Proneness to Boredom and Risk Behaviors During Adolescents’ Free Time

Abstract

As a clinical condition, boredom is an emotional state, widespread among young people, characterized by unpleasant feelings, lack of motivation, and low physiological arousal in which the level of stimulation is perceived as unsatisfactorily low. Boredom is an important emotional state due to its spread among young people. Recent research has mainly studied the complex connection between boredom and leisure-time experiences, which may be involved in adolescents’ risk-taking behaviors. The current study aims to investigate boredom proneness, conceived as a personality trait, in adolescents’ free time, and its involvement in more extreme behaviors, such as binge drinking and addictive Internet use, which may represent ways to cope with the search for additional stimuli. Data from a large sample of Italian adolescents (n = 478, aged between 14 years and 19 years, M = 16.31, SD = 1.47) revealed significant differences between low-boredom and high-boredom subjects. Both girls and boys with high boredom proneness used technology more, engaged less in hobbies and activities such as sports, more frequently consumed strong drinks and binge drank, and were more at risk of Internet addiction than non-bored adolescents. These findings suggest a hypothetical risk profile linked to boredom proneness in adolescence. The results are discussed in light of the literature from a psychosocial and clinical perspective.

Keywords

Adolescence boredom proneness binge drinking Internet addiction leisure activities

Introduction

Boredom: A silent emotion

Boredom is a psychological state that is experienced by most individuals over their lifetime. It is a particularly common experience in adolescence and may be functional for psychological growth. Conversely, when boredom occurs as a persistent condition, it produces existential suffering and might even hamper growth and positive development (Hunter & Csikszentmihalyi, 2003).

In the clinical psychology field, boredom is commonly defined as an affective state characterized by unpleasant feelings, lack of stimulation, and low physiological arousal in which the level of stimulation is perceived as unsatisfactorily low (Harris, 2000; Mikulas & Vodanovich, 1993). Leisure boredom has been described as the subjective perception that the free experiences available to an individual are not sufficiently frequent, engaging, exciting, varied, or original (Iso-Ahola & Weissinger, 1990). Individuals have described boredom as stressful and agitating (Martin, Sadlo, & Stew, 2006) and as tiring, miserable, and frustrating (Mann & Robinson, 2009). Other symptoms include a prolonged subjective sense of time (i.e., the feeling that that “time stands still”), the tendency to flee the situation causing boredom through behavioral or mental disengagement (e.g., daydreaming), and slow and monotonous speech (Goetz & Frenzel, 2006).

Thus, scholars have argued that boredom is a condition of under-stimulation, under-arousal, and lack of psychological involvement associated with dissatisfaction, and individuals try to cope with boredom by seeking additional stimulation (Brissett & Snow, 1993).

Other research has reported that boredom as an unpleasant experience of an absence of interest (Martin et al., 2006). Boredom arises in a situation where none of the things that a person can realistically or potentially do are appealing (Mann & Cadman, 2014), which makes the person in question inactive and generally unmotivated. Although lack of interest can cause boredom, it is not identical to boredom. In contrast to the “torments of boredom,” lack of interest per se is affectively neutral and does not determine distress or suffering (Berlyne, 1960, p. 192). Moreover, lack of interest and boredom have different outcomes (Goetz & Frenzel, 2006); whereas boredom implies a longing to avoid an unpleasant situation, lack of interest involves a lack of desire to engage in an activity but not the urge to flee.

Traditionally, boredom has received less attention from scholars than emotions such as anxiety, anger, and loneliness, and it has not been adequately considered in the leading theories of emotion (Scherer, Schorr, & Johnstone, 2001). One possible reason for this paucity of interest is that boredom is “a silent emotion” that is poorly understood (Eastwood, Frischen, Fenske, & Smilek, 2012) in comparison with more evident affective states, such as anger or anxiety. Instead, from the clinical perspective, it seems to have psychopathological relevance: proneness to boredom may have negative consequences just as other better-known negative emotions do. Specifically, it has been shown to be related to alcohol consumption (Wiesner, Windle, & Freeman, 2005), psychoactive substance use (Anshel, 1991), and many other health problems (Thackray, 1981).

Proneness to boredom, adolescents’ free time, and risk behaviors

Adolescents are more prone to boredom due to the typical criticality of this specific stage of development (Caldwell, Darling, Payne, & Dowdy, 1999). Some classic psychoanalytically oriented authors have described boredom as an intrapsychic state of drive tension without an object of satisfaction that leads to frustration and loneliness (e.g., Fenichel, 1951; Winnicott, 1961).

The most recent research has mainly studied boredom in adolescence from a psychosocial perspective, focusing on leisure time experiences (e.g., Kleiber, Larson, & Csikszentmihalyi, 2014). Leisure time has been described as a “fourth environment” for adolescents beyond home, school, and work (Caldwell et al., 1999), where teens are more likely to engage in exploratory behaviors, novel experiences, and sensation seeking than in other environments (Chassin, Hussong, & Beltran, 2009; Lerner & Steinberg, 2009; Coatsworth, Palen, Sharp, & Ferrer-Wreder, 2006; Dahl, 2004; Steinberg, 2008). Leisure time and boredom have a complex relationship in which adolescents’ risk-taking behaviors may potentially be involved. To date, there has been clear evidence that adolescents report significant experiences of boredom during their free time experiences (Caldwell, Goff, & Stanley, 1992; Larson & Richards, 1991; Shaw, Caldwell, & Kleiber, 1996). It is also globally acknowledged that adolescence is a time of high risk for experimenting with health-compromising behaviors such as smoking, drinking, and drug use, especially during leisure time (National Survey of American Attitudes on Substance Abuse XVII: Teens, 2012).

To investigate the relationship between boredom in leisure and risk behaviors, Caldwell and Smith (1995) conducted a survey of 2756 adolescents and found that approximately 9% were bored during their free time. Compared with their peers, these youths were more likely to engage in a number of unhealthy behaviors, including smoking, heavy alcohol use, and deliberate vomiting. Ziervogel, Ahmed, Flisher, and Roberston (1998) used qualitative methods to identify and understand the social context of alcohol misuse among high schooled male binge drinkers. One of the most significant reasons for the boys’ use of alcohol was that it alleviated boredom. In addition, Biolcati, Passini, & Mancini (2016) confirmed that evidence of boredom proneness is predictive of adolescents’ binge drinking (721 subjects, aged between 13 and 19 years). The mediation model showed that boredom proneness affected binge drinking via the mediation of psychological drinking expectancies, i.e., the notion that alcohol promotes disinhibition and relieves pain and anxiety. Moreover, other studies have suggested that boredom proneness is related to alcoholism and the abuse of other types of substances, such as marijuana, psychedelics, and other stimulants (Iso-Ahola & Crowley, 1991; Hunter & Csiksgentmihalyi, 2003).

In today’s society, investigating leisure in adolescence requires considering new technologies and the Internet, to which teenagers devote a considerable amount of their time (Biolcati, Cani, & Badio, 2013; Biolcati & Cani, 2015). In recent years, Internet misuse in leisure time has emerged as an addictive behavior among boredom-related adolescent experiences (Lin & Yu, 2008). For instance, Internet addiction is described as a disorder that involves symptoms such as restlessness or irritability when not online or feeling the need to spend more time online (Rücker, Akré, Berchtold, & Suris, 2015) to escape boredom (Durkee et al., 2012). Researchers have suggested that boredom avoidance is one of the major motivations for Internet use (Lin & Yu, 2008), and boredom in leisure has become a predictor of social network addiction in late adolescence (Zhou, 2010). Although very few studies have explicitly examined boredom in a Problem Internet Use context (e.g., Lin, Lin, & Wu., 2009), boredom has been associated with general Internet use (Davis 2001; LaRose, Lin, & Eastin, 2003) and the use of social network sites (Pempek, Yermolayeva, & Calvert, 2009).

Based on the aforementioned literature, the present survey aims to investigate boredom proneness in leisure time and its involvement in some adolescents’ risk behaviors. First, we explore adolescents’ free time activities and risk behaviors, exploring gender differences. Then, we investigate the differences between high-boredom and low-boredom adolescents in terms of free time activities and certain risk behaviors, i.e., alcohol use, binge drinking, drinking expectancies, substance use, and addictive Internet use. We assume that boredom proneness, conceived as a personality trait, correlates with more extreme behaviors, such as binge drinking and addictive Internet use, which may represent ways to cope with it by seeking additional stimuli. Hence, we expect that compared with non-bored adolescents, bored adolescents are more prone to engage in risk behaviors and less prone to engage in adaptive free time activities (e.g., sports).

Method

Participants

A total of 478 Italian adolescents (60% males) from three secondary schools in a medium-sized city in Northern Italy took part in the survey: 203 (65.5% males) attended a technical school, 168 (52.4% males) attended a grammar school, and 107 (61.7% males) attended a vocational school. Their ages ranged between 14 and 19 years (M = 16.31, SD = 1.47).

Procedure

The study procedure fulfilled the ethical requirements defined by the American Psychological Association (APA). Approval of data collection was secured through the school board and participation was preceded by an informed-consent procedure that required the active consent of both students and their parents.

In each school, the researcher explained the procedure and guaranteed confidentiality and anonymity. All students who were present in the classrooms responded to the same questionnaire packet. Participation required between 25 and 30 minutes. No teachers were present when the students filled in the questionnaires.

Measures

An anonymous self-report questionnaire was administered in 2015. The tool consisted of closed questions that investigated adolescents’ free time activities, current drinking behavior, drinking expectancies, binge drinking, substance use, Internet addiction, and proneness to boredom.

Frequency of free time activities

Adolescents were asked to indicate how frequently they engaged in the 20 leisure activities presented in their free time (e.g., sports, watching TV, playing a musical instrument, etc.) on a 4-point scale (from 1 = never to 4 = every day). The ad hoc list of activities resulted from a preliminary focus group on free time activities that involved 10 adolescents.

Frequency of alcohol use

Adolescents were asked to indicate how frequently they drank (1) beer, (2) wine, (3) strong drinks, and (4) cocktails on a 6-point scale (from 1 = never to 6 = every day).

Binge drinking

Adolescents were asked (1) whether in the last three months they had drunk more than five (four for women) drinks in a single night (“yes” or “no”) and (2) how many drinks they consumed on a typical night out (1 = one/two drinks, 2 = three drinks, 3 = four drinks, and 4 = five or more drinks). We specified that for drinks we meant “standard drinks’ (e.g., one half pint of beer, a measure of spirits or one glass of wine), a policy adopted by the majority of studies (Gill, 2002).

Drinking expectancies

The adolescents answered the Positive Drinking Expectancy Scale (PDMS; D’Alessio, Laghi, & Baiocco, 2006), which consists of 12 items rated on a 5-point scale (from 1 = “absolutely untrue for me” to 5 = “absolutely true for me”). Authors reported that Internal reliability of PDMS ranges from 0.76 to 0.83. The scale evaluates three fundamental drinking expectations: (1) sexual behavioral disinhibition, e.g., “drinking alcohol helps me to be more uninhibited,” (2) relief from pain, anxiety, and stress, e.g., “drinking alcohol helps me not to think about my problems,” and (3) interpersonal and social confidence, e.g., “drinking alcohol helps me to be nicer.” The Cronbach alpha for these subscales was .73, .75, and .70 respectively.

Frequency of substance use

Adolescents were asked to indicate how frequently they used (1) hashish, (2) marijuana, (3) cocaine, and (4) ecstasy on a 6-point scale (from 1 = never to 6 = every day). We selected this list of illicit substances because they are the most abused in Italy (Molinaro et al., 2011), and they appertain to different pharmacological categories (cannabinoids, psychostimulants, and designer drugs).

Internet addictive use

The Internet Addiction Test (IAT; Young, 1998) consists of 20 items that measure mild, moderate, and severe levels of Internet addictive use. Responses range from “0” (does not apply) to “5” (always). A high score across the scale items (e.g., “How often do you find that you stay online longer than you intended?”) is indicative of addiction. Young’s IAT is one of the most popular assessment instruments to screen for problematic Internet usage and has demonstrated a strong internal reliability and validity across studies (e.g., Laconi, Rodgers, & Chabrol, 2014; Panayides & Walker, 2012). The Cronbach alpha for this scale is .90.

Boredom proneness

The Boredom Proneness Scale (BPS, Farmer & Sundberg, 1986) consists of 28 items that range from 1 (highly disagree) to 7 (highly agree). A high score across the scale items (i.e., “Time always seems to be passing slowly” and “In any situation I can usually find something to do or see to keep me interested”) is indicative of high boredom proneness. Authors reported that the internal consistency of the BPS is .79. Evidence of the scale validity has been provided through relationships with other measures of boredom and similar constructs (Dahlen, Martin, Ragan, & Kuhlman, 2005). The theoretical background of this scale considers boredom proneness as a pathological personality trait that is significantly and positively associated with undesirable emotional states such as depression, hopelessness, loneliness, and amotivational orientation and is negatively related to life satisfaction and autonomy orientation (Craparo, Faraci, Fasciano, Carrubba, & Gori, 2013). The Cronbach alpha for this scale is .75.

Analytical procedures

First, the frequencies and means were computed for each variable. Second, chi-square and analysis of variance (ANOVA) were used to evaluate differences between genders and the high-boredom group vs. the low-boredom group among the study variables. The data were analyzed with SPSS (SPSS Inc. Released 2009. PASW Statistics for Windows, Version 18.0. Chicago: SPSS Inc.).

Results

Leisure time activities

In their free time, the adolescents reported that they mostly engaged in activities such as using a smartphone or an iPhone, listening music, and using a PC (chatting, surfing, gaming). The least popular activities were writing, volunteering, playing in a band, and doing a theater course, as presented in Table 1 (top left). Regarding sex differences (see Table 1, top right), an ANOVA showed that boys practiced sports, played on game consoles, collected things, attended game rooms, and played in a band more so than girls. Conversely, compared with boys, girls more commonly engaged in activities such as using their smartphone or iPhone, listening music, watching TV, watching movies, going shopping, going to the cinema, going to the library, reading books, and writing. No gender differences were found in PC use (chatting, surfing, gaming).

Table 1.

Means (1–4 point scale) and ANOVA differences for gender in free time activities.

			Gender
Measures	M	SD	M boys	M girls	F	ŋ
Free time activities
Using smartphones or iPhones (calling, messaging, chatting, etc.)	3.44	.74	3.37	3.54	6.42*	.013
Listening music	3.23	.79	3.12	3.39	14.18***	.029
Using PCs (chatting, surfing, gaming)	3.09	.79	3.06	3.14	1.09	.002
Watching TV	2.90	.82	2.84	2.99	4.09*	.009
Watching movies	2.79	.71	2.72	2.89	6.48*	.013
Sports	2.52	1.03	2.68	2.29	16.75***	.034
Shopping	2.52	.72	2.33	2.80	53.71***	.102
Bars, pubs etc.	2.25	.89	2.26	2.22	.22	.000
Playing on game consoles/ handheld devices	2.24	.91	2.59	1.72	132.68***	.219
Going around doing nothing	2.24	.92	2.19	2.30	1.42	.003
Cinema	2.12	.64	2.07	2.19	4.32*	.008
Park	2.11	.79	2.12	2.10	.098	.000
Reading books, newspapers	1.98	.82	1.80	2.24	33.88***	.067
Disco	1.79	.87	1.78	1.81	.14	.000
Playing an instrument	1.45	.85	1.47	1.42	.37	.001
Collecting objects	1.42	.72	1.53	1.25	16.99***	.035
Game room	1.38	.61	1.44	1.28	7.68**	.016
Library	1.31	.61	1.23	1.44	13.02***	.027
Writing	1.28	.61	1.16	1.45	26.42***	.053
Volunteer work	1.23	.61	1.19	1.29	3.36	.007
Playing in a band	1.15	.56	1.20	1.08	5.81*	.011
Doing a theater course	1.10	.45	1.07	1.16	4.76*	.010

Note. The numbers in brackets represent the scale range.

p < .05.

p < .01.

***

p < .001.

Risk behaviors

Regarding risk behaviors, adolescents reported drinking two or three drinks on average on a night out. Almost half of participants (45.2%) said that in general, they had “1 or 2 drinks”, 13.4% had “3 drinks”, 8.6% had “4 drinks”, and 10.5% had “more than 5 drinks” on a single night. It should be noted that 22.4% of the participants did not respond. Consumption of beer and strong drinks was more frequent among boys than girls, while no significant differences were found for wine and cocktail consumption (see Table 2, upper part).

Table 2.

Means and ANOVA gender differences in variables.

			Gender
Measures	M	SD	M boys	M girls	F	ŋ
Frequency of alcohol use
Wine (1, 6)	2.33	1.36	2.42	2.19	2.50	.007
Beer (1, 6)	3.03	1.51	3.39	2.44	38.80***	.095
Strong drinks (1, 6)	2.61	1.35	2.72	2.42	4.51*	.012
Cocktails (1, 6)	2.98	1.45	2.98	2.98	.001	.000
Binge drinking (1, 4)	1.80	1.09	1.96	1.53	14.14***	.037
Drinking expectancies
Sexual behavior disinhibition (1, 5)	2.13	.94	2.16	2.08	.55	.001
Pain relief (1, 5)	2.13	.94	2.11	2.18	.49	.001
Interpersonal and social security (1, 5)	1.57	.68	1.62	1.48	3.60	.010
Substance use
Hashish (1, 6)	2.16	1.56	2.38	1.61	5.03*	.049
Marijuana (1, 6)	3.46	1.66	3.63	3.04	2.65	.027
Cocaine (1, 6)	1.14	.61	1.08	1.29	2.24	.023
Ecstasy (1, 6)	1.09	0.41	1.11	1.04	.74	.008
IAT total score	38.97	12.75	40.70	36.42	12.52***	.027
BPS total score	98.92	17.12	98.93	98.92	.00	.000

Note. The numbers in brackets represent the scale range. ANOVA: analysis of variance.

p < 0.05.

p < 0.01.

***

p < 0.001.

With regard to alcohol misuse, 168 participants (35.1%) declared that they had engaged in binge drinking (more than five drinks for men and four drinks for women drinks on a single night) in the past three months, 208 (43.5%) declared that they had not and 102 (21.3%) did not answer. The chi-square test revealed that boys said they had engaged in binge drinking (χ² (1, N = 376) = 8.81, p < .005; 50.6% vs. 35.0%) more than girls.

With regard to drinking expectancies (see Table 2), the adolescents generally gave moderate scores to sexual behavioral disinhibition and relief from stress and anxiety, while they assigned very low scores to interpersonal and social confidence as motivations for alcohol use. No differences were found between genders.

With regard to substance use, the adolescents reported frequencies of use of marijuana in the upper half of the scale, followed by hashish. They assigned very low scores to ecstasy and cocaine use. Boys declared they used hashish more frequently than girls. It is worth noting that only one-third of participants (30%) answered substance-use questions.

In addition, two clinical variables were investigated.

On the IAT, most participants (74.9) scored between 20 and 49, which represents average online use. Nineteen percent scored 50–79, which indicates that Internet use causes occasional or frequent problems and 0.4% scored 80–100, which indicates that Internet use causes significant problems. A total of 5.6% of participants did not answer. An ANOVA analysis comparing genders showed that boys scored higher than girls (see Table 2, lower part).

On the BPS, the adolescents generally gave moderate-high scores to boredom; specifically, more than half of participants (62.1%) scored from 81 to 117 (a medium level of boredom proneness) and 13% scored above 117, indicating a very high level of boredom proneness. A total of 10.3% did not answer. No gender differences were found (see Table 2, lower part).

High boredom vs. low boredom

To investigate the differences between high-boredom and low-boredom adolescents in free time activities, we split the sample at the median value (99) (min 48; max 157; mean = 98.92 (SD = 17.12)); the group that scored from 48 to 99 was named “Low Boredom” (LB) (47.3%, n = 226), and the group that scored from 100 to 157 was named “High Boredom” (HB) (42.5%, n = 203). The LB group was then compared with the HB group. No gender differences were found between the two groups. An ANOVA analysis (see Table 3) revealed significant differences between the LB group and the HB group in free time activities. The HB group used a PC, played with a game console, and watched TV and movies more than the LB group. Conversely, individuals in the LB group played sports, read books, and collected objects more than individuals in the HB group.

Table 3.

Significant differences between Low Boredom and High Boredom groups in free time activities (1–4 point scale).

Measures	M Low Boredom	M High Boredom	F	ŋ
Free time activities
Using smartphones or iPhones (calling, messaging, chatting, etc.)	3.36	3.53	5.38*	.012
Using PCs (chatting, surfing, gaming)	2.91	3.27	22.32***	.050
Watching TV	2.80	3.05	10.57**	.024
Watching movies	2.71	2.88	6.12*	.014
Sports	2.68	2.39	8.29**	.019
Playing with game consoles hand-held devices	2.10	2.41	13.07***	.030
Reading books, newspapers	2.09	1.88	7.16**	.017
Collecting objects	1.50	1.32	6.44*	.015

Note. The numbers in brackets represent the scale range.

p < .05.

p < .01.

***

p < .001.

With regard to risk behaviors, ANOVA analysis (see Table 4 upper part) revealed significant differences between the LB group and the HB group in terms of drinking behaviors. Individuals in the HB group drank more strong drinks and practiced more binge drinking than individuals in the LB group. Individuals in the HB group obtained higher scores on all three factors of the Positive Drinking Expectancy Scale. Surprisingly, the only significant difference found with regard to substance use was the use of hashish: individuals in the LB group declared that they more frequently used hashish than individuals in the HB group.

Table 4.

Means and ANOVA differences for boredom proneness on variables.

			Boredom proneness
Measures	M	SD	M low boredom	M high boredom	F	ŋ
Frequency of alcohol use
Wine (1, 6)	2.33	1.36	2.49	2.10	6.99**	.020
Beer (1,6)	3.03	1.51	2.88	3.11	1.99	.006
Strong drinks (1, 6)	2.61	1.35	2.39	2.74	5.74*	.017
Cocktails (1, 6)	2.98	1.45	2.82	3.09	3.10	.009
Binge Drinking (1, 4)	1.80	1.09	1.67	1.89	3.67*	.011
Drinking expectancies
Sexual behavioral disinhibition (1, 5)	2.13	.94	1.96	2.28	10.26**	.030
Pain relief (1, 5)	2.13	.94	1.90	2.38	23.01***	.065
Interpersonal and social security (1, 5)	1.57	.68	1.44	1.69	11.59**	.034
Substance use
Hashish (1, 6)	2.16	1.56	2.58	1.83	4.53*	.051
Marijuana (1, 6)	3.46	1.66	3.85	3.25	2.68	.031
Cocaine (1, 6)	1.14	0.61	1.24	1.09	1.08	.013
Ecstasy (1, 6)	1.09	0.41	1.09	1.06	0.245	.003
IAT total score	38.97	12.75	34.64	43.25	52.83***	.114

Note. The numbers in brackets represent the scale range.

p < .05.

p < .01.

***

p < .001.

With regard to the IAT, individuals in the HB group scored higher than individuals in the LB group (see Table 4, lower part).

Discussion

The present study investigated differences between high-boredom and low-boredom adolescents in terms of free time activities and risk behaviors. Our results may contribute to the literature on free time use and adolescent risk-taking in several ways.

Leisure time activities

First, the study explored leisure activities during free time. In general, the Italian adolescents surveyed spend most of their free time using technology (i.e., smartphone, iPhone, PC) or listening music and watching movies; these activities require the mediation of an instrument and do not necessarily involve peer presence. The most popular free time activities were forms of sedentary activity, including television or movie watching, using a PC, or talking on the phone while seated.As argued by scholars (Delle Fave, Bassi, & Massimini, 2003), people are increasingly sedentary and adolescents are spending more time in front of different screens. Our results confirm that adolescents today rely heavily on technology to stay connected with the ‘world.’ As shown by Calamaro, Mason, & Ratcliffe (2009), on average, teenagers have more than two of their own electronic devices, and they may be at risk of excessive technology use. As increasing screen time during this period of growth may replace time spent playing sports, it is possible that adverse health outcomes will ensue. The positive effect of physical activity on adolescents’ health in general is well known (Aarnio, Winter, Peltonen, Kujala, & Kaprio, 2002). Among our adolescent sample, the most practiced activities were “relaxed leisure” activities (Larson & Kleiber, 1993), which do not necessarily represent opportunities for developing specific skills. They differ from “transitional leisure” activities such as sports and hobbies, which have more structure, a clear set of rules associated with personal engagement, concentration and effort directed to achieving goals. According to a previous study (Dalle Fave, Bassi, & Massimini, 2003), relaxed leisure activities take up large amounts of Italian adolescents’ time; in our sample, unstructured activities such as PC use or going around doing nothing in particular were more frequent activities than playing an instrument. However, the adolescents were involved in both structured leisure (with sports ranking sixth most common) and relaxed leisure, with some gender differences in their preferences. In general, boys were more interested in sports, playing on game consoles, going to a game room, and playing in a band. Girls were more often involved in a wider range of activities and appeared more interested in engaging in activities other than sports (e.g., reading books). Furthermore, this study confirms the results of previous surveys that showed that girls are more likely to use their own cell phone more than males (Sánchez-Martínez & Puime, 2010). It is worth noting that both genders engaged in entertainment such as using a PC, going to bars, discos, or the park or going around doing nothing, which are phase-specific but not gender-specific activities.

Risk behaviors

According to both international (e.g., Wilsnack, Wilsnack, Kristjanson, Vogeltanz-Holm, & Gmel, 2009) and Italian studies (e.g., Gallimberti et al., 2011; Laghi, Baiocco, Lonigro, Capacchione, & Baumgartner, 2012), our findings show that drinking is widespread, especially among adolescent males. In our sample, boys were more likely to consume beer and spirits than girls (e.g., Wilsnack, Vogeltanz, Wilsnack, & Harris, 2000), but no gender differences for wine and cocktail consumption were found. Our results are in line with those of other studies (e.g., Bagiu, Vlaicu, Onisei, Onisei, & Bagiu, 2016) showing that males consume more beer and spirits than girls and that the prevalence of wine consumption is similar. We can assume that even as female drinking has become more normalized, there has long been a clear distinction between “men’s drinks,” such as beer, and “women’s drinks,” such as wine and cocktails, even in Mediterranean countries. With the consumption of more “feminine” drinks, females tend to conform to social expectations of femininity through their beverage choices (Nicholls, 2016).

Almost all studies on gender differences have found that boys are more likely to engage in binge drinking behaviors (e.g., Kuntsche, Rehm, & Gmel, 2004).

Our data are in line with national surveys investigating heavy alcohol consumption. The European School Survey Project on Alcohol and Other Drugs (Hibell et al., 2012) shows that binge drinking involves 35% of young Italians, slightly less than the European average. In our sample, 35.1% of adolescents declared that they had engaged in binge drinking in the past three months, and this percentage was more than 50% for boys. Our findings are in agreement with studies (e.g., Room, 2010) that underline how so-called Mediterranean cultures are changing their drinking patterns to align more closely with Northern European customs. That is, if Mediterranean cultures traditionally tend to drink alcohol (particularly wine) as an accompaniment to meals, more recently, adolescents are starting to adopt the so-called “Northern European drinking patterns,” characterized by heavy episodic drinking, often for the purpose of intoxication (Biolcati et al., 2016; Calafat et al., 2011). Thus, Italian adolescents seem to be influenced by a so-called “hedonistic culture” (Fry, 2001), where binge drinking is a typical pattern that represents a clear demarcation line between non-leisure and leisure time.

Furthermore, no differences were observed with regard to drinking expectancies. In general, expectancies are considered strong predictors of alcohol consumption for adolescents (McNally & Palfai, 2001). Expectancies are based on the assumption that people consume alcohol to attain certain valued outcomes (Cooper, 1994). Research findings demonstrate that drinking expectancies have a similar salience for both genders (Kairouz, Gliksman, Demers, & Adlaf, 2002). In addition, in our study, adolescents attached more importance to sexual behavioral disinhibition and relief from anxiety than to interpersonal and social confidence as motivations for alcohol use, in line with a previous study (Biolcati et al., 2016).

With regard to gender differences in substance use, our findings were similar to those obtained by Young et al. (2002) who reported greater use among males than females. The consumption of other substances is very low for both genders and the response rate is indeed too low to make assumptions.

Moreover, our findings indicate that addictive Internet use is gender sensitive and that the risk of Internet addiction is higher among adolescent boys than girls. Similarly, previous studies have shown that males are more prone to become Internet addicted than females (e.g., Ostovar et al., 2016).

Finally, in accordance with other researchers (Melton & Schulenberg; 2009; Biolcati et al., 2016), the data from our study did not reveal differences between girls and boys with regard to boredom proneness scores.

High Boredom vs. Low Boredom

Our findings highlight some differences between bored and non-bored adolescents in terms of free time leisure activities. Bored adolescents more frequently engaged in “relaxed leisure” activities and non-bored adolescents more frequently engaged in “transitional leisure” activities. In particular, girls and boys with high-boredom proneness more frequently used technology such as smartphones, PCs, TV, game consoles, and handheld game devices than adolescents with a low level of boredom; moreover, they engaged less in hobbies such as sports, reading, and collecting. We may suppose that the uncomfortable feeling of boredom triggers psychological effects such as “relaxing” and/or “escaping from problems” through screen leisure activities or through seeking additional stimuli in exciting online activities rather than by seeking more structural hobbies that require engagement and specific skills. For example, sports practice requires dedication and consistency, while reading a newspaper is not always stimulating enough to escape from one’s daily routine. Thus, bored adolescents seem to engage in free time activities to achieve “optimal arousal” and to reduce stress. Previous mood management studies have generally found that users select media that enhance positive mood and reduce negative mood (Dillman Carpentier et al., 2008). Along the same lines, Bryant and Zillmann (1984) assumed that viewers use TV to relieve stress. Similarly, bored adolescents may turn to screen activities to obtain agreeable arousal levels for entertainment, diversion, and relaxation (Leung, 2015). Low levels of arousal drive adolescents to seek entertainment from media. It is reasonable to believe that bored people will use media technology more frequently to drive their emotional state to an optimal level.

As expected, our data suggest that a certain level of boredom may lead adolescents to drink as an “antidote” to a lack of optimal stimulation. Indeed, in our sample, bored adolescents engaged in drinking and binge drinking more than non-bored adolescents did; interestingly, the two groups did not differ with regard to wine consumption. We may think that wine is a more traditional and social beverage and adolescents consume strong drinks and binge drink for the purposes of “intoxication” and sensation seeking.

In our sample, hashish and marijuana appeared to be unrelated to boredom; in fact, low-boredom teenagers used hashish more than high-boredom ones. It is also worth noting that the highest rates of use occur in early adulthood (Quinn & Harden, 2013). The onset of substance use may often occur due to boredom but the continuation and escalation of use coincides with a drop in sensation seeking (Kotov, Gamez, Schmidt, & Watson, 2010).. In addition, hashish is not considered among the most arousing substances but it is often used as a socializing experience among peers.

Finally, in our sample, bored adolescents were more at risk of Internet addiction than non-bored adolescents. In line with previous studies (e.g., Lin, Lin, & Wu, 2009), our results suggest that bored individuals fail to achieve an optimal experience from routine activities and may be motivated to seek alternatives on the Internet to satisfy their psychological needs (e.g., sensation-seeking and pleasure-seeking) and to lose control. Finally, the main result of the study is the evidence of a strong relationship between boredom proneness and Internet addiction.

Limitations and conclusions

Our analysis does not allow us to draw causal conclusions. Longitudinal studies of boredom traits are an important future direction for researchers to explore. Furthermore, all variables were measured with adolescent self-reports, which are prone to social desirability bias and may result in either under- or overestimated reports of behavioral patterns. Moreover, for some of the questions, the percentage of missing responses was very high.

However, the findings of our exploratory study suggest a hypothetical risk profile. Boredom proneness in adolescence is strongly related to risk behaviors that involve some specific “consumer goods” (alcohol and technologies) due to their easy accessibility and the expected arousal properties. Among adolescents, boredom proneness, conceived as an individual trait, has been linked to externalizing problems (Krueger, McGue, & Iacono, 2001), alcohol abuse (Flory, Pytte, Hurd, Ferrell, & Manuck, 2011), binge drinking (Biolcati et al., 2016), substance use (Anshel, 1991), and addictive Internet use (Pempek et al., 2009) as attempts to cope with this unpleasant and painful condition. An initial result of this study confirms the importance of acting preventatively in early adolescence (Lammers, Kuntsche, Engels, Wiers, & Kleinjan, 2013) because during this phase of life, some personality vulnerabilities are associated with at-risk drinking patterns, drinking expectancies, and addictive Internet use. Second, our results may have implications for clinical interventions. From a clinical point of view, we believe that interventions should suitably reflect boredom proneness among adolescents (Conrod, Castellanos, & Mackie, 2008; Lammers et al., 2013). These interventions ought to increase adolescents’ ability to cope with boredom and they should be targeted toward emotion regulation skills and the ability to allow time for the fulfilment of adolescents’ “urgent needs.” Moreover, from a psychosocial point of view, interventions should provide more adaptive alternatives to risk behaviors, such as more structured and socializing activities that also reward adolescents. Interventions should be designed to increase adolescents’ internal motivation to engage in meaningful and challenging leisure time activities (Caldwell, 2004). Activity-based interventions may have a greater impact on adolescents’ risk-taking behaviors.

Footnotes

Article Notes

References

Aarnio

Winter

Peltonen

Kujala

U. M.

Kaprio

(2002) Stability of leisure-time physical activity during adolescence—A longitudinal study among 16-, 17- and 18-year-old Finnish youth. Scandinavian Journal of Medicine & Science in Sports 12: 179–185.

Anshel

M. H.

(1991) A survey of elite athletes on the perceived causes of using banned drugs in sport. Journal of Sport Behavior 14: 283.

Bagiu

I. C.

Vlaicu

Onisei

Bagiu

R. V.

(2016) Types of alcohol associated to binge drinking behaviour among students from Timis County. Revista de Chimie 67: 1684–1687.

Berlyne

D. E.

(1960) Conflict, arousal, and curiosity, New York, NY: McGraw-Hill Book Company.

Biolcati

Cani

(2015) Feeling alone among friends: Adolescence, social networks and loneliness. Webology 12(2): 1.

Biolcati

Cani

Badio

(2013) Teenagers and Facebook: Online privacy management. Psicologia clinica dello sviluppo 17(3): 449–478.

Biolcati

Passini

Mancini

(2016) “I cannot stand the boredom.” Binge drinking expectancies in adolescence. Addictive Behaviors Reports 3: 70–76.

Brissett

Snow

R. P.

(1993) Boredom: Where the future isn’t. Symbolic Interaction 16: 237–256.

Bryant

Zillmann

(1984) Using television to alleviate boredom and stress: Selective exposure as a function of induced excitational states. Journal of Broadcasting & Electronic Media 28: 1–20.

10.

Calafat

Blay

N. T.

Hughes

Bellis

Juan

Duch

Kokkevi

(2011) Nightlife young risk behaviours in Mediterranean versus other European cities: Are stereotypes true? The European Journal of Public Health 21: 311–315.

11.

Calamaro

C. J.

Mason

T. B.

Ratcliffe

S. J.

(2009) Adolescents living the 24/7 lifestyle: Effects of caffeine and technology on sleep duration and daytime functioning. Pediatrics 123: e1005–e1010.

12.

Caldwell

K. B.

Goff

H. D.

Stanley

D. W.

(1992) A low-temperature scanning electron microscopy study of ice cream. II. Influence of selected ingredients and processes. Food Structure 11: 2.

13.

Caldwell

L. L.

(2004) TimeWise: Taking charge of leisure time curriculum for middle school students, Scotts Valley, CA: ETR Associates.

14.

Caldwell

L. L.

Smith

E. A.

(1995) Health behaviors of leisure alienated youth. Loisir et Société/Society and Leisure 18: 143–156.

15.

Caldwell

L. L.

Darling

Payne

L. L.

Dowdy

(1999) “Why are you bored?”: An examination of psychological and social control causes of boredom among adolescents. Journal of Leisure Research 31: 103.

16.

Chassin

Hussong

Beltran

(2009) Adolescent substance use. In: Lerner

R. M.

Steinberg

L. D.

(eds) Handbook of adolescent psychology, Hoboken, NJ: John Wiley & Sons.

17.

Coatsworth

J. D.

Palen

L. A.

Sharp

E. H.

Ferrer-Wreder

(2006) Self-defining activities, expressive identity, and adolescent wellness. Applied Developmental Science 10: 157–170.

18.

Conrod

P. J.

Castellanos

Mackie

(2008) Personality-targeted interventions delay the growth of adolescent drinking and binge drinking. Journal of Child Psychology and Psychiatry 49: 181–190.

19.

Cooper

M. L.

(1994) Motivations for alcohol use among adolescents: Development and validation of a four-factor model. Psychological Assessment 6: 117.

20.

Craparo

Faraci

Fasciano

Carrubba

Gori

(2013) A factor analytic study of the Boredom Proneness Scale (BPS). Clinical Neuropsychiatry 10: 164–171.

21.

Dahl

R. E.

(2004) Adolescent brain development: A period of vulnerabilities and opportunities. Keynote address. Annals of the New York Academy of Sciences 1021: 1–22.

22.

Dahlen

E. R.

Martin

R. C.

Ragan

Kuhlman

M. M.

(2005) Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis & Prevention 37(2): 341–348.

23.

D'Alessio

Baiocco

Laghi

(2006) The problem of binge drinking among Italian university students: A preliminary investigation. Addictive Behaviors 31(12): 2328–2333.

24.

Davis

R. A.

(2001) A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior 17: 187–195.

25.

Dillman Carpentier

F. R.

Brown

J. D.

Bertocci

Silk

J. S.

Forbes

E. E.

Dahl

R. E.

(2008) Sad kids, sad media? Applying mood management theory to depressed adolescents' use of media. Media Psychology 11: 143–166.

26.

Durkee

Kaess

Carli

Parzer

Wasserman

Floderus

Brunner

(2012) Prevalence of pathological internet use among adolescents in Europe: Demographic and social factors. Addiction 107: 2210–2222.

27.

Eastwood

J. D.

Frischen

Fenske

M. J.

Smilek

(2012) The unengaged mind: Defining boredom in terms of attention. Perspectives on Psychological Science 7: 482–495. doi:10.1177/1745691612456044.

28.

Farmer

Sundberg

N. D.

(1986) Boredom proneness—The development and correlates of a new scale. Journal of Personality Assessment 50: 4–17.

29.

Fave

A. D.

Bassi

Massimini

(2003) Quality of experience and risk perception in high-altitude rock climbing. Journal of Applied Sport Psychology 15: 82–98.

30.

Fenichel

(1951) On the psychology of boredom. In: Rapaport

(ed.) Organization and pathology of thought, New York, NY: Columbia University Press, pp. 349–361.

31.

Flory

J. D.

Pytte

C. L.

Hurd

Ferrell

R. E.

Manuck

S. B.

(2011) Alcohol dependence, disinhibited behavior and variation in the prodynorphin gene. Biological Psychology 88: 51–56.

32.

Fry

M. L.

(2011) Seeking the pleasure zone: Understanding young adult’s intoxication culture. Australasian Marketing Journal (AMJ) 19: 65–70.

33.

Gallimberti

Chindamo

Buja

Forza

Tognazzo

Galasso

Baldo

(2011) Underage drinking on saturday nights, sociodemographic and environmental risk factors: A cross-sectional study. Substance Abuse Treatment, Prevention, and Policy 6: 1.

34.

Gill

J. S.

(2002) Reported levels of alcohol consumption and binge drinking within the UK undergraduate student population over the last 25 years. Alcohol and Alcoholism 37: 109–120.

35.

Goetz

Frenzel

A. C.

(2006) Phänomenologie schulischer Langeweile [Phenomenology of boredom at school]. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie 38: 149–153. doi:10.1026/0049-8637.38.4.149.

36.

Harris

M. B.

(2000) Correlates and characteristics of boredom proneness and boredom. Journal of Applied Social Psychology 30: 576–598.

37.

Hibell, B., Guttormsson, U., Ahlström, S., Balakireva, O., Bjarnason, T., Kokkevi, A., & Kraus, L. (2012). Substance use among students in 36 European countries. The 2011 ESPAD report (pp. 123–134). Stockholm, Sweden: Monitoring Centre for Drugs and Drug Addiction.

38.

Hunter

J. P.

Csikszentmihalyi

(2003) The positive psychology of interested adolescents. Journal of Youth and Adolescence 32: 27–35.

39.

Iso-Ahola, S. E., & Crowley, E. D. (1991). Adolescent substance abuse and leisure boredom. Journal of leisure Research, 23, 260.

40.

Iso-Ahola

S. E.

Weissinger

(1990) Perceptions of boredom in leisure: Conceptualization, reliability and validity of the leisure boredom scale. Journal of leisure Research 22: 1.

41.

Kairouz

Gliksman

Demers

Adlaf

E. M.

(2002) For all these reasons, I do … drink: A multilevel analysis of contextual reasons for drinking among Canadian undergraduates. Journal of Studies on Alcohol 63: 600–608.

42.

Kleiber

Larson

Csikszentmihalyi

(2014) The experience of leisure in adolescence. In: Mihaly Csikszentmihalyi (ed.) Applications of flow in human development and education, New York, NY: Springer Netherlands, pp. 467–474.

43.

Kotov, R., Gamez, W., Schmidt, F., & Watson, D. (2010). Linking “big” personality traits to anxiety, depressive, and substance use disorders: A meta-analysis. Psychological Bulletin, 136, 768–821.

44.

Krueger

R. F.

McGue

Iacono

W. G.

(2001) The higher-order structure of common DSM mental disorders: Internalization, externalization, and their connections to personality. Personality and Individual Differences 30: 1245–1259.

45.

Kuntsche

Rehm

Gmel

(2004) Characteristics of binge drinkers in Europe. Social Science & Medicine 59: 113–127.

46.

Laconi

Rodgers

R. F.

Chabrol

(2014) The measurement of Internet addiction: A critical review of existing scales and their psychometric properties. Computers in Human Behavior 41: 190–202.

47.

Laghi

Baiocco

Lonigro

Capacchione

Baumgartner

(2012) Family functioning and binge drinking among Italian adolescents. Journal of Health Psychology 17: 1132–1141.

48.

Lammers

Kuntsche

Engels

R. C.

Wiers

R. W.

Kleinjan

(2013) Mediational relations of substance use risk profiles, alcohol-related outcomes, and drinking motives among young adolescents in the Netherlands. Drug and Alcohol Dependence 133: 571–579.

49.

LaRose

Lin

C. A.

Eastin

M. S.

(2003) Unregulated Internet usage: Addiction, habit, or deficient self-regulation? Media Psychology 5: 225–253.

50.

Larson

R. W.

Kleiber

D. A.

(1993) Structured leisure as a context for the development of attention during adolescence. Loisir et Societe/Society and Leisure 16: 77–98.

51.

Larson

R. W.

Richards

M. H.

(1991) Boredom in the middle school years: Blaming schools versus blaming students. American Journal of Education 99: 418–443.

52.

Lerner, R. M., & Steinberg, L. (2009). Handbook of adolescent psychology, volume 1: Individual bases of adolescent development. New Jersey: John Wiley & Sons.

53.

Leung

(2015) Using tablet in solitude for stress reduction: An examination of desire for aloneness, leisure boredom, tablet activities, and location of use. Computers in Human Behavior 48: 382–391.

54.

Lin

C. H.

S. F.

(2008) Adolescent Internet usage in Taiwan: Exploring gender differences. Adolescence 43: 317–331.

55.

Lin

C. H.

Lin

S. L.

C. P.

(2009) The effects of parental monitoring and leisure boredom on adolescents' internet addiction. Adolescence 44: 993.

56.

Mann

Cadman

(2014) Does being bored make us more creative? Creativity Research Journal 26: 165–173.

57.

Mann

Robinson

(2009) Boredom in the lecture theatre: an investigation into the contributors, moderators and outcomes of boredom amongst university students. British Educational Research Journal 35: 243–258.

58.

Martin

Sadlo

Stew

(2006) The phenomenon of boredom. Qualitative Research in Psychology 3: 193–211.

59.

McNally

A. M.

Palfai

T. P.

(2001) Negative emotional expectancies and readiness to change among college student binge drinkers. Addictive Behaviors 26: 721–734.

60.

Melton

A. M.

Schulenberg

S. E.

(2009) A confirmatory factor analysis of the boredom proneness scale. The Journal of Psychology 143: 493–508.

61.

Mikulas

W. L.

Vodanovich

S. J.

(1993) The essence of boredom. The Psychological Record 43: 3.

62.

Molinaro

Siciliano

Curzio

Denoth

Salvadori

Mariani

(2011) Illegal substance use among Italian high school students: Trends over 11 years (1999–2009). PloS One 6(6): e20482.

63.

National Survey of American Attitudes on Substance Abuse XVII: Teens. (2012). The National Center of Addiction and Substance Abuse at Columbia University. Retrieved from https://www.centeronaddiction.org/addiction-research/reports/national-survey-american-attitudes-substance-abuse-teens-2012.

64.

Nicholls

(2016) ‘What on earth is she drinking?’ Doing Femininity through Drink Choice on the Girls’ night out. Journal of International Women’s Studies 17: 77–91.

65.

Ostovar

Allahyar

Aminpoor

Moafian

Nor

M. B. M.

Griffiths

M. D.

(2016) Internet addiction and its psychosocial risks (depression, anxiety, stress and loneliness) among Iranian adolescents and young adults: A structural equation model in a cross-sectional study. International Journal of Mental Health and Addiction 14: 257–267.

66.

Panayides

Walker

M. J.

(2012) Evaluation of the psychometric properties of the Internet Addiction Test (IAT) in a sample of Cypriot high school students: The Rasch measurement perspective. Europe’s Journal of Psychology 8: 327.

67.

Pempek

T. A.

Yermolayeva

Y. A.

Calvert

S. L.

(2009) College students’ social networking experiences on Facebook. Journal of Applied Developmental Psychology 30: 227–238.

68.

Quinn

P. D.

Harden

K. P.

(2013) Differential changes in impulsivity and sensation seeking and the escalation of substance use from adolescence to early adulthood. Development and Psychopathology 25: 223–239.

69.

Room

(2010) ‘Wet’ and ‘dry’ cultures in the age of globalization. Salute e Società 9: 229–237.

70.

Rücker

Akré

Berchtold

Suris

J. C.

(2015) Problematic Internet use is associated with substance use in young adolescents. Acta Paediatrica 104: 504–507.

71.

Sánchez-Martínez

Puime

Á. O.

(2010) Usos de Internet y factores asociados en adolescentes de la Comunidad de Madrid. Atención Primaria 42: 79–85.

72.

Scherer, K. R., Schorr, A., & Johnstone, T. (Eds.). (2001). Appraisal processes in emotion: Theory, methods, research. New York, NY: Oxford University Press.

73.

Shaw

S. M.

Caldwell

L. L.

Kleiber

D. A.

(1996) Boredom, stress and social control in the daily activities of adolescents. Journal of Leisure Research 28: 274.

74.

Steinberg

(2008) A social neuroscience perspective on adolescent risk-taking. Developmental Review 28: 78–106.

75.

Thackray

R. I.

(1981) The stress of boredom and monotony: a consideration of the evidence. Psychosomatic Medicine 43: 165–176.

76.

Wiesner

Windle

Freeman

(2005) Work stress, substance use, and depression among young adult workers: An examination of main and moderator effect model. Journal of Occupational Health Psychology 10: 83.

77.

Wilsnack

R. W.

Vogeltanz

N. D.

Wilsnack

S. C.

Harris

T. R.

(2000) Gender differences in alcohol consumption and adverse drinking consequences: Cross-cultural patterns. Addiction 95: 251–265.

78.

Wilsnack

R. W.

Wilsnack

S. C.

Kristjanson

A. F.

Vogeltanz-Holm

N. D.

Gmel

(2009) Gender and alcohol consumption: Patterns from the multinational GENACIS project. Addiction 104: 1487–1500.

79.

Winnicott

D. W.

(1961) Adolescence: The family and individual development, London, England: Tavistock.

80.

Young

K. S.

(1998) Caught in the net: How to recognize the signs of internet addiction—And a winning strategy for recovery, New York, NY: John Wiley & Sons.

81.

Young

S. E.

Corley

R. P.

Stallings

M. C.

Rhee

S. H.

Crowley

T. J.

Hewitt

J. K.

(2002) Substance use, abuse and dependence in adolescence: Prevalence, symptom profiles and correlates. Drug and Alcohol Dependence 68: 309–322.

82.

Zhou, S. X. (2010). Gratifications, loneliness, leisure boredom and self-esteem as predictors of SNS-game addiction and usage pattern among Chinese college students (Doctoral dissertation). The Chinese University of Hong Kong, China.

83.

Ziervogel

C. F.

Ahmed

Flisher

A. J.

Roberston

B. A.

(1998) Alcohol misuse in South African male adolescents: A qualitative investigation. International Quarterly of Community Health Education 17: 25–41.