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Gender differences in the relationships between meaning in life, mental health status and digital media use during Covid-19



The COVID-19 pandemic has had a significant impact on individuals’ social lives, mental health status, and meaning in life (MIL). Globally, the use of different types of digital media has become a proxy for pre-COVID social lives for many people. This study investigated gender differences in the relationship between use of digital media, mental health status and MIL, during COVID-19 in Hong Kong.


This cross-sectional study surveyed 1,488 young people recruited via city-wide random sampling in 2021. Respondents completed a phone survey on digital media use, Patient Health Questionnaire (PHQ-2), Generalized Anxiety Disorder (GAD-2), COVID-19 impact, meaning in life, and demographics. Gender differences in MIL were tested with an independent sample t-test. Gender-specific multiple linear regression models tested associations between MIL and explanatory variables of age, educational level, history of diagnosis, digital media use, and mental health status.


There was a significant gender difference in MIL (males (M = 12.90, SD = 4.12); females (M = 13.45, SD = 3.96); t (1485) = -2.656, p = .008). For males, all variables significantly associated to MIL (F (9, 759) = 15.731, p < .000, R2 = .157). However, for females, while the overall model for MIL was significant (F (9, 709) = 12.105, p < .001, R2 = .133), the only significant associated variable was mental health status.


Females had significantly better MIL under COVID-19 than males. Digital media use contributed to MIL in males but not females, and there were gender-specific associated factors of MIL.

Peer Review reports


During the period of August 2, 2021, to December 17, 2021, Hong Kong faced sporadic outbreaks of COVID-19 cases, which included localized clusters and imported cases. To curb the spread of the virus and mitigate its impact on public health and the economy, the government implemented a range of measures. These measures encompassed the promotion of vaccination, strict border control measures, mass testing and contact tracing. In particular, many new policies, including reduction of dine-in services, closure of schools, implementation of work-from-home arrangements, etc. have been implemented to restrict social activities and limit spread of infection. Most people now stay at home, with a much-restricted social life, and consequently, many have become disconnected or isolated from others [1, 2].

Gender differences in reactions to COVID-19 have been observed, with studies showing differences in psychological responses between males and females [3, 4]. Laufer and Shechory Bitton found that females in particular experienced higher levels of anxiety, depression, and somatization due to isolation during COVID-19 [3]. Higher levels of resilience was found among men than among women [3]. Mental health status is an important predictor of meaning in life (MIL) [5,6,7]. MIL refers to the manner in which individuals feel their lives have purpose [8]. Steger et al. reported significant gender differences in MIL, with females having higher levels of the presence of, and searching for, MIL than males [9]. Pre-COVID-19, depression and anxiety were reported as negatively-correlated with MIL [8], and these findings hold true in the context of COVID-19 [10].

Moreover, recent findings suggested a negative relationship between interpersonal alienation and MIL during COVID-19 [11]. Interpersonal relationships have been widely regarded as a source of MIL [12,13,14]. According to the Meaning Maintenance Model (MMM), people have an inherent need to perceive events within a framework of mental representations that provide structure and meaning to their understanding of the world [15]. This drive is fulfilled by cognitive processes that apply pre-existing mental constructs to incoming information. When people encounter situations that challenge their existing mental constructs, such as a large discrepancy between actual and expected interpersonal relationships, they may experience an elevated level of interpersonal alienation that leads to a sense of meaninglessness. In other words, when our expectations of how our relationships should be are not met, it can disrupt our sense of meaning and purpose in life [16]. In other words, social isolation experienced during the pandemic may produce adverse effects on people’s development of MIL. One possible implication is that improving interpersonal connections may also improve people’s level of MIL. One feasible way during pandemic restrictions to alleviate social isolation is to increase the usage of virtual communication via digital media [17, 18].

The convenience and speed of information dissemination on social media has led to overwhelming news about COVID-19, such as reports on its prevalence, mortality rates, confirm cases, and high contagion, causing stress and heightened depression and anxiety [19, 20]. According to the study Digital 2021 Hong Kong, average total daily time spent on the internet by people in Hong Kong increased from 6 h 16 min in 2019 to 7 h 15 min in 2020 [21]. This report also indicated that Hongkongers aged from 16 to 64 years spend an average of two hours a day on social media in 2021 [21]. It is suggested that individuals have increasingly relied on digital media for social connection during the COVID-19 pandemic [22]. Nguyen et al. found that females were more likely to increase the amount of virtual communication by video calls and text messages, while males were more likely to do so over online games [23]. Another study indicates that among females, the frequent use of social media as a coping strategy is linked to higher levels of perceived negative impacts on academic performance and stress levels compared to males [24]. However, both genders experience similar negative mental health consequences with frequent social media use [24].

The growing body of research in this area suggest that mental health status is an important predictor of MIL [8, 25,26,27,28,29]. However, little attention has been paid to the effect of digital media use on MIL during COVID-19, when it was often the only viable way to communicate with friends and family because of social contact restrictions. Islam et al. suggest that problematic smartphone use (PSPU) and problematic social media use (PSMU) were significantly associated with adverse psychological well-being outcomes such as anxiety and depression amidst the ongoing pandemic, given that psychological well-being is a construct closely related to MIL as demonstrated by prior research [7, 30,31,32]. We identified only one study in the United States on social media use and MIL during the pandemic, which found no significant relationship [33]. No study has directly examined whether MIL is associated with digital media such as video communication apps (i.e., Zoom, Skype, Facetime, Microsoft Teams), or whether males and females use these apps differently.

This study aims to investigate gender differences in the relationship between use of digital media, mental health status and MIL, during COVID-19 in Hong Kong. Examining these gender differences in app utilization and the subsequent influence on MIL is essential for understanding the distinct coping strategies employed by each gender in response to social isolation. This knowledge can guide the development of more inclusive, gender-sensitive digital platforms tailored to the specific needs and preferences of both males and females. By addressing these factors, targeted interventions can be designed to bolster mental health and MIL, ultimately fostering improved mental health outcomes for both genders during the pandemic.


Study design

This study is a cross-sectional study and was approved by the Human Research Ethics Committee for Non-Clinical Faculties of HKU (Research Ethics Approval ID: EA1709039).


Permanent residents of Hong Kong aged between 18 to 35 years of age, were eligible to participate.

Sample generation and procedures

Our data was a subset of the Hong Kong Mobile Phone Survey on Youth Mental Health and Internet Usage, initiated by the Hong Kong Jockey Club Centre for Suicide Research and Prevention (CSRP). From 2 August 2021 to 17 December 2021, a random sample of mobile phone numbers was generated using mobile number prefixes published by the Office of the Communications Authority (OFCA). For our study, a random sample of 125,746 mobile phone numbers was extracted. Potential respondents were contacted using the Computer-Assisted Telephone Interviewing (CATI) System, calling from 6:30 pm to 10:30 pm on weekdays. If the respondent was in the included age bracket, the study aim was described to potential participants as research to gain insights into the younger generation’s general well-being and usage of the internet. Participants gave informed verbal consent before completing the survey, and they understood that they could withdraw anytime. Should they experience distress during the survey, they were encouraged to seek help from emotional support services and hotlines, whose contact information was provided at the end of the survey.



Participants answered questions about age, gender and educational level, and a binary-response mental health question: “Have you been diagnosed with major depressive disorder, schizophrenia, social phobia, or avoidant personality disorder?”

Digital media use

This was measured as use of social media platforms (i.e., Facebook, Instagram, Twitter, Weibo, WeChat Moment) and video communication Apps (i.e., Zoom, Skype, Facetime, Microsoft Teams). The two binary-response questions were “Consider your recent communication habits, have you ever used social media platforms to update personal status?” and “Consider your recent communication habits, have you ever used video communication apps to make video phone calls?”

Patient Health Questionnaire 2-item (PHQ-2)

This is an ultra-brief self-report instrument for depression that contains the first two items of the PHQ-9 [34]. Each item in PHQ-2 asked about the frequency of a depressive symptom experienced in the last two weeks. The score for each item ranges from 0 (never) to 3 (almost every day), with a total score of 6. A cut off score of 3 is suggested as indicating a possible diagnosis of depressive disorder. Items were “Little interest or pleasure in doing things” and “Feeling down, depressed or hopeless”. In our current sample, Cronbach’s alpha of PHQ-2 was acceptable (0.61).

Generalized Anxiety Disorder 2-item (GAD-2)

It is an ultra-short screening tool that includes the first two items of GAD-7. Its scores are the same as the PHQ-2. Items were “Feeling nervous, anxious or on edge” and “Not being able to stop or control worrying”. A cut-off of 3 or greater is recommended in the general population to screen for GAD [34]. In our current sample, Cronbach’s alpha of GAD-2 was acceptable (0.75).

Impact of COVID-19

Emotional distress under COVID-19 was assessed by one question “Have you been emotionally distressed by the COVID-19 pandemic?” using a five-point scale ranging from 1 (not at all) to 5 (very serious).

Meaning in Life Questionnaire – Short Form (MLQ-SF)

Three items from the MLQ presence subscale were included in the study [35], “My life has a clear meaning or purpose”, “I have found a satisfactory meaning in life” and “I have a clear sense of what gives meaning to my life.” Items were rated from 1 (Absolutely untrue) to 7 (Absolutely true). The MLQ-SF was used in national health surveillance research in the United States, revealing good reliability and validity [36]. In our current sample, the Cronbach’s alpha of MLQ-SF was good (.85).

Statistical analysis

All analyses were conducted utilizing IBM SPSS v. 25. Our analysis encompasses multiple stages to explore the relationships between our variables of interest.

First, we aimed to examine the difference in the level of Meaning in Life (MIL) between genders. To achieve this, we performed an independent sample t-test, a parametric test used to compare the means of MIL scores between two independent groups: males and females.

Next, a multivariate approach was used to investigate the relationships between our explanatory variables and MIL, separately for each gender. This was done through the construction of gender-specific multiple linear regression models. In our models, MIL served as the dependent variable, while age, educational level, history of diagnosis, digital media use (encompassing social media platform usage, chat application usage, and video communication app usage), and mental health status (as measured by PHQ-2 for depression, GAD-2 for anxiety, and a self-report measure for emotional distress under COVID-19) were the explanatory variables.

For the categorical variables of digital media use (social media platform and video communication apps), we utilized a binary coding scheme: “never use” was coded as 1 and “used” was coded as 2. This coding scheme allowed us to examine the effects of any usage versus non-usage of these digital platforms on MIL.

Significant factors in the models were identified using a 5% significance level. The effect size, direction, and statistical significance of each explanatory variable were reported. The overall fit of the model was also evaluated and reported using R2, which represents the proportion of the variance for a dependent variable that’s explained by the independent variables in the regression model. A larger value of R2 represents a larger proportion of the variance in the dependent variable can be predicted from the independent variables.


A total of 1,501 participants completed a community-based mobile survey on digital media use and general well-being. Data with missing values or from those who could not complete the questionnaire were excluded. The final sample consisted of 1,488 respondents. Of these, 51.7% (n = 769) were male and 48.3% (n = 719) were female. The mean age for males was 27.2 years (SD = 3.91), and for females, it was 27.2 years (SD = 3.85). The majority of both males (90.6%) and females (92.0%) had an educational level of post-secondary or above. Over 70% of both male and female participants had used social media platforms, while over 50% had used video communication apps. The prevalence of depressive symptoms and anxiety symptoms was 15.7% (Male = 58.5%) and 16.0% (Female = 51.7%) respectively during the COVID-19 outbreak.

Table 1 presents the results of a chi-square analysis comparing education level, history of diagnosis, social media platform use, and video communication app use across genders. Significant differences emerged between genders in the use of social media platforms (p < .001) and video communication apps (p < .001). However, no significant differences were observed in education level or history of diagnosis between males and females.

Table 1 Chi-square analysis comparing education level, history of diagnosis, social media platform use, and video communication app use by gender

Table 2 delineates the findings of an independent-samples t-test comparing age, Meaning in Life (MIL), PHQ-2, GAD-2, and emotional distress under COVID-19 by gender. A significant gender difference in MIL levels was identified (p = .008). Young males exhibited significantly lower MIL scores (M = 12.90, SD = 4.12) compared to young females (M = 13.45, SD = 3.96). Additionally, a significant gender difference emerged in GAD-2 scores (p = .007). No significant differences were detected between males and females in terms of age, PHQ-2 scores, or emotional distress under COVID-19.

Table 2 Independent Sample t-test comparing age, meaning in life, PHQ-2, GAD-2, and emotional distress under COVID-19 by gender

For young males, the model explained a significant amount (15.7%) of the variance in MIL (p < .000, R2 = .157). All variables contributed significantly to the model (Table 3). For young females, the model also explained a significant amount of variance (13.3%) in MIL (p < .001, R2 = .133) (Table 4), however only PHQ-2 (p < .000), GAD-2 (p = .013) and emotional distress under COVID-19 (p = .037) contributed significantly to the model.

Table 3 Regression coefficients for factors associated with meaning in life under COVID-19 (Male samples)
Table 4 Regression coefficients for factors associated with meaning in life under COVID-19 (Female samples)


This paper reports the first known information on how digital media use contributed to MIL in young Hong Kong residents during Covid-19. Gender differences in MIL suggested that gender-specific models for understanding these associations were required. In males, usage of social media platforms, usage of video communication Apps, older age, and higher educational level positively affected MIL, whilst higher levels of depression and anxiety, higher levels of emotional distress under COVID-19, and previously-diagnosed mental health disorder negatively affected MIL. Two of the associated factors (use of social media platforms and video communication Apps), indicated that males who used them had higher MIL than males who did not. For females, the model explained slightly less of the MIL variance (13.3%) than for males, although similar to males, higher levels of depression and anxiety, and higher levels of emotional distress under COVID-19 negatively affected MIL. However, no variable positively associated with MIL for females, and no form of digital media use associated with MIL under COVID-19.

While mental health outcomes were significantly associated with of MIL in both genders, only males seemed to be benefited from digital media use, given that higher level of MIL correlated with lower state anxiety and lower COVID-19 stress [10]. One of the plausible explanations may be that the overuse of social media could produce negative effects that outweigh the beneficial effects of digital media use. A recent study found that females were more prone to overusing digital media during COVID-19, compared to males [37]. A recent study found that cyberbullying, lack of sleep, and lower physical activity mediated the link between digital media use and mental health among females but not among males [38]. Also, females may be more likely to engage in relational aggression and males in physical aggression, a difference seemingly exacerbated by media exposure [39,40,41]. These gender differences suggest that digital media use might have a more negative impact on females’ well-being than males. Furthermore, this study found significant gender differences in MIL. Digital media use was positively associated with MIL only for males, although MIL was associated with mental health for both genders. Future studies should attempt to measure and model more factors to increase understanding of the factors associated with MIL, particularly under pandemic conditions. Factors of interest could include life satisfaction, sense of purpose, intrinsic and extrinsic aspirations [25, 42].

This study presents a limitation in temporal specificity. The phrase ‘recent communication habits’ was used, but the precise time frame—intended to refer to the COVID-19 pandemic period—was not explicitly defined. This could have led to varied interpretations among respondents, potentially influencing the reliability of the responses. Future research should provide clear time frames to ensure more consistent understanding and improved data accuracy. Moreover, the cross-sectional study design precludes investigation of causal relationships between media use and MIL. Future studies should aim to test this using randomized controlled trials to compare the effects on MIL of different types of digital media. This study only targets young adults. During the pandemic, increased use of digital media became more common, even for elderly [43], thus further research is warranted to examine the use of digital media in different age groups, and its effect on MIL. It is imperative to acknowledge that numerous studies have established a correlation between excessive social media usage and negative effects on mental health [44,45,46,47,48,49]. However, our study adopted a binary approach (with or without) and did not consider the degree of social media use, which precludes a precise exploration of the impact of social media on mental health. In conclusion, based on our current findings, it remains inconclusive whether social media plays a positive role in fostering MIL during the pandemic. Further research is necessary to understand the extent to which social media affects mental health and to ascertain the appropriate measures that individuals can take to avoid any adverse consequences.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.



Centre for Suicide Research and Prevention


Computer-Assisted Telephone Interviewing


Generalized Anxiety Disorder 2-item


Meaning in Life


Meaning in Life Questionnaire – Short Form


Office of the Communications Authority


Patient Health Questionnaire 2-item


Statistical Package for the Social Sciences


  1. Banerjee D, Rai M. Social isolation in Covid-19: the impact of loneliness. Int J Soc Psychiatry. 2020;66(6):525–7.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hwang T-J, Rabheru K, Peisah C, Reichman W, Ikeda M. Loneliness and social isolation during the COVID-19 pandemic. Int Psychogeriatr. 2020;32(10):1217–20.

    Article  PubMed  Google Scholar 

  3. Laufer A, ShechoryBitton M. Gender differences in the reaction to COVID-19. Women Health. 2021;61(8):800–10.

    Article  PubMed  Google Scholar 

  4. Zamarro G, Perez-Arce F, Prados MJ. Gender differences in the impact of COVID-19. United States: KTLA; 2021. Accessed  16 July 2020.

  5. Halama P, Dedova M. Meaning in life and hope as predictors of positive mental health: do they explain residual variance not predicted by personality traits? Stud Psychol. 2007;49(3):191.

    Google Scholar 

  6. Joaquín G-A. Does meaning in life predict psychological well-being? Eur J Couns Psychol (Trier). 2015;3(2):89–98.

    Article  Google Scholar 

  7. Rathi N, Rastogi R. Meaning in life and psychological well-being in pre-adolescents and adolescents. J Indian Acad Appl Psychol. 2007;33(1):31–8.

    Google Scholar 

  8. Maryam Hedayati MA, Mahmoud Khazaei MA. An investigation of the relationship between depression, meaning in life and adult hope. Procedia Soc Behav Sci. 2014;114:598–601.

    Article  Google Scholar 

  9. Steger MF, Oishi S, Kashdan TB. Meaning in life across the life span: levels and correlates of meaning in life from emerging adulthood to older adulthood. J Posit Psychol. 2009;4(1):43–52.

    Article  Google Scholar 

  10. Trzebiński J, Cabański M, Czarnecka JZ. Reaction to the COVID-19 pandemic: the influence of meaning in life, life satisfaction, and assumptions on world orderliness and positivity. J Loss Trauma. 2020;25(6–7):544–57.

    Article  Google Scholar 

  11. Hu Q, Liu Q, Wang Z. Meaning in life as a mediator between interpersonal alienation and smartphone addiction in the context of Covid-19: a three-wave longitudinal study. Comput Hum Behav. 2022;127:107058–107058.

    Article  Google Scholar 

  12. Saarelainen S-M, Mäki-Petäjä-Leinonen A, Pöyhiä R. Relational aspects of meaning in life among older people – a group-interview gerontechnology study. Ageing Soc. 2022;42(5):1035–53.

    Article  Google Scholar 

  13. Yu EA, Chang EC. Relational meaning in life as a predictor of interpersonal well-being: a prospective analysis. Pers Individ Differ. 2021;168:110377.

    Article  Google Scholar 

  14. Ebersole P. Types and depth of written life meanings. United States: Lawrence Erlbaum Associates Publishers; 1998.

  15. Heine SJ, Proulx T, Vohs KD. The meaning maintenance model: on the coherence of social motivations. Pers Soc Psychol Rev. 2006;10(2):88–110.

    Article  PubMed  Google Scholar 

  16. Proulx T, Inzlicht M. The five a s of meaning maintenance: finding meaning in the theories of sense-making. Psychol Inq. 2012;23(4):317–35.

    Article  Google Scholar 

  17. Masur PK. Digital communication effects on loneliness and life satisfaction. In: Oxford research encyclopedia of communication. 2021.

    Google Scholar 

  18. Pittman M, Reich B. Social media and loneliness: why an Instagram picture may be worth more than a thousand Twitter words. Comput Hum Behav. 2016;62:155–67.

    Article  Google Scholar 

  19. Tsoy D, Tirasawasdichai T, Kurpayanidi KI. Role of social media in shaping public risk perception during COVID-19 pandemic: a theoretical review. Int J Manag Sci Bus Admin. 2021;7(2):35–41.

    Google Scholar 

  20. Venegas-Vera AV, Colbert GB, Lerma EV. Positive and negative impact of social media in the COVID-19 era. Rev Cardiovasc Med. 2020;21(4):561–4.

    Article  PubMed  Google Scholar 

  21. DataReportal. Digital 2021: Hong Kong. 2021.

    Google Scholar 

  22. Nguyen MH, Gruber J, Fuchs J, Marler W, Hunsaker A, Hargittai E. Changes in digital communication during the COVID-19 global pandemic: implications for digital inequality and future research. Soc Med Soc. 2020;6(3):205630512094825–2056305120948255.

    Article  Google Scholar 

  23. Nguyen MH, Hargittai E, Marler W. Digital inequality in communication during a time of physical distancing: the case of COVID-19. Comput Hum Behav. 2021;120:106717–106717.

    Article  Google Scholar 

  24. Prowse R, Sherratt F, Abizaid A, Gabrys RL, Hellemans KG, Patterson ZR, McQuaid RJ. Coping with the COVID-19 pandemic: examining gender differences in stress and mental health among university students. Front Psych. 2021;12:650759.

    Article  Google Scholar 

  25. Humphrey A, Vari O. Meaning Matters: self-perceived meaning in life, its predictors and psychological stressors associated with the COVID-19 pandemic. Behav Sci. 2021;11(4):50.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Martela F, Ryan RM, Steger MF. Meaningfulness as satisfaction of autonomy, competence, relatedness, and beneficence: comparing the four satisfactions and positive affect as predictors of meaning in life. J Happiness Stud. 2017;19(5):1261–82.

    Article  Google Scholar 

  27. Grouden ME, Jose PE. Do sources of meaning differentially predict search for meaning, presence of meaning, and wellbeing? Int J Wellbeing. 2015;5(1):33–52.

    Article  Google Scholar 

  28. Kleftaras G, Psarra E. Meaning in life, psychological well-being and depressive symptomatology: a comparative study. Psychology (Irvine, Calif). 2012;3(4):337–45.

    Article  Google Scholar 

  29. King LA, Hicks JA, Krull JL, Gaiso AKD. Positive affect and the experience of meaning in life. J Pers Soc Psychol. 2006;90(1):179–96.

    Article  PubMed  Google Scholar 

  30. Islam MS, Sujan MSH, Tasnim R, Mohona RA, Ferdous MZ, Kamruzzaman S, Toma TY, Sakib MN, Pinky KN, Islam MR. Problematic smartphone and social media use among Bangladeshi college and university students amid COVID-19: the role of psychological well-being and pandemic related factors. Front Psych. 2021;12:647386.

    Article  Google Scholar 

  31. García-Alandete J. Does meaning in life predict psychological well-being? Eur J Couns Psychol. 2015;3(2):89–98.

    Article  Google Scholar 

  32. Dezutter J, Casalin S, Wachholtz A, Luyckx K, Hekking J, Vandewiele W. Meaning in life: an important factor for the psychological well-being of chronically ill patients? Rehabil Psychol. 2013;58(4):334.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Helm PJ, Jimenez T, Galgali MS, Edwards ME, Vail KE, Arndt J. Divergent effects of social media use on meaning in life via loneliness and existential isolation during the coronavirus pandemic. J Soc Pers Relat. 2022;39(6):1768–93.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kroenke K, Spitzer RL, Williams JBW. The patient health questionnaire-2: validity of a two-item depression screener. Med Care. 2003;41(11):1284–92.

    Article  PubMed  Google Scholar 

  35. Steger MF, Frazier P, Oishi S, Kaler M. The meaning in life questionnaire: assessing the presence of and search for meaning in life. J Couns Psychol. 2006;53(1):80–93.

    Article  Google Scholar 

  36. Kobau R, Sniezek J, Zack MM, Lucas RE, Burns A. Well-being assessment: an evaluation of well-being scales for public health and population estimates of well-being among US adults. Appl Psychol Health Well Being. 2010;2(3):272–97.

    Article  Google Scholar 

  37. Luo T, Chen W, Liao Y. Social media use in China before and during COVID-19: preliminary results from an online retrospective survey. J Psychiatr Res. 2021;140:35–8.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Viner RM, Gireesh A, Stiglic N, Hudson LD, Goddings A-L, Ward JL, Nicholls DE. Roles of cyberbullying, sleep, and physical activity in mediating the effects of social media use on mental health and wellbeing among young people in England: a secondary analysis of longitudinal data. Lancet Child Adolesc Health. 2019;3(10):685–96.

    Article  PubMed  Google Scholar 

  39. Martins N, Weaver A. The role of media exposure on relational aggression: a meta-analysis. Aggress Violent Beh. 2019;47:90–9.

    Article  Google Scholar 

  40. Card NA, Stucky BD, Sawalani GM, Little TD. Direct and indirect aggression during childhood and adolescence: a meta-analytic review of gender differences, intercorrelations, and relations to maladjustment. Child Dev. 2008;79(5):1185–229.

    Article  PubMed  Google Scholar 

  41. Ostrov JM, Gentile DA, Crick NR. Media exposure, aggression and prosocial behavior during early childhood: a longitudinal study. Soc Dev. 2006;15(4):612–27.

    Article  Google Scholar 

  42. Karataş Z, Uzun K, Tagay Ö. Relationships between the life satisfaction, meaning in life, hope and COVID-19 fear for Turkish adults during the COVID-19 outbreak. Front Psychol. 2021;12:633384–633384.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Litwin H, Levinsky M. Social networks and mental health change in older adults after the Covid-19 outbreak. Aging Ment Health. 2022;26(5):925–31.

    Article  PubMed  Google Scholar 

  44. Zhao N, Zhou G. Social media use and mental health during the COVID-19 pandemic: moderator role of disaster stressor and mediator role of negative affect. Appl Psychol Health Well Being. 2020;12(4):1019–38.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Ulvi O, Karamehic-Muratovic A, Baghbanzadeh M, Bashir A, Smith J, Haque U. Social media use and mental health: a global analysis. Epidemiologia. 2022;3(1):11–25.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Shutzman B, Gershy N. Children’s excessive digital media use, mental health problems and the protective role of parenting during COVID-19. Comput Hum Behav. 2023;139:107559.

    Article  Google Scholar 

  47. Zhong B, Jiang Z, Xie W, Qin X. Association of social media use with mental health conditions of nonpatients during the COVID-19 outbreak: insights from a national survey study. J Med Internet Res. 2020;22(12):e23696.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Schønning V, Hjetland GJ, Aarø LE, Skogen JC. Social media use and mental health and well-being among adolescents–a scoping review. Front Psychol. 1949;2020:11.

    Google Scholar 

  49. Lin CY, Namdar P, Griffiths MD, Pakpour AH. Mediated roles of generalized trust and perceived social support in the effects of problematic social media use on mental health: a cross-sectional study. Health Expect. 2021;24(1):165–73.

    Article  PubMed  Google Scholar 

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We are grateful for the support from members of the Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong for various roles such as project coordination and data cleaning. We also thank all the people who filled the questionnaire.


The study described in this protocol was generously funded by the Hong Kong Jockey Club Charities Trust and a Collaborative Research Fund (C7151-20G) of the Research Grants Council. The funding body did not have any role in the design of the study, collection, analysis, or interpretation of data or writing the manuscript.

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Authors and Affiliations



WS, BW and PY contributed substantially to the conception and design of the study. WS, BW and CW were involved in the acquisition and analysis of the data. WS and BW participated in the interpretation of the data. WS and BW drafted the paper. All authors critically revised the manuscript and gave final approval for the submission.

Corresponding author

Correspondence to Paul Siu Fai Yip.

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Ethics approval and consent to participate

Verbal informed consent was obtained from all participants prior to any data collection and ethics approval to conduct the survey was approved by the Human Research Ethics Committee for Non-Clinical Faculties of the University of Hong Kong (Research Ethics Approval ID: EA1709039). All methods were carried out in accordance with relevant guidelines and regulations.

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Not applicable.

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The authors declare no competing interests.

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So, W.W.Y., Woo, B.P.Y., Wong, C. et al. Gender differences in the relationships between meaning in life, mental health status and digital media use during Covid-19. BMC Public Health 23, 1770 (2023).

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