problems, which will be considered separately in the present study. The SDQ further provided a Prosocial behavior score. In the current study, internal consistency is α = .68 for the Internalizing problems scale and α = .75 for the Externalizing problems one.

Smartphone Addiction inventory—Italian version

The Italian version of the Smartphone Addiction Inventory (SPAI; Pavia et al., 2016) comprises 24 items measured on a 4-point Likert scale (1 = “Strongly disagree”; 4 = “Strongly agree”) and is aimed at measuring smartphone addiction. Higher scores index greater smartphone addiction, while lower scores index poorer or absent addictive tendencies. The SPAI provides a total final score referred to the level of difficulty in stopping smartphone use as well as the amount of time and resources devoted to its use, the distress experienced when deprived of its use, and the associated feelings of anxiety, irritation, and tension. It further considers how pervasive is the need for smartphone use during daily life activities while also measuring the presence and level of interpersonal problems it may cause. Additional domains refer to the compulsivity and persistence associated with the use of smartphones comprising items investigating withdrawal and avoidance related behaviors, the level of sleep disturbance, and the relation between smartphone use and reduced sleeping time. In the present study, internal consistency is highly satisfactory (α= .91).

Self-Injurious Thought and Behavior Questionnaire-Non Suicidal

The Self-Injurious Thought and Behavior Questionnaire-Non Suicidal (SITBQ-NS), derived from the original interview version (Nock et al., 2007), is a self-report measure validated in Italy by D'Agostino and colleagues (2018) on a sample of self-harming adolescents. It comprises 28 items measured on a 5-point Likert scale, half referring to self-injurious thoughts and the other half investigating self-injurious behavior. For those answering to this second set of items, beyond self-harming thoughts and behavior, the questionnaire further investigates if the person has received medical treatments. The tool specifically aims to measure self-injurious thoughts and behaviors related to self-harming acts without suicidal intent. It provides three final scores referring to “Self-Injurious Thoughts,” “Self-Injurious behavior,” and the “Self-Injurious Spectrum”; the present study will solely consider the latter. Lower scores index less frequent and pervasive self-harming thoughts and behavior, while higher scores refer to a more critical and pervasive situation. Internal consistency is α= .91.

Toronto Alexithymia Scale

The Toronto Alexithymia Scale (TAS; Bagby et al., 1994a, 1994b) validated in Italy by Bressi and colleagues (Bressi et al., 1996) is a self-report measure composed by 20 items rated on a 5-point Likert scale (1 = “strongly disagree”; 5= “strongly agree”). Higher scores index greater alexithymia traits. It is aimed at assessing alexithymia in a continuum going from the absence of alexithymia-related aspects to being alexithymic. The TAS provides a final total score, which will be considered in the present study, comprising three sub-scales, namely, Difficulty Identifying Feelings, Difficulties Describing Feelings, and Externally Oriented Thinking. Internal consistency is α= .80.

Difficulties in Emotion Regulation Scale

The Difficulties in Emotion Regulation Scale (DERS; Gratz and Roemer, 2004) is a self-report measure aimed at measuring difficulties in the capacity for emotion regulation. It comprises 36 items rated on a 5-point Likert scale (1= “almost never”; 5 = “almost always”), with higher scores indexing greater difficulties in emotion regulation. The Italian validation (Sighinolfi et al., 2010) shows a highly significant internal consistency α = .90. The instrument provides a final total score comprising six sub-scales germane to impulse control difficulties, lack of emotional awareness, limited access to emotion-regulation strategies, lack of emotional clarity, and difficulties engaging in goal-directed behaviors. In the present study, internal consistency is α= .91.

Data analysis

Descriptive statics and pair-wise Pearson r’s correlations (p-value > .05) were assessed through SPSS v20.0 (IBM Corp., 2011). Correlations and the graphical representation of paired-variables association were performed to assess multicollinearity among variables. Multiple regression power calculations and regression analysis were performed through R (R Core Team, 2019).

Multiple regression power calculation was performed using R package pwr to define the minimum sample size necessary to maintain a power of at least .80 for hypotheses testing. Results yield that the multivariate regression models should have a sample size of at least 67 participants. The hypothesized proportion of variance explained by the regression models was set at 30%, for a number of predictors ranging from 8 to a maximum of 20. The significance level was set at .05.

Simple linear regressions were initially performed to assess the association between each independent variable (i.e., Age; BSCS; SDQ-int; SDQ-ext; TAS; DERS) with either SITBQ-NS or SPAI and to assess if SITBQ-NS and SPAI are associated. Multivariate linear regressions were then performed. Two models were assessed through backward and forward step-wise selection. In the first model (M1), the dependent variable was SITBQ-NS total score while all the above-mentioned variables, as well as SPAI, were included in the model as predictors. The second model (M2) considers SPAI as a dependent variable and includes the same predictors used in M1 as well as SITBQ-NS total score. In both models, age and gender differences were assessed by including Age (11–13 years = 0; 14–19 years = 1) and Gender (Male = 0; Female = 1) as dummy variables and also assessing the presence of interactions between either one of the dummies and each independent variable. Age was not included as a continuous variable since through simple linear regression Age does not show an association with SITBQ-NS and, although showing a significant association with SPAI, the sample age is asymmetrically distributed on the left; therefore, Age as a dichotomous variable allows a greater balance among age-groups, untimely better accounting for age differences. Slope analysis was then performed to further investigate the significant interaction effects. Durbin–Watson (Durbin and Watson, 1950, 1951, 1971) and Shapiro–Wilk (Shapiro and Wilk, 1965) tests were also performed to assess the independence and distribution normality of the models’ residuals. Predictors were considered significant at p-value < .05.

Results

Participants are N = 78 adolescents presenting NSSI thoughts and behaviors; of them, N = 57 (73.1%) are female and N = 51 (65.4%) attend high school. Means and standard deviations of measurement scores are shown in Table 1.

Table 1.

Descriptive information.

	Mean	SD	Min	Max
Age	14.24	1.56	11	19
BSCS	2.79	0.53	1.69	4.31
SPAI	2.06	0.57	1	3.71
SDQ_int	9.59	3.77	2	18
SDQ_ext	8.83	3.78	1	19
SITBQ	92.87	29.84	25	173
TAS	62.06	11.15	36	89
DERS	3.24	0.65	1.75	4.61

Note: BSCS = self-control; SPAI = Smartphone Addiction; SDQ_int = internalizing problems; SDQ_ext = externalizing problems; SITBQ-NS = NSSI; TAS = alexithymia; DERS = emotion dysregulation.

Correlations

Pair-wise Pearson correlations are shown in Table 2. SPAI shows significant positive correlations with SDQ_ext, SITBQ-NS, TAS, and DERS and a significant negative correlation with BSCS. SITBQ-NS significantly and positively correlates with SDQ_int, SDQ_ext, TAS, and DERS, while BSCS significantly and negatively correlates with SDQ_ext, TAS, and DERS. Moreover, both SDQ_int and SDQ_ext significantly and positively correlate with TAS as well as DERS. SDQ_pros significantly and positively correlates with BSCS and SDQ_int.

Table 2.

Pair-wise Pearson correlation.

	Age	BSCS	SPAI	SDQ_int	SDQ_ext	SITBQ	TAS	DERS
BSCS	−.026	—
SPAI	−.002	−.328^**	—
SDQ_int	−.066	0.02	0.06	—
SDQ_ext	−.094	−.592^**	.308^**	.350^**	—
SITBQ	−.194	−0.19	.223^*	.312^**	.366^**	—
TAS	−.027	−.288^*	.344^**	.599^**	.493^**	.333^**	—
DERS	−.056	−.276^*	.319^**	.590^**	.592^**	.499^**	.648^**	—

Note: *p-value< .05; **p-value< .01. BSCS = self-control; SPAI = Smartphone Addiction; SDQ_int = internalizing problems; SDQ_ext = externalizing problems; SITBQ-NS = NSSI; TAS = alexithymia; DERS = emotion dysregulation.

Regression models

Simple linear regressions were initially performed with either SITBQ-NS or SPAI as dependent variable and the following variables as independent variables: Age, BSCS, SDQ_int, SDQ_ext, TAS, and DERS. Simple regressions performed with SITBQ-NS as dependent variable show a significant positive association with SDQ_int (β = 2.46; p = .006; R² =9.72%), SDQ_ext (β = 2.88; p < .001; R² = 13.42%), TAS (β = .89; p = .003; R² = 11.06%), DERS (β = 22.82; p < .001; R² = 24.95%), and SPAI (β = 11.57; p = .05; R² = 4.96%). BSCS showed no significant association with SITBQ-NS. Simple regressions performed with SPAI as dependent variable show comparable positive associations (SDQ_ext, β = 0.05; p < .001; R² = 9.48%; TAS, β = 0.018; p < .001; R² = 11.87%; DERS, β = 0.28; p < .001; R² = 10.18%; SITBQ-NS, β = .004; p = .05; R² = 4.96%) although also showing a negative and significant association with BSCS (β = −0.35; p < .001; R² = 10.75%). On the other hand, SDQ_int showed no significant association with SPAI. Age as continuous variable does not significantly associate with either SPAI or SITBQ-NS.

Two multivariate step-wise linear regression models were performed with either SITBQ-NS (M1) or SPAI (M2) as the dependent variable, respectively. M1 (Table 3) accounts for a satisfactory amount of variance (R² = 43.83%) although the only significant variables are SPAI, DERS, the interaction between Age and SPAI, and Age and TAS. M2 (Table 4) also accounts for a proper amount of variance (R² = 33.69%). In M2, the variables showing a significant association with SPAI are BSCS, Age, SITBQ-NS, and the interaction between Age and SITBQ-NS.

Table 3.

Step-wise multivariate linear regression model (M1).

M1
	β	SE	t	p-value
BSCS	9.30	9.24	1.007	0.317
Age1	18.74	56.09	0.334	0.739
SPAI	26.81	9.20	2.913	0.0048**
TAS	−0.90	0.47	−1.910	0.06
DERS	21.19	5.55	3.818	< .001***
BSCS:Age1	−21.43	11.46	−1.870	0.066
Age1: SPAI	−31.98	11.19	−2.858	0.0056**
Age1: TAS	1.47	.056	2.608	0.0112*

Residual standard error (df)	23.62 (69)
R²	43.83%
F (p-value)	6.73(< .001)

Note: *p-value < .05; **p-value <.01; ***p-value < .001; D.V.: SITBQ (i.e., NSSI); I.V.: Age = dichotomous dummy variable (11–13 = 0; 14–19 = 1); BSCS = self-control; SPAI = Smartphone Addiction; TAS = alexithymia; DERS = emotion dysregulation. Gender = dichotomous dummy variable (Males=0; Females=1).

Table 4.

Step-wise multivariate linear regression model (M2).

M2
	β	SE	t	p-value
BSCS	−0.28	0.123	−2.252	0.0276*
Gender1	−0.22	0.731	−1.245	0.218
Age1	1.06	0.416	2.544	0.0132*
SDQ_int	−0.04	0.021	−1.68	0.098
TAS	0.01	0.008	1.665	0.101
DERS	−0.13	0.233	−0.574	0.568
SITBQ	0.008	0.003	2.294	0.0249*
Gender1: DERS	0.38	0.234	1.604	0.1134
Age1: SITBQ	−0.01	0.004	−2.478	0.0157*

Residual standard error (df)	0.4978 (68)
R²	33.69%
F (p-value)	3.839 (< .001)

Note: *p-value < .05; **p-value <.01; ***p-value < .001; D.V.: SPAI = Smartphone Addiction; I.V.: Age = dichotomic dummy variable (11–13 = 0; 14–19 = 1); SITBQ = NSSI; BSCS = self-control; SDQ_int = internalizing problems; TAS = alexithymia; DERS = emotion dysregulation. Gender = dichotomic dummy variable (Males=0; Females=1).

Interaction effect plots are shown in Figure 1, referring to the interaction effects emerged in both M1 and M2. Slope analysis results showed that, in M1, SPAI positively and significantly associated with SITBQ-NS only among pre-adolescents aged 11–13 years (a; β = 26.81; p <.001), while the effect was not significant among older adolescents aged 14–19 years. From the interaction plot showing the interaction effect between Age and TAS (b), it can instead be observed a trend whereby among older adolescents aged 14–19 years, TAS and SITBQ-NS show a positive associative trend, while pre-adolescents significantly differed in that TAS, and SITBQ-NS instead showed a negative associative trend. Moreover, referring to M2, and coherent with M1 results, the interaction effect between Age and SITBQ-NS showed that SITBQ-NS only significantly, and positively, associated with SPAI among pre-adolescents aged 11–13 years (c; β = 0.01; p =.02), while the effect was not significant among older adolescents.

Figure 1.

Interaction effect plots. Note: Age: 0 = 11–13 years, 1 = 14–19 years.

Discussion

The purpose of the present study is to exploratorily identify the variables accounting for NSSI and Smartphone Addiction in a sample of adolescents performing NSSI. This study is part of the broader LOOK@ME research-intervention project (The Net-ONLUS, 2017), which goal is to develop ad hoc interventions and focus groups to improve self-control, emotion-regulation capacities, and well-being in adolescents showing NSSI idealization and behavior and Smartphone Addiction and addiction tendencies. Both NSSI and Smartphone Addiction are maladaptive behaviors that can be conceptualized as emotion-regulation strategies (Mikolajczak et al., 2009; Chester et al., 2014;Billiewux et al., 2007; Cho et al., 2017; Jeong et al., 2019; Kim et al., 2018; Zhang et al., 2019). By identifying the variables that account for the two behaviors, it might be possible to set some initial bases on the aspects that could lead to greater Smartphone Addiction in adolescents performing NSSI, thus hypothetically dealing with their sufferance through multiple and easy to access maladaptive strategies. Their joint investigation is thus relevant to the study of emotion regulation since “compromised abilities in emotional regulation may confer a further overall risk for all kinds of psychopathology, independently from its clinical appearance” (De Berardis et al., 2020: 2; Young et al., 2019). The increased spreading of the NSSI behavior is largely favored by the use of the Internet and SNSs (Arendt et al., 2019; Dyson et al., 2016; Memon et al., 2018; Purington and Whitlock, 2010), both readily accessible through the smartphone. The current article supports knowledge regarding aspects relevant to both NSSI and Smartphone Addiction in a community sample of adolescents showing relevant clinical symptoms, yet not under clinical attention, and the first which attempts to report a direct association between the investigated behaviors.

In the present study, findings show that Smartphone Addiction and NSSI significantly and positively predict each other, which seems particularly true for pre-adolescents as compared to adolescents, although Smartphone Addiction tendencies seem to increase as age increases. This is a relevant finding as it could signify that the specific neuro-developmental immaturity of pre-adolescents and associated poor ER capacities (Ahmed et al., 2015) might account for the significant association between NSSI and Smartphone Addiction reported in both regression models. Interestingly, findings also show that low self-control is a significant variable only in the Smartphone Addiction model. The non-significance of self-control for NSSI could be imputed to the assessment tool itself (i.e., BSCS) as it provides a unidimensional view of self-control (Tangney et al., 2004). Others have instead proposed a two-dimensional specification of the tool as it better differentiates between inhibitory (i.e., the capacity to refrain from impulse-driven behaviors) and initiatory (i.e., referred to the initiation of goal-directed behaviors) self-control (De Ridder et al., 2011) as NSSI is better explained by low inhibitory self-control (Allen and Hooley 2015). In this regard, initiatory self-control better predicts desirable behavior, while inhibitory self-control undesirable behavior (De Ridder et al., 2011) with the two being interrelated yet different constructs (De Ridder et al., 2011; Lindner et al., 2015). By comparing the two factorial structures (i.e., unidimensional vs two-dimensional), it was observed for De Ridder and colleagues’ (2001) perspective to better predict outcome variables (De Ridder et al., 2011; Lindner et al., 2015) ultimately concluding for BSCS as not satisfactorily accounting for the two distinct self-control facets (Lindner et al., 2015). As such, the non-significance of self-control concerning NSSI might be explained by a lack of proper differentiation between the two self-control facets for which the investigation of NSSI self-control related variables necessitates a more punctual consideration, as supported by the association between NSSI and inhibitory self-control (Allen and Hooley, 2015) or impulse-control difficulties (i.e., impulsivity; Hamza et al., 2015) as well as emotion dysregulation (Emery et al., 2016; Gratz et al., 2017). Coherently, in the present sample, greater emotion dysregulation associated with NSSI increased pervasiveness and severity (Emery et al., 2016; Gratz et al., 2017). Following the line of the above-reasoning, this finding might be explained by the emotional reactivity to negative emotions and physiological arousal typical of pre-adolescents and adolescents (Ahmed et al., 2015; Lucarelli, 2010), which supports the conceptualization of NSSI as an attempt at emotion regulation (Mikolajczak et al., 2009; Chester et al., 2014). The negative affectivity associated with different levels of physiological arousal influences how youth interprets it (Bradley, 2003). Physiological arousal implies an activation of the body that goes beyond the one normally experienced; its association with an unpleasant state is particularly relevant during the developmental period, especially as regards the subjective distress pre-adolescents and adolescents might experience (Bradley, 2003). As such, maladaptive behaviors and regulation strategies such as NSSI and Smartphone Addiction might develop as a consequence of a difficulty in regulating both negative feelings and arousal (Ahmed et al., 2015; Lucarelli, 2010) potentially leading to their mutual reinforcement. The risks arising from this mutual reinforcement, strengthened by some of the smartphone’s usage possibilities (e.g., to access SNSs and Google searches), could lead to a normalization (Arendt et al., 2019; Dyson et al., 2016; Kirsch, 2012) and thus reinforcement of NSSI behavior by sharing NSSI-related content (Arendt et al., 2019; Dyson et al., 2016; Memon et al., 2018) by utilizing the multiple platforms available on smartphones (e.g., SNSs, WhatsApp, Telegram, and SMS). On the other hand, this could, in turn, lead to greater Smartphone Addiction and addiction tendencies, since the smartphone would allow for the satisfaction of social needs (Mohd Suki, 2013) by speculatively gaining emotional and instrumental support, as indeed observed specifically referring to the association between NSSI and SNSs (Memon et al., 2018). Furthermore, it could also be speculated that adolescents rely on the smartphone also as a means for attentional-deployment, which also requires little cognitive effort (Mahapatra, 2019), also favoring the suppression of the unpleasant and overwhelming affectivity associated to NSSI when the behavior cannot be performed, thereby acquiring a regulatory function toward the negative and dysregulated emotionality associated with NSSI. Such mechanism might as well account for the mutual reinforcement among the two behaviors, coherent with a recent study (Rozgonjik and Elhai, 2019) showing that the “process use” of the smartphone (i.e., a non-social use of the tool, thereby referred to media consumption such as watching videos, playing games, browsing online, etc.) totally mediated the association between emotional suppression and Smartphone Addiction severity.

Referring to Gratz and colleagues’ (Gratz and Roemer, 2004) conceptualization of ER as a multidimensional construct, for which alexithymia could be considered as one of its facets (Stasiewicz et al., 2012), results showed also that, although alexithymia per-se was not significantly associated with NSSI when controlling for the effect of all other variables, adolescents and pre-adolescents significantly differed in the direction of effect acquired by alexithymia, thus showing a positive associative trend among adolescents performing NSSI, and a negative one among pre-adolescents. Differently, the more complex emotion dysregulation resulted relevant, and significantly associated with NSSI, regardless of age. These differences in effect significance and age associated influence of alexithymia versus emotion dysregulation might be explained by the specific aspects that are accounted for by the two constructs. Alexithymia is defined as a difficulty in recognizing, comprehending, as well as verbalizing feelings and emotions (Sifneos, 1973; Taylor and Bagby, 2012); yet, the more complex emotion dysregulation refers to dysfunctions in either the understanding and acceptance of emotional experiences, the ability to engage in goal-directed behaviors while inhibiting the impulse-driven ones when experiencing negative emotions, the capacity to flexibly use adaptive regulatory strategies, or the willingness to experience negative emotions as part of pursuing meaningful activities in life (Gratz and Roemer, 2004). By referring to the construct’s conceptualization, it can be speculated for NSSI to be more generally due to a difficulty in properly and more broadly modulating, more than merely recognizing, emotions and feelings and related strategies. By contrast, the influence of alexithymia might acquire a greater influence on NSSI behavior during late adolescence as resulting from a deficit in developing a proper emotional maturity and awareness, thus pointing to the importance of psycho-educational interventions.

These findings illustrate the complexity of the association between Smartphone Addiction and NSSI, which particularly refers to the reported age differences, with implications for treatment and prevention practices. Differently, and contrary to expectations, no gender differences have emerged and gender, in general, did not result as a significant variable in either model. The reported age differences suggest distinct clinical pictures for adolescents and pre-adolescents and specifically referring to the role of emotion-regulation capacities as regards the adoption of maladaptive regulatory strategies and the attempt to compensate for their sufferance by using multiple dysfunctional strategies. As such, the results referring to the association between NSSI and Smartphone Addiction in pre-adolescence seem of particular relevance, since if during the whole adolescence period connectivity among structures does not balance itself out (as happens in clinical populations) (Ahmed et al., 2015) higher-order cerebral areas, although mature, might still not be able to properly modulate physiological arousal nor to properly mentalize it, thus not allowing proper emotion regulation (Ahmed et al., 2015). This might then result in the permanence and/or worsening of emotion dysregulation, and related aspects, as age increases. Results from a recent meta-analysis (Kraiss et al., 2020) indeed highlighted the importance of supporting proper emotion regulation in the context of mental health issues. This would favor greater well-being, which is important to support symptoms remission and overall recovery (De Vos et al., 2017; Mead and Copeland, 2000; Pitt et al., 2007), while also reducing the risk for later psychopathology (De Berardis et al., 2020; Young et al., 2019).

Still, the current findings necessitate replication and the associated speculations deeper investigation.

Limitations and future research

Findings should be considered in light of some limitations as well as potentialities for future research. The present study relies on self-report measures for which bias could occur. The study followed a cross-sectional design; ethnicity and socioeconomic status were not considered, which could limit the generalizability of findings. Moreover, the present study considers a quite small sample, yet the power analysis performed supports the reliance of findings. A further limitation is the unbalanced distribution of gender in favor of females, although it supports past findings reporting a greater prevalence of NSSI among females (Bresin and Schoenleber, 2015; Burton, 2014; Spears et al., 2014; Morey et al., 2017; Schwartz-Mette and Lawrence, 2019; Skegg et al., 2004).

The present study also provides useful suggestions for future investigations. More specifically, as regards self-control, it could be interesting to further investigate how different self-control facets associate with the two behaviors, for instance, by adopting the Adolescent Self Consciousness questionnaire (Li et al., 2015). Moreover, referring to the association between NSSI and Smartphone Addiction, the present findings highlight the relevance of age differences which necessitate deeper consideration and investigation, particularly as regards the hypothetical different role of alexithymia and emotion dysregulation, since it might have significant clinical implication.

Conclusions

To present knowledge, no prior study has ever considered both NSSI and Smartphone Addiction, nor their direct association. Albeit its limitations, the present study provides some initial bases regarding the joint consideration of the two behaviors, in line with past evidence highlighting the association between NSSI and maladaptive use of the smartphone like nocturnal smartphone use (Oshima et al., 2012). Moreover, the joint investigation of NSSI and Smartphone Addiction further highlighted the relevance of age differences for the two behaviors with implications for clinical practice. As such, projects like the LOOK@ME research-intervention project (The Net-ONLUS, 2017) are of paramount importance to limit and counteract the development or worsening of psychological sign and symptoms relevant to both NSSI and Smartphone Addiction, particularly since onset is usually in adolescence (Beckman et al., 2018; Cerutti et al., 2011). Moreover, in light of the increasing reliance smartphones are acquiring in pre-adolescents and adolescents’ lives as well as the increasing possibilities for their use (Abi-Jaude et al., 2020), together with its noxious aspects also its positive ones could be investigated. This could provide new means for psychological support while sensitizing toward the risks resulting from its misuse, particularly among those pre-adolescents that report clinical symptoms in a non-strictly clinical population. Indeed, it is important to make the most of pre-adolescents’ neuronal plasticity (Ahmed et al., 2015) and thus fostering interventions favoring better emotion regulation and the use of more adaptive and flexible regulatory strategies, thereby supporting their mental health in adulthood (Ahmed et al., 2015; De Berardis et al., 2020).

Footnotes

Acknowledgments

Authors are thankful to the “the Net-ONLUS” and the Department of Developmental Psychology and Socialization of the Univeristy of Padova for supporting this research. The “the Net-ONLUS” LOOK@ME research-intervention project won the OPV prize 2017 for innovation in assessment and clinical research.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Elisa Mancinelli

References

Abi-Jaoude

Treurnicht Naylor

Pignatiello

(2020) Smartphone, social media use and youth mental health. CMAJ 192: 136–141. DOI: 10.1503/cmaj.190434.

Ahmed

Bittencourt-Hewitt

Sebastian

(2015) Neurocognitive bases of emotion regulation development in adolescence. Developmental Cognitive Neuroscience 15: 11–25. DOI: 10.1016/j.dcn.2015.07.006.

Allen

KJD

Hooley

(2015) Inhibitory control in people who self-injurie: Evidence for impairment and enhancement. Psychiatry Research 225: 631–637. DOI: 10.1016/j.psychres.2014.11.033.

American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders. 5th edition. American Psychiatric Association Publishing.

Arendt

Scherr

Romer

(2019) Effects of exposure to self-harm on social media: Evidence from a two-wave panel study among young adults. New Media and Society 21(11-12): 2422–2442. DOI: 10.1177/1461444819850106.

Armey

Crowther

Miller

(2011) Changes in ecological momentary assessment reported affect associated with episodes of nonsuicidal self-injury. Behavior Therapy 42(4): 579–588. DOI: 10.1016/j.beth.2011.01.002.

Bagby

Parker

JDA

Taylor

(1994a) The twenty-item Toronto Alexithymia Scale: Item selection and cross-validation of the factor structure. Journal of Psychosomatic Research 38: 23–32.

Bagby

Parker

JDA

Taylor

(1994b) The twenty-item Toronto Alexithymia Scale: II. Convergent, discriminant, and concurrent validity. Journal of Psychosomatic Research 38: 33–40.

Barrocas

Hankin

Young

, et al. (2012) Rates of nonsuicidal self-injury in youth: age, sex, and behavioral methods in a community sample. Pediatrics 130(1): 39–45. DOI: 10.1542/peds.2011-2094.

10.

Beckman

Lysell

Haglund

, et al. (2018) Prognoses after self-harming in youth: exploring the gender factor. Social Psychiatry and Psychiatric Epidemiology 54(4): 437–444. DOI: 10.1007/s00127-018-1618-7.

11.

Bentley

Nock

Barlow

(2014) The four-function model of nonsuicidal self-injury: Key directions for future research. Clinical Psychological Science 2(5): 638–656. DOI: 10.1177/2167702613514563.

12.

Billieux

Van der Linden

D’Acremont

, et al. (2007) Does impulsivity relate to perceived dependence on and actual use of the mobile phone?. Applied cognitive psychology 21(4): 527–537. DOI: 10.1002/acp.1289.

13.

Bradley

(2003) Affect Regulation and the Development of Psychopathology. New-York-London: The Guilford Press.

14.

Bresin

Schoenleber

(2015) Gender differences in the prevalence of nonsuicidal self-injury: A meta-analysis. Clinical Psychology Review 38: 55–64. DOI: 10.1016/j.cpr.2015.02.009.

15.

Bressi

Taylor

Parker

, et al. (1996) Cross validation of the Toronto Alexithymia Scale: An Italian Multicenter Study. Journal of Psychosomatic Research 41(6): 551–559.

16.

Burton

(2014) Self-harm: working with vulnerable adolescents. Practicing Nursing 25(5): 245–251. DOI: 10.1007/s00038-016-0900-2.

17.

Castaldo

Serra

Piga

, et al. (2020) Suicidal behaviour and non-suicidal self-injury in children and adolescents seen at an Italian paediatric emergency department. Annali dell'Istituto superiore di sanità 56(3): 303–314. DOI: 10.4415/ANN_20_03_08.

18.

Cerutti

Manca

Presaghi

, et al. (2011) Prevalence and clinical correlates of deliberate self-harm among a community sample of Italian adolescents. Journal of adolescence 34(2): 337–347. DOI: 10.1016/j.adolescence.2010.04.004.

19.

Cha

Seo

(2018) Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use. Health Psychology Open 5(1): 205510291875504. DOI: 10.1177/2055102918755046.

20.

Chapman

Gratz

Brown

(2006) Solving the puzzle of deliberate self-harm: The experiential avoidance model. Behaviour Research and Therapy 44, 371–394: 10.1016/j.brat.2005.03.005.

21.

Chiesa

Bonacchi

Lau

, et al. (2020) Measuring self-control across gender, age, langeage, and clinical status: A validation study of the Italian version of the Brief Self-Control Scale (BSCS). PLoS One 15(8): e0237729. DOI: 10.1371/journal.Pone.0237729.

22.

Chester

Merwin

DeWall

(2014) Maladaptive perfectionism’s link to aggression and self-harm: Emotion regulation as a mechanism. Aggressive Behavior 41(5): 443–454. DOI: 10.1002/AB.21578.

23.

Cho

Kim

Park

(2017) Stress and adult smartphone addiction mediation by self-control, neuroticism, and extraversion. Stress Health 33(5): 624–630. DOI: 10.1002/smi.2749.

24.

Choi

Kim

Choi

, et al. (2015) Comparison of risk and protective factors associated with smartphone addiction and Internet addiction. Journal of Behavioral Addictions 4(4): 308–331. DOI: 10.1556/2006.4.2015.043.

25.

Cicchetti

Rogosch

(2002) A developmental psychopathology perspective on adolescence. Journal of Counselling and Clinical Psychology 70(1): 6–20. DOI: 10.1037/0022-006X.70.1.6.

26.

Cipriano

Cella

Cotrufo

(2017) Nonsuicidal self-injury: a systematic review. Frontiers in Psychology 8. DOI: 10.3389/fpsyg.2017.01946.

27.

D’Agostino

Pepi

Aportone

, et al. (2018) Self-Injurious Thoughts and Behaviors Questionnaire-Nonsuicidal (SITBQNS): Development and validation of a revised version of the Self-Injurious Thoughts and Behaviors Interview (SITBI) for the self-assessment of nonsuicidal self-injury. Clinical Neuropsychiatry 15(6): 344–352.

28.

De Berardis

Fornaro

Orsolini

, et al. (2020) Emotional dysregulation in adolescents: Implications for the development of severe psychiatric disorders, substance abuse, and suicidal ideation and behaviors. Brain Sciences 10(9): 591. DOI: 10.3390/brainsci10090591.

29.

De Ridder

Lensvelt-Mulders

Finkenauer

, et al. (2012) Taking stock of self-control: A meta-analysis of how trait self-control relates to a wide range of behaviors. Personality and Social Psychology Review 16(1): 76–99. DOI: 10.1177/1088868311418749.

30.

De Ridder

DTD

De Boer

Lugtig

, et al. (2011) Not doing bad things is not equivalent to doing the right thing: Distinguishing between inhibitory and initiatory self-control. Personality and Individual Differences 50: 1006–1011. DOI: 10.1016/j.paid.2011.01.015.

31.

de Vos

La Marre

Radstaak

, et al. (2017) Identifying fundamental criteria for eating disorder recovery: a systematic review and qualitative meta-analysis. Journal of Eating Disorders 5: 34.

32.

Dewi

Efendi

Has

EMM

, et al. (2018) Adolescents’ smartphone use at night, sleep distrubance and depressive symptoms. International Journal of Adolescence Medicine and Health. DOI: 10.1515/ijamh-2018-0095.

33.

Dyson

Hartling

Shulhan

, et al. (2016) A systematic review of social media use to discuss and view deliberate self-harm acts. PLoS One 11(5): e0155813. DOI: 10.1371/journal.pone.0155813.

34.

Durbin

Watson

(1950) Testing for serial correlation in least squares regression. Biometrika 37(3-4): 409–428. DOI: 10.1093/biomet/37.3-4.409.

35.

Durbin

Watson

(1951) Testing for serial correlation in least squares regression. II. Biometrika 38(1-2): 159–178. DOI: 10.1093/biomet/38.1-2.159.

36.

Durbin

Watson

(1971) Testing for serial correlation in least squares regression, III. Biometrika 58: 1–19.

37.

Elhaia

Dmitri

Caglar

, et al. (2019) Worry and anger associated with latent classes of problematic smartphone use severity among college students. Journal of Affective Disorders 246: 209–2016. DOI: 10.1016/j.jad.2018.12.047.

38.

Emery

Heath

Mills

(2016) Basic psychological need satisfaction, emotion dysregulation, and non-suicidal self-injury engagement in young adults: an application of self-determination theory. Journal of Youth and Adolescence 45: 612–623. DOI: 10.1007/s10964-015-0405-y.

39.

Fischr-Grote

Kothgassner

Felnhofer

(2019) Risk factors for problematic smartphone use in children and adolescents: a review of existing literature. Neuropsychiatry 33(4): 179–190. DOI: 10.1007/s40211-019-00319-8.

40.

Wang

Zhao

, et al. (2019) Can emotion regulation difficulty lead to adolescent problematic smartphone use? A moderated mediation model of depression and perceived social support. Children and Youth Services Review 108: 104660. DOI: 10.1016/j.childyouth.2019.104660.

41.

Gao

Zhang

, et al. (2018) The influence of alexithymia on mobile phone addiction: The role of depression, anxiety and stress. Journal of Affective Disorders 225: 761–766. DOI: 10.1016/j.jad.2017.08.020.

42.

Garisch

Wilson

(2015) Prevalence, correlates, and prospective predictors of non-suicidal self-injury among New Zealand adolescents: Cross-sectional and longitudinal survey data. Child and adolescent psychiatry and mental health 9(1): 28. DOI: 10.1186/s13034-015-0055-6.

43.

Gatta

Dal Santo

Rago

, et al. (2016) Alexithymia, impulsiveness, and psychopathology in non-suicidal self-injured adolescents. Neuropsychiatric disease and treatment 12: 2307. DOI: 10.2147/NDT.S106433.

44.

Goodman

Lamping

Ploubidis

(2010) When to use broader internalising and externalising subscales instead of the hypothesised five subscales on the Strengths and Difficulties Questionnaire (SDQ): data from British parents, teachers and children. Journal of Abnormal Child Psychology 38: 1179–1191. DOI: 10.1007/s10802-010-9434-x.

45.

Gratz

Chapman

Dixon-Gordon

, et al. (2017) Exploring the association of deliberate self-harming with emotional relief using a novel implicit association test. Personality disorders: Theory, Research and Treatment 7(1): 91–102. DOI: 10.1037/per0000138.

46.

Gratz

Roemer

(2004) Multidimentional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the difficulties in emotion regulation scale. Journal of Psychopathology and Behavioral Assessment 26(1): 41–54. DOI: 10.1007/s10862-008-9102-4.

47.

Herpertz-Dahlmann

Bühren

Remshmidt

(2013) Growing up is hard - mental disorders in adolescence. Continuing Medical Education 110(25): 432–440.

48.

Hamza

Willoughby

Heffer

(2015) Impulsivity and nonsuicidal self-injury: A review and meta-analysis. Clinical Psychology Review 38: 13–24. DOI: 10.1016/j.cpr.2015.02.010.

49.

Hughes

King

Krazler

, et al. (2019) Anxious and overwhelming affects and repetitive negative thinking as ecological predictors of self-injurious thought and behaviour. Cognitive Therapy and Research 43: 88–101. DOI: 10.1007/s10608-019-09996-9.

50.

Hussain

Griffiths

Sheffield

(2017) An intervention into problematic smartphone use: The role of narcissism, anxiety, and personality factors. Journal of Behavioural Addiction 6(3): 378–386. DOI: 10.1556/2006.6.2017.052.

51.

IBM Corp. Released (2011) IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.

52.

Ipsos (2017) Il consenso in ambiente digitale: percezione e consapevolezza tra i teen. Available at: https://www.savethechildren.it/sites/default/files/files/comunicati.

53.

ISTAT (2019) Indagine conoscitiva su bullismo e cyberbullismo. Available at: www.istat.it/it/archivio/228976.

54.

Jeong

Suh

Gweon

(2019) Is smartphone addiction different from internet addiction? comparison of addiction-risk factors among adolescents. Behaviour and Information Technology 39(5): 578–593. DOI: 10.1080/0144929X.2019.1604805.

55.

Kardefelt-Winther

(2017) Conceptualizing internet use disorders: Addiction or coping process?. Psychiatry and Clinical Neuroscience 71: 459–466. DOI: 10.1111/pcn.12413.

56.

Kessler

Berglund

Demler

, et al. (2005) Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Archives of General Psychiatry 62(6): 593–602.

57.

Kim

Min

, et al. (2018) Relationship among family environment, self-control, friendship quality, and adolescents' smartphone addiction in South Korea: Findings from nationwide data. PLoS One 13(2): e0190896. DOI: 10.1371/journal.pone.0190896.

58.

Kim

Son

Lee

, et al. (2012) Abnormal brain activation of adolescent internet addict in a ball-throwing animation task: possible neural correlates of disembodiment revealed by fMRI. Progress in Neuro-Psychopharmacology and Biological Psychiatry 39(1): 88–95. DOI: 10.1016/j.pnpbp.2012.05.013.

59.

Kirsch

(2012) The influence of social contagion and technology on epidemic non-suicidal self-injury. Available at https://digitalscholarship.unlv.edu/award/8.

60.

Kraiss

ten Klooster

Moskowitz

, et al. (2020) The relationship between emotion regulation and well-being in patients with mental disorders: A meta-analysis-Comprehensive Psychiatry. DOI: 10.1016/j.comppsych.2020.152189.

61.

Kranzler

Fehling

Anestis

, et al. (2016) Emotional dysregulation, internalizing symptoms, and self-injurious and suicidal behavior: structural equation modeling analysis. Death Studies 40(60): 358–366. DOI: 10.1080/07481187.2016.1145156.

62.

Latina

Statting

(2018) Adolescents who self-harm: the patterns in their interpersonal and psychological difficulties. Journal of Adolescent Research 28(4): 824–838. DOI: 10.1111/jora.12368.

63.

Lee

(2016) Psychological characteristics of self-harming behaviour in Korean adolescents. Asian Journal of Psychiatry 23: 119–124. DOI: 10.1016/j.ajp.2016.07.013.

64.

Lewis

Mahdy

Michal

, et al. (2014) Googling self-injury: the state of health information obtained through online searches for self-injury. JAMA Pediatrics 168(5): 443–449. DOI: 10.1001/jamapediatrics.2014.187.

65.

Lewis

Rosenrot

Messner

(2012) Seeking validation in un-likely places: the nature of online questions about non-suicidal self-injury. Archives of Suicide Research 16(3): 263–272. DOI: 10.1080/13811118.2012.695274.

66.

Delvecchio

Lis

, et al. (2015) Parental attachment, self-control, and depressive symptoms in Chinese and Italian adolescents: Test of mediation model. Journal of Adolescence 43: 159–170. DOI: 10.1016/j.adolescence.2015.06.006.

67.

Lindner

Nagy

Retelsdorf

(2015) The dimensionality of the brief self-control scale—An evaluation of unidimensional and multidimensional applications. Personality and Individual Differences 86: 465–473. DOI: 10.1016/j.paid.2015.07.006.

68.

Lockwood

Daley

Townsend

, et al. (2016) Impulsivity and self-harm in adolescence: a systematic review. European Child and Adolescent Psychiatry 26(4): 387–402. DOI: 10.1007/s00787-016-0915-5.

69.

Lucarelli

(2010) Sindromi affettive. In: Ammaniti

Massimo

(ed) Psicopatologia dello sviluppo: Modelli teorici e percorsi a rischio. Milano: Raffaello Cortina Editore, 133–172.

70.

Mahapatra

(2019) Smartphone addiction and associated consequences: role of loneliness and self-regulation. Behavioural and Information Technology 38(8): 833–844. DOI: 10.1080/0144929X.2018.1560499.

71.

Marzocchi

Capron

Di Pietro

, et al. (2004) The use of Strength and Difficulties Questionnaire (SDQ) in southern European countries. European Child and Adolescent Psychiatry 13(2): ii40–ii46. DOI: 10.1007/s00787-004-2007-1.

72.

McLaughlin

Hatzenbuehler

Mennin

, et al. (2011) Emotion dysregulation and adolescent psychopathology: A prospective study. Behaviour research and therapy 49(9): 544–554. DOI: 10.1016/j.brat.2011.06.003.

73.

Mead

Copeland

(2000) What recovery means to us: consumers’ perspectives. Com- munity Mental Health Journal 36: 315–328.

74.

Mei

Gao

, et al. (2018) The relationship between college students’ alexithymia and mobile phone addiction: Testing mediation and moderation effects. BMC Psychiatry 18(1): 1–7. DOI: 10.1037/t01318-000.

75.

Memon

Shiva

Satyajit

, et al. (2018) The role of online social networking on deliberate self-harming and suicidality in adolescents: A systematized review of literature. Indian Journal of Psychiatry 60(4): 384–392. DOI: 10.4103/psychiatry.IndianJPsychiatry_414_17.

76.

Mikolajczak

Petrides

Hurry

(2009) Adolescents choosing self-harm as an emotion regulation strategy: The protective role of trait emotional intelligence. British Journal of Clinical Psychology 48(2): 181–193. DOI: 10.1348/014466508x386027.

77.

Milyavskaya

Inzlicht

(2017) What’s so great about self-control? Examining the importance of effortful self-control and temptation in predicting real-life depletion and goal attainment. Social Psychological and Personality Science 8: 603–611. DOI: 10.1177/1948550616679237.

78.

Mohd

(2013) Students’ dependence on smart phones: The influence of social needs, social influences and convenience. Campus-Wide Information System 20(2): 124–134. DOI: 10.1108/10650741311306309.

79.

Moller

Rober

Byrne

(2013) Deliberate self-harm, substance use, and negative affect in nonclinical samples: a systematic review. Substance Abuse 34(2): 188–207. DOI: 10.1080/08897077.2012.693462.

80.

Moran

Coffey

Romaniuk

, et al. (2012) The natural history of self-harming from adolescence to young adulthood: a population-based cohort study. Lancet 327(9812): 236–243. DOI: 10.1016/S0140-6736(11)61141-0.

81.

Morey

Mellon

Dailami

, et al. (2017) Adolescent self-harm in the community: An update on prevalence using a self-report survey of adolescents aged 13-18 in England. Journal of Public Health 39(1): 58–64. DOI: 10.1093/pubmed/fdw010.

82.

Muehlenkamp

Engel

Wadeson

, et al. (2009) Emotional states preceding and following acts of non-suicidal self-injury in bulimia nervosa patients. Behaviour Research and Therapy 47(1): 83–87. DOI: 10.1016/j.brat.2008.10.011.

83.

Nock

Holmberg

Photos

, et al. (2007) Self-injurious thought and behaviors interview: development, reliability, and validity in an adolescent sample. Psychological Assessment 19: 309–317. DOI: 10.1037/1040-3590.19.3.309.

84.

Nock

(2009) Why do people hurt themselves? New insights into the nature and functions of self-injury. Current Direction in Psychological Science 18: 78–83. DOI: 10.1111/j.1467-8721.2009.01613.x.

85.

Oliva

Antolin-Suarez

Rodriguez-Merinhos

(2019) Uncovering the link between self-control, age, and psychological maladjustment among spanish adolescents and young adults. Psychosocial Intervention 28(1): 49–55. DOI: 10.5093/pi2019a1.

86.

Olson

Sandra

Colucci

, et al. (2020) Smartphone addiction is increasing across the world: A meta-analysis of 24 countries. DOI: 10.31234/osf.io/fsn6v. [Preprint]

87.

Oshima

Nishida

Shimodera

, et al. (2012) The suicidal feelings, self-injury, and mobile phone use after lights out in adolescence. Journal of Pediatric Psychology 37(9): 1023–1130. DOI: 10.1093/jpepsy/jss072.

88.

Panayiotou

Panteli

Vlemincx

(2019) Adaptive and maladaptive emotion processing and regulation, and the case of alexithymia. Cognition and Emotion 26: 1–12. DOI: 10.1080/02699931.2019.1671322.

89.

Panova

Xavier

(2018) Is smartphone addiction really an addiction?. Journal of Behavioral Addictions 7(2): 252–259. DOI: 10.1556/2006.7.2018.49.

90.

Pavia

Cavani

Di Blasi

, et al. (2016) Smartphone addiction Inventory: psychometric properties and confirmatory factors analysis. Computer in Human Behaviour 63: 170–178. DOI: 10.1016/j.chb.2016.05.039.

91.

Pitt

Kilbride

Nothard

, et al. (2007) Researching recovery from psychosis: a user-led project. Psychiatric Bulletin 31: 55–60.

92.

Plener

Schumacher

Munz

, et al. (2015) The longitudinal course of non-suicidal self-injury and deliberate self-harm: a systematic review of the literature. Borderline Personality Disorder and Emotion Dysregulation 2(1): 2. DOI: 10.1186/s40479-014-0024-3.

93.

Purington

Whitlock

(2010) Non-suicidal self-injury in the media. The prevention Research 17(1): 11–13.

94.

R Core Team (2019) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: https://www.R-project.org/.

95.

Roberts

Yaya

Manolis

(2014) The invisible addiction: Cell-phone activities and addiction among male and female college students. Journal of behavioral addictions 3(4): 254–265. DOI: 10.1556/jba.3.2014.015.

96.

Rozgonjuk

Elhai

(2019) Emotion regulation in relation to smartphone use: Process smartphone use mediated the association between expressive supression and problematic smartphone use. Current Psychology 40: 3246–3255. DOI: 10.1007/s12144-019-00271-4.

97.

Schwartz-Mette

Lawrence

(2019) Peer socialization of non-suicidal self-injury in adolescents’ close friendships. Journal of Abnormal Child Psychology 47(1): 1851–1862. DOI: 10.1007/s10802-019-00569-8.

98.

Shapiro

Wilk

(1965) Analysis of variance test for normality (complete samples). Biometrika 52: 591–611.

99.

Sighinolfi

Norcini Pala

Rocco Chiri

, et al. (2010) Difficulties in emotion regulation scale (DERS): the italian translation and adaptation. Psicoterapia Cognitivo-Comportamentale 16(2): 141–170.

100.

Sifneos

(1973) The prevalence of “alexithymic” characteristics in psychosomatic patients. Psychotherapy and Psychosomatics 22(2-6): 255–262. DOI: 10.1159/000286529.

101.

Singhal

Ross

Seminog

, et al. (2014) Risk of self-harm and suicide in people with specific psychiatric and physical disorders: comparisons between disorders using English national record linkage. Journal of the Royal Society of Medicine 107(5): 194–204. DOI: 10.1177/0141076814522033.

102.

Skegg

Nada-Raja

Moffitt

(2004) Minor self-harm and psychiatric disorder: a population-based study. Suicide and Life-Threating Behaviour 34(2): 187–196. DOI: 10.1521/suli.34.2.187.32790.

103.

Spears

Montgomery

Gunnell

, et al. (2014) Factors associated with the development of self-harm amongst a socioeconomically deprived cohort of adolescents in Santiago, Chile. Social Psychiatry and Psychiatric Epidemiology 49(4): 629–637. DOI: 10.1007/s00127-013-0767-y.

104.

Stasiewicz

Bradizza

Gudleski

, et al. (2012) The relationship of alexithymia to emotional dysregulation within an alcohol dependent treatment sample. Addictive Behaviour 37(4): 469–476. DOI: 10.1016/j.addbeh.2011.12.011.

105.

Statista (2020) Forecast of smartphone users’ numbers in Italy 2017-2025. Available at: https://www.statista.com/statistics/467179/forecast-of-smartphone-users-in-italy/(Accessed 16 June 2021).

106.

Tangney

Baumeister

Boone

(2004) High self-control predicts good adjustment, less pathology, better grades and interpersonal success. Journal of Personality 72(2): 271–324. DOI: 10.1111/j.0022-3506.2004.00263.x.

107.

Taylor

Bagby

(2012) The Alexithymia Personality Dimension. Oxford Handbooks Online.

108.

The Net-ONLUS (2017). Available at: http://thenetonlus.com/2017/06/07/look-at-me/.

109.

van Deursen

AJAM

Bolle

Hegner

, et al. (2015) Modeling habitual and addictive smartphone behaviour: the role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Computers in Human Behaviour 45: 411–420. DOI: 10.1016/j.chb.2014.12.039.

110.

Whitlock

Eckenrode

Silverman

(2006) Self-injurious behaviors in a college population. Pediatrics 117(6): 1939–1948. DOI: 10.1542/peds.2005-2543.

111.

Whitlock

Eells

Cummings

, et al. (2009) Nonsuicidal self-injury in college populations: Mental health provider assessment of prevalence and need. Journal of College Student Psychotherapy 23(3): 172–183. DOI: 10.1080/87568220902794366.

112.

World Health Organization (2019) Adolescent development. Available at: https://www.who.int/maternal_child_adolescent/topics/adolescence/development/en/.

113.

Cheung

, et al. (2013) Psychological risk factors of addiction to social networking sites among Chinese smartphone users. Journal of Behavioral Addictions 2(3): 160–166. DOI: 10.1556/jba.2.2013.006.

114.

Yau

Crowley

Mayes

, et al. (2012) Are Internet use and video-game-playing addictive behaviors? Biological, clinical and public health implications for youths and adults. Minerva Psichiatrica 53(3): 153–170.

115.

Young

Sandman

Craske

(2019) Positive and negative emotion regulation in adolescence: links to anxiety and depression. Brain Sciences 9(4): 76.

116.

Yusainy

Lawrence

(2014) Relating mindfulness and self-control to harm to the self and to others. Personality and Individual Differences 64: 78–83. DOI: 10.1016/j.paid.2014.02.015.

117.

Zhang

Tan

Lei

(2019) Parental attachment and problematic smartphone use among Chinese young adults: a moderated mediation model of interpersonal adaptation and self-control. Journal of Adult Development 27: 49–57. DOI: 10.1007/s10804-019-09331-2.

Self-injury and Smartphone Addiction: Age and gender differences in a community sample of adolescents presenting self-injurious behavior

Abstract

Keywords

Smartphone Addiction

Non-suicidal self-injury

The current study

Method

Participants and procedure

Measurement tools

Brief Self-Control Questionnaire

Strength and Difficulties Questionnaire

Smartphone Addiction inventory—Italian version

Self-Injurious Thought and Behavior Questionnaire-Non Suicidal

Toronto Alexithymia Scale

Difficulties in Emotion Regulation Scale

Data analysis

Results

Correlations

Regression models

Discussion

Limitations and future research

Conclusions

Footnotes

Acknowledgments

Declaration of conflicting interests

Funding

ORCID iD

References