Be Aware! If You Start Using Facebook Problematically You Will Feel Lonely: Phubbing,Loneliness,Self-esteem,and Facebook Intrusion. A Cross-Sectional Study

Abstract

Smartphones are an integral part of people’s life. The aim of the study was to expand the knowledge on the individual determinants of “phubbing.” Two dimensions of phubbing—communication disturbance and phone obsession—were taken into consideration. We tested the model of relations between phubbing, self-esteem, loneliness, and satisfaction with life. We administered the Phubbing Scale, the Facebook Intrusion Scale, the Loneliness Scale, the Satisfaction With Life Scale, and the Self-Esteem Scale. The participants in the online study were 597 Polish users of mobile phones, with a mean age of M = 21.22 (SD = 6.52, range: 16–78). The results showed Facebook intrusion to be a cause of phubbing. Women scored higher than men on both dimensions of phubbing. A low level of self-esteem and satisfaction with life predicted Facebook intrusion. Additionally, a low level of loneliness was a predictor of Facebook intrusion, which in turn was a predictor of loneliness. The results demonstrate the multidimensional nature of phubbing and suggest new directions for future studies.

Keywords

phubbing Facebook intrusion loneliness self-esteem satisfaction with life mobile addiction

Smartphones are an integral part of people’s life. In this article, we will use the terms “smartphone” and “mobile” interchangeably. What we have in mind whenever we say mobile is a phone with access to the Internet. They are mobile phones highly advanced in terms of technology, computing capabilities, and connectivity, with functions similar to those of computers. In fact, people prefer their smartphones to computers while surfing online (Ofcom, 2015). In 2016, the number of smartphone users was estimated at reach 2.08 billion (Statista, 2015). The digital divide between less advanced economies and developed economies was 31 points in 2015 for smartphone ownership. In Poland, 41% of respondents declared owning a smartphone; 75% of them were young people aged 18–34. According to Pew Research Center (2015), 15% of Americans aged 18–29 are dependent on a smartphone for Internet access. Beside calling, texting, and basic Internet browsing, smartphones are used for online banking, seeking information about jobs, obtaining class materials or educational contents, obtaining driving directions in public transport, and many other purposes. When asked, smartphone owners described their devices as “helpful” rather than “annoying,” “connecting” rather than “distracting,” and representing “freedom” rather than functioning as a “leash.” According to a study done on students, using smartphones during classes hampers the ability to concentrate and makes academic performance worse (Samaha & Hawi, 2016).

With the increasing number of smartphones, not only the benefits of using them but also the side effects should be discussed. A new phenomenon is emerging, called phubbing. The word “phubbing” is derived from two words: “phone” and “snubbing.” It means looking at one’s mobile during real conversation with another person (Karadağ et al., 2015). Phubbing is also defined as an act of snubbing the interlocutor in a social setting by looking at one’s phone instead of paying attention (see http://stopphubbing.com/). Karadağ and colleagues (2015) indicated that phubbing is associated with mobile addiction and with deprivation in situations of being far from one’s phone; they also indicated that the mobile phone is used as a tool helping in situations of loneliness, anxiety, and worry. They distinguished two factors of phubbing: communication disturbance and phone obsession (Karadağ et al., 2015). A previous study showed that gender can be a moderator of the relationships between phubbing and mobile phone, short message service (SMS), social media, and Internet addictions; in the female group, phubbing was found to be related to mobile, SMS, and social media addictions, whereas in the male group it was associated with Internet and game addictions (Karadağ et al., 2015). This can be linked with differences in preferences concerning media use. Phubbing also has an impact on interpersonal relationships and personal well-being (Roberts & David, 2016). It seems to be very strongly connected with the way of using other tools; Karadağ and colleagues (2015) suggested that it has certain dimensions such as mobile phone addiction, Internet addiction, or social media addiction. They suggest that people first become addicted to SMS and social media, which then leads to mobile addiction, which in turn results in phubbing.

Problematic phone use is seen as a behavioral addiction. Phubbing is an umbrella term covering three dimensions, one of them being mobile phone addiction. Choliz (2010) found that mobile phone addiction and phubbing have similar main symptoms of dependence, such as lack of impulse control, using a mobile to avoid unpleasant moods, having problems because of using a mobile, intensive online activity, sending a large number of text messages, and devoting a large amount of time to using the mobile every day.

Facebook is one of the most popular social networking sites (for a review, see Błachnio, Przepiórka, & Rudnicka, 2013). The terms used in the literature to refer to Facebook dependency are “Facebook intrusion” (Elphinston & Noller, 2011) or “Facebook addiction” (Andreassen & Pallesen, 2014), which can be defined as excessive involvement in Facebook, disrupting day-to-day activities and interpersonal relationships (Elphinston & Noller, 2011). The authors mention three phases of this phenomenon, namely, (1) withdrawal, (2) relapse and reinstatement, and (3) euphoria. People who are strongly involved in Facebook feel distress when they cannot use it. What is more, they have unsuccessfully attempted to reduce Facebook use. They also feel a connection with other people who use Facebook (Elphinston & Noller, 2011).

A large body of studies have been devoted to the relationship between behavioral dependency and personal traits. Because of the small number of studies on phubbing, our project was eclectic and intuitive. Our assumptions were based on behavior dependency studies in general. Self-esteem, loneliness, and satisfaction with life were selected as antecedents for phubbing.

The previous study indicated that Internet addiction was related to a low level of self-esteem (Błachnio, Przepiórka, Senol-Durak, Durak, & Sherstyuk, 2016) and that self-esteem was a negative predictor of Facebook intensity (Błachnio, Przepiorka, & Rudnicka, 2016) and addictive use of social media (Hawi & Samaha, 2016).

Some authors have assumed that loneliness is an important determinant of social media use (Ye & Lin, 2015; Song et al., 2014). Some studies suggest that loneliness can predict Internet addiction (Özdemir, Kuzucu, & Ak, 2014). Loneliness has been found to be a positive predictor of Facebook addiction, standard Facebook use, and Facebook entertainment (Błachnio, Przepiorka, Boruch, & Bałakier, 2016). As indicated by previous results, problematic mobile phone use is associated with loneliness and depression (Güzeller & Coşguner, 2012).

Satisfaction with life is defined as the global assessment of one’s life. A previous study indicated that Facebook addiction was negatively associated with satisfaction with life (Błachnio, Przepiorka, & Pantic, 2016), and another one showed that phubbing was associated with personal well-being (Roberts & David, 2016).

Our assumptions were based on behavioral dependency studies in general. We tested a model of associations between phubbing, self-esteem, loneliness, and satisfaction with life, based on the following hypotheses (Figure 1):

Hypothesis 1: Facebook intrusion enhances phubbing behavior.

Hypothesis 2: Self-esteem is negatively associated with phubbing.

Hypothesis 3: Loneliness is positively associated with phubbing.

Hypothesis 4: Satisfaction with life is negatively associated with phubbing.

Hypothesis 5: Self-esteem is positively related to satisfaction with life.

Figure 1.

Theoretical model.

Method

Participants and Procedure

The participants were 597 Polish mobile users; 67.5% of them were women. The participants’ mean age was M = 21.22 years (SD = 6.52, range: 16–78).

The snowball sampling procedure was used as a method of reaching a large group of respondents diverse in terms of social status, education level, Internet literacy, and mobile use proficiency. An electronic version of the questionnaires was prepared and the link was sent to undergraduate students with a request to post the link on their Facebook walls. The questionnaires were also sent to Facebook users with a request to spread the message among their Facebook friends. The participants volunteered for the study and received no monetary reward. They were informed about the anonymity of the study.

Measures

The Phubbing Scale (Karadağ et al., 2015) consists of 10 items. The scale has two factors, with 5 items per each: communication disturbance (e.g., “People complain about my dealing with my mobile phone,” Cronbach’s α = .81) and phone obsession (e.g., “I feel incomplete without my mobile phone,” Cronbach’s α = .67). The items are rated on a 5-point Likert-type scale from 1 (never) to 5 (always).

The Facebook Intrusion Questionnaire, developed by Elphinston and Noller (2011), is based on behavioral addiction components and on a scale measuring phone involvement. It consists of 8 items (e.g., “I have been unable to reduce my Facebook use”) measuring the relations between Facebook involvement and eight aspects of behavioral addiction, namely, cognitive salience, behavioral salience, interpersonal conflict, conflict with other activities, euphoria, loss of control, withdrawal, as well as relapse and reinstatement. The items are rated on a 7-point Likert-type scale from 1 (strongly disagree) to 7 (strongly agree). The items were standardized and then the sum score was computed. Cronbach’s α was .86.

Rosenberg’s Self-Esteem Scale (SES) as adapted into Polish by Łaguna, Lachowicz-Tabaczek, and Dzwonkowska (2007). The Rosenberg’s SES contains 10 items rated on a 4-point Likert-type scale and provides an overall evaluation of a person’s self-esteem (e.g., “I feel that I have a number of good qualities”). The reliability of the scale (Cronbach’s α) was .73.

The De Jong Gierveld Loneliness Scale as adapted into Polish by Grygiel, Humenny, Rebisz, Świtaj, and Sikorska (2013) measures the sense of loneliness. The scale consists of 11 items, such as “I miss having really close friends.” Six of them are negatively and five positively formulated. The items are rated on a 5-point Likert-type scale (1 = completely disagree; 5 = completely agree). Cronbach’s α was .88.

The Satisfaction With Life Scale (Diener, Emmons, Larsen, & Griffin, 1985) consisting of 5 items designed to measure global cognitive judgments of satisfaction with life (e.g., “The conditions of my life are excellent”). The reliability of the scale (Cronbach’s α) was .86.

Results

Table 1 shows the descriptive statistics and correlations between the variables: phubbing, Facebook intensity, self-esteem, loneliness, and satisfaction with life. We found that neither communication disturbance nor phone obsession correlated with age. Communication disturbance was positively related to phone obsession, Facebook intrusion, and loneliness, and negatively to self-esteem and satisfaction with life, while phone obsession was positively correlated only with Facebook intrusion.

Table 1.

Means, Standard Deviations, and Correlations Between Variables.

Variables	M	SD	1	2	3	4	5	6
1. Age	21.66	6.52
2. Communication disturbance	1.99	0.73	.03
3. Phone obsession	2.96	0.084	−.04	.48***
4. Facebook intrusion	2.89	1.12	−.16***	.41***	.38***
5. Loneliness	2.34	0.79	.05	.13***	.08	.15***
6. Self-esteem	2.28	0.44	.14***	−.11**	.03	−.12***	−.38***
7. Satisfaction with life	4.20	1.30	.01	−.11**	−.01	−.02	−.48***	.38***

*p < .05. **p < .01. ***p < .001.

As regards gender differences, women scored higher than men on both factors of phubbing on communication disturbance, M _women = 2.05, SD = 0.73, M _men = 1.86, SD = 0.70, F(1, 613) = 9.370, p = .002, and on phone obsession, M _women = 3.06, SD = 0.82, M _men = 2.73, SD = 0.83, F(1, 613) = 20.634, p < .001.

The proposed research model was tested by means of structural equations for interrelations between the variables. Using structural equation models, we tested the theoretical model (see Figure 1) of relations between phubbing, self-esteem, loneliness, and satisfaction with life. The initial model showed a poor fit, and we attempted to improve it by dropping all insignificant paths and adding those suggested by modification indices; we included those that made theoretical sense.

The structural model was tested using the lavaan package (0.5-23.1097). The values of RMSEA should be lower than .08 to indicate that the model is well fitted, and optimally they should be lower than .05 (Konarski, 2010). The comparative fit index (CFI) should be higher than .90 for an acceptable model and equal to 1.0 for a perfect model (Hu & Bentler, 1998). The Tucker–Lewis Index (TLI) should range from 0 to 1: the higher the value, the better the fit (Schermelleh-Engel, Moosbrugger, & Müller, 2003). The goodness-of-fit indices demonstrated that the model was very well fitted to our data: maximum likelihood χ² = 11.60, df = 6, p = .072, χ²/df = 1.93, CFI = .99, TLI = .98, RMSEA = .04 [.01, −.07].

As we can see in Table 2, an increase in the level of self-esteem increases the level of satisfaction with life and reduces the risk of Facebook intrusion. Low satisfaction with life increases the likelihood of Facebook intrusion, but it also increases loneliness. A low level of loneliness contributes to Facebook intrusion, but a high level of Facebook intrusion leads to an increase in loneliness and influences both dimensions of phubbing, namely, communication disturbance and phone obsession.

Table 2.

Standard Path Coefficients.

Dependent	Predictor	SPC	Z	p
Satisfaction with life	Self-esteem	0.38	−10.26	<.001
Facebook intrusion	Self-esteem	−0.89	2.04	.042
	Satisfaction with life	−1.24	−1.75	.079
	Loneliness	−3.29	−1.84	.065
Communication disturbance	Facebook intrusion	0.41	11.01	<.001
Phone obsession	Facebook intrusion	0.37	9.81	<.001
Loneliness	Satisfaction	−0.46	−5.89	<.001
Loneliness	Facebook intrusion	1.86	2.74	.006

Note. SPC = standardized path coefficient.

Discussion

The main aim of the study was to analyze the model of relations between phubbing, Facebook intrusion, self-esteem, loneliness, and satisfaction with life. To the best of our knowledge, this is the first study to examine such relations. We found that people who had a compulsion to use their phone during contact with other people more often used Facebook in an excessive way. They also felt more lonely and scored lower on self-esteem and satisfaction with life.

Our results indicate that women scored higher than men on both factors of phubbing: communication disturbance and phone obsession. The findings of the present study are consistent with the results obtained by Chiu, Hong, and Chiu (2013) in a study on Taiwanese sample. Even in the sample of elementary school students mobile phone use was higher in girls (Kim, Lee, & Choi, 2015), and more females in the secondary school (Warzecha & Pawlak, 2017) or college sample (Deng, Huang, Gui, Niu, & Zhou, 2015) were at risk of mobile phone addiction. Despite having been obtained by means of different measures of mobile phone addiction, all these results highlight consistent findings on gender differences. This may stem from the fact that mobile phones are mostly used for communication; female university students make more phone calls and send more text messages (Hong, Chiu, & Huang, 2012). As shown in our study, people with a phone obsession more often use Facebook in problematic ways. This is in line with Karadağ et al. (2015) who suggested that people become addicted to social media first and that this subsequently leads to phubbing.

Facebook intrusion is a predictor of phubbing (Hypothesis 1). There are similarity between the findings of our study, which confirmed that Facebook intrusion was a predictor of communication disturbance and phone obsession, and those presented by Karadağ et al. (2015). People might use their mobile phones for Facebook activity.

A low level of satisfaction with life and a low level of self-esteem were predictors of Facebook intrusion (Hypotheses 2 and 4). These results are consistent with those of a previous study, in which three groups of Facebook users were compared: normal, intensive, and addicted (Błachnio, Przepiorka, & Pantic 2016). The Facebook-addicted group was characterized by lower levels of both self-esteem and satisfaction with life in comparison with normal Facebook users.

A low level of loneliness was a predictor of Facebook intrusion, while Facebook intrusion was a predictor of loneliness (Hypothesis 3). Previous results suggested that for shy people and for those with low social support Facebook can be a place of compensation for their social problems (Song et al., 2014). Our results may seem to be unexpected, but one possible explanation is fact that people who have many friends are probably open in social contact and start to use Facebook. Some of them change the real contact into online contact via Facebook; they give up off-line interactions and, as a result, they start to feel lonely. Similar results were obtained by Błachnio, Przepiorka, Boruch, and Bałakier (2016). Another study (Zhang, Zhou, & Pei, 2015) also revealed that loneliness was a positive predictor of mobile phone addiction.

A high level of self-esteem turned out to be a predictor of satisfaction with life (Hypothesis 5). The same results were obtained in previous studies (e.g., Bozoglan, Demirer, & Sahin, 2013). A low level of satisfaction with life was, in turn, a predictor of loneliness. This result is in accordance with other findings (e.g., Musich, Wang, Hawkins, & Yeh, 2015; Bozoglan et al., 2013).

The current study has certain limitations that should be acknowledged. Firstly, its design was cross-sectional and does not allow for drawing conclusions about causality. A longitudinal study would afford a better insight into problematic Facebook use and the development of phubbing. Secondly, there was imbalance in terms of age and gender, with an overrepresentation of females and young adults. This makes the findings difficult to generalize. In future studies, examining gender and age differences in using Facebook and mobile phones would bring new insight into this phenomenon. Thirdly, the data were collected online by means of the snowball procedure, which is not free from biases (Tyrer & Heyman, 2016). Although we collected the data with these disadvantages in mind, other research has shown that this procedure is valid (Meyerson & Tryon, 2003) and yields reliable results (e.g., Blachnio, Przepiorka & Rudnicka, 2016).

Despite these limitations, the present findings may have some important implications. Further research in this area should include other psychological and cultural variables such as emotional problems, Internet addiction, or time management. Additionally, our model can be compared across cultures.

Conclusion

To sum up, our aim was to obtain a comprehensive picture of how phubbing is determined by Facebook intrusion and some psychological characteristics. The contribution of the present study is 2-fold. Firstly, we have identified the determinants of phubbing understood as a mobile phone addiction and communication disturbance. Secondly, we have examined the nature of Facebook use more deeply and identified the antecedents of excessive involvement in Facebook. More precisely, we tested a model of relations between phubbing, Facebook intrusion, self-esteem, loneliness and satisfaction with life. We found that Facebook intrusion is a predictor of phubbing, and that a low level of self-esteem and satisfaction with life can predict Facebook intrusion. Additionally, a low level of loneliness was a predictor of Facebook intrusion, while Facebook intrusion was a predictor of loneliness. These relations might shed light on the new social phenomenon called phubbing and inspire future studies.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

References

Andreassen

C. S.

Pallesen

(2014). Social network site addiction—An overview. Current Pharmaceutical Design, 20, 4053–4061.

Błachnio

Przepiorka

Boruch

Bałakier

(2016). Self-presentation styles, privacy, and loneliness as predictors of Facebook use in young people. Personality and Individual Differences, 94, 26–31. doi:10.1016/j.paid.2015.12.051

Błachnio

Przepiorka

Pantic

(2016). Association between Facebook addiction, self-esteem and life satisfaction: A cross-sectional study. Computers in Human Behavior, 55, 701–705. doi:10.1016/j.chb.2015.10.026

Błachnio

Przepiorka

Rudnicka

(2016). Narcissism and self-esteem as predictors of dimensions of Facebook use. Personality and Individual Differences, 90, 296–301. doi:10.1016/j.paid.2015.11.018

Błachnio

Przepiórka

Rudnicka

(2013). Psychological determinants of using Facebook: A research review. International Journal of Human-Computer Interaction, 29, 775–787. doi:10.1080/10447318.2013.780868

Błachnio

Przepiórka

Senol-Durak

Durak

Sherstyuk

(2016). The role of self-esteem in Internet addiction: A comparison between Turkish, Polish and Ukrainian samples. The European Journal of Psychiatry, 30, 149–155.

Bozoglan

Demirer

Sahin

(2013). Loneliness, self-esteem, and life satisfaction as predictors of Internet addiction: A cross-sectional study among Turkish university students. Scandinavian Journal of Psychology, 54, 313–319. doi:10.1111/sjop.12049

Chiu

S.-I.

Hong

F.-Y.

Chiu

S.-L.

(2013). An analysis on the correlation and gender difference between college students’ Internet addiction and mobile phone addiction in Taiwan. ISRN Addiction, 2013, 360607. doi:10.1155/2013/360607

Choliz

(2010). Mobile phone addiction: A point of issue. Addiction, 105, 373–374.

10.

Deng

Huang

Gui

Niu

Zhou

(2015). Mobile phone dependence, parenting style and subjective well-being in college students. Chinese Mental Health Journal, 29, 68–73.

11.

Diener

Emmons

R. A.

Larsen

R. J.

Griffin

(1985). The satisfaction with life scale. Journal of Personality Assessment, 49, 71–75.

12.

Elphinston

R. A.

Noller

(2011). Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology, Behavior and Social Networking, 14, 631–635. doi:10.1089/cyber.2010.0318

13.

Grygiel

Humenny

Rebisz

Świtaj

Sikorska

(2013). Validating the polish adaptation of the 11-Item De Jong Gierveld Loneliness Scale. European Journal of Psychological Assessment, 29, 129–139. doi:10.1027/1015-5759/a000130

14.

Güzeller

C. O.

Coşguner

(2012). Development of a problematic mobile phone use scale for Turkish adolescents. Cyberpsychology, Behavior, and Social Networking, 15, 205–211.

15.

Hawi

N. S.

Samaha

(2016). The relations among social media addiction, self-esteem, and life satisfaction in university students. Social Science Computer Review, 35, 576–586.

16.

Hong

Chiu

Huang

(2012). A model of the relationship between psychological characteristics, mobile phone addiction and use of mobile phones by Taiwanese university female students. Computers in Human Behavior, 28, 2152–2159. doi:10.1016/j.chb.2012.06.020

17.

Bentler

P. M.

(1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453. doi:10.1037//1082-989X.3.4.424

18.

Karadağ

Tosuntaş

Ş. B.

Erzen

Duru

Bostan

Şahin

B. M.

… Babadağ

(2015). Determinants of phubbing, which is the sum of many virtual addictions: A structural equation model. Journal of Behavioral Addictions, 4, 60–74. doi:10.1556/2006.4.2015.005

19.

Kim

Lee

Choi

(2015). Mobile phone overuse among elementary school students in Korea: Factors associated with mobile phone use as a behavior addiction. Journal of Addictions Nursing, 26, 81–85. doi:10.1097/JAN.0000000000000074

20.

Konarski

(2010). Modele równań strukturalnych. Teoria i praktyka. Warszawa, Poland: PWN.

21.

Łaguna

Lachowicz-Tabaczek

Dzwonkowska

(2007). Skala samooceny SES Morrisa Rosenberga—polska adaptacja metody. Psychologia Społeczna, 2, 164–176.

22.

Meyerson

Tryon

W. W.

(2003). Validating Internet research: A test of the psychometric equivalence of Internet and in-person samples. Behavior Research Methods, Instruments, & Computers: A Journal of The Psychonomic Society, Inc, 35, 614–620.

23.

Musich

Wang

S. S.

Hawkins

Yeh

C. S.

(2015). The impact of loneliness on quality of life and patient satisfaction among older, sicker adults. Gerontology and Geriatric Medicine, 1, 1–9. doi:10.1177/2333721415582119

24.

Ofcom. (2015). The communications market report 2015. England.

25.

Özdemir

Kuzucu

Ş.

(2014). Depression, loneliness and Internet addiction: How important is low self-control? Computers in Human Behavior, 34, 284–290. doi:10.1016/j.chb.2014.02.009

26.

Pew Research Center. (2015). Retrieved from http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/

27.

Roberts

J. a.

David

M. E.

(2016). My life has become a major distraction from my cell phone: Partner phubbing and relationship satisfaction among romantic partners. Computers in Human Behavior, 54, 134–141. doi:10.1016/j.chb.2015.07.058

28.

Samaha

Hawi

N. S.

(2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior, 57, 321–325. doi:10.1016/j.chb.2015.12.045

29.

Schermelleh-Engel

Moosbrugger

Müller

(2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8, 23–74.

30.

Song

Zmyslinski-Seelig

Kim

Drent

Victor

Omori

Allen

(2014). Does Facebook make you lonely?: A meta analysis. Computers in Human Behavior, 36, 446–452.

31.

Statista. (2015). Retrieved from http://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide

32.

Tyrer

Heyman

(2016). Sampling in epidemiological research: Issues, hazards and pitfalls. BJPsych Bulletin, 40, 57–60. doi:10.1192/pb.bp.114.050203

33.

Warzecha

Pawlak

(2017). Pathological use of mobile phones by secondary school students. Archives of Psychiatry & Psychotherapy, 19, 27–36. doi:10.12740/APP/67931

34.

Lin

(2015). Examining relations between locus of control, loneliness, subjective well-being, and preference for online social interaction. Psychological Reports, 116, 164–175.

35.

Zhang

Zhou

Pei

(2015). Mediating effect of loneliness on relationship between interpersonal adaptation and mobile phone addiction in college students. Chinese Mental Health Journal, 29, 774–779.