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Gender Differences in the Association of Smartphone Use with the Vitality and Mental Health of Adolescent Students

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Title: Gender Differences in the Association of Smartphone Use with the Vitality and Mental Health of Adolescent Students
Language: English
Authors: Yang, Shang-Yu, Lin, Chung-Ying, Huang, Yueh-Chu, Chang, Jer-Hao
Source: Journal of American College Health. 2018 66(7):693-701.
Availability: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 9
Publication Date: 2018
Document Type: Journal Articles
Reports - Research
Education Level: Two Year Colleges
Higher Education
Postsecondary Education
Descriptors: Mental Health, Gender Differences, Telecommunications, Handheld Devices, Community Colleges, Student Attitudes, Correlation, Addictive Behavior, Well Being, Undergraduate Students, Foreign Countries
Geographic Terms: Taiwan
DOI: 10.1080/07448481.2018.1454930
ISSN: 0744-8481
Abstract: Objective: The present study examined variations in the degree of smartphone use behavior among male and female adolescents as well as the association between various degrees of smartphone use behavior and the vitality and mental health of each gender. Participants: A total of 218 adolescents were recruited from a junior college in September 2014. Methods: All the participants were asked to answer questionnaires on smartphone use. Results: The findings showed that adolescent females as compared with adolescent males exhibited significantly higher degrees of smartphone dependence and smartphone influence. Positive correlations were observed between the duration of smartphone use on weekends and the vitality/mental health of the male adolescents; negative correlations were found between smartphone dependence and the vitality/mental health of males. Conclusion: The findings demonstrate that adolescent females are deeply affected by their smartphone use. Smartphone dependence may decrease the vitality and mental health of male adolescents.
Abstractor: As Provided
Number of References: 41
Entry Date: 2018
Accession Number: EJ1200400
Database: ERIC
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  Value: <anid>AN0133640617;acl01oct.18;2018Dec20.13:31;v2.2.500</anid> <title id="AN0133640617-1">Gender differences in the association of smartphone use with the vitality and mental health of adolescent students </title> <p>Objective: The present study examined variations in the degree of smartphone use behavior among male and female adolescents as well as the association between various degrees of smartphone use behavior and the vitality and mental health of each gender. Participants: A total of 218 adolescents were recruited from a junior college in September 2014. Methods: All the participants were asked to answer questionnaires on smartphone use. Results: The findings showed that adolescent females as compared with adolescent males exhibited significantly higher degrees of smartphone dependence and smartphone influence. Positive correlations were observed between the duration of smartphone use on weekends and the vitality/mental health of the male adolescents; negative correlations were found between smartphone dependence and the vitality/mental health of males. Conclusion: The findings demonstrate that adolescent females are deeply affected by their smartphone use. Smartphone dependence may decrease the vitality and mental health of male adolescents.</p> <p>Keywords: Adolescent students; mental health; smartphone use; vitality</p> <hd id="AN0133640617-2">Introduction</hd> <p>Vitality is operationalized as a positive energy state and the subjective experience of energy level, and it is associated with both physical and psychological factors.1<sups>,</sups>2 Therefore, vitality and mental health should be investigated if a healthcare provider intends to assess the health of an individual, especially in the case of adolescents. During adolescence, adolescents experience both physical and psychosocial changes, including sexual maturation, physical growth, and psychological development.3 These changes become challenges for adolescents to successfully transit into adulthood. Therefore, in order to make a good transition from adolescence to adulthood, vitality and mental health are important components that should be paid attention to. Specifically, adolescents with low vitality or poor mental health in these transition periods may incur insufficient physical activity at the time or costly health care in the future.4<sups>,</sups>5 Indeed, studies have revealed that adolescents are at risk for various and diverse problems in terms of their physical health, mental health, and behavior even when they self-perceive good health.,, Therefore, taking good care of the vitality and mental health of adolescents is a critical and practical issue for healthcare providers. However, modern technology, particularly smartphone use, is currently interfering with the vitality and mental health of adolescents. Smartphone use has increased rapidly, particularly among adolescents;9 for example, 84% of adolescents in Japan had a smartphone in 2008.10 Smartphones have become more common than computers because they are multifunctional in terms of such activities such as browsing the Internet, playing games, and checking emails.11 Moreover, numerous types of applications, or "apps," can be downloaded to smartphones for entertainment purposes (e.g., listening to music), and such apps are frequently used by adolescents.12 Consequently, smartphones have had a profound effect on the lives of adolescents.</p> <p>Heavy smartphone use, defined as using a smartphone more than three hours per weekday,11 can lead to poor physical (including vitality) and mental health.10<sups>,</sups>,,, The duration of smartphone use has been shown to correlate positively with musculoskeletal symptoms among university students.13 Adolescents tend to overuse mobile phones at night, and this overuse is associated with increasing physical symptoms such as tiredness, headache, and rapid exhaustion.14 This implies that physical vitality may be affected by heavy smartphone use. Specifically, Lemola, Perkinson-Gloor, Brand, Dewald-Kaufmann, Grob 15 assessed 362 adolescents and found that smartphone ownership among adolescents was related to late bedtimes and excess smartphone use, especially in bed before sleep. Excessive use of smartphones among adolescents at night is associated with sleep disturbances, poor sleep quality, insomnia symptoms, and depressive symptoms.10<sups>,</sups>15 Moreover, another study16 reported that smartphone abuse is likely to cause irritability, insomnia, low self-esteem, and other psychological problems. However, these earlier studies did not explore the impact of smartphone use, especially the effects of inappropriate use, according to gender among adolescents although the patterns of smartphone use appear to differ in this regard.</p> <p>Smartphone dependence is defined as excessive smartphone use that leads to effects on physical, psychological, and social functioning.17 Smartphone influence is defined as excessive smartphone use influencing the user's interpersonal relationships, work, and daily life.17 About 70% of adolescents in Taiwan show different degrees of smartphone dependence, and long-term use of smartphones is highly likely to influence their routines. For example, nearly 40% of students use smartphones to chat (or text) at night, and 55% of students incur high expenses for smartphone use.17 Thus, smartphone dependence and influence are two focal factors that may affect smartphone users' physical and mental health.</p> <p>Although past studies have presented differences in smartphone use behavior between men and women, to the best of our knowledge, no studies have considered the degree of smartphone use (e.g., heavy or nonheavy users). In sum, it is important to investigate gender differences in smartphone use patterns, so healthcare providers can design gender-specific strategies to prevent the negative impacts of technology products. As such, the present study has the following two aims: (<reflink idref="bib1" id="ref1">1</reflink>) to understand the various degrees (heavy or nonheavy) of smartphone use behavior seen in male and female adolescents and (<reflink idref="bib2" id="ref2">2</reflink>) to determine the association between various degrees of smartphone use and the behavior and vitality and mental health of both genders.</p> <hd id="AN0133640617-3">Hypotheses</hd> <p>On the basis of our literature review, we propose the following hypotheses:</p> <p>Female adolescents exhibit greater smartphone dependence and are more influenced by smartphones than are male adolescents regardless of whether they are heavy users or nonheavy users.</p> <p>Although males and females perceive technology in different ways18, recent evidence indicates that the gender gap in smartphone use is diminishing.19<sups>,</sups>20 Thus, studies regarding specific gender-based differences in degree of use on behavior and the effects of smartphone use are needed. Compared with male adolescents, one study found that a higher percentage of female users tended to call or text in bed before sleeping and were more likely to be heavy smartphone users.21 Billieux et al.22 also indicated that females are more likely to exhibit mobile phone abuse, and these results were supported by Beranuy et al.23 Furthermore, females have a higher likelihood of developing habitual smartphone behavior.24 However, given no evidence on an interaction between gender and degree of smartphone use, we simply hypothesized no interaction between the two variables.</p> <p>According to specific indices, female adolescents are affected negatively more in terms of vitality and mental health by smartphone use than male adolescents regardless of whether they are heavy users or nonheavy users.</p> <p>Female college students have been shown to exhibit a greater proportion of negative consequences of maladaptive mobile phone use compared with their male counterparts that have been attributed to psychological distress.23 However, the subjects of these studies were general smartphone users. The results imply that, compared with males, females are more likely to be smartphone-dependent than males, and thus that usage will have more influence. However, given no evidence of an interaction between gender and degree of smartphone use behavior, we simply hypothesized no interaction between the two variables.</p> <hd id="AN0133640617-4">Methods</hd> <p></p> <hd id="AN0133640617-5">Participants</hd> <p>A convenience sampling method was adopted in this study to recruit participants from a junior college in Kaohsiung, Taiwan in September 2014. The only inclusion criterion was smartphone ownership. This study was approved by the National Cheng Kung University Human Research Ethics Committee (No. 102-096). The researchers explained the purpose of the study to eligible students and invited the students to volunteer for the survey in class. The students who were willing to participate completed the questionnaires. A total of 218 students were recruited in the study. These students were asked to fill out the questionnaires once. More than half of the participants were males (n = 127, 58%), and nearly half of them were 18 to 19 years old (male = 46%, female = 52%). The mean and standard deviation (SD) for age were 18.23 ± 0.91, respectively. Approximately 80% of the participants (83% male and 78% female) had used a smartphone for more than one year.</p> <hd id="AN0133640617-6">Questionnaire</hd> <p>There were three parts to the questionnaire: The first part comprised demographic information, including gender, age, school year, and duration of smartphone ownership. The second part measured smartphone use behavior through questions modified from those in previous surveys by Sahin et al.25 and Hakala et al.26 The questions include "smartphone use," "talking on the phone," "texting (which included using apps such as Facebook and Line)," and "using ancillary functions (e.g., games, music, or other apps)" with a stem of "how many hours you spend in the following activity per weekday (or per weekend day)." All the questions were answered using an ordinal scale with the following choices: less than or equal to 1 hour, 1-2 hours (not including 1 hour), 2-3 hours (not including 2 hours), 3-4 hours (not including 3 hours), and more than 4 hours. There was a multiple-choice question in the second part: "When do you most often use a smartphone?", for which the options included "after school," "during recess," "waiting for transportation," "while using transportation," "while eating," "in class," and "other." Additionally, for this part, a five-point Likert scale was used to measure smartphone dependence (three questions) and the degree of influence (three questions) according to Tsai's study17, with scores ranging from one (<emph>strongly disagree</emph>) to five (<emph>strongly agree</emph>). The following three questions were used to measure smartphone dependence: "Do you feel uncomfortable or anxious when you do not have a phone?"; "Do you voluntarily watch your phone?", and "Do you check your phone immediately after waking up?" The following three questions were used to measure the degree of smartphone influence: "Does smartphone use affect your time spent studying?"; "Do you check your phone in class?", and "Does using a smartphone affect your rest or sleep time?" The content validity of smartphone dependence and influence was evaluated by an educational expert with a PhD degree. The expert critically reviewed all six questions and indicated agreement that they all belong to the embedded concept. In addition, the Cronbach's α coefficients were 0.63 for smartphone dependence and 0.68 for smartphone influence in the current study. We additionally used an exploratory factor analysis to test the validity of the six items, and the results showed them to be clustered into two factors as anticipated (eigenvalues = 1.60 and 1.20) with relatively high factor loadings in each factor: dependence (0.45 to 0.98) and influence (0.49 to 0.85).</p> <p>The third part of the questionnaire employed the vitality and mental health indices of the Short Form 36-Item Health Survey Taiwan version (SF-36).27 The SF-36 is a self-report, health-related questionnaire designed for both general populations and those with specific conditions.28 The SF-36 questionnaire contains 36 items measuring eight dimensions, including vitality (four items) and mental health (five items) indices, all of which are measured with a five-point Likert scale. The vitality items includes those asking whether the respondents feel tired, worn out, or full of energy; the mental health items ask whether they feel nervous, depressed, peaceful, happy, or calm.28 The reliability of 'vitality' and 'mental health' in the study were found to be satisfactory (Cronbach's α were 0.75-0.81).</p> <hd id="AN0133640617-7">Data analysis</hd> <p>Participant demographics, smartphone use behavior, and the vitality and mental health indices were evaluated using a descriptive analysis. Subsequently, a Pearson's chi-square test and a Fisher's exact test were employed to compare the results of the demographic data and smartphone use behavior for each gender. Then, for each gender, the participants were divided into two groups according to the duration of their smartphone use. Participants who used their smartphones more than three hours per weekday were placed into the heavy-use group; those who used smartphones for less time than this were categorized into the nonheavy-use group.11 The above tests were then used again to understand the differences between heavy-use and nonheavy-use users. Because junior college seniors use mobile phones more frequently than their younger counterparts29, the age variable was adjusted in the subsequent analysis. Furthermore, An Analysis of Covariance (ANCOVA) that controlled for age was used to detect the participants' mean differences in the following outcomes (smartphone dependence and influence, vitality, and mental health) between genders. Because we hypothesized that the gender differences in the outcomes would be similar between heavy users and nonheavy users, we tested the interaction terms of gender and level of smartphone use. Multiple regressions adjusted for age were used to determine the correlations between smartphone use behavior and the vitality and mental health indices for both genders. In addition, the reliability of smartphone dependence and smartphone influence were determined by the Cronbach's α coefficient. All data analyses were performed using SPSS Version 22.0. Sample size was estimated using G-power software (3.1.0). With a type I error set at 0.05, a power of 0.8, and a medium effect size (f<sups>2</sups> = 0.1), the estimated sample size was 81 for each gender. Therefore, the appropriate minimum sample size was determined to be 162 for both genders.</p> <hd id="AN0133640617-8">Results</hd> <p>The characteristics of all the participants are shown in Table 1. There were no significant between-gender differences in age, school year, the duration of smartphone ownership, hours spent talking, and hours spent using ancillary functions. However, males spent more time using smartphones on weekends than females did (<emph>p</emph> < 0.05). The females spent significantly less time texting than the males (<emph>p</emph> < 0.05).</p> <p>The characteristics (n = 218) of all the participants.</p> <p> <ephtml> <table border="1" cellpadding="5"><tbody><tr><td align="left" /><td align="center" /><td align="center" colspan="2" /><td align="center">Pearson's chi-squared test</td></tr><tr><td align="left">Category</td><td align="center" /><td align="center">Male (%)</td><td align="center">Female (%)</td><td align="center"><italic>p</italic> value</td></tr></tbody><tbody><tr><td align="left">Number</td><td align="center" /><td align="center">127</td><td align="center">91</td><td align="center" /></tr><tr><td align="left">Age</td><td align="left">16-17 years old</td><td align="char">11(9)</td><td align="char">14(15)</td><td align="char">0.15</td></tr><tr><td align="center" /><td align="left">17-18 years old</td><td align="char">33(26)</td><td align="char">18(20)</td><td align="center" /></tr><tr><td align="center" /><td align="left">18-19 years old</td><td align="char">62(49)</td><td align="char">37(41)</td><td align="center" /></tr><tr><td align="center" /><td align="left">Above 19 years old</td><td align="char">21(17)</td><td align="char">22(24)</td><td align="center" /></tr><tr><td align="left">School year</td><td align="left">Grade 2</td><td align="char">35(28)</td><td align="char">28(31)</td><td align="char">0.48</td></tr><tr><td align="center" /><td align="left">Grade 3</td><td align="char">11(9)</td><td align="char">7(8)</td><td align="center" /></tr><tr><td align="center" /><td align="left">Grade 4</td><td align="char">61(48)</td><td align="char">48(53)</td><td align="center" /></tr><tr><td align="center" /><td align="left">Grade 5</td><td align="char">20(16)</td><td align="char">8(9)</td><td align="center" /></tr><tr><td align="left" rowspan="3">Duration of smartphone ownership</td><td align="left"><1 year</td><td align="char">22(17)</td><td align="char">20(22)</td><td align="char">0.56</td></tr><tr><td align="left">1-2 years</td><td align="char">51(40)</td><td align="char">31(34)</td><td align="center" /></tr><tr><td align="left">More than 2 years</td><td align="char">54(43)</td><td align="char">40(44)</td><td align="center" /></tr><tr><td align="left" rowspan="5">Hours of smartphone use on weekends</td><td align="left"><1 hour</td><td align="char">1(1)</td><td align="char">7(8)</td><td align="char">0.03<xref ref-type="fn" rid="bfn1"><sup>*</sup></xref></td></tr><tr><td align="left">1-2 hours</td><td align="char">15(12)</td><td align="char">16(18)</td><td align="center" /></tr><tr><td align="left">2-3 hours</td><td align="char">28(22)</td><td align="char">23(25)</td><td align="center" /></tr><tr><td align="left">3-4 hours</td><td align="char">34(27)</td><td align="char">17(19)</td><td align="center" /></tr><tr><td align="left">>4 hours</td><td align="char">49(39)</td><td align="char">28(31)</td><td align="center" /></tr><tr><td align="left" rowspan="5">Hours spent talking</td><td align="left"><1 hour</td><td align="char">88(69)</td><td align="char">63(69)</td><td align="char">0.78</td></tr><tr><td align="left">1-2 hours</td><td align="char">19(15)</td><td align="char">15(17)</td><td align="center" /></tr><tr><td align="left">2-3 hours</td><td align="char">11(9)</td><td align="char">7(8)</td><td align="center" /></tr><tr><td align="left">3-4 hours</td><td align="char">3(2)</td><td align="char">4(4)</td><td align="center" /></tr><tr><td align="left">>4 hours</td><td align="char">6(5)</td><td align="char">2(2)</td><td align="center" /></tr><tr><td align="left" rowspan="5">Hours spent texting</td><td align="left"><1 hour</td><td align="char">22(17)</td><td align="char">34(37)</td><td align="char">0.01<xref ref-type="fn" rid="bfn1"><sup>*</sup></xref></td></tr><tr><td align="left">1-2 hours</td><td align="char">36(28)</td><td align="char">25(28)</td><td align="center" /></tr><tr><td align="left">2-3 hours</td><td align="char">42(33)</td><td align="char">21(23)</td><td align="center" /></tr><tr><td align="left">3-4 hours</td><td align="char">14(11)</td><td align="char">4(4)</td><td align="center" /></tr><tr><td align="left">>4 hours</td><td align="char">13(10)</td><td align="char">7(8)</td><td align="center" /></tr><tr><td align="left" rowspan="5">Hours spent using ancillary functions</td><td align="left"><1 hour</td><td align="char">20(16)</td><td align="char">11(12)</td><td align="char">0.71</td></tr><tr><td align="left">1-2 hours</td><td align="char">47(37)</td><td align="char">32(35)</td><td align="center" /></tr><tr><td align="left">2-3 hours</td><td align="char">35(28)</td><td align="char">24(26)</td><td align="center" /></tr><tr><td align="left">3-4 hours</td><td align="char">11(9)</td><td align="char">13(14)</td><td align="center" /></tr><tr><td align="left">>4 hours</td><td align="char">14(11)</td><td align="char">11(12)</td><td align="center" /></tr><tr><td align="left" rowspan="7">When do you most often use a smartphone?</td><td align="left">After school</td><td align="char">101(80)</td><td align="char">78(86)</td><td align="char">0.26</td></tr><tr><td align="left">During recess</td><td align="char">94(74)</td><td align="char">66(73)</td><td align="char">0.81</td></tr><tr><td align="left">Waiting for transportation</td><td align="char">85(67)</td><td align="char">50(55)</td><td align="char">0.07</td></tr><tr><td align="left">While using transportation</td><td align="char">75(59)</td><td align="char">58(64)</td><td align="char">0.49</td></tr><tr><td align="left">While eating</td><td align="char">75(59)</td><td align="char">46(51)</td><td align="char">0.21</td></tr><tr><td align="left">In class</td><td align="char">18(14)</td><td align="char">18(20)</td><td align="char">0.27</td></tr><tr><td align="left">Other</td><td align="char">12(9)</td><td align="char">11(12)</td><td align="char">0.42</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>p</emph> < 0.05;</p> <p>2 <emph>p</emph> < 0.01.</p> <p>Participant characteristics in the heavy- and nonheavy-use groups are shown in Table 2. The heavy-use group consisted of 70 males and 44 females; the nonheavy-use group consisted of 57 males and 47 females (χ<sups>2</sups> = 0.28). In the nonheavy-use group, the age was significantly different between the males and females(<emph>p</emph> < 0.05). In the heavy-use group, more than half of the participants used smartphones to talk for less than one hour a day (64% of the males and 52% of the females), but more than half used their phones for texting (78% male and 64% female) or ancillary functions (67% of the males and 89% of the females) for more than two hours. Similarly, in the nonheavy-use group, most of the participants used smartphones to talk for less than one hour a day (75% of the males and 85% of the females), whereas 70%-79% of the males and 52%-76% of the females used smartphones for texting or using ancillary functions for more than one hour. Based on the Fisher exact test results, no statistically significant (<emph>p</emph> > 0.05) relationship was observed between gender and participant school year, duration of smartphone use, or time spent talking on the phone in either the heavy- and nonheavy-use groups. In the heavy-use group, the females spent more time using ancillary functions (<emph>p</emph> < 0.05). However, in the nonheavy-use group, the females used smartphones less frequently on weekends and sent fewer texts on weekdays than the males (<emph>p</emph> < 0.05).</p> <p>. Participant characteristics after grouping (n = 218).</p> <p> <ephtml> <table border="1" cellpadding="8"><tbody><tr><td align="center" /><td align="center" /><td align="center" colspan="2">Heavy-use group (n = 114)</td><td align="center">Fisher exact test</td><td align="center" colspan="2">Nonheavy-use group (n = 104)</td><td align="center">Fisher exact test</td></tr><tr><td align="left">Category</td><td align="center" /><td align="center">Male</td><td align="center">Female</td><td align="center"><italic>p</italic> value</td><td align="center">Male</td><td align="center">Female</td><td align="center"><italic>p</italic> value</td></tr><tr><td align="left">Number</td><td align="center" /><td align="center">70</td><td align="center">44</td><td align="center" /><td align="center">57</td><td align="center">47</td><td align="center" /></tr><tr><td align="left" rowspan="4">Age</td><td align="left">16-17 years old</td><td align="char">4(6)</td><td align="char">6(14)</td><td align="center">0.16</td><td align="char">7(12)</td><td align="center">8(17)</td><td align="center">0.05<xref ref-type="fn" rid="bfn3"><sup>*</sup></xref></td></tr><tr><td align="left">17-18 years old</td><td align="char">24(34)</td><td align="char">8(18)</td><td align="center" /><td align="char">9(16)</td><td align="center">10(21)</td><td align="center" /></tr><tr><td align="left">18-19 years old</td><td align="char">31(44)</td><td align="char">24(55)</td><td align="center" /><td align="char">31(54)</td><td align="center">13(28)</td><td align="center" /></tr><tr><td align="left">Above 19 years old</td><td align="char">11(16)</td><td align="char">6(14)</td><td align="center" /><td align="char">10(18)</td><td align="center">16(34)</td><td align="center" /></tr><tr><td align="left" rowspan="4">School year</td><td align="left">Grade2</td><td align="char">23(33)</td><td align="char">12(27)</td><td align="center">0.29</td><td align="char">12(21)</td><td align="center">16(34)</td><td align="center">0.45</td></tr><tr><td align="left">Grade3</td><td align="char">6(9)</td><td align="char">5(11)</td><td align="center" /><td align="char">5(9)</td><td align="center">2(4)</td><td align="center" /></tr><tr><td align="left">Grade4</td><td align="char">31(44)</td><td align="char">25(57)</td><td align="center" /><td align="char">30(53)</td><td align="center">23(49)</td><td align="center" /></tr><tr><td align="left">Grade5</td><td align="char">10(14)</td><td align="char">2(5)</td><td align="center" /><td align="char">10(18)</td><td align="center">6(13)</td><td align="center" /></tr><tr><td align="left" rowspan="3">Duration of smartphone ownership</td><td align="left"><1 year</td><td align="char">10(14)</td><td align="char">5(11)</td><td align="center">0.21</td><td align="char">12(21)</td><td align="center">15(32)</td><td align="center">0.48</td></tr><tr><td align="left">1-2 years</td><td align="char">32(46)</td><td align="char">14(32)</td><td align="center" /><td align="char">19(33)</td><td align="center">17(36)</td><td align="center" /></tr><tr><td align="left">More than 2 years</td><td align="char">28(40)</td><td align="char">25(57)</td><td align="center" /><td align="char">26(46)</td><td align="center">15(32)</td><td align="center" /></tr><tr><td align="left" rowspan="5">Hours of smartphone use on weekends</td><td align="left"><1 hour</td><td align="char">0(0)</td><td align="char">0(0)</td><td align="center">0.85</td><td align="char">1(2)</td><td align="center">7(15)</td><td align="center">0.01<xref ref-type="fn" rid="bfn4"><sup>**</sup></xref></td></tr><tr><td align="left">1-2 hours</td><td align="char">2(3)</td><td align="char">0(0)</td><td align="center" /><td align="char">13(23)</td><td align="center">16(34)</td><td align="center" /></tr><tr><td align="left">2-3 hours</td><td align="char">6(9)</td><td align="char">4(9)</td><td align="center" /><td align="char">22(39)</td><td align="center">19(40)</td><td align="center" /></tr><tr><td align="left">3-4 hours</td><td align="char">18(26)</td><td align="char">13(30)</td><td align="center" /><td align="char">16(28)</td><td align="center">4(9)</td><td align="center" /></tr><tr><td align="left">>4 hours</td><td align="char">44(63)</td><td align="char">27(61)</td><td align="center" /><td align="char">5(9)</td><td align="center">1(2)</td><td align="center" /></tr><tr><td align="left" rowspan="5">Hours spent talking</td><td align="left"><1 hour</td><td align="char">45 (64)</td><td align="char">23(52)</td><td align="center">0.46</td><td align="char">43(75)</td><td align="center">40(85)</td><td align="center">0.31</td></tr><tr><td align="left">1-2 hours</td><td align="char">10(14)</td><td align="char">9(21)</td><td align="center" /><td align="char">9 (16)</td><td align="center">6 (13)</td><td align="center" /></tr><tr><td align="left">2-3 hours</td><td align="char">6(9)</td><td align="char">6(14)</td><td align="center" /><td align="char">5(9)</td><td align="center">1(2)</td><td align="center" /></tr><tr><td align="left">3-4 hours</td><td align="char">3(4)</td><td align="char">4(9)</td><td align="center" /><td align="center">—</td><td align="center">—</td><td align="center" /></tr><tr><td align="left">>4 hours</td><td align="char">6(9)</td><td align="char">2(5)</td><td align="center" /><td align="center">—</td><td align="center">—</td><td align="center" /></tr><tr><td align="left" rowspan="5">Hours spent texting</td><td align="left"><1 hour</td><td align="char">5(7)</td><td align="char">11(25)</td><td align="center">0.08</td><td align="char">17(30)</td><td align="center">23(49)</td><td align="center">0.03<xref ref-type="fn" rid="bfn3"><sup>*</sup></xref></td></tr><tr><td align="left">1-2 hours</td><td align="char">11(16)</td><td align="char">5(11)</td><td align="center" /><td align="char">25(44)</td><td align="center">20(43)</td><td align="center" /></tr><tr><td align="left">2-3 hours</td><td align="char">27(39)</td><td align="char">17(39)</td><td align="center" /><td align="char">15(26)</td><td align="center">4 (9)</td><td align="center" /></tr><tr><td align="left">3-4 hours</td><td align="char">14(20)</td><td align="char">4(9)</td><td align="center" /><td align="center">—</td><td align="center">—</td><td align="center" /></tr><tr><td align="left">>4 hours</td><td align="char">13(19)</td><td align="char">7(16)</td><td align="center" /><td align="center">—</td><td align="center">—</td><td align="center" /></tr><tr><td align="left" rowspan="5">Hours spent using ancillary functions</td><td align="left"><1 hour</td><td align="char">8(11)</td><td align="char">0(0)</td><td align="center">0.04<xref ref-type="fn" rid="bfn3"><sup>*</sup></xref></td><td align="char">12(21)</td><td align="center">11(23)</td><td align="center">0.93</td></tr><tr><td align="left">1-2 hours</td><td align="char">15(21)</td><td align="char">5(11)</td><td align="center" /><td align="char">32(56)</td><td align="center">27(57)</td><td align="center" /></tr><tr><td align="left">2-3 hours</td><td align="char">22(31)</td><td align="char">15(34)</td><td align="center" /><td align="char">13(23)</td><td align="center">9(19)</td><td align="center" /></tr><tr><td align="left">3-4 hours</td><td align="char">11(16)</td><td align="char">13(30)</td><td align="center" /><td align="char">—</td><td align="center">—</td><td align="center" /></tr><tr><td align="left">>4 hours</td><td align="char">14(20)</td><td align="char">11(25)</td><td align="center" /><td align="char">—</td><td align="center">—</td><td align="center" /></tr><tr><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center"><italic>Pearson's chi-squared test</italic></td><td align="center" /><td align="center" /><td align="center"><italic>Pearson's chi-squared test</italic></td></tr><tr><td align="left" rowspan="7">When do you most often use a smartphone?</td><td align="left">After school</td><td align="char">61(87)</td><td align="char">38(86)</td><td align="center">0.44</td><td align="char">40(70)</td><td align="center">40(85)</td><td align="center">0.07</td></tr><tr><td align="left">During recess</td><td align="char">58(83)</td><td align="char">32(73)</td><td align="center">0.20</td><td align="char">36(63)</td><td align="center">34(72)</td><td align="center">0.32</td></tr><tr><td align="left">Waiting for transportation</td><td align="char">51(73)</td><td align="char">26(59)</td><td align="center">0.13</td><td align="char">34(60)</td><td align="center">24(51)</td><td align="center">0.38</td></tr><tr><td align="left">While using transportation</td><td align="char">48(69)</td><td align="char">28 (64)</td><td align="center">0.59</td><td align="char">27(47)</td><td align="center">30 (64)</td><td align="center">0.09</td></tr><tr><td align="left">While eating</td><td align="char">45(64)</td><td align="char">25(57)</td><td align="center">0.43</td><td align="char">30(52)</td><td align="center">21(45)</td><td align="center">0.42</td></tr><tr><td align="left">In class</td><td align="char">13(19)</td><td align="char">11(25)</td><td align="center">0.41</td><td align="char">5(9)</td><td align="center">7(15)</td><td align="center">0.33</td></tr><tr><td align="left">Other</td><td align="char">6(9)</td><td align="char">6(14)</td><td align="center">0.39</td><td align="char">6(11)</td><td align="center">5(11)</td><td align="center">0.52</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>p</emph> < 0.05;</item> <item>4 <emph>p</emph> < 0.01.</item> </ulist> <p>Tables 1 and 2 indicate that the students often used smartphones after school, during recess, and while waiting for transportation. Table 3 shows that the females exhibited higher smartphone dependence and a greater degree of smartphone influence than their male counterparts in all groups; however, the differences were nonsignificant (<emph>p </emph>> 0.05) for the heavy- and nonheavy-use groups. In the heavy-use group, the females had significantly higher mental health indices than the males (<emph>p </emph>< 0.01). In addition, the interaction terms of gender and level of smartphone use were all nonsignificant (F = 0.05, <emph>p</emph> = 0.83 for dependence; F = 0.39, <emph>p</emph> = 0.54 for influence; F = 0.24, <emph>p</emph> = 0.62 for vitality; F = 1.84, <emph>p</emph> = 0.18 for mental health).</p> <p>. Means, standard deviation, and ANCOVA results for smartphone dependence, degree of influence, and vitality and mental health indices.</p> <p> <ephtml> <table border="1" cellpadding="10"><tbody><tr><td align="left" /><td align="center" colspan="3">Total group</td><td align="center" colspan="3">Heavy-use group</td><td align="center" colspan="3">Nonheavy-use group</td></tr><tr><td align="left">Category</td><td align="center">Male</td><td align="center">Female</td><td align="center"><italic>p</italic></td><td align="center">Male</td><td align="center">Female</td><td align="center"><italic>p</italic></td><td align="center">Male</td><td align="center">Female</td><td align="center"><italic>p</italic></td></tr><tr><td align="left">Smartphone dependence</td><td align="char">8.68 ± 2.27</td><td align="char">9.35 ± 2.10</td><td align="char">0.02<xref ref-type="fn" rid="bfn5"><sup>*</sup></xref></td><td align="char">9.04 ± 2.18</td><td align="char">9.84 ± 2.07</td><td align="char">0.10</td><td align="char">8.23 ± 2.31</td><td align="char">8.89 ± 2.05</td><td align="char">0.10</td></tr><tr><td align="left">Degree of smartphone influence</td><td align="char">7.77 ± 2.11</td><td align="char">8.45 ± 2.55</td><td align="char">0.01<xref ref-type="fn" rid="bfn5"><sup>*</sup></xref></td><td align="char">8.11 ± 2.00</td><td align="char">9.05 ± 2.58</td><td align="char">0.06</td><td align="char">7.35 ± 2.17</td><td align="char">7.89 ± 2.42</td><td align="char">0.13</td></tr><tr><td align="left">Vitality</td><td align="char">43.23 ± 17.35</td><td align="char">44.07 ± 20.67</td><td align="char">0.78</td><td align="char">45.36 ± 15.98</td><td align="char">45.23 ± 22.41</td><td align="char">0.76</td><td align="char">40.61 ± 18.71</td><td align="char">42.98 ± 19.07</td><td align="char">0.47</td></tr><tr><td align="left">Mental health</td><td align="char">38.90 ± 17.40</td><td align="char">43.91 ± 21.12</td><td align="char">0.04<xref ref-type="fn" rid="bfn5"><sup>*</sup></xref></td><td align="char">38.86 ± 17.22</td><td align="char">47.55 ± 18.63</td><td align="char"><0.01<xref ref-type="fn" rid="bfn6"><sup>**</sup></xref></td><td align="char">38.95 ± 17.77</td><td align="char">40.51 ± 22.90</td><td align="char">0.55</td></tr></tbody></table> </ephtml> </p> <ulist> <item>5 <emph>p</emph> < 0.05;</item> <item>6 <emph>p</emph> < 0.01.</item> </ulist> <p>Age was adjusted in the ANCOVA.</p> <p>Table 4 presents the multiple regressions results. Both the vitality (β = 0.30, <emph>p</emph> < 0.05) and mental health (β = 0.36, <emph>p</emph> < 0.01) of the males were significantly and positively correlated with hours of smartphone use on weekends. In the case of the males, the degree of smartphone dependence was significantly and negatively correlated with both vitality (β = -0.24, <emph>p</emph> < 0.05) and mental health (β = -0.23, <emph>p</emph> < 0.05). The mental health of the males was negatively associated with using ancillary functions (β = -0.24, <emph>p</emph> < 0.05).</p> <p>Multiple regressions among smartphone use behavior and vitality and mental health adjusted for age.</p> <p> <ephtml> <table border="1" cellpadding="5"><tbody><tr><td align="center" /><td align="center" colspan="2">Vitality</td><td align="center" colspan="2">Mental health</td></tr><tr><td align="center" /><td align="center">Male<xref ref-type="fn" rid="bfn9"><sup>a</sup></xref></td><td align="center">Female<xref ref-type="fn" rid="bfn10"><sup>b</sup></xref></td><td align="center">Male<xref ref-type="fn" rid="bfn11"><sup>c</sup></xref></td><td align="center">Female<xref ref-type="fn" rid="bfn12"><sup>d</sup></xref></td></tr><tr><td align="left">Variable</td><td align="center">β</td><td align="center">β</td><td align="center"><bold>β</bold></td><td align="center">β</td></tr><tr><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">Hours of use on weekends</td><td align="char">0.30<xref ref-type="fn" rid="bfn7"><sup>*</sup></xref></td><td align="char">0.06</td><td align="char">0.36<xref ref-type="fn" rid="bfn8"><sup>**</sup></xref></td><td align="char">0.11</td></tr><tr><td align="left">Phoning</td><td align="char">0.06</td><td align="char">-0.23</td><td align="char">−0.05</td><td align="char">−0.15</td></tr><tr><td align="left">Texting</td><td align="char">−0.02</td><td align="char">0.08</td><td align="char">−0.08</td><td align="char">0.02</td></tr><tr><td align="left">Using ancillary functions</td><td align="char">−0.06</td><td align="char">0.12</td><td align="char">−0.24<xref ref-type="fn" rid="bfn7"><sup>*</sup></xref></td><td align="char">0.14</td></tr><tr><td align="left">Smartphone dependence</td><td align="char">−0.24<xref ref-type="fn" rid="bfn7"><sup>*</sup></xref></td><td align="char">0.06</td><td align="char">−0.23<xref ref-type="fn" rid="bfn7"><sup>*</sup></xref></td><td align="char">0.07</td></tr><tr><td align="left">The degree of influence</td><td align="char">0.12</td><td align="char">−0.16</td><td align="char">0.12</td><td align="char">−0.13</td></tr></tbody></table> </ephtml> </p> <ulist> <item>7 <emph>p</emph> < 0.05;</item> <item>8 <emph>p</emph> < 0.01.</item> <item>9 R<sups>2</sups> = 0.33;</item> <item>10 R<sups>2</sups> = 0.30;</item> <item>11 R<sups>2</sups> = 0.11;</item> <item>12 R<sups>2</sups> = 0.06.</item> </ulist> <hd id="AN0133640617-9">Comment</hd> <p>Our study aims were to understand the various degrees (heavy or nonheavy) of smartphone use behavior in male and female adolescents and to examine the associations among various degrees of smartphone use, behavior, vitality, and mental health. Some characteristics of smartphone use between different genders were discovered, and only Hypotheses 1 was supported.</p> <hd id="AN0133640617-10">Gender differences existed in the smartphone use</hd> <p>The results shown in Table 1 indicate that significant gender differences existed in the number of hours of smartphone use on weekends and hours spent texting. In more detail, the figures revealed that the males had a greater tendency to use smartphones on weekends than the females. Further, Table 2 shows that only in the nonheavy-use group, the males used smartphones more than the females. In terms of students in the heavy-use group, both genders used smartphones heavily on the weekends. Since texting is a fast, convenient, and informal way to communicate with others compared to voice communication, and frequent texting can facilitate faster and earlier social engagement with one's peers, this can lead to the development of social relationships more quickly than under other conditions.30 Table 2 also shows that these gender differences only existed in the nonheavy-use group and that the males liked to text more than the females. Since male students are usually more active in school clubs or classes, it is possible that among the study participants, male non-heavy smartphone users spend more time texting than female users. Further research is recommended on this issue.</p> <p>Our participants used smartphones mainly to access apps, particularly in the case of the heavy-use group (Table 2). More than 67% and 19% of the students in the heavy- and nonheavy-use groups, respectively, spent more than two hours per day using smartphone ancillary functions, namely apps. Specifically, the females in the heavy-use group who accessed smartphone ancillary functions used apps more frequently than their male counterparts did. This finding is related to the fact that females have a greater tendency to use social networking apps, such as Facebook, to upload their photos or share their feelings;31 therefore, females had a higher degree of smartphone attachment.</p> <p>The results supported our first hypothesis and concurred with those of previous studies:15<sups>,</sups>21<sups>,</sups>23 female adolescents (in both the heavy-use and nonheavy-use group) exhibited significantly higher degrees of smartphone dependence and smartphone influence than was seen among the male adolescents (although there were no significant between-group differences, cf. Table 3). Female adolescents have been reported to enjoy establishing and maintaining social relationships, which requires more frequent connections and emotional engagement.19<sups>,</sups>23 Females also like to use smartphones to deal with emotionally loaded issues;23 thus, they may use and check their smartphones more frequently than males do. In contrast, males may use technology products to send and retrieve information, which requires a substantial amount of texting time.32 Hence, we speculated that male adolescents prefer to send messages and that female adolescents exhibit a higher frequency of smartphone use. Also, males may spend more time texting than females do. However, the purpose of smartphone use for both genders needs to be further investigated in order to explain the discrepancy in more detail. In addition, from the three questions on the degree of smartphone influence, we found that smartphone use can affect the time spent studying in the case of the females since they also often check their phones in class. Such behavioral changes could lead to their being easily distracted in class, which could in turn result in poor learning outcomes. Moreover, excessive habitual checking may cause stress in smartphone users.33</p> <p>Lee, Chang, Lin, Cheng12 reported that males have less self-control over smartphone use and that males are more likely to be compulsive users than females. Our results are the opposite of theirs. The possible reason for this may be the different degree of maturation (the mean age in Lee et al.'s study was 28.98 ± 9.34, and the mean age in our study was 18.23 ± 0.91). The degree of maturation in terms of mental state varies between males and females, so the impacts of smartphone use on health outcomes may differ in different life periods. Specifically, females usually mature earlier than males do;34 thus, female adolescents may experience the impacts of smartphone use earlier than male adolescents, while female adults may have better control than the male adults in terms of smartphone use.</p> <hd id="AN0133640617-11">Association between smartphone use behavior and vitality and mental health</hd> <p>Our results revealed that only the males who spent more time using smartphones on weekends had superior vitality and mental health. This result (Table 4) did not support our second hypothesis or the results of previous studies.23<sups>,</sups>35<sups>,</sups>36 According to our findings, more than 66% of the males spent more than three hours using smartphones on weekends. On weekdays, they were not allowed to freely use their smartphones at school. However, during weekends or holidays, the males could seek entertainment through apps or interacting with friends, resulting in high levels of pleasure and satisfaction. The males could thus have developed positive moods through chatting, texting, playing games, visiting social networking sites, or through using other ancillary functions on their smartphones. Because of these positive moods, these heavy smartphone users may have felt more energetic. Thus, they exhibited a high vitality score even though they were physically tired.37 With the exception of this result, the variables for smartphone dependence correlated negatively with vitality and mental health in males. This result confirmed the findings of previous studies suggesting that excess smartphone use affects both physical and mental health.10<sups>,</sups>,,<sups>,</sups>36 This result is quite interesting because the females were more smartphone-dependent than the males, but only the males' health was significantly affected by smartphone dependence. This could be because females tended to use smartphones at high frequencies for short periods of time, but smartphone-dependent males tended to use smartphones at high frequencies for long periods of time. However, further research is needed to examine this relationship.</p> <p>Our findings indicated that the more males used the ancillary smartphone functions, the poorer their mental health (score) was. Previous studies have also shown that smartphone overuse is significantly correlated with sleep disturbances and low social skills,38<sups>,</sups>39 both of which lead to poor mental health and disruptions in academic training. Moreover, low self-esteem has been associated with heavier amounts of internet use.40 All of these findings are very similar, therefore, the mental health of male adolescents in smartphone overuse needs to pay more attention to deal with.</p> <hd id="AN0133640617-12">Limitations and future research</hd> <p>This study has several limitations. First, convenience sampling was adopted in this study to recruit participants, and students were invited to volunteer for the survey in class. The recruitment method may have affected the representativeness of the sample because students who are willing to volunteer for studies might also be more enthusiastic or active in school activities and therefore might spend more time texting to communicate with others. Second, our sample size might not be able to provide sufficient power to detect gender differences given that we stratified our sample into smaller groups based on heavy and non-heavy use. However, we believed that our results can provide some initial evidence for future studies to investigate the issue of gender difference. Additionally, the participants were all from the same junior college, so our results have limited generalizability. Third, there were several confounders affecting individual participant vitality and mental health that were not controlled for in this study. Although we controlled for two crucial demographic variables (i.e., age and gender) in our analysis, future studies may benefit from controlling for other confounders (e.g., the socio-economic status of the families, for example). Fourth, the questionnaire was self-reported by the students, so the information might have been inaccurate because of memory bias or social desirability bias. Fifth, the Cronbach's alphas in the smartphone dependence and smartphone influence were not satisfactory, so the reliability and validity of the two variables may come into question. However, we did ensure the expert validity of the two variables. Given that a somewhat low Cronbach's alpha (e.g., ∼0.6) is acceptable for a newly developed instrument,41 such as our measures on smartphone dependence and influence, we believed that our study appropriately measured the two variables. Nevertheless, future studies are needed to improve the measures on the two variables. Sixth, we only measured two specific quality of life domains. Specifically, vitality and mental health questions were retrieved from the SF-36, a comprehensive instrument used to measure quality of life. Since quality of life provides a holistic view of an individual's health, we suggest that future studies assess quality of life as a whole instead of examining only vitality and mental health in order to explore how smartphones affect quality of life at the individual level through implementing a more precise assessment. Future researchers should also perform longitudinal research to more thoroughly investigate the influence of smartphones on adolescents. In addition to studies that mitigate the limitations of this study, future studies can be conducted to develop intervention or prevention programs based on our study results. For example, investigating which types of apps are most frequently used among female heavy users to help them avoid becoming addicted to smartphones or developing cognition-related programs that prevent smartphone dependence in male users (either heavy-users or nonheavy-users) that could help to protect their mental health.</p> <hd id="AN0133640617-13">Conclusion</hd> <p>This study investigated gender differences in terms of vitality and mental health among adolescents engaging in various degrees of smartphone use. The findings demonstrate that adolescent females as compared with adolescent males exhibited significantly higher degrees of smartphone dependence and smartphone influence. The duration of smartphone use on weekends had a mild positive correlation with the vitality and mental health of the adolescent males who were heavy users. Overusing ancillary functions and smartphone dependence may both decrease the vitality and mental health of male adolescents. Based on the findings, it seems necessary to assist adolescents with self-control related to the use of smartphones. Overall, this study provides clinicians with a deeper understanding of the impact of smartphone use as it relates to gender.</p> <hd id="AN0133640617-14">Conflict of interest disclosure</hd> <p>The authors have no conflicts of interest to report. The authors confirm that the research presented in this article met the ethical guidelines, including adherence to the legal requirements, of Taiwan and received approval from the National Cheng Kung University Human Research Ethics Committee.</p> <hd id="AN0133640617-15">Funding</hd> <p>This study was supported by Shu-Zen Junior College of Medicine and Management.</p> <ref id="AN0133640617-16"> <title> References </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Hirsch JK, Molnar D, Chang EC, Sirois FM. 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Consumer Res.</emph>. 1994 ; 21 ( 2 ): 381−391. doi: 10.1086/209405.</bibtext> </blist> </ref> <aug> <p>By Shang-Yu Yang; Chung-Ying Lin; Yueh-Chu Huang and Jer-Hao Chang</p> </aug>
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  Data: Gender Differences in the Association of Smartphone Use with the Vitality and Mental Health of Adolescent Students
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Shang-Yu%22">Yang, Shang-Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Lin%2C+Chung-Ying%22">Lin, Chung-Ying</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Yueh-Chu%22">Huang, Yueh-Chu</searchLink><br /><searchLink fieldCode="AR" term="%22Chang%2C+Jer-Hao%22">Chang, Jer-Hao</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+American+College+Health%22"><i>Journal of American College Health</i></searchLink>. 2018 66(7):693-701.
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  Data: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: 9
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  Data: 2018
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Two+Year+Colleges%22">Two Year Colleges</searchLink><br /><searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Mental+Health%22">Mental Health</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunications%22">Telecommunications</searchLink><br /><searchLink fieldCode="DE" term="%22Handheld+Devices%22">Handheld Devices</searchLink><br /><searchLink fieldCode="DE" term="%22Community+Colleges%22">Community Colleges</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Addictive+Behavior%22">Addictive Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Well+Being%22">Well Being</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Taiwan%22">Taiwan</searchLink>
– Name: DOI
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  Data: 10.1080/07448481.2018.1454930
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  Label: ISSN
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  Data: 0744-8481
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: The present study examined variations in the degree of smartphone use behavior among male and female adolescents as well as the association between various degrees of smartphone use behavior and the vitality and mental health of each gender. Participants: A total of 218 adolescents were recruited from a junior college in September 2014. Methods: All the participants were asked to answer questionnaires on smartphone use. Results: The findings showed that adolescent females as compared with adolescent males exhibited significantly higher degrees of smartphone dependence and smartphone influence. Positive correlations were observed between the duration of smartphone use on weekends and the vitality/mental health of the male adolescents; negative correlations were found between smartphone dependence and the vitality/mental health of males. Conclusion: The findings demonstrate that adolescent females are deeply affected by their smartphone use. Smartphone dependence may decrease the vitality and mental health of male adolescents.
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  Data: 2018
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  Data: EJ1200400
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        PageCount: 9
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      – SubjectFull: Mental Health
        Type: general
      – SubjectFull: Gender Differences
        Type: general
      – SubjectFull: Telecommunications
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      – SubjectFull: Community Colleges
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      – SubjectFull: Addictive Behavior
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      – SubjectFull: Well Being
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      – SubjectFull: Undergraduate Students
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Taiwan
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      – TitleFull: Gender Differences in the Association of Smartphone Use with the Vitality and Mental Health of Adolescent Students
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