The effect of loneliness on depression: A meta-analysis

Abstract

Background:

Negative emotions, which have a common, chronic and recurrent structure, play a vital role in the development and maintenance of psychopathology. In this study, loneliness as a negative emotion was considered to be a predisposing factor in depression.

Aim:

The aim of this meta-analysis is to determine the effect of loneliness on depression.

Method:

Initially, a literature scan was performed and all related literature was pooled together (n = 531). Based on scales determined by the researchers, it was decided to include 88 studies in the analysis. This study obtained a sampling group of 40,068 individuals.

Results:

The results of using a random effects model for analysis showed that loneliness had a moderately significant effect on depression. None of the variables of study sampling group, type of publication and publication year were found to be moderator variables.

Conclusion:

According to the results of the research, loneliness may be said to be a significant variable affecting depression. The findings obtained are discussed in light of the literature.

Keywords

Loneliness depression meta-analysis

Introduction

Depression, which is distinguished by symptoms of low motivation, appetite or uncomfortable sleep, falling self-worth, feelings of guiltiness, falling of energy, dissatisfaction and lack of interest, is a mood disorder that takes hold of all society in the world. Nowadays, it is estimated that approximately 350 million people are affected by depression. Results of the World Mental Health Survey revealed that almost 1 in 20 people is exposed to depression episodes (World Health Organization (WHO), 2012). Given the etiology of depression, the effects of biological, psychological and social components have been shown. The effect of loneliness, which is accepted as one of the social determinants of depression (Cacioppo, Fowler, & Christakis, 2009), on depression was assessed in this study.

Loneliness is a universal and common situation with emotional, cognitive and motivational scales (Galanaki, 2004). Loneliness, contrary to human nature due to the disposition toward social communication and unity (Cacioppo & Patrick, 2008), is a negative situation occurring due to the insufficient quality and quantity of social relationship networks of an individual. Research related to loneliness has demonstrated the correlation of loneliness to factors such as lack of emotional support (Alkan, 2014; Stickley et al., 2015), stress (Burke & Segrin, 2014), entrapment (Perron, Cleverley, & Kidd, 2014), social deficiency (Zhang et al., 2014), low self-esteem (Świtaj, Grygiel, Anczewska, & Wciórka, 2015), hopelessness (Chang, Lian, et al., 2015), shyness (Clark, Loxton, & Tobin, 2015) and low levels of emotional intelligence (Wols, Scholte, & Qualter, 2015). However, among factors that are affected by loneliness, there is much research on the variable of depression (Çağan & Ünsal, 2014; Chang, Muyan, & Hirsch, 2015; Grov, Golub, Parsons, Brennan, & Karpiak, 2010; Holvast et al., 2015; Weiss, 1973; Yadegarfard, Ho, & Bahramabadian, 2013; Yao & Zhong, 2014).

As with loneliness, research related to depression has aimed to find the causes of depression and treatment aims to predict interventions to these variables. Many studies completed by researchers have revealed that loneliness is a significant variable among causes of depression, with different results and values found by these studies (Drageset, Espehaug, & Kirkevold, 2012; Grov et al., 2010; Jaremka et al., 2013; Peerenboom, Collard, Naarding, & Comijs, 2015; Şahin & Tan, 2012). However, it appears there is no study on the total effect value of loneliness on depression. With this aim, this study attempts to determine the degree of effect of loneliness on depression from different values obtained by previous studies to identify the total effect value.

This study researched the effect of loneliness on depression. In addition, the following variables were considered to be moderators affecting the mean effect size obtained by the study: (1) sample used in the research, (2) type of publication of research and (3) year of publication of research. Together with these variables in light of previous research results, this study attempted to test the following hypotheses:

H₁. Loneliness has a positive effect on depression.

H₂. Sampling group is a moderator of the positive effect of loneliness on depression.

H₃. Type of publication is a moderator of the positive effect of loneliness on depression.

H₄. Publication year is a moderator of the positive effect of loneliness on depression.

Method

Study design

This study tested the effect of loneliness on depression using the method of meta-analysis. Meta-analysis combines the results of many independent studies on a certain topic and is a method of statistically analyzing the obtained research findings (Littel, Corcoran, & Pillai, 2008). The current meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines (PRISMA) (Moher, Liberatti, Tetzlaff, & Altman, 2009).

Eligibility criteria and search strategy

To determine which research would be included in the meta-analysis, initially a literature scan of the Web of Knowledge and ProQuest academic databases was completed. In this stage, depression was taken as a basis and the term ‘loneliness’ was used, and the title, keywords and abstracts were downloaded after the scanning procedure. The last date for research to be included in this study was determined to be January 2018.

The study used several strategies to determine which research was appropriate for inclusion in the meta-analysis. Initially, title, keywords and abstract were downloaded during scanning and a study pool of all research related to loneliness and depression (531 studies) was created. Later, the full text of publications was downloaded from the databases. According to the criteria below, 443 studies were excluded from the research after literature investigation. The descriptive statistics relating to the 88 studies are presented in Table 1.

Table 1.

Characteristics of studies included in the meta-analysis.

Options		1	2	3	4
Publication year		Before 1990	1990–1999	2000–2009	After 2010
	n	7	15	32	34
	%	7.95	17.04	36.36	38.65
Publication type		Thesis	Paper
	n	31	57
	%	35,22	64,73
Sampling group		Patient	Career	Elderly	Student	Other
	n	10	8	23	35	12
	%	11.36	9.09	26.13	39.77	13.65

The inclusion criteria determined for this study are as follows: (1) contains required statistical information for correlational meta-analysis (n and r or R² values), and (2) studies measuring the relationship between loneliness and depression. The exclusion criteria for studies in the meta-analysis are as follows: (1) no determination of any quantitative data, (2) no correlation values included in the research, (3) no acceptance of loneliness as a measured unit and (4) no access to the data through lack of access to the full text.

Coding is the term used for the procedure of extracting data, understanding confusing data in the studies and adapting it for use. Before proceeding to statistical analysis, a coding form was created in this study and coding was completed in accordance with this form. Our basic aim in creating this form was to develop a special coding system that would generally allow a view of all research and yet not allow the characteristics of a single research to be skipped. The coding form created for this study comprised the following components: (1) source of the research, (2) sampling information, (3) data collection method(s) and (4) methodological information, quantitative values.

Statistical procedure

The effect size obtained from meta-analysis is a standard scale value used to determine the strength and direction of the relationship in the study (Borenstein, Hedges, Higgins, & Rothstein, 2009). The effect size in this study was calculated as the Pearson correlation coefficient (r). As the correlation coefficient is a value between +1 and –1, this r value can be transformed into a value on z tables and used for calculations (Hedges & Olkin, 1985). As correlational meta-analysis studies provide more than one correlation value for the same structural category, there are two different approaches which can be used for meta-analysis (Borenstein et al., 2009; Kulinskaya, Morgenthaler, & Staudte, 2008). In this study, (1) initially if the correlations were independent, all related correlations were analyzed and accepted as independent studies, or (2) where dependent correlations are given, the mean of the correlations is used. Although there are different methods to correct the mean correlations, the majority of these methods cause high correlation estimates (Schyns & Schilling, 2013). As a result of this criticism, to use mean correlations all correlations are created from a conservative estimate, so this study used conservative estimates.

There are two basic models for meta-analysis studies: fixed effects model and random effects model. While deciding which model to use, the characteristics of studies included in the meta-analysis are examined to determine which model preconditions they abide by (Borenstein et al., 2009; Hedges & Olkin, 1985; Kulinskaya et al., 2008). For the fixed effects model, there is (1) an assumption that the research is functionally identical and (2) includes the aim of calculating the effect size only for the defined population. If the research is not believed to be functionally equivalent and if the calculated effect size is intended to be generalized to a larger population, the model that should be used is the random effects model. When these conditions were assessed, the meta-analysis procedure in this study used the random effects model. The meta-analysis procedure benefited from the Comprehensive Meta-Analysis program.

Moderator variables

Moderator analysis is an analysis method allowing testing of the difference in mean effect size of variables (moderators) and direction of difference between subgroups. Moderator analysis in a meta-analysis study should be planned appropriately with the aim of the study and should be performed in accordance with this plan (Littel et al., 2008). Statistical significance of differences between moderator variables is tested with the Q statistical method developed by Hedges and Olkin (1985). In this method, Q is divided into two as Q_between (Q_b) and Q_within (Q_w) and analyses are performed on these two different Q. Q_w tests the homogeneity within the moderator variable used, while Q_b tests the homogeneity between the groups (Borenstein et al., 2009; Hedges and Olkin, 1985; Kulinskaya et al., 2008).

In this study, only the statistical significance of differences between moderators was examined, so only Q_b values were used. Four moderator variables considered to play a role in the mean effect size in this study were determined. One of these was the sample the research was completed with. Considering that the sampling group used for depression and loneliness may have affected the effect size, it was evaluated as a possible moderator. Second, possible differences between the effect of loneliness on depression in Turkey and other countries were considered and source of publication was determined as a moderator. Third, due to reporting of positive and negative data and inclusion of results with different aspects, the type of publication was considered a moderator. Finally, the year of publication of the research was concluded to be a possible moderator due to changing working conditions through the years and the resulting effects on depression.

Publication bias

The basis of publication bias is the assumption that all research on a topic may not have been published. Especially research that does not identify statistically significant correlations or find low-level correlations is not considered valuable for publication, affecting total effect levels negatively and increasing bias of mean effect size (Borenstein et al., 2009; Hanrahan, Field, Jones, & Davey, 2013; Kulinskaya et al., 2008). The effect of this publication bias, which we can call data loss, is that it negatively affects the total effect in meta-analysis studies. In this case, the possibility of publication bias should be considered in meta-analysis studies. To investigate publication bias in this study, the following questions were answered: (1) Is there any evidence of any publication bias? (2) Can general effect size be a result of publication bias? (3) What amount of the total effect can be linked to publication bias?

A range of calculation methods are used to provide a statistical answer to the questions related to the above possibilities in meta-analysis. The first of these is the funnel plot method. The shape provided by this method may not be fully objective but allows us to observe whether there is an effect of publication bias in the studies. The funnel plot of all research included in the meta-analysis in this study is presented in Figure 1. There is no evidence in Figure 1 of any effect linked to publication bias in the studies included in the meta-analysis. With publication bias, the funnel plot is expected to be severely asymmetric. The clustering of research plotted in lower parts of the funnel, especially on one side of the line showing mean effect size (especially the right side), shows the possibility of publication bias. In 74 studies included in the meta-analysis in this study, no evidence of publication bias was observed.

Figure 1.

Funnel plot of effect size related to publication bias.

Although no evidence relating to publication bias was observed on the funnel plot, there was publication bias observed in the Duval–Tweedie’s trim and fill tests used to evaluate the amount of effect linked to publication bias in the effect size obtained from the meta-analysis. As a result, 18 studies were removed from the analysis. As observed in Table 2, there is a difference between the observed effect size value and the virtual effect size created to correct the effect due to publication bias.

Table 2.

Results of Duval–Tweedie’s trim and fill test.

	Studies removed	Point estimate	95% CI		Q
	Studies removed	Point estimate	Lower	Upper
Observed values		0.5008	0.4428	0.5546	4,618.54
Corrected values	22	0.4191	0.3561	0.4784	7,216.32

CI: confidence interval.

Results

The meta-analysis results for depression and loneliness are found in Table 3. The findings show a positive relationship between depression and loneliness, supporting H₁. The effect value of loneliness on depression was significant (r = .50, p < .01; 95% BCa = [.44, .55]). This value shows that loneliness has a moderate (see Cohen, 1988) effect on depression.

Table 3.

Effect of loneliness on depression.

Variable	k	r	95% CI		Q	Q_b
			Lower	Upper
Depression	88	.50**	.44	.55	4,618.54**
Moderator (sampling group)						1.20
Patient	10	.54**	.38	.67
Carer	8	.57**	.40	.70
Elderly	23	.49**	.39	.59
Student	35	.50**	.41	.58
Other	12	.44**	.16	.66
Moderator (publication type)						0.20
Paper	57	.52**	.45	.58
Thesis	31	.49**	.41	.49
Moderator (publication year)						4.31
Before 1990	7	.36**	.16	.53
1990–1999	15	.52**	.40	.61
2000–2009	32	.50**	.42	.57
After 2010	34	.56**	.46	.65

CI: confidence interval.

p < .05; **p < .01.

Moderator analysis did not support H₂ that the sample used in the research played a moderator role in the effect levels of loneliness on depression. However, the research included in the meta-analysis of patients (r = .54, p < .01; 95% BCa = [.38, .67]), carers (r = .57, p < .01; 95% BCa = [.40, .70]), elderly (r = .49, p < .01; 95% BCa = [.39, .59]), students (r = .50, p < .01; 95% BCa = [.41, .58]) and other participants (r = .44, p < .01; 95% BCa = [.16, .66]) showed that loneliness had a moderate and significant effect on depression. The strongest effect belonged to the sample of carers. Although the effect values of different samples on depression change, the moderator analysis according to the random effects model found that the effect difference of sampling type used to measure depression was not statistically significant (Q_b = 1.20, p > .05).

Findings did not support H₃ that type of publication played a moderating role in the effect of loneliness on depression. In moderator analysis, the difference in effect levels between theses and papers was not found to be statistically significant (Q_b = 0.20, p > .05); however, a moderately significant effect was identified in papers (r = .52, p < .01; 95% BCa = [.45, .58]) and theses (r = .49, p < .01; 95% BCa = [.41, .49]).

The research did not support H₄ that publication year played a moderator role in the effect of loneliness on depression. The moderator analysis performed found that the difference in effect between publication years was not statistically significant (Q_b = 4.31, p > .05). In this meta-analysis, research published in 2010 and after (r = .56, p < .01; 95% BCa = [.16, .53]), research published between 2000 and 2009 (r = .50, p < .01; 95% BCa = [.40, .61]), research published between 1990 and 1999 (r = .52, p < .01; 95% BCa = [.42, .57]) and research published before 1990 (r = .36, p < .01; 95% BCa = [.46, .65]) found a small level of effect of loneliness on depression.

Discussion

This study performed meta-analysis to determine what level of effect loneliness has on depression. Thus, general results can be obtained from papers and theses from the past to the present. In addition, the study examined whether the variables of sampling group, source of publication, publication year and type of publication played a moderator role in the effect of loneliness on depression.

The findings show that loneliness has a significant effect at moderate levels on depression. If analyzed for moderator variables, the variables determined in this study did not indicate a moderator effect on loneliness affecting depression. The findings of the sampling group analysis showed that lonely carers had greater depressive tendencies. Similar results were identified for patients, students, elderly and other victims of depression. This result shows that society leaves patients, carers and elderly alone with their problems and they distance from daily life, experiencing depression. In addition, problems experienced in the period of puberty leads teenagers who believe that no one understands them to experience feelings of loneliness which may cause depression. The need to feel a sense of belonging may be seen as the common characteristic between these two groups with very different age profiles and social environments. One of the variables used to explain the effect of loneliness on depression by research is feeling a sense of belonging (Baskin, Wampold, Quintana, & Enright, 2010). Research shows that belonging alone is effective on loneliness (Waytz, Chou, Magee, & Galinsky, 2015) and that individuals entering a new environment experience great difficulties in the period when they have not formed a social environment (Wohn & LaRose, 2014), leading to the consideration that loneliness in social environments may indirectly trigger depression. Social sufficiency (Zhang et al., 2014) may be a factor affecting the individual in youth and old age and may cause this result. Individuals in puberty are in a situation where their expertise is incomplete in social terms. In elderly people, these skills begin to be lost. In both situations, a distancing of the individual from social environments is the issue. This rupture means the individual loses the power to affect their environment. The tendency of individuals losing power to experience loneliness (Waytz et al., 2015) and the formation of a factor that reduces loneliness in their communication with their environment (Zhang, Gao, Fokkema, Alterman, & Liu, 2015) appear to be factors explaining the moderator role of the age factor.

Moderator analysis according to type of publication showed that type of publication did not have a significant effect on loneliness affecting depression. Both theses and papers had significant effect values, indicating there were significant differences within data from both theses and papers of the effect of loneliness on depression. This situation may be interpreted as results providing the possibility of differences in the effect of loneliness on depression in both theses and papers. This result showed that the relationship between depression and loneliness is a constant result and may be interpreted as not being affected by publication bias.

Finally, there did not appear to be a significant difference due to the moderator of publication year. Contrarily, all years had moderate level and significant effects. This result shows that the effect of loneliness on depression does not change from year to year; in other words, the effect of loneliness is shown to be a permanent factor in depression.

Footnotes

Acknowledgements

Some part of the study was presented as an oral presentation at XIII. National Psychological Counseling and Guidance Congress, 7–9 September 2015, Turkey.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Özkan Çikrikci

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