Social Networks and Internet Emotional Relationships on Mental Health and Quality of Life in Students: Structural Equation Modelling

doi:10.21203/rs.3.rs-1147915/v1

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Social Networks and Internet Emotional Relationships on Mental Health and Quality of Life in Students: Structural Equation Modelling

https://doi.org/10.21203/rs.3.rs-1147915/v1

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Background: Social networks and relationships create a sense of belonging and social identity and therefore have a major effect on mental health and quality of life, especially in young people. The present study was conducted to determine the predictor role of social networks and Internet emotional relationships on mental health and quality of life in students.

Methods: The present cross-sectional study was conducted in 2021 on 350 students at Alborz University of Medical Sciences selected by convenience sampling. Data were collected using five questionnaires: Socioeconomic Status, Social Networks, Internet Emotional Relationships Mental Health, Quality of Life and a checklist of demographic details. Data were analyzed in SPSS-25, PLS-3, and Lisrel-8.8.

Results: According to the path analysis results, mental health had the most significant positive causal relationship with Internet emotional relationships in the direct path (B=0.22) and the most negative relationship with socioeconomic status (B=-0.09). Mental health was assessed using DASS-21, in which higher scores mean higher mental disorders. Quality of life had the highest negative causal relationship with the DASS-21 score in the direct path (B=-0.26) and the highest positive relationship with socioeconomic status in the indirect path (B=0.023). The mean duration of using social networks (B=-0.067) and Internet emotional relationships (B=-0.089) had the highest negative relationship with quality of life.

Conclusion: The use of the Internet and virtual networks, Internet emotional relationships and unfavourable socioeconomic status were associated with mental disorders and reduced quality of life in the students. Since students are the future of any country, it is necessary for policymakers to further address this group and their concerns.

Health Policy

Social Networks

Internet Emotional Relationships

Mental Health

Quality of Life

Students

Path Analysis

The 21^st century is a century of rapid spread of social networks on the Internet, and in contrast to traditional media in which users are passive recipients, social media enable people to create and share content and have become a very popular means of social interactions [1], and this popularity has led to dramatic changes in people's lifestyles [2].

According to reports, 521 million new users have joined social networks by April 2021 [3], and the majority of the users of social networks are young people [2]. Despite people's increased use of and access to the Internet, the social consequences of its long-term use and associated crises have been neglected [4]. Because of their many similarities to human society, social networks allow people to retain their existing relationships, find new friends and discover information about people they know offline, which can have both positive and negative consequences [5].

Some researchers believe that by creating a sense of belonging and public social identity, social relationships significantly affect mental and psychological health and improve the quality of life [6]. As a matter of fact, people with greater social interactions have more favourable physical and mental health [7]. On the one hand, social research and criticisms often emphasize the negative impact of using the Internet [8]. For example, in a study conducted on 1573 youth, Kim et al. found that internet addiction and using virtual networks is associated with high rates of depression and suicidal ideations [9]. Moreover, some studies observed that the excessive use of virtual networks and the Internet is associated with stress, personality disorder, and sleep disorder [10, 11]. In other studies, some researchers reported the negative impact of using the Internet and virtual networks on the quality of life [12]. On the other hand, researchers such as Khalaila et al. (2018) and Schmidt et al. (2021) reported that using the Internet has a positive effect on older adults' quality of life [13, 14].

The youth and children are more inclined to use virtual networks due to being alone and not having adequate social connections and support [15, 16]. Kawachi et al. (2001) found that being involved in social networks and private relationships facilitates access to various forms of support that play a protective role against psychological problems, including stress. It describes several pathways through which participation in social networks can affect psychological well-being. Social influence refers to the way members of a social network obtain normative guidance about health-relevant behaviours, such as physical activity. Behaviours such as regular exercise may, in turn, exert a beneficial influence on mental health. And also maybe have a positive effect on mental health by the neuroendocrine response (Fig. 1) [17]. Grino et al. (2017) also found that loneliness affects both mental and physical quality of life through two paths. Mediated by mental health and resilience, loneliness has a relationship with the mental and physical quality of life (Fig.2) [18].

Given these issues and also due to the contradictions existing in relation to the effect of social networks on mental health and social support, the importance of mental health and quality of life among the youth, who make the future of any country, and the lack of studies assessing all these elements together in one model, the present study was conducted to determine The predictor role of social networks and Internet emotional relationships on quality of life and mental health in students using the structural equation modeling.

We aimed to answer these questions:

1- What is the effect of social network and internet emotional relationships (direct/indirect) on mental health of college student?

2- What is the effect of social network and internet emotional relationships (direct/indirect) on quality of life of college student?

3- What is the effect of mental health (direct/indirect) on quality of life of college student?

4- What is the effect of demographic factors (age, Education,) on mental health and quality of life of college student?

2.1. Study design:

The present cross-sectional study was conducted in 2021 on students at Alborz University of Medical Sciences. This University is located in Alborz Province, neighboring Tehran Province (where the Capital of Iran is located), and has six schools: Medicine, dentistry, pharmacy, health, nursing, and para-medicine.

2.2. Study population

Based on a study conducted by Ziggi et al. [19] and considering 𝒄𝒐𝒓𝒓𝒆𝒍𝒂𝒕𝒊𝒐𝒏=𝟎.𝟏𝟓, 𝜷=𝟎.𝟐, 𝜶=𝟎.𝟎𝟓, the sample size was determined as 345 using the following formula, but it was then increased to 350 to take account of a potential withdrawal of 10%. A weight was assigned to each school based on the number of students in it, and then, based on the weight assigned to each school, the eligible students were selected from each school until the required sample size was reached.

n={(Z_α+Z_β)/C}²+3

C=0.5ln[(1+r)/(1-r)]

2.2.1. Inclusion criteria

Male and female Iranian students aged 18 to 29 years who had passed one academic semester, did not use psychotropic and narcotic drugs, did not take antidepressants, had physical and mental health according to their educational records and self-report, and had a cellphone that enabled them to use virtual networks on their cellphone were included in the study.

2.2.2. Exclusion criteria

The students who withdrew from the study for whatever reason or who quit studying, experienced adverse events during the study such as the death of parents and returned incomplete questionnaires were excluded.

2.3. Definition and Instrument

In This study, data were collected using five questionnaires and a checklist, as follows:

2.3.1. Checklist of demographic details

This Checklist inquired about participants' personal details, including age, gender, nationality, marital status, education, the field of study, academic semester, occupation, use of virtual networks on their cellphone, being a virtual network user, type of virtual network used, and the mean duration of using social networks per day (in hours).

2.3.2. Socioeconomic status scale

The socioeconomic status questionnaire comprising five main items and six demographic items developed by Ghodratnama in 2013 was used to evaluate four dimensions of the socioeconomic status, i.e. income level, economic class, Education and housing status. The items were scored on a five-point scale ranging from 1: very low 5: very high. Eslami et al. confirmed the face and content validity of this questionnaire in Iran. They also confirmed its reliability by calculating a Cronbach's alpha of 0.83 (2013) [20]

2.3.3. Internet Emotional Relationships questionnaire

Internet emotional relationship is friendly interactions online in the virtual world through chatting, email, Yahoo Messenger, social networks such as Facebook, telegram, etc. In other words, the Internet Social relations. In this study, the valid and reliable questionnaire developed by Barghi-Irani et al. [21] was used to assess online emotional relationships. This 28-item questionnaire has five components, including trust, honesty, enjoyment, sexual desire and preferring virtual relationships, and is scored based on a five-point Likert scale (from totally disagree to totally agree). The validity and reliability of the questionnaire were confirmed with Cronbach's alpha coefficients of 0.73 for trust, 0.70 for honesty, 0.71 for enjoyment, 0.79 for sexual desire, 0.84 for preferring virtual communication, and 0.90 for the whole scale [21]. In the present study, the overall reliability of this tool was confirmed with Cronbach's alpha of 0.85.

2.3.4. Stress, Anxiety, Depression Scale (DASS21), Lovibond (1995) [22]

Mental health means the absence of mental disorders like Stress, Depression, and Anxiety… [10]. In this study, The Depression, Anxiety and Stress Scale - 21 Items (DASS-21) was used to assess the students' mental health. It has 21 items in the three dimensions of stress, anxiety, and depression, each with seven items, and the final score of each subscale and the total score are found by summing up the scores of the items in that subscale. Each item is given a score between zero (did not apply to me at all) to three (applied to me very much). Since DASS-21 is the short form of the original scale with 42 items, the final score of each subscale has to be doubled. The validity and reliability of this scale were confirmed in Iran by Sahebi et al. within a range of 0.77 to 0.79 [23]. The lower is the score on this scale, the better is the respondent's mental health.

2.3.5. Item Short-Form Health Survey (SF-36):

Quality of life is defined as people's perception of their position in life in the context of the culture and value systems in which they live and their goals, expectations, standards and concerns. For assessing it, a Short form health survey (SF-36) was used. This questionnaire is a prevalent instrument for evaluating Health-Related Quality of Life. It was designed in the US by Ware and Sherbourne (1992), and its validity and reliability were assessed in different groups of patients [24). The survey contains 36 items in eight dimensions, including physical functioning, role limitations due to physical problems, role limitations due to emotional problems, energy/fatigue, emotional well-being, social functioning, pain and general health. Moreover, two general subscales called physical health and mental health are obtained by combining these subscales. A lower total score in this questionnaire indicates lower quality of life and vice versa. In Iran, Montazeri et al. (2005) confirmed the validity of this questionnaire as 0.58 to 0.95 and its reliability as 0.77 to 0.90 [25].

2.3.6. Social Networks Questionnaire

Social networks refer to the online space students use to communicate, share, communicate or connect with others for Education, entertainment, socialization and so on [26]. For assessing it, we used the Iranian questionnaire that was developed by Jahanbani (2018).it has 19-items containing three dimensions: the rate of usage, type of use, and the users' trust in networks. Scoring is based on a five-point Likert scale from very little to very much. Jahanbani (2018) confirmed the reliability of this questionnaire with Spearman's correlation coefficient of 0.90. The internal consistency of this questionnaire was also confirmed with Cronbach's alpha coefficient of 0.85 [26].

2.4. Procedure:

The study began after obtaining the necessary permissions from the University and a code of ethics from the University ethics committee. Due to the Covid-19 situation and the impossibility of physical presence of the students, a consent form for participation in the study was first sent to the students through related online networks, such as the Student Deputy, the Student Research Committee and student groups. Eligible students willing to take part were selected by convenience sampling. Then, the online questionnaires were forwarded to the students through these networks and they were asked to complete them in the specified time frame (minimum two weeks). The researcher's phone number was given to the students to contact for responding to any possible ambiguities.

The students were assured of the confidentiality of all their data and that they had no obligation to take part in the study or continue their cooperation and that they would not face any problems or restrictions if they decided not to participate in the study.

2.5. Statistical analysis:

This study assessed the fit of a conceptual model for examining the concurrent effect of social networks and Internet emotional relationships on mental health and quality of life in students(Fig3). First, the normal distribution of the quantitative variables was assessed using the Kolmogorov-Smirnov test. The Path analysis method is an extension of the usual regression that shows the direct effects as well as indirect effects and impact of each variable on the dependent variables, and the results of this model can provide a rational interpretation of the relationships and correlations observed. We checked the construct validity of all questionnaires at the conceptual model with each other by PLS.

Data were analyzed in SPSS-25 [27], PLS3 [28] and Lisrel-8.8 [29]. The correlation results were presented as Pearson's correlation coefficient and the Path analysis results as regression coefficient, Standardized Beta with a significance level of T-value>1.96.

Path analysis is considered a causal modeling technique; it can be performed with either cross-sectional or longitudinal data. All variables in a path model can be described as either endogenous or exogenous. Endogenous variables are variables that are diagrammed as being influenced by other variables in the model. The variables diagrammed as independent of any influence are the exogenous variables. Dependent variables are always endogenous, but some independent (or predictor) variables can be endogenous if they are influenced by other independent variables in the model [30]. In this study, exogenous variables were Education, Average time of use the Internet, SES, Internet Emotional Relationships and the endogenous variable were mental health and quality of life. Social Networks was exogenous for mental health and quality of life but endogenous for other variables.

The data from 350 students at Alborz University of Medical Sciences were assessed in this study. Participants' mean age was 22.42±2.8 years and their mean duration of Education was 14.99±1.4 years. The mean score of quality of life was 66.48±15.57, mental health 41.31±14.15, Internet emotional relationships 61.42±17.50, and social networks 49.87±9.3.

According to Pearson's correlation test results, the use of social networks (r=-0.155) and mental health (DASS-21) (r=-0.260) had a negative and significant correlation with quality of life, with DASS-21 having the highest negative and significant correlation. In other words, the higher was the DASS-21 score, i.e., the higher was mental disorder, including stress, anxiety, and depression, the lower is the score of quality of life (r=-0.260).

Mental health had a positive and significant correlation with the mean duration of using social networks (r=0.137) and Internet emotional relationships (r=0.222). Among these two variables, Internet emotional relationships had the highest positive and significant correlation with the mental health score (DASS-21 (r=0.222) (Table 1).

In order to test the model, the above questionnaires were first assessed in the model. The factor loadings of the items of each questionnaire and the validity and reliability of the tools used in the model were assessed in PLS. According to the results, the factor loadings of all the questionnaires items was higher than 0.4 and all the items were retained after the final testing of the model. To determine the convergent and divergent validity, indices including composite reliability (CR), average variance extracted (AVE), maximum shared variance (MSV), and average shared variance (ASV) were used. According to the results, CR>AVE and AVE>0.5; therefore, the subscales of this tool have convergent validity, and since MSV<AVE and ASV<AVE, the divergent validity is also desirable (Table 2). Table 3 presents the heterotrait-monotrait ratio of correlations (HTMT), which is used to assess the divergent validity of two variables. Divergent validity is acceptable when this ratio is less than 0.9 [31].

According to the Path analysis results, in the direct path, mental health had the most positive significant causal relationship with Internet emotional relationships (B=0.22) and the most negative relationship with socioeconomic status (B=-0.09). In this study, mental health was assessed using DASS-21, in which higher scores mean unfavorable mental health or higher mental disorders (stress, anxiety, and depression). Thus, with a one-unit increase in the score of online emotional relationships, mental disorders (DASS-21) increase by 0.22 units, and with a one-unit increase in socioeconomic score, mental disorders reduce by -0.09 units.

Among the variables related to quality of life, in the direct path, quality of life had the highest negative causal relationship with the mental health or DASS-21 score (B=-0.26); in other words, with a one-unit increase in the score of mental disorders, quality of life reduced by 0.26 units. Among the variables with a significant causal relationship with quality of life in the indirect path, socioeconomic status had the highest positive relationship with quality of life (B=0.023), while the mean duration of using social networks (B=-0.067) and Internet emotional relationships (B=-0.089) had the highest negative relationship with this index. In other words, a one-unit increase in socioeconomic score increased the quality of life score by 0.023 units, and one-unit increases in the score of Internet emotional relationships and the mean duration of using social networks reduced the quality of life score by 0.089 and 0.067 units, respectively. An increase in the quality of life score meant a more favorable quality of life and vice versa (Table 4)(figure4).

The model fit indices showed that the model has a good fit and is highly compatible and the adjusted relationships of the variables based on the conceptual model are rational. Accordingly, there is no significant difference between the fitted model and the conceptual model (Table 5).

In the course of their Education, students experience a unique transition from high school to adulthood [32], which marks the beginning of a major period of psychological and social development that can have different impacts on students' health and life [33]. In this study, we assessed the effect of the selected variable on mental health and quality of life separately.

According to the results obtained, among the variables with a significant causal relationship with mental health in the students, socioeconomic status had the highest direct negative relationship. In other words, the more unfavorable is a student's socioeconomic status, the greater is his risk of mental disorders such as stress, depression and anxiety. This finding agrees with the results obtained by Silva et al., who found in their review study of 2004 to 2014 that there is an independent relationship between mental health and socioeconomic status [34]. In a cohort study conducted on adolescents, Reiss et al. (2020) found that socioeconomic status is an independent predictor of mental health problems. Adolescents and youth with unfavorable socioeconomic conditions experience various stressful situations in their life, thus exposing them to mental problems or their exacerbation [35]. The results of many studies indicate that there are more stressful situations in the life of families with a lower socioeconomic status, such as parents' mental illness or accidents, severe financial crisis, job loss, academic problems, etc. compared to other groups, which can contribute to the development of mental disorders [34-36].

In the present study, Internet emotional relationships had a positive causal relationship with mental health in the direct path. In other words, students' mental disorders increased as their score of Internet emotional relationships scored. The consequences of introducing new communication technologies include the changes in the type and method of communication between the two sexes, mutual emotional interactions and different patterns of intimacy [37]. Today, a new type of relationship has formed in the context of the Internet, especially virtual networks that is very different from its traditional form. Researchers have found conflicting results about these forms of friendship.On the one hand, online friendships are defined as fragile relationships with less attachment, commitment and self-disclosure that deter people from pursuing social activities and impair their development of social relationships. On the other hand, online friendships are regarded as an alternative to positive experiences and beneficial and stable relationships [38]. Researchers have found that feeling left out of the family and society and the absence of a robust social network like friends persuade people to pursue online relationships, make up for their emotional deficiencies, and cause different levels of internet addiction [39]. In a study conducted on university students, Seyyed Salman Alavi et al. found that there is a significant relationship between mental disorders including depression and internet dependencies, which agrees with the present findings [40], but disagrees with the results of the study by Hachebi (2001), who argued that social media provide a good opportunity for people and can affect their mental health [41]. These contradictions can be caused by the method and level of use of social networks and the level of emotional-social dependence due to cultural-social limitations. The rise in these contradictions is alarming not only for policymakers and civil community groups, but also for the citizens [42].

Another investigated variable was students' quality of life. Poor mental health has a direct, negative, causal effect on quality of life, such that an increase in mental problems reduces students' quality of life. The present findings agree with the results of many studies, including a qualitative systematic review study by Connell et al. (2012) which found that a good quality of life is correlated with a good sense of control, a positive self-image, and hope and optimism, and conversely, a low quality of life is correlated with mental disorders, anxiety and poor self-esteem [43]. In another study conducted on university students, Jenkins et al. (2020) confirmed the effect of mental problems on quality of life and argued that students with anxiety, depression or other mental problems had a poorer quality of life [44].

Other factors with a significant causal relationship with quality of life included the average time spent on the Internet and online emotional relationships, which had the greatest negative relationship with quality of life. Some researchers have found that internet use is significantly associated with reduced communication with friends and social networks, which causes further loneliness and a decrease in some aspects of quality of life [45]. In a study conducted on nursing students, Ragheb et al. (2018) found that inappropriate use of the Internet adversely affects the students' quality of life. They believed that excessive use of the Internet was a way for the students to run away from the pressures of real life or parental conflicts (46), which agrees with the present findings. Nonetheless, a study conducted on older adults by Khalaila et al. (2017) showed that using the Internet and social networks has a positive role in the quality of life in this age group [14]. Such contradictions can be due to the differences in age groups examined, study type and data collection instruments.

According to the results, using the Internet and virtual networks, online emotional relationships, and unfavorable socioeconomic status are associated with mental disorders and reduced quality of life in students. Since students make the future of every country, it is essential for policymakers to further address this group and their concerns. The researchers recommend further training of the public on proper internet use and virtual networks and their role in improving the quality of life.

This study was conducted in Pandemic of COVID-19. Because of that, we had some limitations to access students, and another limit was using the questionnaire, which we had to trust answers.

EDU =Education

TUS= Average time of use of the Internet

INT= Internet Emotional Relationships

SOM= Social Network Score

DASS= Depression, Anxiety and Stress Scale

SES= Socioeconomic Status

SF36=36-Item Short-Form Health Survey

Ethics approval and consent to participate:

Informed consent was obtained from all the participants entering the study. Relevant guidelines and regulations were observed for all methods. All experimental protocols were approved by the Ethics Committee of Alborz University of Medical Sciences (Abzums.Rec.1399.234). All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

All Of Authers have Consent for publication.

Availability of supporting data

The data that support the findings of this study are available from the corresponding author upon reasonable request

Competing interests

The authors declare that they have no competing interests

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors' contributions

F, A. designed and collected the data, performed the statistical analysis and wrote the manuscript. Z, M. supervised the study, contributed to the study design and conducted the analysis, and also helped write the manuscript. M, Q. analyzed the data and helped write the manuscript. L, S. served as the study’s scientific advisor, designed and helped write the manuscript. Z, M T. helped collect the data and write the manuscript.

All author) s (have read and approved the final manuscript

Acknowledgements

The present study is the result of a master's thesis in midwifery counseling that was conducted with the support of the Research Deputy and the Education Deputy of Alborz University of Medical Sciences. The researchers wish to express their gratitude to these organizations and all the participating students

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Table 1:The correlation matrix between social networks and Internet emotional relationships with mental health and quality of life in students 2021(n=350)
SFE6	SOM	SES	DASS	INT	TUS	EDU	Age
							1	Age	1
						1	0.508**	EDU	2
					1	-0.088	-0.108*	TUS	3
				1	0.097	-0.134*	-0.120*	INT	4
			1	0.222*	0. 137*	0.072	-0.014	DASS	5
		1	-0.093	0.027	-0.156**	0.070	-0.153**	SES	6
	1	-0.041	0.161**	0.265**	0.497**	-0.014	-0.085	SOM	7
1	-0.155**	- 0.032	0.260**	-0.074	-0.056	-0.012	0.018	SF36	8
= ** P >0/01 =*P>0/05
EDU =Education. TUS= Average time of use of the Internet, INT= Internet Emotional Relationships SOM= Social Network Score, DASS= Depression, Anxiety and Stress Scale, SES= Socioeconomic Status SF36=36-Item Short-Form Health Survey

Table 2- The results of the confirmatory factor analysis and the combination of CR) and the mean of variance extracted (AVE) quality of life questionnaire, mental health, social networks and Internet emotional relationships in the test model
Alpha Cronbach	ASV	MSV	Rho-A	Average variance extracted	Composite reliability	Questionnaires
0.899	0.484	0.551	0.904	0.832	0.937	DASS
0.898	0.389	0.562	0.901	0.712	0.925	Internet emotional relationship
0.878	0.435	0.501	0.938	0.74	0.961	Quality of Life (SF36)
0.865	0.367	0.340	0.869	0.75	0.887	Social networks

Table 3- Heterotrait-heteromethod ratio of correlations (HTMT) Matrix Quality of Life, Mental health, Social Networks and Internet emotional relationships in the Test Model
SOM	SF36	INT	DASS
				DASS	1
			0.252	INT	2
		0.205	0.750	SF36	3
	0.252	0.331	0.209	SOM	4
INT= Internet Emotional Relationships SOM= Social Network Score, DASS= Depression, Anxiety and Stress Scale, SF36=36-Item Short-Form Health Survey

Table 4 Direct and indirect effects of social networks and Internet emotional relationships with mental health and quality of life
Effectively	Indirect effects	Direct effects		mental health (DASS21)
0.12*	0.36	0.12*	EDU
0.11	0.288	0.08	TUS
-0.09*	0.0012	-0.09*	SES
0.22*	0.013	0.22*	INT
0.06	-	0.06	SOM
-0.03*	-0.03*	0.04	EDU	Quality of life(SF-36)
-0.067*	-0.067*	0.04	TUS
0.0234*	0.0234*	-0.06	SES
-0.089*	-0.089*	0.02	INT
-0.14	-	-0.14*	SOM
-0.26*	-	-0.26*	DASS
EDU =Education. TUS= Average time of use of the Internet, INT= Internet Emotional Relationships SOM= Social Network Score, DASS= Depression, Anxiety and Stress Scale, SES= Socioeconomic Status SF36=36-Item Short-Form Health Survey *=significant

Table 5.model Fitting Indicators

RMSEA (root mean squared error of approximation)

NFI (Bentler-Bonett Normed fit index)

GFI (Goodness of fit index)

CFI (comparative fit index)

X²/df

X²

0000

0/99

1/06

3/18

No competing interests reported.

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Social Networks and Internet Emotional Relationships on Mental Health and Quality of Life in Students: Structural Equation Modelling

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