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Abstract

The COVID-19 pandemic has presented significant challenges to mental health worldwide, exacerbating mental health symptoms across various populations. This meta-analysis aims to evaluate the relationship between social support and mental health symptoms, specifically depression, general anxiety, and stress, during the pandemic. We conducted a comprehensive literature search that identified 210 studies involving a total of 216,104 participants. Data were analyzed using robust variance estimation with random effects to assess correlations between social support and mental health symptoms, while also examining potential moderating factors such as age, gender, and sample types. Our analysis revealed a significant negative correlation between social support and overall mental health symptoms (r = −0.259; 95% CI: −0.29, −0.24; p < 0.01). Notably, high-risk populations exhibited a stronger association (r = −0.302) compared to students (r = −0.263) and the general population (r = −0.219). Furthermore, the correlation between social support and depression (r = −0.304) was significantly stronger than that for generalized anxiety (r = −0.238) and stress (r = −0.220), underscoring the critical role of family support during this period. These findings suggest that while social support positively influences mental health symptoms, its effectiveness may be more limited than anticipated. As the pandemic continues to impact mental well-being, there is an urgent need for targeted strategies to enhance the effectiveness of social support in addressing mental health challenges. This research highlights the importance of prioritizing social support mechanisms in public health responses to future crises.

Keywords

COVID-19 social support depression anxiety stress meta-analysis

Introduction

The past two years have been profoundly challenging due to the COVID-19 pandemic, which has led to an alarming number of infections and deaths, resulting in unprecedented global health crises (Prati & Mancini, 2021). During this period, there has been a growing focus on the mental health of the general population, as the psychological impacts of isolation, grief, and uncertainty have become increasingly evident (Yang et al., 2020; Zhang et al., 2021). Scholars from around the world have highlighted a significant surge in mental health symptoms during the pandemic, particularly concerning depression, general anxiety, and stress (Fan et al., 2020; Hou et al., 2021; Jones et al., 2021; Raihan, 2021; Twenge & Joiner, 2020). These symptoms have even treated as critical indicators of mental health symptoms faced by individuals during this crisis (Guo et al., 2021; Mahamid et al., 2023). This underscores the urgent need for targeted interventions and support systems to address these escalating mental health issues effectively. Social support is universally recognized as a key predictor and protector of mental health. Its significance is particularly amplified during crises such as the pandemic, where it serves as a critical buffer against stress, general anxiety, and depression (Cohen & Wills, 1985; Taylor, 2011). To effectively promote mental health education in the post-epidemic era and enhance overall well-being, this study aims to conduct a comprehensive analysis of the relationship between social support and mental health symptoms, as well as to explore potential moderating factors during the epidemic period. Specifically, we seek to synthesize findings regarding the direct impact of social support on mental health symptoms. The insights gained from this analysis will be valuable for informing mental health education initiatives in the post-epidemic context.

Social support and mental health

Social support refers to the assistance and resources that an individual can access through their social connections with other individuals, groups, and the broader community (Lin et al., 1979), and has long been recognized as a critical buffer against the adverse effects of distress on psychological and physical well-being. Theoretical frameworks, such as the buffering hypothesis (Cohen & Wills, 1985), suggest that social support mitigates the impact of stress by providing emotional, informational, and practical resources. During the COVID-19 pandemic, the need for social support has become even more pronounced as individuals face isolation and social distancing measures aimed at “flattening the curve.” These measures have disrupted usual social networks, often leading to feelings of loneliness, deprivation, and increased psychological vulnerability (Szkody et al., 2021). Empirical research consistently underscores the protective role of social support in maintaining mental health during this crisis. Studies have demonstrated that social support significantly alleviates symptoms of depression, general anxiety, and stress amid the pandemic (Fan et al., 2020; Fang et al., 2021; Raihan, 2021). In fact, social support has emerged as one of the most robust predictors of mental health resilience during these challenging times. This underscores the critical role of strong social connections in fostering psychological resilience, particularly in the face of prolonged adversity and uncertainty.

In sum, we could speculate that social support and mental health symptoms will show a relatively close relationship during the COVID-19 pandemic. However, in empirical investigations of the links between social support and mental health symptoms during the COVID-19 pandemic, there is substantial variance in the magnitude of reported associations. For example, many studies have found social support is highly negatively correlated with depression (e.g., |rs| > 0.50) (Fang et al., 2021; Ghafari et al., 2021; Marroquín et al., 2020) and generalized anxiety (e.g., |rs| ≥ 0.50) (Nazim et al., 2022; Olashore et al., 2021). In contrast, other studies have reported a more moderate–low negative correlation between social support and depression (e.g., |rs| < 0.10) (Graupensperger et al., 2020; Park et al., 2023), generalized anxiety (e.g., |rs| < 0.10) (Çelik et al., 2022; Muyor-Rodríguez et al., 2021), and stress (e.g., |rs| < 0.10) (Zhou & Yao, 2020; Zhou, et al., 2020), and some studies even found a positive correlation between social support and these mental health symptoms (Akhther, & Sopory. 2022; Fong et al., 2021; Spinola et al., 2020). According to the matching hypothesis (Cohen & Wills, 1985; Cutrona, 1990), the effectiveness of social support in mitigating psychological symptoms following pandemic-related stress is contingent upon a match between the specific coping needs elicited by the stress and the functions of the support received. When this alignment is lacking, even available social support may fail to buffer against adverse mental health outcomes, leading to inconsistent findings in the literature regarding the relationship between social support and mental health symptoms. Several factors may contribute to this inconsistency: first, measurement variability arises from different studies employing varying definitions and methods to assess both social support and mental health symptoms. Second, the types of support play a critical role, for instance, individuals who perceive high levels of social support may experience better mental health outcomes than those who receive tangible support but still feel isolated, highlighting the importance of distinguishing between perceived and received support. Third, temporal factors also influence observed relationships, cross-sectional studies capture data at a single point in time, potentially overlooking changes in mental health symptoms or social support over time, whereas longitudinal studies can reveal how these variables interact and evolve. Lastly, sample variability introduces additional complexity, differences in study populations—such as demographic groups (e.g., adolescents or elderly individuals) or occupational backgrounds—that can lead to different results due to varying social dynamics and contextual factors. Additionally, sample size and recruitment methods can affect the generalizability of findings. Collectively, these factors underscore the intricate relationship between social support and mental health symptoms, emphasizing the need for nuanced interpretations of research outcomes and tailored interventions. The true extent of the positive effects of social support on mental health symptoms during the COVID-19 pandemic is yet to be determined. Moreover, previous studies investigating the relationship between social support and mental health symptoms during the COVID-19 pandemic often focus on only one specific indicator of mental health symptoms (Liu et al., 2024). Additionally, the sample sizes of the studies included in these meta-analyses tend to be relatively small (Gabarrell-Pascuet et al., 2023), which may hinder a more holistic understanding of the important relationship between social support and mental health symptoms during the COVID-19 pandemic.

Overview of the present meta-analysis

In our study, we conducted a quantitative meta-analysis to examine the association between social support and mental health symptoms, including depression, general anxiety, and stress (note, stress is defined as general distress in response to stressful life events in our study) within diverse populations and cultures during the COVID-19 pandemic. Considering the unique circumstances of the pandemic, we expect a strong correlation between social support and mental health symptoms, lying within a moderate–high range of association. By synthesizing existing research, our analysis aimed to provide a more precise estimation of the true correlation between social support and mental health symptoms. In addition, we were also interested in evaluating participants’ age, gender (percentage of women), study types (cross-sectional vs. longitudinal studies), types of social support (enacted social support [ESS] or perceived social support [PSS]), sample types (general samples, student samples or high-risk samples), and social support measure used (Multidimensional Scale of Perceived Social Support [MSPSS or Non-MSPSS]) as potential moderators of the association between social support and mental health symptoms during the COVID-19 pandemic.

First, developmental psychology suggests that the impact of social support on mental health may vary across different age groups due to life-stage differences in coping mechanisms and social network structures (López et al., 2020). Although some studies indicate that, compared to younger people, older adults experienced more severe mental health symptoms during the pandemic (Tian et al., 2020), most research suggests that, individuals in early adulthood exhibited more pronounced mental health symptoms than older people during this period (Ahmed et al., 2020; Gambin et al., 2021; Huang, & Zhao, 2020). Perhaps this is because they have experienced a more significant disruption to their daily routines, faced greater economic challenges, and have fewer personal or cognitive resources to draw upon. In addition, studies have consistently shown age-related differences in the effectiveness of social support. López et al. (2020) indicated that older adults benefit more from perceived social support due to the added isolation they face. And they often rely on emotional and instrumental support from family members and close friends, as their social networks may be more limited. Conversely, younger adults and adolescents may rely more on peer-based support, which plays a significant role in buffering mental health symptoms during the pandemic (Trevino et al., 2021), and it is important to note that such peer interactions have also been significantly limited due to social distancing measures and lockdowns. Thus, the moderating role of age is crucial for understanding how social support functions differently across life stages, especially in crisis situations. Consequently, the age of participants may moderate the association between social support and mental health symptoms during the COVID-19 pandemic.

Second, numerous studies have shown that women are at a greater risk of experiencing mental health symptoms compared to men due to the interaction of biological factors and social determinants, including gender stereotypes (Afifi, 2007; Riecher-Rössler, 2010, 2017). Evidence from numerous studies conducted during the COVID-19 pandemic also suggests that women have experienced disproportionately higher levels of mental health distress compared to their male counterparts (Mir et al., 2023; Proto & Quintana-Domeque, 2021; Saw et al., 2022; Xiong et al., 2020). This disparity highlights the need for targeted mental health interventions for women in the context of global crises. Gender differences in the experience of social support can be understood through social role theory, which posits that men and women are socialized to express and seek support differently. Women are more likely to seek emotional support and are generally more attuned to social networks, while men tend to rely on instrumental support and may underreport emotional distress (Taylor et al., 2000). In the context of mental health challenges arising from the COVID-19 pandemic, the gender differences in coping strategies (problem-focused vs. emotion-focused) (Cholankeril et al., 2023) may enable women to better utilize social support as a means of alleviation on mental health symptoms. Another study partially supports this idea. Johansen et al. (2021) indicated that social support had a stronger role as a protective factor for mental distress among young women, compared to young men and older persons. Thus, women’s mental health symptoms may benefit more from social support than men. Consequently, the gender of participants may moderate the association between social support and mental health symptoms during the COVID-19 pandemic.

Third, recent research indicates that social support and mental health symptoms, such as depression, general anxiety, and stress, may exhibit a stronger correlation when measured concurrently rather than at different times (Ke et al., 2023). Several studies have suggested that the relationship between social support and mental health outcomes can vary significantly depending on whether the research employs cross-sectional or longitudinal designs (Mauer et al., 2024; Song et al., 2024). Therefore, we investigated the potential moderating effect of cross-sectional versus longitudinal studies on the relationship between social support and mental health symptoms during the COVID-19 pandemic.

Four, social support is a multifaceted construct that can be categorized into two main types: enacted social support (ESS) and perceived social support (PSS) (Haber et al., 2007; Lakey et al., 2010; Leskela et al., 2009; Wethington & Kessler, 1986; Xia et al., 2012). ESS, also known as objective or received social support, refers to the tangible assistance actually offered by an individual’s social network, emphasizing the quantitative and objective aspects of support. In contrast, PSS pertains to an individual’s subjective evaluation of the support they receive, encompassing their feelings and experiences regarding that support. The buffering hypothesis (Cohen & Wills, 1985) suggests that PSS serves as a psychological resource that protects against the harmful effects of stress by fostering a sense of belonging and reassurance. On the other hand, ESS, such as tangible assistance, may be beneficial but is less consistently protective than perceived support in times of crisis (Cohen & Wills, 1985). Recent studies have reaffirmed that PSS is particularly important in alleviating mental health symptoms during the pandemic. For example, Rui and Guo (2022) found that, those with high PSS are better equipped to cope stress from COVID-19 news, and PSS could directly dampen media effects on stress. This underscores the critical role of PSS in supporting psychological well-being during challenging times. This is because PSS enhances individuals’coping capabilities and provides a sense of emotional safety, especially in contexts of prolonged isolation. During the pandemic, even with limited tangible assistance, individuals who feel supported are better equipped to cope with stress and maintain a positive outlook. Consequently, the types of social support may moderate the association between social support and mental health symptoms during the COVID-19 pandemic.

Five, the relationship between social support and mental health symptoms may also be moderated by sample types. There is evidence that high-risk groups such as health-care workers, older adults, pregnant women, patients with confirmed COVID-19 infection, and people with other diseases or disabilities, are more likely to experience mental health symptoms during the COVID-19 pandemic (Brooks et al., 2022; Fujita et al., 2020; He et al., 2023; Hu et al., 2020; López-Morales et al., 2021; Spoorthy et al., 2020). Social support may play a greater role in alleviating mental health symptoms among high-risk samples. In addition to high-risk samples, research on the relationship between social support and mental health symptoms during the COVID-19 pandemic has mainly focused on the general populations (Budimir et al., 2021; Tull et al., 2020; Simon et al., 2021) and student populations (Barros & Sacau-Fontenla, 2021; Ozrudi et al., 2021; Torres et al., 2023), and scholars from around the world have conducted a substantial amount of studies in these two populations. Different types of samples may moderate the relationship between social support and mental health symptoms during the pandemic. Thus, in the present meta-analysis, we focused on possible moderating effects of sample types (i.e., general samples, student samples or high-risk samples) on the links between social support and mental health symptoms during the COVID-19 pandemic.

Six, PSS is the most commonly assessed measure of social support (Ibarra-Rovillard & Kuiper, 2011), largely because it is easy to quantify and has been shown to be a more effective predictor of mental health than other measures (Brissette et al., 2002; Dour et al., 2014; Levendosky et al., 2002). PSS was mainly measured by Multi-Dimensional Scale of Perceived Social Support (MSPSS), including family, friend, and significant other support dimensions. We are interested in determining whether the social support measurements employed include MSPSS or Non-MSPSS may moderate the association between social support and mental health symptoms during the COVID-19 pandemic. Non-MSPSS measurement tools encompass various other types of social support assessments, such as the Social Support Rating Scale, which is regarded as a measure of ESS (Xia et al., 2012), as well as scales that evaluate material or alternative forms of social support. In addition, the dimensions of PSS also may influence the relationship between social support and mental health symptoms. Because the epidemic has disrupted the normal rhythm of interpersonal relationships (Hu et al., 2022), these three dimensions may have varied effects on mental health symptoms during such difficult periods. In particular, because of the implementation of epidemic prevention and control measures, family support may play a more important role. Therefore, we examined the potential moderating effect of social support measures (i.e., MSPSS or non-MSPSS) and the dimensions of PSS on the association between social support and mental health symptoms during the COVID-19 pandemic.

Methods

Search procedures

We retrieved peer-reviewed articles and master/doctoral dissertations investigating the relation between social support and mental health symptoms in July 2024. (Note that at the time of preregistration, the cutoff date for literature retrieval in our study was established as October 2021. To incorporate more recent data, we have updated the literature retrieval cutoff date to July 2024.) We did so by systematically searching the Science Direct, PubMed, Web of Science (ISI), ProQuest central, and Google Scholar databases. Our search keywords included many possible combinations of terms reflecting social support (support, social support) and mental health symptoms (“mood” OR “depression” OR “anxiety” OR “stress” OR “mental health” OR “internalizing”), and key words that reflect the context of COVID-19 (“Coronavirus” OR “COVID-19”) will also be added. Subsequently, abstracts of articles were reviewed and the full text of an article was read whenever a paper’s title or abstract indicated that the study might be relevant to the present analyses. Finally, if we could not compute effect sizes from the information provided in the articles, we sent an email to the corresponding author(s). Figure 1 shows the study selection process. This meta-analysis was preregistered on the Open Science Framework (OSF; https://doi.org/10.17605/OSF.IO/M824S).

Figure 1.

Flow chart of the study selection process.

Eligibility criteria

Studies were incorporated into this meta-analysis if they (1) examined human participants during the COVID-19 pandemic; (2) reported Pearson’s correlation coefficients between social support (including PSS, ESS, or other more specific types of social support, such as instrumental support, informational support, and appraisal support) and the indicators of mental health symptoms (i.e., depression, general anxiety and stress); (3) adopted subjective and complete measures of social support and mental health symptoms using questionnaire surveys; (4) utilized cross-sectional and longitudinal studies; and (5) the study was written in English.

Selection of studies

Our search and study inclusion criteria led to a final sample of 210 studies, most of which were published in peer-reviewed papers (only two master/doctoral dissertations). Of these 210 studies, 146 assessed the correlation between social support and depression (239 correlations), 136 assessed the correlation between social support and generalized anxiety (227 correlations), and 86 assessed the correlation between social support and stress (151 correlations). And the properties of included studies, including the distribution of studies that included each variable of interest, are listed in Appendix Table 1 (see Appendix A). The data utilized for the meta-analysis can be accessed via the OSF platform at the link: https://osf.io/kv6a5/?view_only = 8385ffaea3164e588bf1efbc7b10e836.

Coding of variables

First, we coded whether the study examined social support, and which indicators of mental health (i.e., depression, general anxiety, or stress). We also coded source characteristics, including (a) author, (b) publication year, and (c) country of data collection.

Furthermore, the study- and sample-level properties were also coded, including (d) sample size; (e) participant age range; (f) participant mean age; (g) gender (percentage of women in the study); (h) study types (i.e., longitudinal or cross-sectional); (i) types of social support (i.e., ESS or PSS); (j) measure of mental health symptoms; (k) sample types (i.e., general samples, student samples or high-risk samples); (l) whether the study used the MSPSS or an alternative measure; (m) which specific indicators of social support; (n) which specific indicators of mental health symptoms the study included; (o) the study’s effect sizes; and (p) description of the source of the sample. Among the study- and sample-level properties, age and gender were coded as continuous variables. In contrast, study types, types of social support, sample types, and measure of social support were coded as categorical variables. To compare the associations between social support and mental health symptoms across cross-sectional and longitudinal studies, we focused exclusively on longitudinal associations derived from studies employing longitudinal designs (e.g., correlations between baseline social support and depression at follow-ups). In instances where a study included only one follow-up time point (such as T2), we utilized the correlation obtained at that time point. Conversely, for studies featuring multiple follow-up assessments, we extracted all relevant follow-up correlations and computed the mean of these correlations to provide a comprehensive overview of the relationship over time.

Finally, if studies provided multiple samples, we included the effect sizes from each sample in the current meta-analysis. When studies conducted separate analyses for men and women, we treated these analyses as two distinct samples in our meta-analysis. When studies reported multiple outcomes (such as depression, general anxiety, and stress), multiple effect sizes were coded.

To enhance coding accuracy, two authors (XZ and YYX) independently coded all primary studies included in the meta-analysis. Inter-rater reliability was evaluated using intraclass correlation (ICC) for continuous variables and kappa coefficients (k) for categorical variables. In case of discrepancies, both coders independently reviewed the study, resolving errors through consensus agreement (Zhang et al., 2022).

Quality assessment

To evaluate the methodological quality of the studies, we utilized an adapted version of the Newcastle–Ottawa Scale (NOS) (Peterson et al., 2011) designed for cross-sectional and longitudinal studies, as employed in previous systematic reviews (Gabarrell-Pascuet et al., 2023; Mohd et al., 2019) (see Appendix B). The NOS checklist consists of three sections that assess various characteristics of the studies: selection, comparability, and outcome. Each item is assigned a score of one or two stars, with the total score for each section determining the overall quality rating of the studies (i.e., 1 = “poor,” 2 = “fair,” or 3 = “good”). Any discrepancies in ratings were resolved through discussion between the reviewers. And the results of the quality assessment will be presented in Appendix C.

Statistical analyses

The effect size of interest was the Pearson’s r between social support and indicators of mental health symptoms (i.e., depression, general anxiety, stress). All correlation coefficients were transformed into Fisher’s adjusted Z scale (z) to normalize their distribution, as correlations become increasingly skewed away from zero (Borenstein et al., 2011; Rosenthal, 1986). Then, the variance (v) and standard error (SE) of each effect size were computed. Finally, all Fisher’s z values were reverted back to correlation coefficients r for easier interpretation (Xie et al., 2020; Zhang et al., 2022). To obtain unbiased estimates of the correlation coefficients, the meta-analyses were carried out using Fisher’s z values (Shadish & Haddock, 2009). To synthesize the effect sizes for the bivariate relationships and examine the moderation effects, the robumeta package (Fisher & Tipton, 2015) in the R statistical environment (R Core Team, 2016) was employed.

Due to the multifaceted nature of mental health symptoms and the multidimensional aspect of social support, many studies reported multiple outcomes. However, averaging effect sizes within studies without considering within-study dependencies may distort or obscure true effect size estimates (Scammacca et al., 2014). Therefore, to address within-study dependencies in effect-size estimates, we employed the meta-analytic technique of robust variance estimation (Hedges et al., 2010; Tanner-Smith & Tipton, 2014; Xie et al., 2020; Zhang & Dong, 2022; Zhang et al., 2022). Specifically, we utilized the robu () function from the robumeta package, version 2.0, in R, version 3.6.1. We applied the correlated weights (Hedges et al., 2010) and incorporated small sample corrections (Tipton, 2015). The value of ρ was set to the recommended 0.80 to address dependency among effect-size estimates (Tanner-Smith & Tipton, 2014). Additionally, we evaluated the magnitude of heterogeneity among study-average effects using τ² (Deeks et al., 2008), and assessed I² to determine the proportion of variability attributable to true effects rather than sampling error (Borenstein et al., 2017; Higgins & Thompson, 2002). A leave-one-out sensitivity analysis was conducted by systematically removing one study at a time to ensure that our findings were not unduly influenced by any individual study. Additionally, to address the issue of multiple outcomes in some studies, we calculated weighted effect sizes. This approach could allow us to assess the stability of our results by examining the impact of each study when it is excluded from the analysis. Subsequently, to examine potential moderation effects, we applied mixed-effects random variance estimation meta-regression models. Consistent with previous studies (Borenstein et al., 2011; Zhang et al., 2022), meta-regression analyses were conducted for outcomes that included at least 10 samples per moderator to explore possible moderation.

Publication bias

To assess for publication bias, funnel plots and Egger’s test (Egger et al., 1997; Zhang et al., 2022) were used. If the funnel plots are symmetric and the p-values from the Egger’s test exceed 0.05, it suggests that publication bias is unlikely to be present to a significant degree.

Results

Overview

As a preliminary step, the effect sizes were examined for potential outliers. Outliers were defined as values that deviated from the mean by more than 3.29 standard deviations in absolute terms, based on the criterion proposed by Tabachnick and Fidell (2007). A total of six outliers were identified; however, further analyses indicated that their presence did not significantly impact the overall results. Therefore, the findings will be presented with the outliers included. The final sample consisted of 210 studies, with a total of 223 samples. The interrater agreement between the two raters was satisfactory (ICCS ranged from 0.937 to 1.00, and kappa ranged from 0.0.926 to 1.00). All disagreements were resolved through discussion, and 100% consensus was reached before data analysis. For the specific correlation coefficient distribution, please see Table 1. For the Heterogeneity test (Q = 13375.20, p < .001, df = 615), we found moderate variation among studies (I² = 96.30%; τ² = 0.030), thus, we choose a random-effects model for further analyses (Borenstein et al., 2009). Based on the results of the leave-one-out sensitivity analysis (see Appendix D), the removal of any single study yielded similar and consistent outcomes. In addition, considering that the meta-analysis includes a variety of measurement scales for mental health symptom indicators, this may introduce confounding factors that could impact the overall findings. Therefore, this study conducted subgroup analyses on the symptom scales. The results indicate that only the heterogeneity index (Q = 10.310, p<0.01) for the stress scale was significant (see Table 3). Thus, we are inclined to conclude that the heterogeneity among the measurement scales for different mental health symptom indicators is relatively small or has a minimal effect on the overall results of the study.

Table 1.

Distribution of correlation coefficient in final analysis in meta-analysis

Social support-mental health symptoms	The number of correlation coefficient
Social support-mental health symptoms	210 (617 correlations)
Social support-depression	146 (239 correlations)
Social support-general anxiety	136 (227 correlations)
Social support-stress	86 (151 correlations).

Table 2.

Moderator analyses for the relation between social support and mental health symptoms during the COVID-19 pandemic

Variable	β-Coefficient	Standard error	t-value	p-value
Study types	−0.069	0.047	−1.461	0.183
Gender	−0.000	0.000	−0.485	0.629
Age	0.000	0.000	1.237	0.219
Sample types
High-risk vs. student	0.090	0.032	2.84	0.005
High-risk vs. general	0.052	0.029	1.83	0.070
Types of social support	0.023	0.044	0.531	0.598
Social support measures Domains of mental health symptoms Depression vs. general anxiety Depression vs. stress	−0.020 0.056 0.090	0.033 0.021 0.032	−0.599 2.71 2.84	0.550 0.008 0.005

Note: Significant (p < 0.05) moderating effects are listed in boldface font.

Table 3.

Moderator analyses of scales of mental health symptoms

	N_effect	r	95% CI	I²	Q (df)
Depression measure used				94.55%	0.0213(1)
BDI	21	−0.315	[0.3835, −0.2675]
DASS	48	−0.291	[−0.3370, −0.2636]
HADS	68	−0.306	[−0.3425, −0.2901]
CESDS	36	−0.319	[−0.3773, −0.2820]
PHQDS	14	−0.236	[−0.3371, −0.1471]
Other	52	−0.303	[−0.3714, −0.2551]
General anxiety measure used				95.77%	3.659(1)
GADS	70	−0.230	[−0.2763, −0.1923]
DASS	52	−0.200	[−0.2366, −0.1690]
HADS	28	−0.247	[−0.3190, −0.1853]
TSSTAI	23	−0.265	[−0.3199, −0.2237]
SAS	19	−0.345	[−0.4138, −0.3068]
Other	35	−0.238	[−0.3224, −0.1623]
Stress measure used				96.42%	10.310(1)**
PSS	76	−0.265	[−0.3164, −0.2261]
DASS	41	−0.182	[−0.2157, −0.1523]
Other	34	−0.156	[−0.2360, −0.0770]

Note: ** p < .01. BDI: Beck Depression Inventory; DASS: Depression, Anxiety, and Stress Scale; HADS: Hospital Anxiety and Depression Scale; CESDS: Center for Epidemiological Studies Depression Scale; PHQDS: Patients’ Health Questionnaire Depression Scale; GADS: Generalized Anxiety Disorder 7-item Scale; TSSTAI: The Trait Scale of the State–Trait Anxiety Inventory; SAS: Self-rating Anxiety Scale; PSS: Perceived Stress Scale.

Quality assessment

The methodological quality assessment revealed that more than 60% of the included studies were rated as either fair or good (N = 133,63.33%). In contrast, less than 40% of the studies received a poor rating on the overall NOS assessment (N = 77, 36.67%). Notably, more than 50% of the included studies achieved high scores in the comparability domain, which assesses potential bias arising from confounding factors. Furthermore, a substantial proportion of studies demonstrated satisfactory performance in the outcome domain. Nevertheless, the overall performance in the selection domain was comparatively lower across the evaluated studies (see Appendix C).

Main effect test

We tested for the association between social support and mental health symptoms during the COVID-19 pandemic. We found that, social support was negatively correlated with mental health symptoms (k = 220, 617 correlations; r = −0.259, 95% CI [−0.29, −0.24], p < 0.01) during the COVID-19 pandemic.

Moderating effects analysis

We tested for the moderating effects of association between social support and mental health symptoms during the COVID-19 pandemic. The results showed that age, gender, study types, types of social support, and measure of social support did not significantly moderate the associations between social support and mental health symptoms during the COVID-19 pandemic. In contrast, sample types did moderate the association between social support and mental health symptoms during the COVID-19 pandemic (see Table 2). The relation between social support and mental health symptoms among the high-risk samples during the COVID-19 pandemic significantly stronger than that of general samples [t (1, 140.4) = 2.84, p = 0.005; high-risk samples −0. 302, general samples −0.219], and showed a trend toward being stronger compared to that of the student sample [t (1, 121.1) = 1.83, p = 0.070; high-risk sample −0.302, student sample −0.263], although this difference did not reach statistical significance at the conventional level (p < 0.05).

In addition, we tested for the possible moderating effects of domains of mental health symptoms and dimensions of PSS. We found the domains of mental health symptoms significantly moderated the associations between social support and mental health symptoms during the COVID-19 pandemic. The relation between social support and depression during the COVID-19 pandemic was significantly stronger than that of social support and generalized anxiety [t (1, 158) = 2.71, p = 0.008; depression −0. 304, generalized anxiety −0.238], and significantly stronger than that of social support and stress [t (1, 130) = 2.84, p = 0.005; depression −0. 304, stress −0.220]. We also found the dimensions of PSS significantly moderated the associations between social support and mental health symptoms during the COVID-19 pandemic. The relation between family support and mental health symptoms during the COVID-19 pandemic was significantly stronger than that of significant other support and mental health symptoms [t (1, 42.6) = 2.25, p = 0.030; family support −0. 293, other support −0.228], and stronger than that of friend support and mental health symptoms [t (1, 44.7) = 1.08, p = 0.286; family support −0. 293, friend support −0.248], however, the differences of the latter are not significant.

Assessment of publication bias

As showed in Figure 2, the funnel plots exhibit a reasonable degree of symmetry. Furthermore, following procedures used in prior research (Peng et al., 2016; Xie et al., 2020; Zhang et al., 2022), we used the robumeta package in R 3.5.1 to conduct an Egger’s test for publication bias. The results indicate that the relationship between social support and mental health symptoms did not yield a significant result [t (40) = 0.127, p = 0.899] during the COVID-19 pandemic, and there appears to be little-to-no publication bias in the included studies.

Figure 2.

Funnel plot of the relation between social support and mental health symptoms during the COVID-19 pandemics.

Discussion

Overall findings

By exploring the relationship between social support and mental health symptoms in the context of the COVID-19 pandemic, this study employed robust meta-analytic techniques to analyze a large dataset from diverse cultural backgrounds. Several key findings emerged from this analysis. First, our results indicate that social support overall contributes to a reduction in mental health symptoms during the pandemic, and the leave-one-out analysis demonstrated that similar results were obtained even after excluding a single study. This finding aligns with previous research suggesting that higher levels of social support are associated with fewer mental health challenges in times of crisis (Schulder et al., 2024). Second, we found that sample types played a moderating role in the relationship between social support and mental health symptoms. Specifically, the association between social support and mental health symptoms was significantly stronger among high-risk samples compared to general samples. This suggests that individuals in high-risk categories may experience unique vulnerabilities that increase their need for the protective effects of social support. Lastly, we observed that different domains of mental health symptoms significantly moderated the associations between social support and mental health outcomes. The relationship between social support and depression exhibited a notably stronger correlation than those with generalized anxiety and stress, indicating that as social support increases, depression symptoms decrease more dramatically compared to general anxiety and stress symptoms. Furthermore, our findings revealed that various dimensions of PSS have differing impacts on mental health symptoms, with family support potentially playing a more crucial role than other types of support during the COVID-19 pandemic. These findings have important implications for effectively leveraging the positive impact of social support on mental health during epidemic situations.

Our main analysis revealed that social support could reduce mental health symptoms during the COVID-19 pandemic. This finding is consistent with the studies consistently emphasizing the importance of social support for mental health in the context of the pandemic (Fang et al., 2021; Raihan, 2021; Szkody et al., 2021). Although the overall effect of social support on mental health symptoms during the COVID-19 pandemic was statistically significant (R = −0.259), the effect size was relatively modest. This suggests that while social support is important, other factors—such as resilience and other coping mechanisms—may also play a significant role in mitigating the mental health impact of the pandemic (Killgore et al., 2020; Schäfer et al., 2022). Although the findings may seem modest, they underscore the importance of cultivating a supportive social environment. Strengthening social networks—particularly family and community support—can serve as a crucial strategy to mitigate mental health symptoms arising from isolation and uncertainty. In comparing our findings with those from other pandemics and major natural disasters, it becomes evident that the impact of COVID-19 has been particularly profound, making the role of social support even more crucial. Unlike previous pandemics or disasters (Lau et al., 2010), the COVID-19 pandemic was characterized by extensive lockdowns and social distancing measures that significantly limited face-to-face interactions and disrupted traditional support networks. This unique context heightened individuals’ reliance on alternative forms of social support, emphasizing its role as a crucial buffer against mental health symptoms during times of crisis. Given these circumstances, our study focuses on the direct effects of social support to identify actionable strategies for mental health interventions. The findings suggest that in similar public health crises, policymakers should prioritize the establishment and maintenance of social support networks, even in the context of lockdowns and social distancing. This includes promoting virtual support systems and ensuring access to mental health resources, as strong social support can mitigate the negative mental health impacts during crises.

Moderator analyses results

We found that sample types played a moderating role in the relationship between social support and mental health symptoms, and the relation between social support and mental health symptoms among the high-risk samples was significantly stronger than general samples. The negative correlation between social support and mental health symptoms was stronger in high-risk populations, suggesting that these individuals benefit more from social support in reducing mental health symptoms compared to student or general populations. High-risk groups, such as pregnant women, the elderly, COVID-19 patients, or medical system personnel, face elevated stress levels and greater vulnerability to mental health issues, which makes social support a more critical buffer in these populations (Khoury et al., 2021; Luo et al., 2020; Pappa et al., 2020; Van Tilburg et al., 2021). In contrast, while social support also helps reduce depression, general anxiety, and stress in general samples, its protective effect appears less pronounced, possibly due to lower baseline mental health symptom levels in these groups. These results highlight the importance of targeted interventions that emphasize strengthening social support networks for high-risk populations, especially in times of crisis, to alleviate psychological distress and prevent worsening mental health outcomes. In the post-pandemic era, there should also be more research focused on the mental health and educational needs of high-risk groups, working together to create a harmonious and stable social environment.

Our findings indicate that the domain of mental health symptoms significantly moderates the negative relationship between social support and mental health outcomes. Specifically, the negative correlation between social support and depression was stronger compared to its correlation with generalized anxiety and stress. This suggests that social support exerts a more pronounced protective effect in alleviating depressive symptoms than in reducing general anxiety and stress. One possible explanation for this finding is that depression, characterized by feelings of hopelessness and social withdrawal, may make individuals particularly sensitive to the availability and quality of social support (Cohen, & Wills, 1985; Liu et al., 2015). The presence of supportive relationships can help individuals reframe negative thoughts and provide emotional reassurance, making social support especially helpful for alleviating depressive symptoms (Zhen et al., 2018). In contrast, general anxiety and stress, which are often triggered by acute external stressors, may require more immediate or targeted interventions, such as problem-solving or stress management strategies, where social support plays a less central role (Hofmann et al., 2012). The differential impact of social support on various mental health symptoms suggests that targeted interventions should be designed based on the specific nature of the mental health concern. For individuals suffering from depression, fostering strong social connections and improving the quality of social support could be key strategies in alleviating symptoms. On the other hand, for individuals dealing with general anxiety and stress, interventions might need to incorporate a combination of social support and more direct coping techniques such as cognitive behavioral therapy or stress reduction programs. These findings, rooted in the context of the pandemic, hold significant implications for mental health education in the post-pandemic era. They suggest that educational programs should emphasize the importance of tailored support systems and coping strategies that address specific mental health issues. By focusing on these targeted approaches, we can better equip individuals to manage their mental health challenges and foster resilience in a changing social landscape.

Furthermore, we found that the dimensions of PSS played a moderating role in the relationship between social support and mental health symptoms. Unexpectedly, family support emerged as a more significant factor in alleviating mental health symptoms than support from friends or significant others, which initially appears to contradict expectations based on developmental theory. According to the developmental theory, the importance of social support from specific sources varies by developmental stages (Kahn & Antonucci, 1980), and the support from parents and family grows less important in late adolescence as youth reach out beyond their family circle into the wider social environment, whereas the support from friends and significant others is becoming more and more important. The participants included in the present meta-analysis are mainly adults, with the establishment and stability of friendship and intimate relationships, the social support from friends and significant others may become increasingly important in the sources of social support among adult populations. This is also supported by evidence from meta-analytic study (Gariépy et al., 2016), that is, sources of social support change significantly across life stages, in childhood and adolescence, parental support is paramount, as individuals transition into adulthood and older age, reliance shifts towards spouses, followed by family and friends. However, our findings challenge this conventional pattern, particularly in the context of the COVID-19 pandemic. The pandemic significantly disrupted normal life rhythms due to strict lockdowns, isolation, and reduced social interactions, which limited access to support from peers and external social networks (Krahé et al., 2024; Li & Xu. 2022). In contrast, family support became more readily available and accessible in this context, and became even more central to psychological well-being, providing a stable source of emotional and practical assistance when other sources are unavailable (Li & Xu. 2022). It plays a crucial role in alleviating depressive symptoms during challenging times (Mariani et al., 2020). During the COVID-19 pandemic, individuals faced unprecedented stress and social isolation, making family support a vital source of emotional security. This support not only alleviated mental health challenges but also promoted resilience and well-being, highlighting the essential role of familial connections in times of crisis. These findings suggest that strengthening family bonds can serve as a protective factor during stressful periods. Mental health interventions should prioritize fostering family relationships to enhance emotional support, ultimately contributing to better mental health outcomes in challenging times. In the post-pandemic era, this underscores the importance of focusing on family dynamics in mental health programs. By promoting healthy family interactions, we can build resilience and improve overall well-being.

Limitations and future studies

Although our study has generated valuable conclusions, it is important to acknowledge the limitations of the current meta-analysis. Firstly, our study only considered three standard indicators of mental health symptoms during the COVID-19 pandemic, neglecting the potential impact on other indicators such as loneliness. This highlights the need for further research to comprehensively explore the mental health issues arising from the pandemic. Secondly, the majority of included studies failed to control for or report socioeconomic status (SES), which has been identified as a potential confounding factor in the relationship between social support and mental health outcomes. The inability to account for SES in our analysis may have influenced the observed associations, as lower SES is associated with both reduced social support and increased mental health symptoms. Future research should prioritize the systematic collection and reporting of SES data to enable more robust adjustment for this critical confounder. Additionally, it is crucial to note that our study specifically focuses on the relationship between social support and mental health symptoms within the context of COVID-19, which may limit the generalization of our findings. As we move into a post-pandemic era, it is essential to explore how these dynamics may evolve. Future research should investigate whether the positive effects of social support on mental health persist when social isolation measures are lifted.

Conclusions

This study investigated the relationship between social support and mental health symptoms during the COVID-19 pandemic using a large, diverse dataset. Our findings demonstrate that social support significantly reduces mental health symptoms overall. However, the association is stronger in high-risk populations compared to general samples, indicating that high-risk individuals may benefit more from social support. Additionally, we found that the impact of social support varies across different mental health domains, with a notably stronger correlation observed for depression compared to generalized anxiety and stress. Furthermore, family support appeared to play a more crucial role than other types of support during this period. These results highlight the importance of enhancing social support, particularly for vulnerable groups, to address mental health challenges in times of crisis.

Supplemental Material

sj-doc-1-pac-10.1177_18344909251324571 - Supplemental material for Social support and mental health symptoms during the COVID-19 pandemic: A comprehensive meta-analysis unveils limited protective effects

Supplemental material, sj-doc-1-pac-10.1177_18344909251324571 for Social support and mental health symptoms during the COVID-19 pandemic: A comprehensive meta-analysis unveils limited protective effects by Xing Zhang, Yanyu Xiao, Peimiao Xu and Shenghong Dong in Journal of Pacific Rim Psychology

Supplemental Material

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Supplemental material, sj-doc-2-pac-10.1177_18344909251324571 for Social support and mental health symptoms during the COVID-19 pandemic: A comprehensive meta-analysis unveils limited protective effects by Xing Zhang, Yanyu Xiao, Peimiao Xu and Shenghong Dong in Journal of Pacific Rim Psychology

Supplemental Material

sj-docx-3-pac-10.1177_18344909251324571 - Supplemental material for Social support and mental health symptoms during the COVID-19 pandemic: A comprehensive meta-analysis unveils limited protective effects

Supplemental material, sj-docx-3-pac-10.1177_18344909251324571 for Social support and mental health symptoms during the COVID-19 pandemic: A comprehensive meta-analysis unveils limited protective effects by Xing Zhang, Yanyu Xiao, Peimiao Xu and Shenghong Dong in Journal of Pacific Rim Psychology

Supplemental Material

sj-doc-4-pac-10.1177_18344909251324571 - Supplemental material for Social support and mental health symptoms during the COVID-19 pandemic: A comprehensive meta-analysis unveils limited protective effects

Supplemental material, sj-doc-4-pac-10.1177_18344909251324571 for Social support and mental health symptoms during the COVID-19 pandemic: A comprehensive meta-analysis unveils limited protective effects by Xing Zhang, Yanyu Xiao, Peimiao Xu and Shenghong Dong in Journal of Pacific Rim Psychology

Footnotes

Acknowledgments

The authors thank all scholars involved in mental health studies in the context of the pandemic, special thanks to the authors who provided additional data for this meta-analysis study.

ORCID iD

Xing Zhang

Transparency and openness statement

The data are accessible via the OSF platform (the link address: . The provided data are adequate for an independent researcher to replicate the results. The data of integrated studies have been listed in the Appendix Table 1 (see Appendix A) of this meta-analysis.

Supplemental material

Supplemental material for this article is available online.

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