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Journal of Adolescence Journal of Adolescence 30 (2007) 569–585 www.elsevier.com/locate/jado

Use of information and communication technology (ICT) and perceived health in adolescence: The role of sleeping habits and waking-time tiredness Raija-Leena Punama¨kia,b,, Marjut Walleniusb, Clase-Ha˚kan Nyga˚rdc, Lea Saarnic, Arja Rimpela¨c a

Research Unit of Pirkanmaa Hospital District, Tampere University Hospital, Finland b Department of Psychology, University of Tampere, Finland c School of Public Health, University of Tampere, Finland

Abstract The first aim for this paper was to examine gender and age differences in the intensity of usage of information and communication technology (ICT: computer for digital playing, writing and e-mailing and communication, and Internet surfing, and mobile phone). Second, we modelled the possible mediating role of sleeping habits and waking-time tiredness in the association between ICT usage and perceived health (health complaints, musculoskeletal symptoms, health status). The participants were 7292 Finns aged 12, 14, 16 and 18 years responding to a postal enquiry (response rate 70%). The results showed that boys played digital games and used Internet more often than girls, whose mobile phone usage was more intensive. Structural equation model analyses substantiated the mediating hypothesis: intensive ICT-usage was associated with poor perceived health particularly or only when it negatively affected sleeping habits, which in turn was associated with increased waking-time tiredness. The associations were gender-specific especially among older adolescents (16- and 18-year olds). Intensive computer usage forms a risk for boys’, and intensive mobile phone usage for girls’ perceived health through the mediating links. Girls were vulnerable to the negative consequences of intensive mobile phone usage, as it associated with perceived health complaints and musculoskeletal symptoms both directly and through deteriorated sleep and

Corresponding author. Tel.: +3583 3551 7024 (work)/+358 40 77 22 55 9 (GSM); fax: +358 3 3551 7345.

E-mail address: Raija-leena.Punamaki@uta.fi (R.-L. Punama¨ki). 0140-1971/$30.00 r 2006 The Association for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.adolescence.2006.07.004

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increased waking-time tiredness. The results of gender-specific ICT usage and vulnerability are discussed as reflecting gendered psychophysiological, psychological and social developmental demands. r 2006 The Association for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved. Keywords: ICT; Adolescents; Health complaints; Musculoskeletal symptoms

Introduction Information and communication technology (ICT) has become a central part of the everyday life of children and adolescents. They use computer for studying, playing games and seeking information on the Internet, and they communicate via mobile phones where and whenever they so wish (for reviews, see Buckingham, 2002; Subrahmanyam, Greenfield, Kraut, & Gross, 2001; Tarpley, 2001). We do not yet well understand how living in cyber environments and learning multiple communication technologies impact the human development and well-being. There are concerns and fears of a negative impact of intensive ICT usage, and research confirms that violent digital plays in particularly increase the risks for children’s aggressiveness (Anderson & Bushman, 2001; Kirsh, 2003). However, also positive impacts of ICT have been reported, especially on children’s cognitive skills, intelligence and school achievement (Subrahmanyam, Greenfield, & Kraut, 2001; Rocheleau, 1995). Researchers also argue that family and peer communication can benefit from modern technology of mobile phoning and e-mailing (Durkin & Barber, 2002; Hughes & Hans, 2001, Parks & Floyd, 1996). Research is still scarce and discrepant about mechanisms through which the modern way of communicating, playing and exploring would impact children’s and adolescents’ well-being. Parallel with the expanding ICT usage, symptoms of physical and mental overload among adolescents are reported to be increasing (Berntsson, 2000; Haugland, Wold, Stevenson, Aaroe, & Woynarowska, 2001; Rimpela¨ et al., 2004). Although the association between the two phenomena may be disputable, there is some evidence of links between ICT usage and health problems. Intensive computer usage has been connected with musculoskeletal complaints, particularly neck–shoulder and low back pain (Alexander & Currie, 2004; Jacobs & Baker, 2002; Hakala, Rimpela¨, Saarni, & Salminen, 2006). Musculoskeletal problems due to computer usage can be attributed to long sitting hours in static positions. Intensive ICT usage may also contribute to health-related problems such as passive lifestyle and overweight by displacing physical activity and other health-enhancing practices (Kautiainen, Koivusilta, Lintonen, Virtanen, & Rimpela¨, 2005). Researchers have also found that the use of interactive media, the Internet particularly, may turn addictive for some adolescents and place them at risk of losing control of their behaviour (Eppright, Allwood, Stern, & Theiss, 1999; Kaltiala-Heino, Lintonen, & Rimpela¨, 2004; Young, 1997). Finally, there is a hypothesis of an increased risk of brain tumours related to intensive mobile phone usage, but the evidence has not been confirmed in epidemiological studies (Ahlbom, Green, Keifets, Savitz, & Swerdlow, 2004;Auvinen, Hietanen, Luukkonen, & Koskela, 2002; Kundi, Mild, Hardell, & Mattsson, 2004). An explanation for the concurrent increase in intensive ICT usage and health problems might be offered by the deteriorated sleeping habits. There is evidence of reduced sleeping times among adolescents in recent decennia; sleeping time has decreased with approximately 1 h since the end of

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the 1970s, particularly in the latter half of the 1990s (Andrade, Benito-Silva, Domenice, Arnhold, & Mennaa-Bareto, 1993; Thorleifsdottir, Bjo¨rnsson, Benediktsdottir, Gislason, & Kristbjarnarson, 2002). ICT may be one of the reasons for the development. Researchers have found that intensive computer use and playing digital games are associated with sleep delay and irregular sleeping pattern (Van den Bulck, 2004). Similarly as excessive television-viewing relates to reduced sleeping time and sleep disturbance in children and adolescents (Tynja¨la¨, Kannas, & Va¨limaa, 1993; Gau & Soong, 1995; Owens et al., 1999; Saarenpa¨a¨-Heikila¨, Rintahaka, Laippala, & Koivikko, 2000). There are behavioural and psychophysiological mechanisms that explain why intensive ICT usage may negatively affect sleeping habits. ICT provides increased opportunities to spend the night surfing the Internet, playing digital games, and participating in chat groups (Carskadon, Vieira, & Acebo, 1993; Carskadon, Wolfson, Acebo, Tzischinsky, & Seifer, 1998). A late playing of computer games can cause a high arousal and alert in the brain, thus interfering with the calming effect that is necessary to sleep (Spear, 2000). There is evidence that interactive computer usage, especially participatory games and chatting, adds to arousal and excitement, which may delay the onset of sleep (Mannir et al., 1997). The reduced sleeping hours are associated with a variety of negative health, developmental and performance outcomes. There is evidence that too short and poor sleep predicts deterioration in higher cognitive performances, such as verbal creativity, problem solving, and abstract thinking in adolescents (Pilcher & Walters, 1997; Randazzo, Muehlbach, Schweitzer, & Walsh, 1998), and increases concentration problems and exhaustion (Dinges et al., 1997). Children and adolescents are also more easily irritable and frustrated after a sleep restriction (Dahl, 1996, 1998; Owens et al., 1998; Wolfson & Carskadon, 1998). Associations have been found between a short night-sleep and daytime sleepiness both in laboratory (Carskadon & Dement, 1981; Thorby, Korman, Spielman, & Glovinsky, 1988) and epidemiological studies (Gau & Soong, 1995; Saarenpa¨a¨Heikila¨ et al., 2000; Tynja¨la¨, Kannas, & Leva¨lahti, 1997; Wolfson & Carskadon, 1998). Day time sleepiness in turn correlate with health problems among adolescents (Fukuda & Ishihara, 2001; Roberts, Roberts, & Chen, 2002; Saarenpa¨a¨-Heikila¨ et al., 2000; Saarenpa¨a¨-Heikkila¨, Laippala, & Koivikko, 2001; Thorleifsdottir et al., 2002; Wolfson & Carskadon, 1998). The lack of sufficient sleep leads to changes in the hormone production (Spiegel, Leprould, & Van Cauter, 1999; Vgontzas et al., 1999) and the metabolic function (Bonnet, Berry, & Arand, 1991). Thus the emotional and hormonal imbalance may be one reason of reduced sleep functioning as a health risk. Earlier research on ICT, sleep and health can be criticized for the lack of developmental perspective and thorough analysis of gender differences. Concerning the usage of ICT, research shows that boys are more active players of digital play (Durkin & Barber, 2002; Yates & Littleton, 1999). General observation is that, parallel with the development of the information society, also younger children become experts and more sophisticated users of ICT (Buckingham, 2002). In mental health, gender and age interact. Until early adolescence boys generally display a larger number of mental health problems, especially aggression and other behavioural problems. In adolescence, however, girls turn out to display more serious psychological distress than boys, especially depressive and anxiety symptoms (Esser, Schmidt, & Woerner, 1990; Polaino-Lorente & Dome`nech, 1993). Research is not explicit about the gender differences in changes in sleep, and we do not know whether boys’ and girls’ health would be differently vulnerable to ICT-related poor

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sleeping habits. Therefore our aim is to model the ICT, sleeping time and regularity and health associations separately according to gender and age. To summarize, the first aim of this study is to examine the age and gender differences in the ICT usage of 12–18-year-old Finnish children and adolescents. Second, we test the hypothesis that an intensive use of ICT is associated with problems of perceived health, particularly (partial mediation) or only (complete mediation) when it is connected with unhealthy sleeping habits and subsequent increased waking-time tiredness. The hypothesized mediating model is illustrated in Fig. 1. To substantiate the mediating links, we first test the possible direct associations between ICT (intensity of computer and mobile phone usage) and perceived health (health complaints, musculoskeletal pain and health status). We then test the mediating role of unhealthy sleeping habits, indicated by irregular and insufficient sleep and subsequent waking-time tiredness. Third, we explore the possible gender- and age-specific mediation by testing the models separately among early adolescent (12- and 14-year olds) and adolescent (16- and 18-year-olds) girls and boys.

Methods Participants and procedure of the study The participants were 7292 Finnish adolescents derived from a nationally representative sample of 12-, 14-, 16- and 18-year olds, collected in 2001. Of them 55.2% were girls and 44.8% boys. The samples were obtained from the Population Register Centre based on given dates of birth, so that all adolescents born on the sample days were included. The original sample size was 10 360 and the response rate was 70%. Table 1 shows the response rates according to gender and age, indicating that the sample is biased towards loss of male and older participants. The response rate varied between 53% and 72% in boys and between 76% and 82% in girls, and it decreased with age. The participants filled in a self-administered structured mailed questionnaire (the Adolescent Health and Lifestyle Survey) and returned it by pre-paid mail. The purpose of the study was explained on the cover page and responding to the questionnaire was voluntary. Two re-inquiries were sent to non-respondents.

ICT: Computer Usage

Sleeping habits

Waking-time tiredness

Perceived health

ICT:Mobile phone Usage

Fig. 1. The hypothesized model mediating between ICT usage and perceived health.

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Table 1 Number of respondents and response rates Age (years)

Boys

Girls

N

%

N

%

12 14 16 18

351 1251 892 774

72 66 62 53

425 1485 1138 976

82 79 82 76

Total

3268

62

4024

79

Total number of respondents: N ¼ 7292. Total response rate: 70%.

Measurements Use of ICT was conceptualized by two dimensions: (1) use of computer and playing digital games, later referred as computer usage and (2) mobile phone usage. Computer usage was measured by three questions. Respondents were inquired how much time they usually spent daily on (1) using computer for writing and e-mailing and (2) playing computer, video or console games (later referred as digital games). The alternatives for both were ‘not at all’, ‘sporadically’, ‘less than an hour’, ‘1–3 h, ‘4–5 h’, ‘over 5 h’. (3) The daily use of the Internet was asked with alternatives: ‘Yes, sometimes’; ‘Not at all’, and ‘If Yes, how many ___ hours a day?’ Mobile phone usage was measured by three questions: (1) Mobile phone ownership was indicated by three alternatives: participant does not have a mobile phone, shares it with family member, or has his/her own. (2) Duration of mobile phone usage was inquired by providing the alternatives: less than six months, 6–11 months, 1–3 years, or over 3 years. (3) The intensity of using mobile phone was indicated by daily hours spent on phoning, text messages, and game playing: ‘not at all’, ‘occasionally’, ‘less than an hour’, ‘1–3 h, ‘4–5 h’, ‘over 5 h’. Sleeping habits were indicated by (1) sleeping hours and (2) regularity of bedtime. In a structured question the participants were asked at what time they usually go to bed and wake up on school or work days. The variable sleeping hours was calculated from these responses. The alternatives for the regularity of bedtime were: very regular, quite regular, rather irregular, or very irregular. Waking-time tiredness was indicated by two questions. (1) Feeling active in the morning-variable is based on the question ‘‘Do you feel active (brisk, energetic) in the morning?’’ with alternatives: often or every morning, quite often, sometimes, or seldom or never. (2) Daytime sleepiness is based on the question how often the participants felt tiredness during the daytime in the last month: not at all, less than once a week, on 1–2 days a week, 3–5 days a week, or daily or almost daily. Perceived health was measured by three self-rated scales: Health status, Health Complaints and Musculoskeletal symptoms. (1) The participants were asked to estimate their general health status

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between five alternatives: very good, quite good, satisfactory, rather poor, or very poor. (2) For health complaints, the participants were asked how often during the preceding 6 months they had experienced the following eight symptoms: stomach aches, tension or nervousness, irritability or outbursts of anger, trouble falling asleep or waking at night, headache, trembling of hands, feeling tired or weak, feeling dizzy. Alternatives were: seldom or not at all, about once a month, about once a week, or almost daily. Two sum variables were constructed counting the number of symptoms occurring weekly and daily. Symptoms of sleeping difficulties (falling asleep, waking up during the night) were omitted from the sum variables to avoid confounding in the tested model. To indicate musculoskeletal symptoms, participants were asked how often they had experienced neck–shoulder and low back pain during the preceding 6 months: seldom or not at all, about once a month, about once a week, or almost daily. A sum variable was constructed to indicate weekly occurrence of the complaints. Statistical analyses The w2 analyses were used to examine univariate differences in ICT usage between girls and boys, and zero-order correlations between manifest indicators of ICT and health. The role of sleeping habits and waking-time tiredness in mediating between ICT-usage and perceived health was tested by the structural equation modelling (SEM) (AMOS-4 software package; Arbuckle & Wothke, 1999). The mediators were conceptualized as variables specifying how, or by which mechanisms, a given effect occurs, here, the possible link between ICT and perceived health. A complete mediating phenomenon is present when (a) the b-coefficients between the predictors, added mediators and outcome variables become significant and the direct association b-coefficient becomes non-significant, and (b) the mediator model shows a better fit to the data than the direct model (Baron & Kenny, 1986). A partial mediation phenomenon is present when not only the b-coefficients of the mediators are significant but also the original direct link remains significant (Baron & Kenny, 1986; Holmbeck, 1997). SEM was chosen to test the fit of the hypothesized mediating models, because it allows simultaneous modelling of multiple predictors and the use of multidimensional constructs, i.e. measurement models (Hoyle & Smith, 1994). For the SEM-analyses the data was divided to early adolescents (12- and 14-year olds) and adolescent (16- and 18-year olds) groups. The analyses were separately conducted among girls and boys. The standard model fitting testing with a maximum likelihood estimation was conducted separately for girls and boys and in both combined age cohorts. To indicate a good model fit, the Comparative fit index (CFI) and Tucker–Lewis Index (TLI) should be higher than .90, and the w2/ df ratio less than 2.00. To indicate sufficient parsimony of the model, the Root-mean-square error of approximation estimate (RMSEA) should be less than .05 (Browne & Cudeck, 1993). The w2tests were applied to evaluate the fit between the model and the data, and their differences to indicate the significant differences between the direct and the mediated models (Hayduk, 1987). Correlating error terms were allowed between single latent variable, but not across them. Subsequently, there were two error terms correlations within both ICT usage manifest variables, four within sleeping habits, and two error terms correlations within both waking-time tiredness and perceived health variables.

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Results ICT-usage according to age and gender The results in Table 2 show that at ages 14, 16 and 18, boys more frequently than girls played digital games, used computer for writing and e-mailing, and Internet surfing. Among the 12-year olds, boys played digital games more frequently than girls, but there were no gender differences in the other ICT usages. Concerning mobile phone usage, girls were more frequent users. Further, they reported more sole phone ownership (rather than family shared) and more intensive daily mobile phone use than boys in all age groups. However, it was only among the 14-year olds that the duration of mobile phone usage of girls exceeded that of boys. SEM and measurement model testing Fig. 1 presented the conceptual model of SEM. The ICT usage is conceptualized as two latent exogenous constructs, computer and mobile phone usage, and the mediation is constructed by two latent constructs of sleeping habits and waking-time tiredness. Finally, the perceived health— latent construct involves the health status, health complaints and musculoskeletal symptoms. The results of the mediating models for early adolescent girls and boys are presented in Fig. 2 and for adolescent girls and boys in Fig. 3. The measurement models for the latent contents were conceptually and technically successful in both gender and age groups, as indicated by significant loadings of the manifest variables on the latent constructs. The loadings to the ‘ICT: Computer usage’-latent construct ranged between .24 and .87 (digital games, writing and e-mailing, the Internet surfing), and to the ‘ICT: Mobile phone usage’-latent construct between .24 and .86 (mobile phone ownership, duration of mobile phone usage, intensity of mobile phone usage). The manifest variables to the ‘sleeping habits’—latent construct ranged between .46 and .78 (sleeping hours, regularity of bedtime) and to the ‘Wakingtime tiredness’ between .54 and .97 (feeling active in the morning, daytime sleepiness). Finally, the manifest variables to the ‘Perceived health’-latent construct ranged between .43 and .86 (health status, weekly health complaints, daily health complaints, weekly musculoskeletal symptoms). Direct and mediating models for ICT and perceived health We first performed a basic line modelling to test the direct associations between the intensity of ICT usage and perceived health, and thereafter added sleeping habits and waking-time tiredness as mediators to the model. The results of testing the direct and mediator models among girls in the early adolescence (12- and 14-year olds) and adolescence (16- and 14-year olds) are presented in Table 3, and among early adolescent and adolescent boys in Table 4. The results show that direct effect models differed to some extent for girls and boys. Among girls, intensive mobile phone usage was significantly directly associated with poor perceived health in both age groups, and intensive computer usage also among early adolescent girls. Among boys, a direct association between ICT mobile phone and poor perceived health was found only in early

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Table 2 Percentage distribution of information and communication usage by age and sex and type of use (p-values: w2 tests between sexes) Variables

12-year olds Girls %

Computer usage Playing digital games Not at all Sporadically Less than an hour 1–3 h 4–5 h or more w2-Value

Boys %

Girls %

16-year olds Boys %

Girls %

18-year olds Boys %

Girls %

Boys %

5.5 13.5 27.1 47.3 6.6

31.7 49.0 12.1 6.7 0.4 925.43

7.1 18.7 18.7 45.2 10.2

46.3 41.9 7.8 3.7 0.3 595.87

10.9 29.7 19.4 33.9 6.1

55.6 37.0 4.6 2.3 0.5 347.17

19.6 41.6 14.5 20.7 3.6

Computer for writing and e-mail Not at all 20.0 Sporadically 50.2 Less than an hour 23.2 1–3 h 6.6 4–5 h or more 0.0 w2-Value 9.13

25.7 45.7 20.2 7.2 1.2

16.3 51.4 21.3 10.2 0.7 48.94

22.3 38.7 25.4 11.8 1.8

13.5 54.1 20.6 10.6 1.2 61.54

16.7 38.8 22.4 19.8 2.4

16.1 56.9 18.4 7.3 1.1 77.49

19.4 38.3 22.7 15.5 4.1

Internet surfing Not at all Sporadically Less than an hour 1–3 h 4–5 h or more w2-Value

19.9 64.3 5.7 9.9 0.2 4.65

23.5 57.0 6.6 12.3 0.6

14.7 65.1 6.6 13.4 0.1 76.67

16.5 52.2 9.6 18.9 2.8

10.7 63.7 9.0 15.4 1.1 68.43

10.7 49.3 8.4 28.4 3.3

11.6 67.8 6.6 12.9 1.1 51.90

12.6 52.6 9.3 22.2 3.3

Mobile phone usage Mobile phone ownership No Family shared Own w2-Value

31.4 60.7 7.9 11.10

42.6 52.4 5.0

15.4 78.4 6.1 40.13

25.1 70.4 4.4

4.7 93.1 2.2 26.96

10.7 86.8 2.5

3.5 95.0 1.6 9.04

6.5 92.3 1.2

Length of mobile phone usage Less than 6 months 28.4 6–12 months 38.8 1–3 years 30.6 More than 3 years 2.2 w2-Value 1.80

33.8 34.9 28.7 2.6

13.5 27.4 53.2 5.8 22.96

20.3 25.3 46.6 7.8

5.0 14.3 66.4 14.3 4.22

6.8 15.9 64.5 12.9

3.1 8.5 58.4 29.9 4.75

4.8 9.7 54.5 31.0

Daily use of mobile phone Not at all Sporadically Less than an hour 1–3 h 4–5 h or more w2-Value

35.8 36.1 20.8 5.5 1.8

13.4 31.1 38.3 12.5 4.7 111.08

22.5 40.5 29.3 5.5 2.1

7.0 33.8 45.7 11.2 2.4 48.21

12.8 42.0 35.1 9.0 1.1

7.0 31.9 50.9 9.2 0.9 25.58

10.5 39.9 41.1 7.7 1.0

 po.001.  po.0001.  po.00001.

16.8 45.1 25.9 12.2 0.0 191.02

14-year olds

21.2 38.8 28.5 9.0 1.5 22.48

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Digital Playing Information seeking

Weakly health complaints

G

B ;a .78

G .56 ; B .62

The Internet surfing

G

.G 25; B.28

G .84; B .87 .76 ;B .77

Daily health complaints

.69

ICT: Computer usage

B .16

***

45 1; B G .4

.52

4 ; B .5 G .63

.59

4

G .69; B .57

Health status

.69

Daytime sleepiness

2; B

;B

.65

G .85; B .7

Sleeping hours

G 53%; B 57%

G. 4

Feeling active in the morning

.76

G

Regularity of bed time

Perceived health

.67** (.64**)

G .97; B

6

6; B .4

G

ICT:Mobile phone usage

4; B

B

Waking-time tiredness G 24%; B 21%2

G -.4

**;

3*

-.4

G-.49*** ;B-46***

G .8

**

1*

-.3

Sleeping habits G 21%; B 14%

Weekly musculoskeletal symptoms

Lenght of mobile phone use

Daily use of mobile phone Mobile phone ownership

Fig. 2. ICT usage and perceived health: sleeping habits and waking-time tiredness as mediators among early adolescent (12 and 14 years) girls (G) and boys (B).

adolescence. The direct effect models accounted for only 2–7% of the variance of perceived health among girls, and for 2–5% among boys. The results substantiated the hypothesized mediation between ICT and perceived health. The SEM goodness-of-fit indices (CFI, TFI and RAMSEA) demonstrate a very good fit between the theoretical mediation models and the data for both genders and age groups. However, w2/df— values exceeded 2.00 in the models of adolescent girls and boys. The w2-values were also highly significant, which is common in large data (Hayduk, 1987). The mediating models explained a substantial amount of the variation of perceived health among girls (45–58%) and boys (40–76%), which was considerably higher than the explained share of direct models. The differences in w2 statistics between direct and mediation models were also significant in all gender and age group analyses, substantiating the phenomenon of mediated effects, either complete or partial. The complete mediation is there if the b-coefficients and their t-values are significant in direct models, but become non-significant in the mediator models. The mediation models, illustrated in Figs. 2 and 3, indicate that computer and mobile phone usage formed a risk for poor perceived health, characterized by health complaints,

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578 Digital games

Daily health complaints

Daily use of Internet Weakly health complaints G .7 9; B .8 0

G .17*; B .15* B -.1

3

Sleeping habits G8% ;B 3%

Feeling active in the morning

.4 B 6; .4 G

G .07%

3

.82

9 ; B .4

Perceived health G 52%; B 48%

Health status

G .43; B .45

G .50

G .55***; B .61***

G .91; B .54

9

; B -5 Regularity of bed time

;B .53

G .86; B

35

G. .85

G .24; B .45

G -.56

ICT:Mobile phone usage

Waking-time tiredness G 29%; B 24%

3; B

**

7*

.2 G-

G -.40***; B -.49***

G .8

ICT: Computer usage

G .59; B .72

8 B .24; B .2

G 82; B .8 1 G.6 6; B. 87

Writing & e-mail

Sleeping hour s

Daytime sleepiness

Weekly neck and low back pain

Lenght of mobile phone use

Daily use of mobile phone

Mobile phone ownership

Fig. 3. ICT usage and perceived health: sleeping habits and waking-time tiredness as mediators among adolescent (16 and 18 years) girls (G) and boys (B).

musculoskeletal pain and poor health status, only or particularly if it was intensive enough to initiate unhealthy sleeping habits and waking-time tiredness. There were differences in the mediation models between girls (G) and boys (B) especially in adolescence. For girls intensive mobile usage and for boys intensive computer usage formed a risk for poor perceived health through deteriorated sleeping habits and waking-time tiredness. Among adolescent girls, the intensive mobile usage was also directly associated with poor perceived health. Similar to girls, intensive mobile phone usage among the 14-year-old boys was associated with poor perceived health via unhealthy sleeping habits and waking-time tiredness.

Discussion Our aim was to examine gender and age differences in the usage of ICT, and to test the role of sleeping habits and waking-time tiredness in mediating between intensive ICT usage and perceived

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Table 3 Direct effect and mediator models for ICT usage and perceived health among early and older adolescent girls Model estimatesa Models for girls

b-coefficientb t-Valueb w2

Direct effect models ITC: computer usage-perceived health 12–14-year olds .09 2.74* 16–18-year olds .03 1.18

df

p(w2)

76.90 28 .001 101.39 28 .03

w2/df CFI TFI RMSEA R2 (% explained variation)

1.99 2.42

.99 .98

.98 .03 .97 .04

.07 (7%) .02 (2%)

ITC: mobile phone usage-perceived health 12–14-year olds .22 5.29*** 76.90 28 .001 2.39 16–18-year olds .20 5.16*** 101.39 28 .0001 1.96

.99 .98

.98 .03 .97 .03

.07 (7%) .04 (4%)

Mediator models ITC: computer usage-perceived health (when sleeping habits and waking-time tiredness in the models) 12–14-year olds .06 2.20 116.59 67 .0001 1.84 1.00 .99 .03 .77 (77%) 16–18-year olds .02 .85 149.67 67 .0001 2.00 .99 .98 .03 .43 (43%) ITC: mobile phone usage-perceived health (when sleeping habits and waking-time tiredness in the models) 12–14-year olds .03 .84 116.59 67 .0001 1.84 .98 .96 .04 .54 (54%) 16–18-year olds .07 2.69* 149.67 67 .0001 2.00 1.00 1.00 .03 .52 (52%) *

po.05; **po.01; ***po.001. a Model estimates are exactly the same for both direct models and both mediator models, because in both cases there are the same two exogenous latent variables and one latent outcome variable. b In the mediator models, the b-coefficients and their t-tests refer to the remained direct effects in the models to which the mediators have been included. Thus, if significant, the mediating associations are only partial.

health. The participants were 12–18-year-old adolescents from a nationwide survey data. The results point toward a distinctly gendered ICT usage and related vulnerability. They also substantiated the hypothesis that the association between intensive ICT usage and poor perceived health is mediated through deteriorated sleeping habits and increased waking-time tiredness. Boys used ICT more for exploring and playing, and girls for communication. Socialization and psychophysiological differences between genders may explain boys’ preferences for digital game playing and Internet surfing, and girls’ preferences for mobile phone communication. Girls are more advanced than boys in emotional and communicative capacities, including emotional expression, awareness of own and others’ feelings (Casey, 1993), and interpreting complex verbal and facial-kinaesthetic messages (Dunn, Bretherton, & Munn, 1987; Saarni, 1999, p. 153). Parents contribute to these differences by encouraging their daughters to express emotions (Dunn et al., 1987; Fivush, 1993), and to console and show empathy toward others (Brody, 1996; Saarni, 1999). Parents communicate about emotions and human relationships with their daughters more than with their sons (Brody, 1996). Boys in turn excel girls in visuo-spatial tasks and problem solving, and are generally encouraged to enhance their cognitive capacities (Halpern, 1992). The gendered use of ICT in adolescence unmistakably reflects these early acquired and trained capabilities. Console and computer games provide excitement and action, and enhance threedimensional and visuo-spatial capacity. They lack social and emotional complexity and involve

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Table 4 Direct effect and mediator models for ICT usage and perceived health among early and older adolescent boys Model estimatesa Models for boys

b-Coefficient

b

t-Valueb w2

df p(w2)

w2/df CFI TFI RMSEA R2 (% explained variation)

Direct effect models ITC: computer usage-perceived health 12–14-year olds .03 .82 16–18-year olds .01 .04

64.90 28 .001 2.13 75.48 29 .0001 1.96

.99 .98

.98 .97

.03 .04

.02 (2%) .03 (4%)

ITC: mobile phone usage-perceived health 12–14-year olds .21 3.43*** 16–18-year olds .15 2.16

64.90 28 .08 1.39 75.48 29 .0001 1.96

.99 .99

.98 .98

.03 .03

.05 (5%) .01 (1%)

Mediator models ITC: computer usage-perceived health (when sleeping habits and waking-time tiredness in the models) 12–14-year olds .04 1.10 211.57 64 .0001 1.84 .97 .99 .04 .58 (58%) 16–18-year olds .05 1.75 149.52 64 .0001 2.00 .98 .97 .03 .48 (48%) ITC: mobile phone usage-perceived health (when sleeping habits and waking-time tiredness in the models) 12–14-year olds .04 1.05 211.57 64 .0001 1.84 .97 .99 .04 .58 (58%) 16–18-year olds .03 .85 149.52 64 .0001 2.00 .98 .97 .03 .48 (48%) ***

po.001. Model estimates are exactly the same for both direct models and both mediator models, because in both cases there are the same two exogenous latent variables and one latent outcome variable. b In the mediator models, the b-coefficients and their t-tests refer to the remained direct effects in the models to which the mediators have been included. Thus, if significant, the mediating associations are only partial. a

predominantly behavioural elements (Anderson, 2004). For boys, Internet surfing and travelling in the cyber world provide opportunities for adventure, exploration, competition, and satisfaction of curiosity. In contrast, girls dedicate their time to ‘home matters’, sharing experiences, gossiping, and maintaining intimate relationships by means of modern communication tools that enable them to engage in a constant human contact and ‘availability’. The mediation hypothesis was substantiated among both genders in early adolescence and adolescence, but models differed for boys and girls especially in adolescence. Among girls it was the intensive mobile phone usage that associated with shorter and more irregular sleep and via waking-time tiredness, resulted in poorer perceived health. Among boys, it was the intensive computer usage, including digital game playing and Internet surfing, that initiated a negative path. Among adolescent girls intensive mobile phone usage was also directly associated with poor perceived health, which indicates girls’ greater vulnerability. The crystallization of gender differences in the ICT-related consequences to health reflects girls’ earlier onset of puberty, and the increased gender differences from middle childhood to adolescence (Esser et al., 1990). In early adolescence, both genders were at equal risk of the intensive ICT mobile phone usage associating with poor sleeping habits and, via waking-time tiredness, to health problems. Sufficient and regular sleep is particularly important during puberty, as the release of growth hormone increases, and testosterone, follicle-stimulating hormone, and luteinizing hormone are

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primarily released in large amounts during sleep (Nolen-Hoeksema, 1990; Pickles et al., 2001). The delicate developmental balance may easily be shaken by the negative impact on sleep of a risky ICT behaviour. Girls’ higher vulnerability was evident in the direct link between intensive mobile phone usage and poor perceived health, including psychosomatic as well as musculoskeletal problems. It may be attributed to girls biological, psychological and social vulnerability in the differentiating developmental paths. Until early adolescence boys are generally more vulnerable to psychological problems, whereas during adolescence girls suffer from psychological distress, especially depressive and anxiety symptoms (Esser et al., 1990; Polaino-Lorente & Dome`nech, 1993). Besides an increase of female hormones, girls undergo changes in the mood-regulating brain functions in adolescence (Pickles et al., 2001). Girls also tend to worry about others and apply negative and self-blaming attributes to their own experiences (Nolen-Hoeksema & Girgus, 1994), and are more than boys exposed to stress and abuse, and their social status is generally lower than that of boys’ (Coperland & Hess, 1995; Petersen, Sarigiani, & Kennedy, 1991). The direct link between intensive mobile phone usage and girls’ poor perceived health in our data suggests that the new technical innovation might entail elevated risks for girls during the sensitive period of adolescence. Why would intensive computer usage associate with less and irregular sleeping among boys but not girls? Earlier research focusing on the sleep consequences of computer and Internet usage has explained the negative impacts by increased excitement and physiological arousal (Mannir et al., 1997). A child’s mind that is absorbed in virtual worlds needs a relatively longer time to calm down from these excitements. However, this explanation was valid for boys, but not for the girls whose sleep was in turn affected by intensive mobile phone usage. The intensive female mobile phone usage may be part of a lifestyle and developmental stage characterized by close friendships, disclosing secrets, and sharing important first-time experiences. The ICT era has provided girls with an effective means of communication, which may not replace the ‘traditional’ face to face friendships, but breaks the time and space limits of togetherness. Intensively communicating girls may sleep less and irregularly simply because they ‘lack the time for it’. Again the biological and neurological explanations would be related to findings demonstrating that an intensive usage of mobile phone activates such brain areas that are responsible for calming down and for sleep (Kundi et al., 2004). The research deserves criticism for a cross-sectional setting, possible dropout biases, and a weak measurement of health concepts. First, cautiousness is needed prior to accepting the directions of the associations. Alternative hypotheses are also possible, for instance, that girls with high levels of health complaints, sleeping difficulties and tiredness may use mobile phone for seeking support, and distracting and soothing pain. In order to be confident about the hypothesized mediating role of poor sleep, we should get information about whether the duration and irregularity of sleep are voluntary or involuntary due to other problems, different from ICT. If the deterioration of sleeping is the key problem, then both ICT usage and perceived health problems can be considered outcomes. Furthermore, it is plausible that there are other, more salient mediating mechanisms than unhealthy sleeping habits to explain the association between intensive ICT usage and poor perceived health. There is evidence, for instance, that intensive usage of digital playing is associated with unpopularity among peers, and loneliness in social relationship (Ho & Lee, 2001; Roe & Muijs, 1998). In adolescence the quality of peer and friendship relationships is crucial for

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well-being and self-worth (Hymel, Vaillancourt, McDougall, & Renshaw, 2002), so a more comprehensive model for ICT and health should include them, too. Another weakness of our research involves lower response rates among boys than among girls, and loss of older adolescents. Therefore, our results concerning the frequencies of ICT usage may not be nationally representative. Finally, a better choice for measuring health complaints would have been a multidimensional scale including both physical and mental health. We also lack evidence about correlations between the perceived health and actual health. Accordingly, the findings should be replicated in other data, using more comprehensive measurements of sleeping habits, psychological functioning, and mental health.

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