Cross-lagged regression study on daily stress,mental health,and psychological burden among young adults during the COVID-19 pandemic

Abstract

During the coronavirus disease 2019 (COVID-19) pandemic, young adults have experienced many maladaptive symptoms that have consequently affected their mental health. Most studies have examined the risk factors of mental health while ignoring the protective factors. This longitudinal study aims to investigate whether daily stress, depression, anxiety and stress, and positive mental health have a predictive effect on the psychological burden of COVID-19. We conducted three follow-up surveys in 2014 (T1), 2015 (T2), and 2020 (T3) to understand the predictive effect of daily stress and mental health on the psychological burden of COVID-19 on young adults. Data were assessed in 2014 (T1) and 2015 (T2) using the depression, anxiety, and stress scale (DASS-21), positive mental health scale (PMH), and brief daily stressor screening scale (BDSS), and in 2020 (T3), where we incorporated the psychological burden of COVID-19 to evaluate its psychological burden status on young adults. A total of 556 young adults participated in three surveys. Cross-lagged analysis indicated that (1) daily stress at T1 significantly predicted DASS and PMH at T2, DASS at T2 significantly predicted the psychological burden of COVID-19 at T3, but PMH at T2 could not predict the psychological burden of COVID-19 at T3; (2) PMH at T1 significantly predicted daily stress and DASS at T2, which significantly predicted the psychological burden of COVID-19 at T3. Individuals with low daily stress and depression, anxiety, and stress symptoms can still maintain a low psychological burden during the pandemic.

Keywords

psychological burden of COVID-19 depression anxiety and stress positive mental health

Introduction

At the beginning of 2021, the coronavirus disease 2019 (COVID-19) pandemic spread to more than 200 countries worldwide. Studies show that people's mental health was challenged during this pandemic. Consequently, their mental health has declined, and the probability of psychological problems has increased, irrespective of whether they are children, undergraduates, or part of the general population (Chang et al., 2020; Wang et al., 2021; Zhu et al., 2020). Some measures, such as maintaining social distancing, established to prevent and control the pandemic, may cause a heavy psychological burden on people. Similarly, the uncertainty of infection and other factors related to the COVID-19 outbreak have overwhelmed the public. Moreover, the psychological burden (caused by COVID-19, hereinafter referred to simply as ‘the psychological burden’) can be characterized by fear, despair, frustration, and confusion, or other negative psychological reactions (Salari et al., 2020; Tang et al., 2020). People's perception and response to the COVID-19 outbreak significantly affect not only their physical and mental health (Taylor et al., 2020) but also their adherence to the pandemic prevention and control measures set by the government. Individuals that experienced high psychological burdens exhibited lower adherence to the measures set by the government (Brailovskaia & Margraf, 2020). In this context, it is important to investigate the predicting factors of the level of the psychological burden of COVID-19. Identifying these will help reduce people's psychological burden, thereby protecting physical and mental health.

Stress is a risk factor for mental health. Stress sources are mainly classified as either discrete or continuous. Discrete stress mainly refers to major life events, including car accidents and natural disasters (such as COVID-19 in this study), whereas continuous stress usually refers to continuous problems, also known as “daily” stressors (Serido et al., 2004). It encompasses various small things that persist in life, such as being late for work (Kohn et al., 1990). Stress sensitization theory suggests that long-term stress can cause an imbalance in the stress-response system, weaken the response threshold and adaptive response to later stress, and increase the risk of later mental disorders (Dienes et al., 2006). Exposure to long-term stressors may result in psychological maladjustment, making individuals more susceptible to subsequent stressors, and also reduce the individual's ability to buffer stress, as the individual's resources for coping with stress decrease with the stress. Fernandez et al. (2020) found that relatively remote, chronic, or even diffuse daily pressures significantly affect the stress-response ability and the psychological adaptation of individuals in the face of major life events. Moreover, an early study during the SARS pandemic also revealed that the adaptive stress response may play a protective role during the outbreak of the pandemic and that higher levels of stress may be a predictor of the incidence of mental illness in the future (Chua et al., 2004). Chen et al. (2017) also found that depressive episodes are related to stressful life events. The greater the daily pressure, the worse the psychological adaptation state, which consequently increases the psychological burden in the face of emergencies (Ponnamperuma & Nicolson, 2018). Additionally, people exposed to more stressors are more likely to experience psychological problems such as depression and anxiety (Fernandez et al., 2020; Portillo-Reyes et al., 2020). Past stressful life events are associated with an increased risk of major depression, anxiety disorders, and perceived stress (Hammen, 2005; McLaughlin et al., 2010). Studies also show that the cumulative effect of daily stressors is a reliable predictor of the severity of anxiety and depression symptoms, as well as a significant risk factor for depression, anxiety, and other psychological symptoms (Conway et al., 2016; Regehr et al., 2013). However, some researchers suggest that stressful events have a positive effect. For example, experiencing manageable stressors may enhance an individual's positive psychological adaptation to adversity (Southwick et al., 2011) and reduce future vulnerability to negative mental health (Updegraff & Taylor, 2000). After experiencing stressful events, participants described themselves as stronger, more mature, and better able to manage other crises (Thomas et al., 1988). Seery et al. (2013) also found that experiencing moderately stressful events may help develop resilience so that individuals exhibit better and more positive psychophysiological responses. Past stressful events may have two different mental health effects on people. Therefore, it is necessary to re-explore the effect of past exposure to stressful events on people's mental health in the face of stress or adversity based on dual-factor models.

Dual-factor models consider mental health more than the absence of mental illness (Keyes, 2005; Suldo & Shaffer, 2008). Additionally, mental health encompasses two interrelated but relatively separate dimensions: positive and negative (Lukat et al., 2016). Positive mental health, originally developed by Keyes (2005), refers to high levels of emotional, psychological, and social well-being. Researchers found that improving the level of positive mental health can enhance an individual's psychological adaptability, which consequently reduces the psychological burden of adverse events (Wu et al., 2020). People with high levels of positive mental health tend to set goals for their lives and pay attention to the positive aspects of things (Keyes et al., 2002). Positive mental health results in more positive emotions and helps individuals better cope with adversity (Hu et al., 2020; Huang et al., 2021), such as stressful life and catastrophic events (Brailovskaia et al., 2020a). Positive mental health buffers the effect of daily stress on suicidal ideation and behavior (Siegmann et al., 2018; Teismann, Brailovskaia et al., 2018). Its buffering effect lasts more than one year (Brailovskaia et al., 2020b) and also reduces the risk of suicidal thoughts among individuals with aggravated depressive symptoms (Teismann, Forkmann et al., 2018). Additionally, individuals with high levels of positive mental health usually have a high sense of control and respond adaptively to emergencies to reduce their psychological burden (Keeton et al., 2008; Niemeyer et al., 2019; Yu et al., 2018). Positive mental health may seem like a protective factor. However, what makes people more vulnerable in the face of stressful events is unclear. According to the diathesis-stress theory, diathesis indicates a factor of cognitive vulnerability, which makes some people more likely to be affected by a certain degree of stress than others (Hammen, 2005). Relatively insignificant stress or “trouble” may cause mental health problems among vulnerable people. Regarding invulnerable people, however, only major catastrophic events may trigger similar reactions. Individuals with higher depressive symptoms usually have cognitive vulnerability factors (including dysfunctional attitudes, negative cognitive styles, and contemplative response styles) (Alloy et al., 1999). They are more likely to pay attention to the negative aspects of events (Hankin et al., 2001) and react unsuitably to uncertain situations, which increases the psychological burden (Fong et al., 2020; Gorday et al., 2018; Huang et al., 2021; Qiu & Wang, 2000). Additionally, depression, anxiety, and stress symptoms can affect an individual's perception of social support, preventing individuals from maximizing social support to combat the effect of emergencies, which further triggers a sense of psychological burden (Rankin et al., 2018; Zhou et al., 2013). Owing to the scale of this pandemic, its psychological effect has become an urgent research item, especially the potential factors that generate negative psychological consequences that may affect mental health and the potential mechanisms for coping with these consequences (Giallonardo et al., 2020; Liu et al., 2020; Satici et al., 2020).

Stamatis et al. (2022) note that most previous studies on mental health risk factors rely on cross-sectional designs. Accordingly, few studies examine the role of risk and protective factors for developing mental health over time in specific responses (i.e., psychological burden) by the pandemic. Based on stress sensitization and diathesis-stress theories, this study adopts longitudinal tracking research to explore the predictive factors of psychological maladaptation during the pandemic. We hypothesize that: (H1) daily stress and mental health can predict each other longitudinally; (H2) daily stress at T1 can predict depression, anxiety, and stress and positive mental health at T2, and indirectly predicts psychological burden of COVID-19 at T3 through depression, anxiety, and stress and positive mental health at T2; (H3) depression, anxiety, and stress at T1 can predict daily stress and positive mental health at T2, and indirectly predicts psychological burden of COVID-19 at T3 through daily stress and positive mental health at T2; (H4) positive mental health at T1 can predict depression, anxiety, and stress and daily stress at T2, and indirectly predicts the psychological burden of COVID-19 at T3 through depression, anxiety, and stress and daily stress at T2.

Materials and method

Participants and procedure

Data were gathered in the context of the Bochum Optimism and Mental Health study program (BOOM) (Brailovskaia & Margraf, 2020; Hu et al., 2020; Siegmann et al., 2018). The BOOM program has been conducting follow-up surveys on college students since 2011. This study added the variable of the psychological burden of COVID-19 to the program in 2020 and used data collected in 2014, 2015, and 2020 (after the outbreak of the COVID-19 pandemic) for analysis. The sample comprised 556 participants from one university in Shanghai, China. Based on the convenience sampling method, participants were recruited during the baseline year, 2014. Surveys were administered to the same participants at three measurement time points: June 2014 (T1: participants included junior undergraduate students), June 2015 (T2: participants included senior undergraduate students), and May 2020 (T3: participants included those five years after graduation and were present during the COVID-19 pandemic). The level of daily stress, depression, anxiety and stress, and positive mental health was evaluated using the first and second surveys. The same three variables and the psychological burden of COVID-19 were further evaluated using the third survey. The instruments at T1 and T2 were applied using pencil and paper. T3, which was affected by COVID-19, used an online questionnaire to conduct the test. The participants received a gift (about 10 yuan) after completing each survey. A total of 635 participants participated in all three surveys. After excluding invalid data with many missing values, incomplete basic personal information, and doubts about the authenticity of the data (that is, the same answer was selected for all questions), a total of 556 valid data were obtained, which included responses from 122 male and 434 female participants. The average age of the sample (T3) was 26.68 ± 0.86 years (age range: 24–31). The participants stated their socioeconomic status (SES) and individual annual income at T3. The demographic information of the subjects was presented in Table 1.

Table 1.

Demographic characteristics of the samples.

	T1	T2	T3
Total	2747	2157	556
Sex
Female = 0	2102 (76.5%)	1704 (78.9%)	434 (78.1%)
Identity status
Undergraduate	2746 (99.9%)	2155 (99.8%)	0
Graduate	0	2 (0.1%)	26 (4.7%)
Doctor	1 (0.1%)	0	15 (2.7%)
Employed	0	0	515 (92.6%)
Mean (SD)
Age	20.94 (0.95)	21.93 (0.93)	26.68 (0.86)
SES
Underclass			50 (9.0%)
Salaried class			411 (73.9%)
Lower middle class			32 (5.8%)
Middle class			57 (10.3%)
Upper middle class			6 (1.1%)
Individual income (year)
≤10,000 yuan			27 (4.9%)
10,001–20,000			32 (5.8%)
20,001–40,000			12 (2.2%)
40,001–60,000			27 (4.9%)
60,001–100,000			91 (16.4%)
100,001–150,000			263 (47.3%)
150,000–250,000			89 (16.0%)
≥250,000 yuan			15 (2.7%)
Attrition rate		21.4%	74.3%

Note. SD = standard deviation; T1 = first survey; T2 = second survey; T3 = third survey.

Measures

Psychological burden by COVID-19

Following Brailovskaia and Margraf (2020), the experience of the burden of COVID-19 was assessed using six items (e.g., “I am burdened by the current social situation,” “I feel restricted in my everyday life,” “I feel socially isolated”). Items were rated on a 7-point Likert scale (1 = I do not agree, 7 = I totally agree). Higher sum scores indicated a higher burden. Before the main analysis, the factor structure of this questionnaire was examined using the explanatory factor analysis (EFA). The questionnaire comprised six items. The principal factor method was further used to identify the factor loadings of each item by setting the number of factors to 1. The last two items had factor loads < 0.4 (Kline, 2015) and could not be loaded in the other four questions of the questionnaire. Therefore, they were excluded. The table of Factor Analysis of Measures of Psychological Burden of COVID-19 in the supplementary. Accordingly, this study uses the first four questions of the questionnaire to evaluate psychological burden. In this study, the Cronbach α of the scale was 0.80.

Depression Anxiety Stress Scale

The Depression Anxiety Stress Scale (DASS) was originally developed by Lovibond and Lovibond (1995) and was used for depression, anxiety, and stress symptoms. The scale has cross-time equivalence among Chinese college students. DASS-21 comprised three subscales, including the depression, anxiety, and stress scale. Each subscale had seven items that were rated on a 4-point Likert scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time). This study evaluates the level of mental illness using DASS-21. Scale reliability: depression dimension: Cronbach α_T1 = 0.79, Cronbach α_T2 = 0.85, Cronbach α_T3 = 0.87; anxiety dimension: Cronbach α_T1 = 0.78, Cronbach α_T2 = 0.81, Cronbach α_T3 = 0.83; stress dimension: Cronbach α_T1 = 0.83, Cronbach α_T2 = 0.84, Cronbach α_T3 = 0.87. The higher the total score, the higher the level of DASS.

Positive mental health

Positive mental health was evaluated using the one-dimensional Positive Mental Health Scale (PMH-Scale; Lukat et al., 2016). The scale assesses the positive aspects of health and life experiences (e.g., I am often carefree and in good spirits; I enjoy my life; I manage well to fulfill my needs; I am in good physical and emotional condition). The scale includes nine items, rated on a 4-point Likert scale ranging from 0 (disagree) to 3 (agree). The higher the total score, the higher the level of positive mental health. Scale reliability: Cronbach α_T1 = 0.91, Cronbach α_T2 = 0.94, Cronbach α_T3 = 0.94.

Daily stress

The Brief Daily Stressor Screening (BDSS) (Scholten et al., 2020) was used to evaluate chronic daily stressful experiences over the last 12 months, including family responsibilities, health problems, and dissatisfaction with research or work, among others. The scale included nine items rated on a five-point Likert scale (score range: 1–5). The higher the total score, the higher the daily stress level. Scale reliability: Cronbach α_T1 = 0.82, Cronbach α_T2 = 0.80, Cronbach α_T3 = 0.83.

Statistical analyses

SPSS22.0 was used to perform descriptive statistics and related analysis on daily stress, positive mental health, negative mental health, and psychological burden. Repeated measures analysis of variance (ANOVA) was used to examine the degree of change in daily stress, positive mental health, and depression, anxiety, and stress across six years. Daily stress, positive mental health, depression, anxiety and stress, and the psychological burden of COVID-19 at T1, T2, and T3 were included in the model. Cross-lagged analysis, which was performed using Amos22.0, was further used to examine the variation of variables over time. The cross-lagged model was used to analyze the interaction between daily stress and mental health (including DASS and PMH) and the prediction of daily stress and mental health on the psychological burden of COVID-19, including the cross-lagged path coefficients (i.e., predictive associations) of daily stress and mental health between T1 and T2, and T2 and T3. Additionally, participants’ self-reported SES and individual annual income at T3 as control variables in the cross-lagged model. The cross-lagged model in this study includes correlations between variables at each measured time point (for T2 and T3, correlations were between residual errors).

Research data

The questionnaire used in the study has been provided in the Online Supplementary Material. All data and analysis code, as well as supplementary content, have been made publicly available via the Open Science Framework (OSF) and can be accessed at https://osf.io/2e3h5/files/osfstorage/62e4fc0a51748d07db285ee8.

Results

Table 2 presents the descriptive statistics of the investigated variables and repeated measurement analysis. A repeated-measures multivariate analysis of variance (ANOVA) was conducted with time (T1, T2, and T3) as the within-group independent variable and the scores on daily stress, depression, anxiety and stress, and PMH as dependent variables. A significant effect of time was observed for daily stress [F (2,554) = 26.84, p < .001, η_P²= 0.09], anxiety [F (2,554) = 8.20, p < .01, η_P² = 0.03], and stress [F (2,554) = 9.76, p < .01, η_P² = 0.02]. The daily stress level increased first and then decreased in an inverted-U tendency. The level of anxiety and stress decreased and reached a peak in T1. However, no significant effect of time was observed for depression [F (2,554) = 0.93, p = 0.39, η_P² = 0.00] and positive mental health [F (2,554) = 1.22, p = 0.14, η_P² = 0.00]. According to G*Power, to obtain a power of 0.80 at an alpha level of 0.05, the effect size (f) needs to be more than 0.10, with 556 participants in the ANOVA analysis. However, the effect size here was relatively low to achieve the required power. The mean of the psychological burden of COVID-19 was M (SD) = 10.85 (4.77), range: 4–28.

Table 2.

Three-year repeated measurement of each variable.

	T1	T2	T3	F	η_P²
	M ± SD	M ± SD	M ± SD	F	η_P²
Daily stress	19.04 ± 5.95	20.80 ± 5.71	19.40 ± 6.16	26.84***	0.09
Depression	2.03 ± 2.58	1.92 ± 2.85	2.10 ± 3.18	0.93	0.00
Anxiety	2.81 ± 2.81	2.47 ± 2.85	2.26 ± 2.90	8.20***	0.03
Stress	3.37 ± 3.29	2.79 ± 3.20	2.97 ± 3.48	9.76***	0.03
PMH	20.64 ± 4.54	20.62 ± 4.99	20.31 ± 5.12	1.22	0.00
Burden	–	–	10.85 ± 4.77	–	–

Note. N = 556; M = mean; SD = standard deviation; PMH = positive mental health; Burden = psychological burden of COVID-19. *p < .05; **p < .01; ***p < .001.

Table 3 presents the correlations among the investigated variables. The burden of COVID-19 was significantly positively correlated with the daily stress of each year, depression, anxiety, and stress of each year and significantly negatively correlated with PMH of each year (ps < .01). Daily stress was significantly positively correlated with depression, anxiety, and stress (ps < .01) and negatively correlated with PMH (ps < .01).

Table 3.

Correlations of each variable at T1, T2, and T3.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
T1 Daily stress	–
T2 Daily stress	.46**	–
T3 Daily stress	.28**	.35**	–
T1 Depression	.34**	.23**	.16**	–
T1 Anxiety	.38**	.24**	.14**	.72**	–
T1 Stress	.41**	.25**	.50**	.40**	.45**	–
T2 Depression	.27**	.37**	.25**	.50**	.40**	.45**	–
T2 Anxiety	.27**	.37**	.21**	.39**	.48**	.46**	.81**	–
T2 Stress	.31**	.36**	.26**	.41**	.45**	.53**	.79**	.82**	–
T3 Depression	.18**	.24**	.41**	.36**	.28**	.25**	.41**	.34**	.36**	–
T3 Anxiety	.22**	.26**	.41**	.26**	.33**	.26**	.34**	.38**	.37**	.80**	–
T3 Stress	.26**	.24**	.44**	.28**	.34**	.33**	.34**	.37**	.41**	.78**	.83**	–
T1PMH	–.32**	–.28**	–.21**	–.55**	–.52**	–.55**	–.40**	–.38**	–.39**	–.29**	–.20**	–.26**	–
T2PMH	–.29**	–.40**	–.25**	–.37**	–.34**	–.38**	.50**	–.44**	–.49**	–.33**	–.27**	–.29**	.56**	–
T3PMH	–.21**	–.26**	–.46**	–.24**	–.22**	–.25**	–.26**	–.23**	–.27**	–.57**	–.50**	–.56**	.39**	.40**	–
Burden	.24**	.25**	.41**	.19**	.18**	.20**	.22**	.21**	.24**	.42**	.40**	.42**	–.17**	–.16**	–.39**	–

Note. 1 = T1 daily stress; 2 = T2 daily stress; 3 = T3 daily stress; 4 = T1 depression; 5 = T1 anxiety; 6 = T1 stress; 7 = T2 depression; 8 = T2 anxiety; 9 = T2 stress; 10 = T3 depression; 11 = T3 anxiety; 12 = T3 stress; 13 = T1 pmh; 14 = T2 pmh; 15 = T3 pmh; 16 = burden.

Based on the correlation analysis, we established a cross-lagged model of daily stress and depression, anxiety, and stress, positive mental health, and the psychological burden of COVID-19 to explore the two-way predictive relationship between daily stress and depression, anxiety, and the stress and psychological burden of COVID-19, and the two-way predictive relationship between positive mental health and the psychological burden of COVID-19.

Cross-lagged analysis of daily stress, depression, anxiety, and stress, positive mental health and psychological burden of COVID-19

We established a cross-lagged regression analysis model of daily stress, depression, anxiety and stress (DASS), positive mental health (PMH), and the psychological burden of COVID-19, as shown in Figure 1.

Figure 1.

Cross-lagged regression analysis results of daily stress, DASS, PMH, and the psychological burden of COVID-19.

The variables measured at the previous measurement time point significantly predicted the variables measured at the next measurement time point, the autoregressive paths coefficient were 0.27–0.49, p < .001. After controlling the three measurement time points correlation and stability between daily stress, DASS and PMH, the daily stress at T1 significantly predicted the DASS at T2 (β = 0.08, p < .05), PMH at T2 (β = -0.10, p < .01), the PMH at T1 significantly predicted daily stress at T2 (β = −0.14, p < .01), and DASS at T2 (β = −0.14, p < .01), simultaneously, the daily stress at T2 (β = 0.19, p < .001) and the DASS at T2 (β = 0.17, p < .001) significantly predicted the psychological burden of COVID-19 at T3, but the PMH at T2 couldn’t significantly predict the psychological burden of COVID-19 at T3 (β = −0.003, p = .950). Additionally, the daily stress at T2 (R²= 0.233) indicated that daily stress, DASS, and PMH at T1 explained 23.3% of the total variation. The daily stress at T3 (R²= 0.148) indicated that daily stress, DASS, and PMH at T2 explained 14.8% of the total variation. The DASS at T2 (R²= 0.306) indicated that daily stress, DASS, and PMH at T1 explained 30.6% of the total variation. The DASS at T3 (R²= 0.210) indicated that daily stress, DASS, and PMH at T2 explained 21.0% of the total variation. The PMH at T2 (R²= 0.331) indicated that daily stress, DASS, and PMH at T1 explained 33.1% of the total variation. The PMH at T3 (R²= 0.187) indicated that daily stress, DASS, and PMH at T2 explained 18.7% of the total variation. The Burden at T3(R²= 0.095) indicated that daily stress, DASS, and PMH at T2 explained 9.5% of the total variation.

In addition, a separate cross-lagged analysis of the three dimensions of DASS was done, and the three-dimensional separation model of DASS in the supplementary.

Discussion

This study collects six years of longitudinal data and uses cross-lagged analysis to examine the bidirectional predictive relationship between daily stress and mental health, as well as the predictive relationship between daily stress and mental health before the outbreak and the psychological burden of COVID-19.

The results of the study were partly expected. The daily stress of undergraduates significantly predicted mental health at T3 (T3: five years after graduation; during the COVID-19 pandemic), and the mental health of undergraduates significantly predicted daily stress at T3 (T3: five years after graduation; during the COVID-19 pandemic), except DASS at T1 could not predict daily stress at T2 (partial confirmation of H1). Additionally, the daily stress and depression, anxiety, and stress symptoms of young adults before the pandemic were significant predictors of the psychological burden of COVID-19 (confirmation of H4). However, positive mental health at T2 could not significantly predict the psychological burden of COVID-19 at T3 (T3: five years after graduation and during the COVID-19 pandemic; H2 and H3 were partially confirmed).

The daily stress level in the past six years has shown a rising then falling trend. The results of repeated measures of variance showed that the daily stress of young adults in the past six years was significantly different, the daily stress of the undergraduate senior year (T2) was significantly higher than that of the undergraduate junior year (T1) and five years after graduation (T3: during the COVID-19 pandemic). The anxiety and stress of the undergraduate junior year (T1) were significantly higher than those of the undergraduate senior year (T2) and five years after graduation (T3: during the COVID-19 pandemic). This is attributable to the fact that during the undergraduate junior and senior period, young adults were about to graduate and had to complete the transformation of their status—from student to new employee. Additionally, a considerable number of students face the pressure of qualifying for postgraduate entrance examinations. Therefore, anxiety and stress symptoms are inevitable at this stage (Wu et al., 2020). And the positive mental health showed a relatively stable state in the past six years. These results indicate that mental health education and intervention measures in colleges and universities can be adjusted according to students’ grades, for example, by providing appropriate employment counseling or mental health education for graduates.

The results of the correlation analysis showed that the daily stress of young adults in the past six years was significantly positively correlated with depression, anxiety, and stress and negatively correlated with positive mental health. This is consistent with previous studies (Lu & Huang, 2010), which state that daily stressful events often trigger negative emotions, reduce the level of positive mental health, and cause symptoms such as depression and anxiety. Further cross-lagged analysis found that early daily stress significantly negatively predicts later positive mental health and vice versa. Early daily stress significantly positively predicts later mental health, including depression anxiety and stress and positive mental health; and early positive mental health also significantly negatively predicts later depression anxiety and stress and daily stress; but early depression anxiety and stress could not significantly predict later positive mental health, and depression anxiety and stress at T1 could not significantly predict daily stress at T2; therefore, H1 was partially confirmed. This result also supports stress sensitization and diathesis-stress theories (Dienes et al., 2006; Qiu & Wang, 2000). Stress-sensitization theory suggests that exposure to stress over a long period not only makes individuals susceptible to subsequent stressors but reduces an individual's buffering capacity to pressure because the individual's resources for coping with stress are depleted as stress builds up and persists. Research also shows that constant stress manifesting over time is important for predicting anxiety (Fu et al., 2021). The greater the daily stress, the more likely depression, anxiety, and stress symptoms, reducing the level of positive mental health. Conversely, mental health can predict daily stress longitudinally. Previous studies examine the effect of daily stress on mental health (Conway et al., 2016; Hammen, 2005; McLaughlin et al., 2010; Seery et al., 2010). Few studies have used mental health as an independent variable to examine its effect on daily stress. The diathesis-stress theory suggests that not everyone exhibits psychological problems under pressure, which is determined by the qualities of individuals. Individuals with severe depression, anxiety, and stress symptoms are more inclined to pay attention to the negative aspects of events and tend to make negative evaluations (Gorday et al., 2018). Therefore, these individuals have more stress. Evidence shows that patients with depression show strong emotional responsiveness to daily stress, perceived negative situations, and perceived stressful events, especially negative emotional responses (Bylsma et al., 2011; Wichers et al., 2007). The results of the study showed that daily stress and positive mental health can significantly predict each other, but DASS cannot significantly predict PMH. The results of this study may indicate that there is a close relationship between daily stress and positive mental health, and it may be a strong predictor of the two. The positive emotions of individuals with higher levels of positive mental health expand the range of thought and attention (Ong et al., 2006; Tugade & Fredrickson, 2004; Zautra et al., 2005). Therefore, individuals will use more positive emotions to face problems when facing daily stress, and they will be more active in the cognitive assessment of stress, therefore reducing the sense of stress.

Daily stress and DASS were significantly positively correlated with the psychological burden of COVID-19, whereas positive mental health was significantly negatively correlated with the psychological burden of COVID-19. The results of the cross-lagged analysis showed that DASS and daily stress were significant predictors of the psychological burden of COVID-19. Daily stress of the undergraduate junior year (T1) indirectly predicted the psychological burden of COVID-19 (T3: after five years of undergraduate graduation) through the DASS of the undergraduate senior year (T2). Individuals with daily stress before the outbreak had more severe psychological burden and mental health problems such as depression, anxiety, and stress symptoms during the pandemic, which is consistent with a recent study (Brailovskaia & Margraf, 2020; Shanahan et al., 2020; Zhu et al., 2020). In other words, the pressure of the past will make people more sensitive and prone to psychological problems in the face of future stressful events or emergencies (Fernandez et al., 2020). The study of Fernandez et al. (2020) also shows that individuals who experience multiple stressors are less well adjusted to natural disasters or major life events than individuals who do not experience more daily stress and are more likely to suffer from PTSD. These adverse consequences, such as isolation and economic recession caused by the pandemic, affect people's mental health. Moreover, the daily routines of young people are interrupted so that they have to deal with many changes in their lives (such as unemployment), resulting in more stress, depression, and psychological burden (Arnett, 2000; Nitschke et al., 2021). People with higher levels of depression, anxiety, and stress tend to have stronger stress responses in the face of danger (Andrews & Wilding, 2004). Studies showed that individuals with depressive symptoms have stronger negative reactions to stressful events and bear a strong sense of psychological burden and despair (Booij et al., 2017). The tracking period of this study is long, and the results showed that the DASS of the undergraduate senior year (T2) significantly predicted the psychological burden of COVID-19 (T3: after graduation and the interval between T2 and T3 was five years). The results may indicate that DASS has a lasting negative effect and on individuals.

In this study, the positive mental health of the undergraduate junior year (T1) indirectly predicted the psychological burden of COVID-19 (T3: five years after graduation) through the DASS and daily stress of the undergraduate senior year (T2). Studies observed that positive affect significantly alleviates stress (Zander-Schellenberg et al., 2020). Stress-buffering models of positive affect suggest that positive affect has the potential to reduce these negative health-harming consequences of psychological stress on the mind and body. This study's results also partially support the notion of the stress-buffering effect of positive mental health. However, the positive mental health of the undergraduate senior year (T2) cannot directly predict the psychological burden of COVID-19 (T3: after five years of undergraduate graduation) longitudinally, which was inconsistent with the study of Brailovskaia and Margraf (2020) and others. The possible reasons for this result are as follows. First, Brarovskaia and Margraf (2020) found that the protective effect of positive mental health is fully manifested through the sense of control. As China's pandemic prevention and control measures were effectively established and implemented by May, the results yielded were remarkable. People's mental health also improved as the pandemic progressed (Zhu et al., 2020), which may reduce not only people's psychological burden but also enhance people's sense of control. A related study shows that a stronger sense of control could protect people's emotional well-being during the outbreak (Yang & Ma, 2020). Therefore, future research can examine the role of sense of control in emergencies or daily stress, which is also beneficial in discovering protective factors for mental health. Second, another possible reason is that the psychological burden of COVID-19 in this study has stronger predictors than positive mental health, such as daily stress and DASS. The current study included daily stress, depression, anxiety and stress, and positive mental health in one model to verify the longitudinal relationship between these three variables and the psychological burden of COVID-19. The results also showed that DASS and daily stress can directly or indirectly significantly predict the psychological burden of COVID-19 longitudinally.

This study has the following limitations. The study investigates the psychological burden of young adults during the COVID-19 pandemic but does not conduct a second follow-up during the pandemic. Second, owing to the long study period, the loss of more subjects resulted in the lack of certain representativeness of the samples. Additionally, the three measurement time-point intervals in the study were not equal. The change in the size of the autoregressive path coefficients of the variables in the study also indirectly indicates that the measurement time-point unevenness and long interval do have an impact on the research results. Therefore, in the future, this should be evaluated at equal intervals. Third, this study did not control other factors affecting the psychological burden, such as marital status. Future studies should explore more possible influence factors of mental health.

Regarding the study implications, the results of this study suggest a significant positive predictive relationship between daily stress and depression, anxiety, and stress, and these appear to be risk factors for people's mental health in the face of adversity. Furthermore, the results of this study indicate that the daily stress and depression, anxiety, and stress have a long-term and strong negative impact on mental health. Notably, at the T2 measurement time point in this study, the participants were about to graduate, and may have endured more stressful events, such as graduation and job-seeking. This sudden increase in stress events deserves more attention. However, positive mental health can play a buffering role in daily stress and depression, anxiety, and stress, which may be a protective factor for mental health. Therefore, when we suffer from the negative psychological effects of suddenly increased stress events, we must seek help from others to enhance positive mental health. It is important to pay more attention to our psychological state in daily life and seek help in time in the face of depression or other negative mental health issues. We should learn to solve psychological problems promptly, adjust our mentality, and maintain a positive and optimistic attitude at all times. This also reminds us to consider the positive and risk factors, including the negative consequences of risk factors, and explore more comprehensive mental health protection methods from the perspective of the dual factors of mental health.

Conclusions

In summary, this study shows that daily stress and depression, anxiety, and stress symptoms before the outbreak are important predictors of the psychological burden during the pandemic. Moreover, individuals with lower daily stress and depression, anxiety, and stress perception before the pandemic also had a lower psychological burden during the pandemic. Positive mental health may play a protective role to a certain extent.

Supplemental Material

sj-doc-1-pac-10.1177_18344909231196269 - Supplemental material for Cross-lagged regression study on daily stress, mental health, and psychological burden among young adults during the COVID-19 pandemic

Supplemental material, sj-doc-1-pac-10.1177_18344909231196269 for Cross-lagged regression study on daily stress, mental health, and psychological burden among young adults during the COVID-19 pandemic by Hongjuan Ding, Jing Zhao, Dan Cai, Xiaochi Zhang and Jürgen Margraf in Journal of Pacific Rim Psychology

Footnotes

Acknowledgments

This work was supported by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning; and Shanghai Shuguang Program by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (grant number 20SG45).

Declaration of conflicting interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Research Base of Online Education for Shanghai Middle and Primary Schools, and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning.

Ethical approval

Two ethical approvals were obtained from the Research Ethics Committee of the corresponding author's university. This first ethical approval was obtained in 2013, before the COVID-19 pandemic, and the second ethical approval was obtained after the COVID-19 outbreak. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Availability of data and material

The dataset generated and material analyzed during this study will be available from the corresponding author upon reasonable request.

ORCID iDs

Hongjuan Ding

Dan Cai

Supplemental material

Supplemental material for this article is available online.

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