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The relationships between mobile phone use and depressive symptoms, bodily pain, and daytime sleepiness in Hong Kong secondary school students
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Addictive Behaviors journal homepage: www.elsevier.com/locate/addictbeh
The relationships between mobile phone use and depressive symptoms, bodily pain, and daytime sleepiness in Hong Kong secondary school students Ka Chun Nga, Lai Har Wub, Hoi Yan Lama, Lai Kuen Lama, Po Yan Nipa, Cho Man Nga, ⁎ Ka Chun Leungc, Sau Fong Leungb, a
Master of Nursing, The Hong Kong Polytechnic University, Hong Kong The School of Nursing at The Hong Kong Polytechnic University, Hong Kong c Bachelor of Medicine and Bachelor of Surgery, The University of Hong Kong, Hong Kong b
HIGHLIGHTS
10% of 686 students were problematic mobile phone users. • Approximately and prolonged mobile phone use correlated with negative health consequences. • Problematic and daytime sleepiness mediated the relationship of mobile phone use with depression. • Pain • This was the first study to translate and use the Chinese-language 10-item Mobile Phone Problem Use Scale. ARTICLE INFO
ABSTRACT
Keywords: Mobile phone Mobile phone problem use Sleep Depression Pain Secondary school students
Introduction: Studies have found that increased mobile phone use (MPU) is associated with multiple health issues such as depression, disordered sleep and pain. However, the current situation and interrelationships of these problems remain unexplored in the Hong Kong population. Objectives: This study aimed to understand the situation and problematic use of mobile phones by Hong Kong secondary school students and to investigate depressive symptoms, bodily pain and daytime sleepiness and the associations of these factors with MPU in Hong Kong secondary school students. Methods: This quantitative cross-sectional design study was based on self-administered questionnaires completed at five secondary schools. The questionnaire comprised five sections: MPU as measured by the Chinese version of the 10-Item Mobile Phone Problem Use Scale (CMPPUS-10); depressive symptoms according to the Depression Anxiety Stress Scale-21 Chinese Version (DASS-21); bodily pain according to the Brief Pain Inventory Short Form Chinese (BPISF-C); daytime sleepiness as measured using the Chinese version of the Epworth Sleepiness Scale (CESS) and socio-demographic questions. Results: A total of 686 students were recruited. The CMPPUS-10 score correlated positively with the average daily duration of MPU and the presence of depression, daytime sleepiness and bodily pain. Problematic mobile phone users received significantly higher scores for depression severity, bodily pain and daytime sleepiness. Health problems were significantly more severe in female than in male students. Bodily pain and daytime sleepiness mediated the relationship of MPU with depression. Conclusions: Problematic MPU was associated with depression, bodily pain and daytime sleepiness. These findings will inform further studies of MPU-related health problems.
1. Introduction In recent years, the popularity of mobile phones (MPs) has increased dramatically worldwide. A MP is a device that connects wirelessly to a
phone system via radio signals and can be used anywhere. A smartphone is a type of MP that can connect to the Internet and be used as a computer (Cambridge University Press, 2015a, 2015b). In Hong Kong, the smartphone penetration rate increased significantly from 2012
Corresponding author at: PQ404, School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. E-mail addresses: [email protected] (K.C. Ng), [email protected] (L.H. Wu), [email protected] (H.Y. Lam), [email protected] (L.K. Lam), [email protected] (P.Y. Nip), [email protected] (C.M. Ng), [email protected] (K.C. Leung), [email protected] (S.F. Leung). ⁎
https://doi.org/10.1016/j.addbeh.2019.04.033 Received 30 October 2018; Received in revised form 26 April 2019; Accepted 29 April 2019 0306-4603/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Ka Chun Ng, et al., Addictive Behaviors, https://doi.org/10.1016/j.addbeh.2019.04.033
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(54.0%) to 2014 (77.2%), and each resident owned an average of 2.40 MPs in 2014 (Census and Statistics Department and Hong Kong, 2015; Office of the Communications Authority, 2015). This increase in smartphone usage was likely driven by the powerful advantages of smartphones, including the ability to connect to the Internet, the availability of social networks such as Facebook, WhatsApp and the development of other smartphone applications used for various functions, including gaming, gambling and music streaming. The Hong Kong Computer Society (HKCS) (2013) revealed that 90.4% of students in Hong Kong owned a MP, which represented a > 10% increase within a 1-year period. A comparison of MP use (MPU) among secondary school students in East Asian areas, including Japan (70.8%) (Cabinet Office, 2014), South Korea (93.2%–94.8%) (Statistics Korea, 2014) and Taiwan (69.3%) (Chang, 2013), reported that the second highest rate was observed in Hong Kong. The high level of MPU has drawn the attention of researchers towards the negative effects and problematic use of these devices (Bianchi & Phillips, 2005; Yen, Ko, Yen, Chang, & Cheng, 2009). In particular, excessive MPU can lead to financial problems, aggressive behaviour (e.g. cyberbullying), self-reported dependence and addiction-like symptoms (e.g. cravings and lack of control) (Billieux, Maurage, LopezFernandez, Kuss, & Griffiths, 2015). Billieux (2012) described a theoretical framework which integrated four pathways to explain the aetiology and maintenance of problematic MPU. According to this framework, problematic MPU by an individual can be driven by i) an impulsive pathway characterised by poor self-control and/or maladaptive regulation of emotions; ii) a relationship maintenance pathway involving a constant need for reassurance, promoted by maladaptive cognition and/or insecure attachment; iii) an extraversion pathway involving an increased desire to communicate with peers and establish new potential relationships and iv) a cyber addiction pathway, characterised by the massive engagement in a wide range of online activities such as video games and social networks. Other researchers currently prefer other terms than problematic MPU including smartphone addiction and smartphone use disorder. The latter term has been coined as a reaction to the Gaming Disorder nomenclature in ICD-11 and the IPACE model as proposed by Brand et al. (2016) in order to strive for unification of terms in the literature. Smartphone use disorder has also been linked with the excessive use of social media platforms. In one study, smartphone use disorder had high and significant associations with the problematic use of WhatsApp (WhatsApp Use Disorder) and Facebook usage (Facebook Use Disorder) (Sha, Sariyska, Riedl, Lachmann, and Montag, in press). The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) does not include a MP-specific addiction disorder (American Psychiatric Association, 2013). However, Internet Gaming Disorder may be eligible for further study as a non-substance-related addiction, similar to a gambling disorder. South Korean researchers have attempted to investigate addictive smartphone behaviour in the context of advanced functions and entertainment services (Kwon et al., 2013; Kwon, Kim, Cho, & Yang, 2014). The rapid increase of MPU among Hong Kong students and the associated problems, including smartphone addiction and smartphone use disorder, should warrant attention. MPU has been associated with health problems such as depression, bodily pain and sleep problems. Multiple studies have highlighted a positive correlation between MPU and depression in adolescents. For example, a Taiwanese study found that depressed individuals were more likely to be problematic MP users (Yen et al., 2009). Similarly, studies of American and European adolescents reported that intensive MP users were more susceptible to depression (Bickham, Hswen, & Rich, 2015; Sánchez-Martínez & Otero, 2009). A systematic review of nine studies revealed a significant association between the severity of depression and problematic or general smartphone use in different samples of high school and college students and adults (Elhai, Dvorak, Levine, & Hall, 2017). However, an online survey of participants aged 16–59 years found no connection between electronic use (including
MPU) and depression (Harwood, Dooley, Scott, & Joiner, 2014). Therefore, the effects of MPU on psychological changes in students remain uncertain. Taiwanese researchers found that students faced the risk of neck and hand pain when texting via MPs (Lin & Peper, 2009). Similarly, a study of Canadian students correlated a longer duration of MPU with an increased risk of neck and shoulder pain (Berolo, Wells, & Amick III, 2011). Moreover, Chinese students who reported prolonged MPU also experienced low back pain (Shan et al., 2013). However, a Finnish study found no relationship between MPU and neck/shoulder pain (Hakala, Rimpelä, Saarni, & Salminen, 2006). In summary, MPU appears to be linked to various forms of bodily discomfort. MPU, including messaging after bedtime, was associated with poor sleep quality in a study of American college students (Adams & Kisler, 2013). Furthermore, Adachi-Mejia, Edwards, Gilbert-Diamond, Greenough, and Olson (2014) observed that more than half of American students left their MPs switched on while sleeping, while more than a third of respondents continued to text after bedtime, which decreased their sleep durations. Mak et al. (2014) compared MPs with televisions, computers and portable video devices and found that only the former was associated with a shorter sleep duration, poorer sleep quality and serious daytime sleepiness among Hong Kong students. Daytime sleepiness has been associated with various sleep problems, such as insomnia and sleep apnoea, which can affect students' development (Chung, 2000; Mak, Lee, Ho, Lo, & Lam, 2012). Therefore, sleepiness as a potential adverse consequence of MPU warrants further examination. In summary, the adverse health effects of MPU have not been fully delineated. This study focused on the relationships of current MPU with depression, daytime sleepiness and bodily pain among Hong Kong secondary school students. 1.1. Objectives The objectives of this study were to understand the situation and problematic MPU among Hong Kong secondary school students, investigate depressive symptoms, bodily pain and daytime sleepiness in the participants, and examine the relationships of MPU with these negative consequences in the study population. 2. Methods 2.1. Research design This study used a quantitative cross-sectional design to examine the current situation regarding MPU and the relationships of this behaviour with depressive symptoms, bodily pain and daytime sleepiness in a sample of secondary school students. 2.2. Sampling Five secondary schools were approached between May and July 2015 via convenience sampling. All schools were located in the three main geographical clusters of Hong Kong (Hong Kong Island, Kowloon and the New Territories). Hong Kong secondary school students are equivalent to junior and senior high school students in the western education system. Therefore, the participant inclusion criteria were status as a Form 1–5 secondary school student at the time of study, an age between 12 and 19 years and the use of a MP within the previous 4 weeks. The exclusion criterion was a lack of MPU within the previous 4 weeks. 2.3. Instruments The self-administered questionnaire comprised five domains, including MPU, depressive symptoms, bodily pain, daytime sleepiness and socio-demographic information. The following instruments were 2
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1991, 2019). A score > 10 is considered to indicate EDS. This study adopted the validated Chinese version, which yielded good test-retest reliability (rho = 0.72) and internal consistency (α = 0.80) (Chung, 2000).
included after a review of appropriateness based on factors such as content validity, reliability and the availability of a Chinese-language version. 2.3.1. Chinese version of the 10-item Mobile phone problem use scale (CMPPUS-10) The MP Problem Use Scale (MPPUS-27) is a 27-item self-reported measure developed and validated by Bianchi and Phillips (2005). This measure has a high level of internal consistency (α = 0.93) and good construct validity, as demonstrated by its strong correlations with other measures of MPU and addiction. The MPPUS-27 is used widely and is considered as a possible ‘gold standard’ for the evaluation of problematic MPU (Lopez-Fernandez, Honrubia-Serrano, Freixa-Blanxart, & Gibson, 2014). The short version, MPPUS-10, was specifically designed to measure MPU in adolescents and was selected for its brevity, good internal consistency (α = 0.85) and strong correlation (r = 0.95) with the original 27-item scale (Foerster, Roser, Schoeni, & Röösli, 2015). Responses to the items on the MPPUS-10 were measured using a 10point Likert scale. A higher total score indicated a higher likelihood of problem MPU. The MPPUS-10 includes five subscales: ‘loss of control’, ‘withdrawal’, ‘negative life consequences’, ‘craving’ related to addictive behaviour and ‘peer dependence’ related to the influences of adolescents and peers. The MPPUS-10 was not constructed with cut-off points. The 15th, 80th and 90th-percentile MPPUS-10 scores have been used to classify occasional, habitual, at-risk and problematic MP users, with reference to a previous study of the MPPUS-27 (Lopez-Fernandez et al., 2014) and general practices adopted in the context of gambling addiction (Chow, Leung, Ng, & Yu, 2009). To address the lack of a validated Chinese-language measure of problem MPU, a Chinese version of the MPPUS-10 (CMPPUS-10) was generated via back translation in this study. First, the research team translated the MPPUS-10 into Chinese. Next, the Chinese version was back translated into English by a thirdparty professional translator. The back-translated version was then compared to the original English version by the team, with comments from an expert panel. The CMPPUS-10 yielded a satisfactory level of internal consistency (α = 0.83) and test-retest reliability, as demonstrated by the intraclass correlation (ICC = 0.79) and Pearson's product-moment correlation (r = 0.87) in this study.
2.3.5. Sociodemographic data Sociodemographic data were collected using a self-designed 14-item instrument. The following data were collected: age, gender, grade of study, family background, chronic illnesses, average daily time spent on MP (ADTS-MP) and current number of owned MPs (MP-current). 2.4. Content validity and reliability A panel of three experts was invited to rate the degree of relevance of the questionnaire. The content validity index (CVI) was 0.88, which was considered acceptable (Lynn, 1986; Polit & Beck, 2004). This CVI also supported the validity of the back-translated CMPPUS-10. Testretest reliability was evaluated in a sample of 30 students who completed the CMPPUS-10 twice at a 2-week interval. The overall reliability measures of these questionnaires, including CMPPUS-10, were acceptable, with satisfactory values for correlation (r = 0.76) and internal consistency (α = 0.84). 2.5. Data collection procedure The permission of secondary school principals was sought through invitation letters. Subsequently, letters with refusal reply slips were distributed to the students for parental approval at least 3 days before the study. These letters stated the purpose of the study, date of the event, anonymous data collection protocol, confidentiality and the students' right to withdraw at any time without penalty. The refusal reply slips were only required from parents who wished to refuse their children's participation. During the survey, only students without refusal reply slips were allowed to participate. The students were given information about the study and the voluntary nature of participation before the questionnaires were distributed. Two research team members were present during data collection to ensure voluntary participation. The students were given 15 mins to complete the questionnaires. Extra time was given upon request. Students who opted out were asked to study in the classroom. The questionnaires were stored in a locked cabinet for 12 months after completion of the research and then destroyed.
2.3.2. Brief pain inventory short form Chinese (BPISF-C) The Brief Pain Inventory Short Form Chinese (BPISF-C) measures two scores determined using a 10-point Likert scale: pain intensity [BPISF-C(PI)] and pain interference to general activity [BPISFC(PIGA)]. A higher score indicates a more severely painful experience. Participants were also given a diagram of the human body on which to indicate the painful body parts. The available Chinese version was found to have good internal consistency (α = 0.89) for pain intensity and (α = 0.92) for BPISF-C(PIGA) (Wang, Mendoza, Gao, & Cleeland, 1996).
2.6. Ethics Ethical approval was granted by the Human Subject Ethics SubCommittee of the Hong Kong Polytechnic University on May 6, 2015. The study adhered to the principles of the Declaration of Helsinki. In this study, refusal reply slips were considered a form of passive consent. This type of consent protocol has been commonly adopted by local schools in consideration of personal data confidentiality and voluntary participation (Mak et al., 2010; Wong, Stewart, Ho, Rao, & Lam, 2005).
2.3.3. Chinese version of the depression anxiety stress Scale-21 (DASS-21) The Chinese version of the Depression Anxiety Stress Scale-21 (DASS-21) comprises seven items intended to measure depression. The items are scored using a four-point Likert scale (α = 0.91 for Depression; α = 0.81 for Anxiety; α = 0.89 for Stress) (Lovibond & Lovibond, 1995). The total scores indicate the level of severity (normal, mild, moderate, severe and extremely severe). The DASS-21 was validated in the Hong Kong Chinese population by determining the intercorrelation phi values for Depression-Anxiety (0.61), Depression-Stress (0.63) and Anxiety-Stress (0.67) (Taouk, Lovibond, & Laube, 2001).
2.7. Statistical analysis The statistical analysis was conducted using the Statistical Package for the Social Science (SPSS), version 18.0. The pairwise deletion method was used to manage missing data (Schlomer, Bauman, & Card, 2010). Spearman's rank order correlation, the Kruskal–Wallis test and the Mann–Whitney U test were performed. Furthermore, multiple linear regression and mediation model analyses were used to investigate the dynamics of the variables. The variables were non-parametric according to the Shapiro–Wilk test (p < .05) and were non-collinear in a multiple regression, with a tolerance > 0.01 and variance inflation factor (VIF) < 10 (Pallant, 2010). The BPISF-C(PI) and BPISF-C(PIGA) scores were combined as a single variable, Factor (Pain) and subjected
2.3.4. Chinese version of the Epworth sleepiness scale (CESS) The Chinese version of the Epworth Sleepiness Scale (CESS) was used to assess excessive daytime sleepiness (EDS) in adolescents. On this scale, the respondents rate the likelihood of falling asleep during eight daily activities using a four-point Likert scale (α = 0.82) (Johns, 3
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among female students, compared to male students (Table 3 and Table 4). The positive significant correlation between the MP-current and CMPPUS-10 score remained in female but not male students. The mean CMPPUS-10 score was 45.21/100 units (SD = 16.14) (Table 1). Scores of 28, 59 and 66 units were identified as the 15th, 80th and 90th percentiles, respectively, and were used to categorise the four types of MP users (occasional, habitual, at-risk, and problematic MP users) (Table 3).
Table 1 Descriptive data of the participants (N = 686).
Age Male Female Grade - Form 1 Form 2 Form 3 Form 4 Form 5 With MP Ownership Without MP Ownership MP-current ADTS-MP (min) CMPPUS-10 (total score) CMPPUS-10 subscale Craving Withdrawal Loss of Control Negative Life Consequences Peer dependence DASS-21 Depression BPISF-C(PI) BPISF-C(PIGA) CESS
n
Mean (SD)/%
Range
680 371 315 146 296 153 25 66 677 8 686 671 686 686 686 686 686 686 685 672 671 686
14.14 (1.49) 54.08% 45.92% 21.28% 43.15% 22.30% 3.64% 9.62% 98.83% 1.17% 1.29 (0.91) 225.11 (177.08) 45.21 (16.14) 6.43 (2.49) 3.66 (1.94) 5.49 (2.43) 2.75 (1.85) 5.82 (2.81) 4.85 (4.67) 2.61 (1.69) 2.32 (1.64) 8.53 (4.09)
12–19 – – – – – – – 0–15 1–1200 1–100 1–10 1–10 1–10 1–10 1–10 0–21 1–10 1–10 0–24
3.2. Health measures of bodily pain, depressive symptoms and daytime sleepiness Bodily pain was reported using two scales from the BPISF-C. The mean scores on the BPISF-C(PI) and BPISF-C(PIGA) were 2.61 (SD = 1.69) and 2.32 (SD = 1.64), respectively (Table 1). A total of 434 (63.27%) students reported pain in at least one body part, and these data were grouped on a graph of the human body. Pain was most frequently reported in the head, neck, shoulder and/or back (n = 325, 47.38%), followed by the lower limbs (n = 132, 19.24%), upper limbs (n = 81, 11.81%), chest or abdomen areas (n = 76, 11.08%) and buttock area (n = 20, 2.92%). The mean DASS-21 score was 4.85 (SD = 4.67) (Table 1). Of the participating students, 401 (58.54%) had a normal mood, while 75 (10.95%), 114 (16.64%), 54 (7.88%) and 41 (5.99%) had mild, moderate, severe and extremely severe depression, respectively. Regarding daytime sleepiness, the mean CESS score was 8.53 (SD = 4.09) (Table 1). Moreover, 204 (29.74%) students received a CESS score > 10 and were classified as having EDS.
Note: The sums of the numbers may not equal 686 because of the pairwise deletion method. MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale.
3.3. Relationships between MPU and health measures
to a factor analysis in the regression analysis.
As shown in Table 2, CMPPUS-10 exhibited a moderate positive correlation (p < .001) with the DASS-21 Depression score (rho = 0.385) and weak positive correlations with the BPISF-C(PI) (rho = 0.224), BPISF-C(PIGA) (rho = 0.292) and CESS scores (rho = 0.249). The ADTS-MP exhibited similarly weak positive correlations (p < .001) with the three health measures. The MP-current exhibited weakly significant correlations with the pain and daytime sleepiness scores. Positive correlations were also detected between other health variables. The DASS-21 Depression score correlated positively with the BPISF-C(PI) (rho = 0.323), BPISF-C(PIGA) (rho = 0.434) and CESS scores (rho = 0.236). The CESS score correlated weakly with the BPISF-C(PI) (rho = 0.221) and BPISF-C(PIGA) scores (rho = 0.200) (Table 2). The Kruskal–Wallis test was used to examine the ADTS-MP, MPcurrent, depression, pain, daytime sleepiness and five CMPPUS-10 scores according to the type of MP user (occasional, habitual, at-risk, problematic) (Table 5). Problematic MP users received the highest scores for depression, BPISF-C(PIGA), daytime sleepiness and all five CMPPUS-10 subscales, and these differences were statistically
3. Results A total of 729 students were accessed, of whom 13 (1.78%) provided refusal slips. Thirty students were excluded according to the prespecified criteria or because they provided invalid data. The final data analysis included 686 participants. The demographics of the participants and descriptive data are reported in Table 1. 3.1. Situation of MPU among students Of the students included in the analysis, 677 (98.33%) owned at least one MP. The average MP-current number was 1.29 (SD = 0.91) (Table 1). The students reported an ADTS-MP of 225.11 mins (SD = 177.08) (Table 1). The ADTS-MP correlated positively with the CMPPUS score (rho = 0.353). The MP-current correlated weakly with the CMPPUS-10 (rho = 0.121) (Table 2). In a gender-stratified analysis, a stronger correlation between ADTS-MP and CMPPUS-10 was observed
Table 2 Relationship of variables according to Spearman's Rank Order Correlation (n = 686). Variables
MP measures Health measures
Spearman's Rank Order (two-tailed) [rho (p)]
CMPPUS-10 ADTS-MP MP-current BPISF-C(PI) BPISF-C(PIGA) CESS
DASS-21 Depression
BPISF-C(PI)
BPISF-C(PIGA)
CESS
0.385 0.188⁎⁎ 0.054 0.323⁎⁎ 0.434⁎⁎ 0.236⁎⁎
0.224 0.162⁎⁎ 0.077⁎ – – –
0.292 0.161⁎⁎ 0.117⁎⁎ 0.649⁎⁎ – –
0.249 0.207⁎⁎ 0.102⁎⁎ 0.221⁎⁎ 0.200⁎⁎ –
⁎⁎
⁎⁎
⁎⁎
⁎⁎
ADTS-MP
MP-current
0.353 – – – – –
0.121⁎⁎ 0.105⁎⁎ – – –
⁎⁎
Note: *p < .05, **p < .01, ***p < .001; MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale. 4
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Table 3 Relationships of variables in female students according to Spearman's Rank Order Correlation. Variables
MP measures Health measures
Spearman's Rank Order (two-tailed) [rho (p)]
CMPPUS-10 ADTS-MP MP-current BPISF-C(PI) BPISF-C(PIGA) CESS
DASS-21 depression
BPISF-C(PI)
BPISF-C(PIGA)
CESS
0.385 0.162⁎⁎ 0.081⁎ 0.323⁎⁎ 0.434⁎⁎ 0.236⁎⁎
0.224 0.161⁎⁎ 0.110⁎⁎ – – –
0.292 0.207⁎⁎ 0.135⁎⁎ 0.649⁎⁎ – –
0.249 0.353⁎⁎ 0.080⁎ 0.221⁎⁎ 0.200⁎⁎ –
⁎⁎
⁎⁎
⁎⁎
⁎⁎
ADTS-MP
MP-current
0.353 – – – – –
0.198⁎⁎ 0.141⁎⁎ – – – –
⁎⁎
Note: *p < .05, **p < .01, ***p < .001; MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale. Table 4 Relationships of variables in male students according to Spearman's Rank Order Correlation. Variables
MP measures Health measures
Spearman's Rank Order (two-tailed) [rho (p)]
CMPPUS-10 ADTS-MP MP-current BPISF-C(PI) BPISF-C(PIGA) CESS
DASS-21 depression
BPISF-C(PI)
BPISF-C(PIGA)
CESS
0.345 0.155⁎⁎ 0.042 0.344⁎⁎ 0.414⁎⁎ 0.243⁎⁎
0.215 0.161⁎⁎ 0.061 – – –
0.289 0.148⁎⁎ 0.106⁎ 0.606⁎⁎ – –
0.257 0.211⁎⁎ 0.125⁎ 0.214⁎⁎ 0.202⁎⁎ –
⁎⁎
⁎⁎
⁎⁎
⁎⁎
ADTS-MP
MP-current
0.294 – – – – –
0.074 0.169⁎⁎ – – – –
⁎⁎
Note: *p < .05, **p < .01, ***p < .001; MP-current: Current number of MP ownerships, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale.
significant (all p < .001). Regarding the ADTS-MP, the results of atrisk MP users were irregular, with a median of 300.00 mins and mean of 318.54 mins versus 240.00 and 315.31 mins, respectively, for problematic MP users. Although the MP-current varied significantly among the sub-groups, the median value was the same for all four types of MP users (1.00). The four types of MP users did not differ significantly with respect to age (p > .05). In a gender-stratified analysis, female students had significantly higher ADTS-MP, CMPPUS-10, DASS-21 Depression, BPISF-C(PI) and BPISF-C(PIGA) scores when compared to male students. However, no significant gender differences were observed in the CESS and MP-current scores (Table 6).
In the regression analysis, CMPPUS-10 was identified as a significant predictor of depression, bodily pain (using the variable ‘Factor (Pain)’ from the factor analysis) and sleepiness measures (p < .001), as shown in Models 1–3 in Table 7. Next, a mediation analysis was performed to further examine the effects of bodily pain and sleepiness on the relationship between the CMPPUS-10 score and depression. The mediating effect of Factor(Pain) on the association between the CMPPUS-10 and DASS21 Depression scores was calculated as [(0.285 * 0.324)/0.353], yielding a value of 26.16% (Fig. 1). Similarly, the mediating effect of the CESS score on the association between the CMPPUS-10 and DASS21 Depression scores was calculated as [(0.262 *
Table 5 Comparison of variables among MP users according to the Kruskal–Wallis test. Types of MP Users (CMPPUS-10 scores, n, %) All (10–100, N = 686, 100%) Variables
Median
ADTS-MP MP-current Age DASS-21 Depression BPISF-C(PI) BPISF-C(PIGA) CESS CMPPUS-10 subscale Craving Withdrawal Loss of Control Negative Life Consequences Peer Dependence
180.00 1.00 14.00 4.00 2.00 1.57 8.00 – 7.00 3.33 5.33 6.00 2.00
Occasional (10–27, n = 101, 14.72%)
Habitual (28–58, n = 441, 64.29%)
At risk (59–65, n = 75, 10.93%)
Problematic (66–100, n = 69, 10.06%)
p
120.00 1.00 14.00 1.00 1.25 1.00 7.00 – 4.00 1.00 1.67 1.00 2.00
180.00 1.00 14.00 3.00 2.00 1.57 8.00 – 7.00 3.33 5.33 2.00 6.00
300.00 1.00 14.00 7.00 3.00 2.71 9.00 – 8.00 5.33 7.33 4.00 8.00
240.00 1.00 14.00 8.00 3.00 3.00 10.00 – 9.00 6.67 8.67 5.00 9.00
0.000 0.000 0.902 0.000 0.000 0.000 0.000 – 0.000 0.000 0.000 0.000 0.000
Note: MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale. 5
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2013; OTCA, 2012, 2015). Local studies have identified entertainment purposes and social communication as the main reasons for MPU by students (Chui, 2015; HKCS, 2013). This study confirmed the association between MPU and depression, consistent with the findings of previous overseas studies (Bianchi & Phillips, 2005; Ikeda & Nakamura, 2014; Sánchez-Martínez & Otero, 2009; Yen et al., 2009). However, the possible mechanisms underlying this association remain obscure. This study demonstrated that bodily pain had a significant mediating effect on the association of MPU with depression. Researchers in Canada, China and South Korea have identified repeated motions and prolonged use as the causes of pain (Berolo et al., 2011; Kim & Kim, 2015; Shan et al., 2013). The current study also observed an association between pain and MPU. The link between pain and depression was studied in depth by Kroenke et al. (2011), who identified a reciprocal effect between these variables in a randomised longitudinal study. The results of our mediation analysis and the previous literature identify bodily pain as a likely mediator in the relationship between MPU and depression. The mediating effects of sleep disturbance on the relationship between MPU and depression have been more extensively studied. İNal, Çetİntürk, Akgönül, and Savaş (2015) demonstrated that sleep problems resulting from MPU mediated psychological problems such as depression and anxiety. Lemola, Perkinson-Gloor, Brand, DewaldKaufmann, and Grob (2015) identified sleep as a mediator between depression and MPU. This study yielded similar results, as problematic MP users reported more daytime sleepiness and higher depression levels. Additionally, daytime sleepiness was found to mediate the association of MPU with depression. This exploratory analysis of crosssectional data led us to hypothesise that pain and daytime sleepiness are two mediators of the relationship between MPU and depression. Particularly, bodily pain may exert a greater mediating effect. These findings may contribute new insights to the dynamics between MPU and negative health outcomes in adolescents. Further observational studies for confirmation may be necessary. In addition to the above findings, Montag et al. (2015) reported that the advent of the smartphone had increasingly replaced zeitgebers or alarm clocks, thus contributing to the overuse of smartphones while in bed. This change may have negative effects on sleep. Importantly, the reintroduction and use of analogue zeitgebers may help to reduce smartphone addictive tendencies. In this study, significantly higher median CMPPUS-10, ADTS-MP and depression scores were reported by female students, compared to male students. Similar results were reported in studies conducted in Australia (Bianchi & Phillips, 2005), Austria (Augner & Hacker, 2012), Taiwan (Yen et al., 2009) and Turkey (Demirci, Akgonul, & Akpinar, 2015). Previous studies suggest that this gender-based difference may be due to differences in the purpose of MPU. Specifically, female subjects tend to use MPs more frequently for social connection, whereas male subjects tended to use MPs for business (Bianchi & Phillips, 2005; Park & Lee, 2012). However, this explanation may not be applicable to the current study population, which had a mean age of 14.14 years.
Table 6 Differences across genders as determined using the Mann–Whitney U Test. Variables
Male
Female
n (male/female)/ (p)
240.00 1.00 49.00 4.00 2.50 1.85 9.00 7.00 3.66 6.00 2.50 7.00
364/307 368/312 371/315 370/315 363/309 362/309 371/315 371/315 371/315 371/315 371/315 371/315
Median ADTS-MP (mins) MP-current CMPPUS-10 DASS-21 Depression BPISF-C(PI) BPISF-C(PIGA) CESS CMPPUS-10 subscale Craving Withdrawal Loss of Control Negative Life Consequences Peer Dependence
150.00 1.00 43.00 3.00 1.75 1.57 8.00 6.00 3.33 5.33 2.00 5.00
(0.000) (0.643) (0.000) (0.001) (0.000) (0.003) (0.552) (0.000) (0.000) (0.003) (0.970) (0.000)
Note: The sums of numbers may not equal 686 because of the pairwise deletion method. MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISFC (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale.
0.153) / 0.353], yielding a value of 11.36% (Fig. 2). A hierarchical regression analysis showed that Factor(Pain) and the CESS score mediated the association between mobile phone-related variables and depression, leading to an increase in the adjusted R2 value (0.229) (Table 8). 4. Discussion This study revealed the current situation of MPU and the associated health problems and interrelationships in a sample of Hong Kong secondary school students. Notably, female students received significantly higher MPU scores and reported more severe depression, pain and sleepiness, compared to their male counterparts. This study also identified the mean CMPPUS-10 score (mean = 45.21) and cut-off point (66 units) for secondary school students classified as problematic MP users (90th percentile). These values were higher than those reported in a 2012 Swiss study which was the first to apply the MPPUS-10 (mean = 30.60, cut-off = 51 units) (Roser, Schoeni, Foerster, & Röösli, 2016). This difference may be attributable to advances in technology and the convenience of mobile data, as suggested by Roser et al. (2016). In this study, the total duration of MPU, 225.11 mins, was also higher than that reported by the HKCS (2013), with more than half of respondents reporting daily MPU durations of 180.00 mins. Since the introduction of the fourth generation (4G) mobile network in Hong Kong in 2012, increasing trends in MP or smartphone ownership have been observed in both students and the overall population (HKCS, Table 7 Regression model of health variables.
Linear regression Multiple linear regression
Model
Dependent
Model R2
Model Predictors
β
Tolerance
VIF
1 2 3 4
DASS-21 Depression Factor(Pain) CESS DASS-21 Depression
0.125 0.081 0.069 0.221
5
DASS-21 Depression
0.146
CMPPUS-10 CMPPUS-10 CMPPUS-10 Factor(Pain) CMPPUS-10 CMPPUS-10 CESS
0.353*** 0.285*** 0.262*** 0.324*** 0.261*** 0.313*** 0.153***
– – – 0.919 0.919 0.931 0.931
– – – 1.089 1.089 1.074 1.074
Note: *p < .05, **p < .01, ***p < .001; β represents the standardised coefficient beta in the linear regression; CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, CESS: Chinese version of the Epworth Sleepiness Scale; Factor(Pain): Factor analysis of Pain intensity and Pain interference with general activity in the Brief Pain Inventory Short Form Chinese; VIF: Variance inflation factor. 6
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Fig. 1. Mediating effect of Factor(Pain) on the association between the CMPPUS-10 and DASS21 Depression scores. Note: β represents the standardised coefficient beta in the linear regression, while β 1 represents the standardised coefficient beta in the multiple linear regression. CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale; Factor(Pain): Factor analysis of Brief Pain Inventory Short Form Chinese.
Fig. 2. Mediating effect of the CESS score on the association between the CMPPUS-10 and DASS21 Depression scores. Note: β represents the standardised coefficient beta in a linear regression, while β 1 represents the standardised coefficient beta in a multiple linear regression. CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale; CESS: Chinese version of the Epworth Sleepiness Scale.
the susceptibility of mood to MPU or merely serves as a confounder that reflects the different functions of MPU are warranted.
Table 8 Results of a hierarchical regression analysis of daytime sleepiness and pain.
Step 1: Demographic Age Gender Grade Adjusted R2 F
Model 1
Model 2
DASS-21 depression
CESS
variables 0.078 0.145* −0.015 0.018 5.006
Step 2: Phone relationship variables With/Without MP 0.068 Ownership MP-current 0.000 ADTS-MP 0.064 CMPPUS-10 0.314*** 2 Adjusted R 0.132 2 Δ Adjusted R 0.114 ΔF 21.821 Step 3: Mediators CESS Factor(Pain) Adjusted R2 Δ Adjusted R2 ΔF
Model 3 Factor(Pain)
4.1. Strengths and limitations
DASS-21 Depression
0.177* 0.020 −0.062 0.011 3.498
0.229** 0.147*** −0.188* 0.027 6.961
0.078 0.145* −0.015 0.018 5.006
0.065
−0.003
0.068
0.046 0.165*** 0.210*** 0.107 0.096 18.458
0.047 0.047 0.265*** 0.104 0.077 14.765
0.000 0.064 0.314*** 0.132 0.114 21.821
To the best of our knowledge, this study was the first to translate the well-validated MPPUS-10 into the Chinese language (CMPPUS-10). Our characterisation of the CMPPUS-10 indicated satisfactory levels of internal consistency (α = 0.83) and test-retest reliability (ICC = 0.79 and r = 0.87), and the overall questionnaire was found to be reliable and valid (CVI = 0.88). The CMPPUS-10 may predict MPU-associated adverse health outcomes, as indicated by the observed associations of high CMPPUS-10 score with depressive symptoms, pain and daytime sleepiness. Moreover, a potential cut-off CMPPUS-10 score (66 units) for the detection of problematic MP users in the local Hong Kong context was identified. Future studies should further evaluate the psychometric properties of the CMPPUS-10 to confirm its validity and reliability. However, several limitations of this study were identified. The study involved a convenient sample, which may have led to selection bias and affected the generalisability of the results. To reduce this limitation, the convenience sample was recruited from three main geographical clusters in Hong Kong. The positive findings generated by this cross-sectional study warrant future longitudinal studies of causation in larger samples. Self-administered questionnaires are susceptible to self-report bias, as participants might have recalled recent information more accurately. However, the questionnaire items only required students to recall information that occurred during the past 4 weeks. Therefore, the responses were less likely to be affected by an inaccurate recall. Moreover, the students' experiences of pain could not be understood in detail, as the areas shaded on body diagrams may have been subject to under- or over-estimation. A future qualitative study might provide more in-depth insights into the patterns and purposes of MP in adolescents. Finally, the MPPUS is not a timely questionnaire to assess problematic MPU anymore, because most research deals with smartphones, which might be a sophisticated form of the mobile phone including constant access to the Internet. Beyond this self-report approaches to assess smartphone usage have been recently questioned.
0.080* 0.311*** 0.229 0.097 41.296
Note: *p < .05, **p < .01, ***p < .001; All standardised regression coefficients were determined from the final mode, n = 686. CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale; ADTS-MP: Average daily time spent on mobile phone; DASS: Depression Anxiety Stress Scale; CESS: Chinese version of the Epworth Sleepiness Scale; Factor(Pain): Factor analysis of the Brief Pain Inventory Short Form Chinese.
Interestingly, Lemola et al. (2015) observed a similar gender difference in MPU and depressive symptoms in a teenage population. In that study, females respondents were significantly more likely to send texts, whereas male respondents more frequently played video games and watched videos. Further studies to verify whether gender truly affects 7
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Psychiatrists' and psychologists' assisted self-reports were found to underestimate numbers of calls and text message, and smartphone use durations, compared with the exact time recorded via a mobile application (Lin et al., 2015; Montag et al., 2015).
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5. Conclusions This study involved 686 Hong Kong secondary school students who reported an average daily MPU duration of 225.11 mins. The study identified 69 (10.06%) students as problematic MP users, 95 (13.87%) with severe levels of depression, 204 (29.74%) with excessive daytime sleepiness and 434 (63.27%) with pain. A longer MPU duration and a higher CMPPUS-10 score correlated positively with depression, pain and sleepiness. Female students generally had higher CMPPUS-10 scores, which indicated more problematic MPU, a longer MPU duration and poorer health conditions. Finally, bodily pain and daytime sleepiness were identified as significant mediators of the relationship between MPU and depression. Funding sources This study was not sponsored, and no sources of funding were received. Declarations of interest None. Acknowledgements This project was supported by the School of Nursing, The Hong Kong Polytechnic University. Chiang C. L., Lam Y. Y., Lee H. and Wu S. T. provided valuable advice regarding the validation of questionnaires. The English back translation was performed by Chow H.Y. The authors would also like to thank all the participating schools, parents and students. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.addbeh.2019.04.033. References Adachi-Mejia, A. M., Edwards, P. M., Gilbert-Diamond, D., Greenough, G. P., & Olson, A. L. (2014). TXT me I'm only sleeping: Adolescents with mobile phones in their bedroom. Family & Community Health, 37(4), 252–257. https://doi.org/10.1097/FCH. 0000000000000044. Adams, S. K., & Kisler, T. S. (2013). Sleep quality as a mediator between technologyrelated sleep quality, depression, and anxiety. Cyberpsychology, Behavior and Social Networking, 16(1), 25–30. https://doi.org/10.1089/cyber.2012.0157. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (DSM-5®) (5th ed.). Washington: American Psychiatric Publishing. Augner, C., & Hacker, G. W. (2012). Associations between problematic mobile phone use and psychological parameters in young adults. International Journal of Public Health, 57(2), 437–441. https://doi.org/10.1007/s00038-011-0234-z. Berolo, S., Wells, R. P., & Amick, B. C., III (2011). Musculoskeletal symptoms among mobile hand-held device users and their relationship to device use: A preliminary study in a Canadian university population. Applied Ergonomics, 42(2), 371–378. https://doi.org/10.1016/j.apergo.2010.08.010. Bianchi, A., & Phillips, J. G. (2005). Psychological predictors of problem mobile phone use. Cyberpsychology & Behavior, 8(1), 39–51. https://doi.org/10.1089/cpb.2005. 8.39. Bickham, D. S., Hswen, Y., & Rich, M. (2015). Media use and depression: Exposure, household rules, and symptoms among young adolescents in the USA. International Journal of Public Health, 60(2), 147–155. https://doi.org/10.1007/s00038-0140647-6. Billieux, J. (2012). Problematic use of the mobile phone: A literature review and a pathways model. Current Psychiatry Reviews, 8(4), 299–307. https://doi.org/10. 2174/157340012803520522. Billieux, J., Maurage, P., Lopez-Fernandez, O., Kuss, D. J., & Griffiths, M. D. (2015). Can disordered mobile phone use be considered a behavioral addiction? An update on
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The relationships between mobile phone use and depressive symptoms, bodily pain, and daytime sleepiness in Hong Kong secondary school students Ka Chun Nga, Lai Har Wub, Hoi Yan Lama, Lai Kuen Lama, Po Yan Nipa, Cho Man Nga, ⁎ Ka Chun Leungc, Sau Fong Leungb, a
Master of Nursing, The Hong Kong Polytechnic University, Hong Kong The School of Nursing at The Hong Kong Polytechnic University, Hong Kong c Bachelor of Medicine and Bachelor of Surgery, The University of Hong Kong, Hong Kong b
HIGHLIGHTS
10% of 686 students were problematic mobile phone users. • Approximately and prolonged mobile phone use correlated with negative health consequences. • Problematic and daytime sleepiness mediated the relationship of mobile phone use with depression. • Pain • This was the first study to translate and use the Chinese-language 10-item Mobile Phone Problem Use Scale. ARTICLE INFO
ABSTRACT
Keywords: Mobile phone Mobile phone problem use Sleep Depression Pain Secondary school students
Introduction: Studies have found that increased mobile phone use (MPU) is associated with multiple health issues such as depression, disordered sleep and pain. However, the current situation and interrelationships of these problems remain unexplored in the Hong Kong population. Objectives: This study aimed to understand the situation and problematic use of mobile phones by Hong Kong secondary school students and to investigate depressive symptoms, bodily pain and daytime sleepiness and the associations of these factors with MPU in Hong Kong secondary school students. Methods: This quantitative cross-sectional design study was based on self-administered questionnaires completed at five secondary schools. The questionnaire comprised five sections: MPU as measured by the Chinese version of the 10-Item Mobile Phone Problem Use Scale (CMPPUS-10); depressive symptoms according to the Depression Anxiety Stress Scale-21 Chinese Version (DASS-21); bodily pain according to the Brief Pain Inventory Short Form Chinese (BPISF-C); daytime sleepiness as measured using the Chinese version of the Epworth Sleepiness Scale (CESS) and socio-demographic questions. Results: A total of 686 students were recruited. The CMPPUS-10 score correlated positively with the average daily duration of MPU and the presence of depression, daytime sleepiness and bodily pain. Problematic mobile phone users received significantly higher scores for depression severity, bodily pain and daytime sleepiness. Health problems were significantly more severe in female than in male students. Bodily pain and daytime sleepiness mediated the relationship of MPU with depression. Conclusions: Problematic MPU was associated with depression, bodily pain and daytime sleepiness. These findings will inform further studies of MPU-related health problems.
1. Introduction In recent years, the popularity of mobile phones (MPs) has increased dramatically worldwide. A MP is a device that connects wirelessly to a
phone system via radio signals and can be used anywhere. A smartphone is a type of MP that can connect to the Internet and be used as a computer (Cambridge University Press, 2015a, 2015b). In Hong Kong, the smartphone penetration rate increased significantly from 2012
Corresponding author at: PQ404, School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. E-mail addresses: [email protected] (K.C. Ng), [email protected] (L.H. Wu), [email protected] (H.Y. Lam), [email protected] (L.K. Lam), [email protected] (P.Y. Nip), [email protected] (C.M. Ng), [email protected] (K.C. Leung), [email protected] (S.F. Leung). ⁎
https://doi.org/10.1016/j.addbeh.2019.04.033 Received 30 October 2018; Received in revised form 26 April 2019; Accepted 29 April 2019 0306-4603/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Ka Chun Ng, et al., Addictive Behaviors, https://doi.org/10.1016/j.addbeh.2019.04.033
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(54.0%) to 2014 (77.2%), and each resident owned an average of 2.40 MPs in 2014 (Census and Statistics Department and Hong Kong, 2015; Office of the Communications Authority, 2015). This increase in smartphone usage was likely driven by the powerful advantages of smartphones, including the ability to connect to the Internet, the availability of social networks such as Facebook, WhatsApp and the development of other smartphone applications used for various functions, including gaming, gambling and music streaming. The Hong Kong Computer Society (HKCS) (2013) revealed that 90.4% of students in Hong Kong owned a MP, which represented a > 10% increase within a 1-year period. A comparison of MP use (MPU) among secondary school students in East Asian areas, including Japan (70.8%) (Cabinet Office, 2014), South Korea (93.2%–94.8%) (Statistics Korea, 2014) and Taiwan (69.3%) (Chang, 2013), reported that the second highest rate was observed in Hong Kong. The high level of MPU has drawn the attention of researchers towards the negative effects and problematic use of these devices (Bianchi & Phillips, 2005; Yen, Ko, Yen, Chang, & Cheng, 2009). In particular, excessive MPU can lead to financial problems, aggressive behaviour (e.g. cyberbullying), self-reported dependence and addiction-like symptoms (e.g. cravings and lack of control) (Billieux, Maurage, LopezFernandez, Kuss, & Griffiths, 2015). Billieux (2012) described a theoretical framework which integrated four pathways to explain the aetiology and maintenance of problematic MPU. According to this framework, problematic MPU by an individual can be driven by i) an impulsive pathway characterised by poor self-control and/or maladaptive regulation of emotions; ii) a relationship maintenance pathway involving a constant need for reassurance, promoted by maladaptive cognition and/or insecure attachment; iii) an extraversion pathway involving an increased desire to communicate with peers and establish new potential relationships and iv) a cyber addiction pathway, characterised by the massive engagement in a wide range of online activities such as video games and social networks. Other researchers currently prefer other terms than problematic MPU including smartphone addiction and smartphone use disorder. The latter term has been coined as a reaction to the Gaming Disorder nomenclature in ICD-11 and the IPACE model as proposed by Brand et al. (2016) in order to strive for unification of terms in the literature. Smartphone use disorder has also been linked with the excessive use of social media platforms. In one study, smartphone use disorder had high and significant associations with the problematic use of WhatsApp (WhatsApp Use Disorder) and Facebook usage (Facebook Use Disorder) (Sha, Sariyska, Riedl, Lachmann, and Montag, in press). The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) does not include a MP-specific addiction disorder (American Psychiatric Association, 2013). However, Internet Gaming Disorder may be eligible for further study as a non-substance-related addiction, similar to a gambling disorder. South Korean researchers have attempted to investigate addictive smartphone behaviour in the context of advanced functions and entertainment services (Kwon et al., 2013; Kwon, Kim, Cho, & Yang, 2014). The rapid increase of MPU among Hong Kong students and the associated problems, including smartphone addiction and smartphone use disorder, should warrant attention. MPU has been associated with health problems such as depression, bodily pain and sleep problems. Multiple studies have highlighted a positive correlation between MPU and depression in adolescents. For example, a Taiwanese study found that depressed individuals were more likely to be problematic MP users (Yen et al., 2009). Similarly, studies of American and European adolescents reported that intensive MP users were more susceptible to depression (Bickham, Hswen, & Rich, 2015; Sánchez-Martínez & Otero, 2009). A systematic review of nine studies revealed a significant association between the severity of depression and problematic or general smartphone use in different samples of high school and college students and adults (Elhai, Dvorak, Levine, & Hall, 2017). However, an online survey of participants aged 16–59 years found no connection between electronic use (including
MPU) and depression (Harwood, Dooley, Scott, & Joiner, 2014). Therefore, the effects of MPU on psychological changes in students remain uncertain. Taiwanese researchers found that students faced the risk of neck and hand pain when texting via MPs (Lin & Peper, 2009). Similarly, a study of Canadian students correlated a longer duration of MPU with an increased risk of neck and shoulder pain (Berolo, Wells, & Amick III, 2011). Moreover, Chinese students who reported prolonged MPU also experienced low back pain (Shan et al., 2013). However, a Finnish study found no relationship between MPU and neck/shoulder pain (Hakala, Rimpelä, Saarni, & Salminen, 2006). In summary, MPU appears to be linked to various forms of bodily discomfort. MPU, including messaging after bedtime, was associated with poor sleep quality in a study of American college students (Adams & Kisler, 2013). Furthermore, Adachi-Mejia, Edwards, Gilbert-Diamond, Greenough, and Olson (2014) observed that more than half of American students left their MPs switched on while sleeping, while more than a third of respondents continued to text after bedtime, which decreased their sleep durations. Mak et al. (2014) compared MPs with televisions, computers and portable video devices and found that only the former was associated with a shorter sleep duration, poorer sleep quality and serious daytime sleepiness among Hong Kong students. Daytime sleepiness has been associated with various sleep problems, such as insomnia and sleep apnoea, which can affect students' development (Chung, 2000; Mak, Lee, Ho, Lo, & Lam, 2012). Therefore, sleepiness as a potential adverse consequence of MPU warrants further examination. In summary, the adverse health effects of MPU have not been fully delineated. This study focused on the relationships of current MPU with depression, daytime sleepiness and bodily pain among Hong Kong secondary school students. 1.1. Objectives The objectives of this study were to understand the situation and problematic MPU among Hong Kong secondary school students, investigate depressive symptoms, bodily pain and daytime sleepiness in the participants, and examine the relationships of MPU with these negative consequences in the study population. 2. Methods 2.1. Research design This study used a quantitative cross-sectional design to examine the current situation regarding MPU and the relationships of this behaviour with depressive symptoms, bodily pain and daytime sleepiness in a sample of secondary school students. 2.2. Sampling Five secondary schools were approached between May and July 2015 via convenience sampling. All schools were located in the three main geographical clusters of Hong Kong (Hong Kong Island, Kowloon and the New Territories). Hong Kong secondary school students are equivalent to junior and senior high school students in the western education system. Therefore, the participant inclusion criteria were status as a Form 1–5 secondary school student at the time of study, an age between 12 and 19 years and the use of a MP within the previous 4 weeks. The exclusion criterion was a lack of MPU within the previous 4 weeks. 2.3. Instruments The self-administered questionnaire comprised five domains, including MPU, depressive symptoms, bodily pain, daytime sleepiness and socio-demographic information. The following instruments were 2
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1991, 2019). A score > 10 is considered to indicate EDS. This study adopted the validated Chinese version, which yielded good test-retest reliability (rho = 0.72) and internal consistency (α = 0.80) (Chung, 2000).
included after a review of appropriateness based on factors such as content validity, reliability and the availability of a Chinese-language version. 2.3.1. Chinese version of the 10-item Mobile phone problem use scale (CMPPUS-10) The MP Problem Use Scale (MPPUS-27) is a 27-item self-reported measure developed and validated by Bianchi and Phillips (2005). This measure has a high level of internal consistency (α = 0.93) and good construct validity, as demonstrated by its strong correlations with other measures of MPU and addiction. The MPPUS-27 is used widely and is considered as a possible ‘gold standard’ for the evaluation of problematic MPU (Lopez-Fernandez, Honrubia-Serrano, Freixa-Blanxart, & Gibson, 2014). The short version, MPPUS-10, was specifically designed to measure MPU in adolescents and was selected for its brevity, good internal consistency (α = 0.85) and strong correlation (r = 0.95) with the original 27-item scale (Foerster, Roser, Schoeni, & Röösli, 2015). Responses to the items on the MPPUS-10 were measured using a 10point Likert scale. A higher total score indicated a higher likelihood of problem MPU. The MPPUS-10 includes five subscales: ‘loss of control’, ‘withdrawal’, ‘negative life consequences’, ‘craving’ related to addictive behaviour and ‘peer dependence’ related to the influences of adolescents and peers. The MPPUS-10 was not constructed with cut-off points. The 15th, 80th and 90th-percentile MPPUS-10 scores have been used to classify occasional, habitual, at-risk and problematic MP users, with reference to a previous study of the MPPUS-27 (Lopez-Fernandez et al., 2014) and general practices adopted in the context of gambling addiction (Chow, Leung, Ng, & Yu, 2009). To address the lack of a validated Chinese-language measure of problem MPU, a Chinese version of the MPPUS-10 (CMPPUS-10) was generated via back translation in this study. First, the research team translated the MPPUS-10 into Chinese. Next, the Chinese version was back translated into English by a thirdparty professional translator. The back-translated version was then compared to the original English version by the team, with comments from an expert panel. The CMPPUS-10 yielded a satisfactory level of internal consistency (α = 0.83) and test-retest reliability, as demonstrated by the intraclass correlation (ICC = 0.79) and Pearson's product-moment correlation (r = 0.87) in this study.
2.3.5. Sociodemographic data Sociodemographic data were collected using a self-designed 14-item instrument. The following data were collected: age, gender, grade of study, family background, chronic illnesses, average daily time spent on MP (ADTS-MP) and current number of owned MPs (MP-current). 2.4. Content validity and reliability A panel of three experts was invited to rate the degree of relevance of the questionnaire. The content validity index (CVI) was 0.88, which was considered acceptable (Lynn, 1986; Polit & Beck, 2004). This CVI also supported the validity of the back-translated CMPPUS-10. Testretest reliability was evaluated in a sample of 30 students who completed the CMPPUS-10 twice at a 2-week interval. The overall reliability measures of these questionnaires, including CMPPUS-10, were acceptable, with satisfactory values for correlation (r = 0.76) and internal consistency (α = 0.84). 2.5. Data collection procedure The permission of secondary school principals was sought through invitation letters. Subsequently, letters with refusal reply slips were distributed to the students for parental approval at least 3 days before the study. These letters stated the purpose of the study, date of the event, anonymous data collection protocol, confidentiality and the students' right to withdraw at any time without penalty. The refusal reply slips were only required from parents who wished to refuse their children's participation. During the survey, only students without refusal reply slips were allowed to participate. The students were given information about the study and the voluntary nature of participation before the questionnaires were distributed. Two research team members were present during data collection to ensure voluntary participation. The students were given 15 mins to complete the questionnaires. Extra time was given upon request. Students who opted out were asked to study in the classroom. The questionnaires were stored in a locked cabinet for 12 months after completion of the research and then destroyed.
2.3.2. Brief pain inventory short form Chinese (BPISF-C) The Brief Pain Inventory Short Form Chinese (BPISF-C) measures two scores determined using a 10-point Likert scale: pain intensity [BPISF-C(PI)] and pain interference to general activity [BPISFC(PIGA)]. A higher score indicates a more severely painful experience. Participants were also given a diagram of the human body on which to indicate the painful body parts. The available Chinese version was found to have good internal consistency (α = 0.89) for pain intensity and (α = 0.92) for BPISF-C(PIGA) (Wang, Mendoza, Gao, & Cleeland, 1996).
2.6. Ethics Ethical approval was granted by the Human Subject Ethics SubCommittee of the Hong Kong Polytechnic University on May 6, 2015. The study adhered to the principles of the Declaration of Helsinki. In this study, refusal reply slips were considered a form of passive consent. This type of consent protocol has been commonly adopted by local schools in consideration of personal data confidentiality and voluntary participation (Mak et al., 2010; Wong, Stewart, Ho, Rao, & Lam, 2005).
2.3.3. Chinese version of the depression anxiety stress Scale-21 (DASS-21) The Chinese version of the Depression Anxiety Stress Scale-21 (DASS-21) comprises seven items intended to measure depression. The items are scored using a four-point Likert scale (α = 0.91 for Depression; α = 0.81 for Anxiety; α = 0.89 for Stress) (Lovibond & Lovibond, 1995). The total scores indicate the level of severity (normal, mild, moderate, severe and extremely severe). The DASS-21 was validated in the Hong Kong Chinese population by determining the intercorrelation phi values for Depression-Anxiety (0.61), Depression-Stress (0.63) and Anxiety-Stress (0.67) (Taouk, Lovibond, & Laube, 2001).
2.7. Statistical analysis The statistical analysis was conducted using the Statistical Package for the Social Science (SPSS), version 18.0. The pairwise deletion method was used to manage missing data (Schlomer, Bauman, & Card, 2010). Spearman's rank order correlation, the Kruskal–Wallis test and the Mann–Whitney U test were performed. Furthermore, multiple linear regression and mediation model analyses were used to investigate the dynamics of the variables. The variables were non-parametric according to the Shapiro–Wilk test (p < .05) and were non-collinear in a multiple regression, with a tolerance > 0.01 and variance inflation factor (VIF) < 10 (Pallant, 2010). The BPISF-C(PI) and BPISF-C(PIGA) scores were combined as a single variable, Factor (Pain) and subjected
2.3.4. Chinese version of the Epworth sleepiness scale (CESS) The Chinese version of the Epworth Sleepiness Scale (CESS) was used to assess excessive daytime sleepiness (EDS) in adolescents. On this scale, the respondents rate the likelihood of falling asleep during eight daily activities using a four-point Likert scale (α = 0.82) (Johns, 3
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among female students, compared to male students (Table 3 and Table 4). The positive significant correlation between the MP-current and CMPPUS-10 score remained in female but not male students. The mean CMPPUS-10 score was 45.21/100 units (SD = 16.14) (Table 1). Scores of 28, 59 and 66 units were identified as the 15th, 80th and 90th percentiles, respectively, and were used to categorise the four types of MP users (occasional, habitual, at-risk, and problematic MP users) (Table 3).
Table 1 Descriptive data of the participants (N = 686).
Age Male Female Grade - Form 1 Form 2 Form 3 Form 4 Form 5 With MP Ownership Without MP Ownership MP-current ADTS-MP (min) CMPPUS-10 (total score) CMPPUS-10 subscale Craving Withdrawal Loss of Control Negative Life Consequences Peer dependence DASS-21 Depression BPISF-C(PI) BPISF-C(PIGA) CESS
n
Mean (SD)/%
Range
680 371 315 146 296 153 25 66 677 8 686 671 686 686 686 686 686 686 685 672 671 686
14.14 (1.49) 54.08% 45.92% 21.28% 43.15% 22.30% 3.64% 9.62% 98.83% 1.17% 1.29 (0.91) 225.11 (177.08) 45.21 (16.14) 6.43 (2.49) 3.66 (1.94) 5.49 (2.43) 2.75 (1.85) 5.82 (2.81) 4.85 (4.67) 2.61 (1.69) 2.32 (1.64) 8.53 (4.09)
12–19 – – – – – – – 0–15 1–1200 1–100 1–10 1–10 1–10 1–10 1–10 0–21 1–10 1–10 0–24
3.2. Health measures of bodily pain, depressive symptoms and daytime sleepiness Bodily pain was reported using two scales from the BPISF-C. The mean scores on the BPISF-C(PI) and BPISF-C(PIGA) were 2.61 (SD = 1.69) and 2.32 (SD = 1.64), respectively (Table 1). A total of 434 (63.27%) students reported pain in at least one body part, and these data were grouped on a graph of the human body. Pain was most frequently reported in the head, neck, shoulder and/or back (n = 325, 47.38%), followed by the lower limbs (n = 132, 19.24%), upper limbs (n = 81, 11.81%), chest or abdomen areas (n = 76, 11.08%) and buttock area (n = 20, 2.92%). The mean DASS-21 score was 4.85 (SD = 4.67) (Table 1). Of the participating students, 401 (58.54%) had a normal mood, while 75 (10.95%), 114 (16.64%), 54 (7.88%) and 41 (5.99%) had mild, moderate, severe and extremely severe depression, respectively. Regarding daytime sleepiness, the mean CESS score was 8.53 (SD = 4.09) (Table 1). Moreover, 204 (29.74%) students received a CESS score > 10 and were classified as having EDS.
Note: The sums of the numbers may not equal 686 because of the pairwise deletion method. MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale.
3.3. Relationships between MPU and health measures
to a factor analysis in the regression analysis.
As shown in Table 2, CMPPUS-10 exhibited a moderate positive correlation (p < .001) with the DASS-21 Depression score (rho = 0.385) and weak positive correlations with the BPISF-C(PI) (rho = 0.224), BPISF-C(PIGA) (rho = 0.292) and CESS scores (rho = 0.249). The ADTS-MP exhibited similarly weak positive correlations (p < .001) with the three health measures. The MP-current exhibited weakly significant correlations with the pain and daytime sleepiness scores. Positive correlations were also detected between other health variables. The DASS-21 Depression score correlated positively with the BPISF-C(PI) (rho = 0.323), BPISF-C(PIGA) (rho = 0.434) and CESS scores (rho = 0.236). The CESS score correlated weakly with the BPISF-C(PI) (rho = 0.221) and BPISF-C(PIGA) scores (rho = 0.200) (Table 2). The Kruskal–Wallis test was used to examine the ADTS-MP, MPcurrent, depression, pain, daytime sleepiness and five CMPPUS-10 scores according to the type of MP user (occasional, habitual, at-risk, problematic) (Table 5). Problematic MP users received the highest scores for depression, BPISF-C(PIGA), daytime sleepiness and all five CMPPUS-10 subscales, and these differences were statistically
3. Results A total of 729 students were accessed, of whom 13 (1.78%) provided refusal slips. Thirty students were excluded according to the prespecified criteria or because they provided invalid data. The final data analysis included 686 participants. The demographics of the participants and descriptive data are reported in Table 1. 3.1. Situation of MPU among students Of the students included in the analysis, 677 (98.33%) owned at least one MP. The average MP-current number was 1.29 (SD = 0.91) (Table 1). The students reported an ADTS-MP of 225.11 mins (SD = 177.08) (Table 1). The ADTS-MP correlated positively with the CMPPUS score (rho = 0.353). The MP-current correlated weakly with the CMPPUS-10 (rho = 0.121) (Table 2). In a gender-stratified analysis, a stronger correlation between ADTS-MP and CMPPUS-10 was observed
Table 2 Relationship of variables according to Spearman's Rank Order Correlation (n = 686). Variables
MP measures Health measures
Spearman's Rank Order (two-tailed) [rho (p)]
CMPPUS-10 ADTS-MP MP-current BPISF-C(PI) BPISF-C(PIGA) CESS
DASS-21 Depression
BPISF-C(PI)
BPISF-C(PIGA)
CESS
0.385 0.188⁎⁎ 0.054 0.323⁎⁎ 0.434⁎⁎ 0.236⁎⁎
0.224 0.162⁎⁎ 0.077⁎ – – –
0.292 0.161⁎⁎ 0.117⁎⁎ 0.649⁎⁎ – –
0.249 0.207⁎⁎ 0.102⁎⁎ 0.221⁎⁎ 0.200⁎⁎ –
⁎⁎
⁎⁎
⁎⁎
⁎⁎
ADTS-MP
MP-current
0.353 – – – – –
0.121⁎⁎ 0.105⁎⁎ – – –
⁎⁎
Note: *p < .05, **p < .01, ***p < .001; MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale. 4
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Table 3 Relationships of variables in female students according to Spearman's Rank Order Correlation. Variables
MP measures Health measures
Spearman's Rank Order (two-tailed) [rho (p)]
CMPPUS-10 ADTS-MP MP-current BPISF-C(PI) BPISF-C(PIGA) CESS
DASS-21 depression
BPISF-C(PI)
BPISF-C(PIGA)
CESS
0.385 0.162⁎⁎ 0.081⁎ 0.323⁎⁎ 0.434⁎⁎ 0.236⁎⁎
0.224 0.161⁎⁎ 0.110⁎⁎ – – –
0.292 0.207⁎⁎ 0.135⁎⁎ 0.649⁎⁎ – –
0.249 0.353⁎⁎ 0.080⁎ 0.221⁎⁎ 0.200⁎⁎ –
⁎⁎
⁎⁎
⁎⁎
⁎⁎
ADTS-MP
MP-current
0.353 – – – – –
0.198⁎⁎ 0.141⁎⁎ – – – –
⁎⁎
Note: *p < .05, **p < .01, ***p < .001; MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale. Table 4 Relationships of variables in male students according to Spearman's Rank Order Correlation. Variables
MP measures Health measures
Spearman's Rank Order (two-tailed) [rho (p)]
CMPPUS-10 ADTS-MP MP-current BPISF-C(PI) BPISF-C(PIGA) CESS
DASS-21 depression
BPISF-C(PI)
BPISF-C(PIGA)
CESS
0.345 0.155⁎⁎ 0.042 0.344⁎⁎ 0.414⁎⁎ 0.243⁎⁎
0.215 0.161⁎⁎ 0.061 – – –
0.289 0.148⁎⁎ 0.106⁎ 0.606⁎⁎ – –
0.257 0.211⁎⁎ 0.125⁎ 0.214⁎⁎ 0.202⁎⁎ –
⁎⁎
⁎⁎
⁎⁎
⁎⁎
ADTS-MP
MP-current
0.294 – – – – –
0.074 0.169⁎⁎ – – – –
⁎⁎
Note: *p < .05, **p < .01, ***p < .001; MP-current: Current number of MP ownerships, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale.
significant (all p < .001). Regarding the ADTS-MP, the results of atrisk MP users were irregular, with a median of 300.00 mins and mean of 318.54 mins versus 240.00 and 315.31 mins, respectively, for problematic MP users. Although the MP-current varied significantly among the sub-groups, the median value was the same for all four types of MP users (1.00). The four types of MP users did not differ significantly with respect to age (p > .05). In a gender-stratified analysis, female students had significantly higher ADTS-MP, CMPPUS-10, DASS-21 Depression, BPISF-C(PI) and BPISF-C(PIGA) scores when compared to male students. However, no significant gender differences were observed in the CESS and MP-current scores (Table 6).
In the regression analysis, CMPPUS-10 was identified as a significant predictor of depression, bodily pain (using the variable ‘Factor (Pain)’ from the factor analysis) and sleepiness measures (p < .001), as shown in Models 1–3 in Table 7. Next, a mediation analysis was performed to further examine the effects of bodily pain and sleepiness on the relationship between the CMPPUS-10 score and depression. The mediating effect of Factor(Pain) on the association between the CMPPUS-10 and DASS21 Depression scores was calculated as [(0.285 * 0.324)/0.353], yielding a value of 26.16% (Fig. 1). Similarly, the mediating effect of the CESS score on the association between the CMPPUS-10 and DASS21 Depression scores was calculated as [(0.262 *
Table 5 Comparison of variables among MP users according to the Kruskal–Wallis test. Types of MP Users (CMPPUS-10 scores, n, %) All (10–100, N = 686, 100%) Variables
Median
ADTS-MP MP-current Age DASS-21 Depression BPISF-C(PI) BPISF-C(PIGA) CESS CMPPUS-10 subscale Craving Withdrawal Loss of Control Negative Life Consequences Peer Dependence
180.00 1.00 14.00 4.00 2.00 1.57 8.00 – 7.00 3.33 5.33 6.00 2.00
Occasional (10–27, n = 101, 14.72%)
Habitual (28–58, n = 441, 64.29%)
At risk (59–65, n = 75, 10.93%)
Problematic (66–100, n = 69, 10.06%)
p
120.00 1.00 14.00 1.00 1.25 1.00 7.00 – 4.00 1.00 1.67 1.00 2.00
180.00 1.00 14.00 3.00 2.00 1.57 8.00 – 7.00 3.33 5.33 2.00 6.00
300.00 1.00 14.00 7.00 3.00 2.71 9.00 – 8.00 5.33 7.33 4.00 8.00
240.00 1.00 14.00 8.00 3.00 3.00 10.00 – 9.00 6.67 8.67 5.00 9.00
0.000 0.000 0.902 0.000 0.000 0.000 0.000 – 0.000 0.000 0.000 0.000 0.000
Note: MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISF-C (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale. 5
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2013; OTCA, 2012, 2015). Local studies have identified entertainment purposes and social communication as the main reasons for MPU by students (Chui, 2015; HKCS, 2013). This study confirmed the association between MPU and depression, consistent with the findings of previous overseas studies (Bianchi & Phillips, 2005; Ikeda & Nakamura, 2014; Sánchez-Martínez & Otero, 2009; Yen et al., 2009). However, the possible mechanisms underlying this association remain obscure. This study demonstrated that bodily pain had a significant mediating effect on the association of MPU with depression. Researchers in Canada, China and South Korea have identified repeated motions and prolonged use as the causes of pain (Berolo et al., 2011; Kim & Kim, 2015; Shan et al., 2013). The current study also observed an association between pain and MPU. The link between pain and depression was studied in depth by Kroenke et al. (2011), who identified a reciprocal effect between these variables in a randomised longitudinal study. The results of our mediation analysis and the previous literature identify bodily pain as a likely mediator in the relationship between MPU and depression. The mediating effects of sleep disturbance on the relationship between MPU and depression have been more extensively studied. İNal, Çetİntürk, Akgönül, and Savaş (2015) demonstrated that sleep problems resulting from MPU mediated psychological problems such as depression and anxiety. Lemola, Perkinson-Gloor, Brand, DewaldKaufmann, and Grob (2015) identified sleep as a mediator between depression and MPU. This study yielded similar results, as problematic MP users reported more daytime sleepiness and higher depression levels. Additionally, daytime sleepiness was found to mediate the association of MPU with depression. This exploratory analysis of crosssectional data led us to hypothesise that pain and daytime sleepiness are two mediators of the relationship between MPU and depression. Particularly, bodily pain may exert a greater mediating effect. These findings may contribute new insights to the dynamics between MPU and negative health outcomes in adolescents. Further observational studies for confirmation may be necessary. In addition to the above findings, Montag et al. (2015) reported that the advent of the smartphone had increasingly replaced zeitgebers or alarm clocks, thus contributing to the overuse of smartphones while in bed. This change may have negative effects on sleep. Importantly, the reintroduction and use of analogue zeitgebers may help to reduce smartphone addictive tendencies. In this study, significantly higher median CMPPUS-10, ADTS-MP and depression scores were reported by female students, compared to male students. Similar results were reported in studies conducted in Australia (Bianchi & Phillips, 2005), Austria (Augner & Hacker, 2012), Taiwan (Yen et al., 2009) and Turkey (Demirci, Akgonul, & Akpinar, 2015). Previous studies suggest that this gender-based difference may be due to differences in the purpose of MPU. Specifically, female subjects tend to use MPs more frequently for social connection, whereas male subjects tended to use MPs for business (Bianchi & Phillips, 2005; Park & Lee, 2012). However, this explanation may not be applicable to the current study population, which had a mean age of 14.14 years.
Table 6 Differences across genders as determined using the Mann–Whitney U Test. Variables
Male
Female
n (male/female)/ (p)
240.00 1.00 49.00 4.00 2.50 1.85 9.00 7.00 3.66 6.00 2.50 7.00
364/307 368/312 371/315 370/315 363/309 362/309 371/315 371/315 371/315 371/315 371/315 371/315
Median ADTS-MP (mins) MP-current CMPPUS-10 DASS-21 Depression BPISF-C(PI) BPISF-C(PIGA) CESS CMPPUS-10 subscale Craving Withdrawal Loss of Control Negative Life Consequences Peer Dependence
150.00 1.00 43.00 3.00 1.75 1.57 8.00 6.00 3.33 5.33 2.00 5.00
(0.000) (0.643) (0.000) (0.001) (0.000) (0.003) (0.552) (0.000) (0.000) (0.003) (0.970) (0.000)
Note: The sums of numbers may not equal 686 because of the pairwise deletion method. MP-current: Current number of owned MPs, ADTS-MP: Average daily time spent on mobile phone, CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, BPISFC (PI): Brief Pain Inventory Short Form-Chinese (Pain intensity), BPISF-C (PIGA): Brief Pain Inventory Short Form-Chinese (Pain interference to general activity), CESS: Chinese version of the Epworth Sleepiness Scale.
0.153) / 0.353], yielding a value of 11.36% (Fig. 2). A hierarchical regression analysis showed that Factor(Pain) and the CESS score mediated the association between mobile phone-related variables and depression, leading to an increase in the adjusted R2 value (0.229) (Table 8). 4. Discussion This study revealed the current situation of MPU and the associated health problems and interrelationships in a sample of Hong Kong secondary school students. Notably, female students received significantly higher MPU scores and reported more severe depression, pain and sleepiness, compared to their male counterparts. This study also identified the mean CMPPUS-10 score (mean = 45.21) and cut-off point (66 units) for secondary school students classified as problematic MP users (90th percentile). These values were higher than those reported in a 2012 Swiss study which was the first to apply the MPPUS-10 (mean = 30.60, cut-off = 51 units) (Roser, Schoeni, Foerster, & Röösli, 2016). This difference may be attributable to advances in technology and the convenience of mobile data, as suggested by Roser et al. (2016). In this study, the total duration of MPU, 225.11 mins, was also higher than that reported by the HKCS (2013), with more than half of respondents reporting daily MPU durations of 180.00 mins. Since the introduction of the fourth generation (4G) mobile network in Hong Kong in 2012, increasing trends in MP or smartphone ownership have been observed in both students and the overall population (HKCS, Table 7 Regression model of health variables.
Linear regression Multiple linear regression
Model
Dependent
Model R2
Model Predictors
β
Tolerance
VIF
1 2 3 4
DASS-21 Depression Factor(Pain) CESS DASS-21 Depression
0.125 0.081 0.069 0.221
5
DASS-21 Depression
0.146
CMPPUS-10 CMPPUS-10 CMPPUS-10 Factor(Pain) CMPPUS-10 CMPPUS-10 CESS
0.353*** 0.285*** 0.262*** 0.324*** 0.261*** 0.313*** 0.153***
– – – 0.919 0.919 0.931 0.931
– – – 1.089 1.089 1.074 1.074
Note: *p < .05, **p < .01, ***p < .001; β represents the standardised coefficient beta in the linear regression; CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale, CESS: Chinese version of the Epworth Sleepiness Scale; Factor(Pain): Factor analysis of Pain intensity and Pain interference with general activity in the Brief Pain Inventory Short Form Chinese; VIF: Variance inflation factor. 6
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Fig. 1. Mediating effect of Factor(Pain) on the association between the CMPPUS-10 and DASS21 Depression scores. Note: β represents the standardised coefficient beta in the linear regression, while β 1 represents the standardised coefficient beta in the multiple linear regression. CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale; Factor(Pain): Factor analysis of Brief Pain Inventory Short Form Chinese.
Fig. 2. Mediating effect of the CESS score on the association between the CMPPUS-10 and DASS21 Depression scores. Note: β represents the standardised coefficient beta in a linear regression, while β 1 represents the standardised coefficient beta in a multiple linear regression. CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale, DASS: Depression Anxiety Stress Scale; CESS: Chinese version of the Epworth Sleepiness Scale.
the susceptibility of mood to MPU or merely serves as a confounder that reflects the different functions of MPU are warranted.
Table 8 Results of a hierarchical regression analysis of daytime sleepiness and pain.
Step 1: Demographic Age Gender Grade Adjusted R2 F
Model 1
Model 2
DASS-21 depression
CESS
variables 0.078 0.145* −0.015 0.018 5.006
Step 2: Phone relationship variables With/Without MP 0.068 Ownership MP-current 0.000 ADTS-MP 0.064 CMPPUS-10 0.314*** 2 Adjusted R 0.132 2 Δ Adjusted R 0.114 ΔF 21.821 Step 3: Mediators CESS Factor(Pain) Adjusted R2 Δ Adjusted R2 ΔF
Model 3 Factor(Pain)
4.1. Strengths and limitations
DASS-21 Depression
0.177* 0.020 −0.062 0.011 3.498
0.229** 0.147*** −0.188* 0.027 6.961
0.078 0.145* −0.015 0.018 5.006
0.065
−0.003
0.068
0.046 0.165*** 0.210*** 0.107 0.096 18.458
0.047 0.047 0.265*** 0.104 0.077 14.765
0.000 0.064 0.314*** 0.132 0.114 21.821
To the best of our knowledge, this study was the first to translate the well-validated MPPUS-10 into the Chinese language (CMPPUS-10). Our characterisation of the CMPPUS-10 indicated satisfactory levels of internal consistency (α = 0.83) and test-retest reliability (ICC = 0.79 and r = 0.87), and the overall questionnaire was found to be reliable and valid (CVI = 0.88). The CMPPUS-10 may predict MPU-associated adverse health outcomes, as indicated by the observed associations of high CMPPUS-10 score with depressive symptoms, pain and daytime sleepiness. Moreover, a potential cut-off CMPPUS-10 score (66 units) for the detection of problematic MP users in the local Hong Kong context was identified. Future studies should further evaluate the psychometric properties of the CMPPUS-10 to confirm its validity and reliability. However, several limitations of this study were identified. The study involved a convenient sample, which may have led to selection bias and affected the generalisability of the results. To reduce this limitation, the convenience sample was recruited from three main geographical clusters in Hong Kong. The positive findings generated by this cross-sectional study warrant future longitudinal studies of causation in larger samples. Self-administered questionnaires are susceptible to self-report bias, as participants might have recalled recent information more accurately. However, the questionnaire items only required students to recall information that occurred during the past 4 weeks. Therefore, the responses were less likely to be affected by an inaccurate recall. Moreover, the students' experiences of pain could not be understood in detail, as the areas shaded on body diagrams may have been subject to under- or over-estimation. A future qualitative study might provide more in-depth insights into the patterns and purposes of MP in adolescents. Finally, the MPPUS is not a timely questionnaire to assess problematic MPU anymore, because most research deals with smartphones, which might be a sophisticated form of the mobile phone including constant access to the Internet. Beyond this self-report approaches to assess smartphone usage have been recently questioned.
0.080* 0.311*** 0.229 0.097 41.296
Note: *p < .05, **p < .01, ***p < .001; All standardised regression coefficients were determined from the final mode, n = 686. CMPPUS-10: Chinese version of the 10-item Mobile Phone Problem Use Scale; ADTS-MP: Average daily time spent on mobile phone; DASS: Depression Anxiety Stress Scale; CESS: Chinese version of the Epworth Sleepiness Scale; Factor(Pain): Factor analysis of the Brief Pain Inventory Short Form Chinese.
Interestingly, Lemola et al. (2015) observed a similar gender difference in MPU and depressive symptoms in a teenage population. In that study, females respondents were significantly more likely to send texts, whereas male respondents more frequently played video games and watched videos. Further studies to verify whether gender truly affects 7
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Psychiatrists' and psychologists' assisted self-reports were found to underestimate numbers of calls and text message, and smartphone use durations, compared with the exact time recorded via a mobile application (Lin et al., 2015; Montag et al., 2015).
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5. Conclusions This study involved 686 Hong Kong secondary school students who reported an average daily MPU duration of 225.11 mins. The study identified 69 (10.06%) students as problematic MP users, 95 (13.87%) with severe levels of depression, 204 (29.74%) with excessive daytime sleepiness and 434 (63.27%) with pain. A longer MPU duration and a higher CMPPUS-10 score correlated positively with depression, pain and sleepiness. Female students generally had higher CMPPUS-10 scores, which indicated more problematic MPU, a longer MPU duration and poorer health conditions. Finally, bodily pain and daytime sleepiness were identified as significant mediators of the relationship between MPU and depression. Funding sources This study was not sponsored, and no sources of funding were received. Declarations of interest None. Acknowledgements This project was supported by the School of Nursing, The Hong Kong Polytechnic University. Chiang C. L., Lam Y. Y., Lee H. and Wu S. T. provided valuable advice regarding the validation of questionnaires. The English back translation was performed by Chow H.Y. The authors would also like to thank all the participating schools, parents and students. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.addbeh.2019.04.033. References Adachi-Mejia, A. M., Edwards, P. M., Gilbert-Diamond, D., Greenough, G. P., & Olson, A. L. (2014). TXT me I'm only sleeping: Adolescents with mobile phones in their bedroom. Family & Community Health, 37(4), 252–257. https://doi.org/10.1097/FCH. 0000000000000044. Adams, S. K., & Kisler, T. S. (2013). Sleep quality as a mediator between technologyrelated sleep quality, depression, and anxiety. Cyberpsychology, Behavior and Social Networking, 16(1), 25–30. https://doi.org/10.1089/cyber.2012.0157. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (DSM-5®) (5th ed.). Washington: American Psychiatric Publishing. Augner, C., & Hacker, G. W. (2012). Associations between problematic mobile phone use and psychological parameters in young adults. International Journal of Public Health, 57(2), 437–441. https://doi.org/10.1007/s00038-011-0234-z. Berolo, S., Wells, R. P., & Amick, B. C., III (2011). Musculoskeletal symptoms among mobile hand-held device users and their relationship to device use: A preliminary study in a Canadian university population. Applied Ergonomics, 42(2), 371–378. https://doi.org/10.1016/j.apergo.2010.08.010. Bianchi, A., & Phillips, J. G. (2005). Psychological predictors of problem mobile phone use. Cyberpsychology & Behavior, 8(1), 39–51. https://doi.org/10.1089/cpb.2005. 8.39. Bickham, D. S., Hswen, Y., & Rich, M. (2015). Media use and depression: Exposure, household rules, and symptoms among young adolescents in the USA. International Journal of Public Health, 60(2), 147–155. https://doi.org/10.1007/s00038-0140647-6. Billieux, J. (2012). Problematic use of the mobile phone: A literature review and a pathways model. Current Psychiatry Reviews, 8(4), 299–307. https://doi.org/10. 2174/157340012803520522. Billieux, J., Maurage, P., Lopez-Fernandez, O., Kuss, D. J., & Griffiths, M. D. (2015). Can disordered mobile phone use be considered a behavioral addiction? An update on
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