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Well-Being Reduces COVID-19 Anxiety: A Three-Wave Longitudinal Study in China

The coronavirus disease 2019 (COVID-19) pandemic threatens human beings’ livelihoods and mental health, which lowers their well-being and gives rise to anxiety. This study examines whether there is a causal relationship (and, if so, in which direction) between people’s well-being and COVID-19 anxiety. Two hundred and twenty-two participants (54.50% female, Mage = 31.53, SD = 8.17) from 26 provinces of China completed measures of subjective well-being (SWB) and COVID-19 anxiety at three key nodes of the development of COVID-19 in China. The results showed that people’s SWB and COVID-19 anxiety fluctuated with the peak (T1), decline (T2), and trough stages (T3) of the COVID-19 pandemic. Meanwhile, the cross-lagged analysis showed that the participants’ SWB at T0 (pre-pandemic stage; the base level of SWB) and T1 could significantly predict their COVID-19 anxiety at T1 and T2 respectively. However, SWB at T2 was not associated with the COVID-19 anxiety at T3. Furthermore, COVID-19 anxiety could not predict subsequent SWB from T1 to T3. The current findings contribute to clarifying the causal relationship between well-being and anxiety through the development of epidemics, as well as finding ways to alleviate people’s COVID-19 anxiety. Supplementary Information The online version contains supplementary material available at 10.1007/s10902-021-00385-2.


Introduction
In December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan, China. Two months later, the epidemic had spread across China and reached its peak level (15,153 daily cases, 254 daily deaths; Chinese Center for Disease Control and Prevention, 2020, February 15th). Because of the policies adopted by the Chinese government, by the end of April, the number of confirmed cases in China had declined sharply and remained below

Relationship between SWB and COVID-19 Anxiety
Existing evidence shows that SWB is negatively related to anxiety (Grieve et al., 2013;Lepp et al., 2014). To understand the relationship between SWB and people's evaluation of life events, Diener (1984) proposed bottom-up and top-down theories. From the perspective of the bottom-up theory, the positive emotion of past pleasant experiences contributes to increasing SWB, whereas the negative emotion of unpleasant experiences might decrease one's current evaluation of the whole life (Stallings et al., 1997). Hence, the emergence of COVID-19 might first have made people feel anxious about their health and safety, which might then have lowered their SWB.
In contrast, the top-down theory of well-being indicates that the reverse may be correct. SWB has a strong effect on several life domains, and higher SWB predicts positive emotional reflection and evaluation towards a certain life event (Diener, 1984). Based on the top-down theory, some psychologists elucidate the role of SWB as a psychological resource in coping with stress and challenges (Fredrickson, 2004;Huppert & Baylis, 2004). They indicate that high SWB may enhance people's resilience against stressors in negative life events. Efficient well-being therapy in anxiety interventions proffers some evidence supporting these theories that well-being may prevent people from becoming anxious. For example, intervention research shows that patients who receive well-being therapy are encouraged to self-observe to increase their SWB, which can result in lower anxiety levels in future assessments (Fava et al., 2017). Therefore, according to the topdown theory, people with lower SWB may hold a more pessimistic view about the development of the epidemic, leading to heightened levels of anxiety. Furthermore, with respect to the potential protective role of SWB in this theory, an individual's SWB level before the epidemic may also have a significant influence on their COVID-19 anxiety.
Although intervention research supports the top-down theory, it remains unclear whether the top-down theory can be applied to the interpretation of the relationship between SWB and anxiety in natural settings, especially during massive epidemics. To determine this, the current study aimed to examine the relationship between individuals' SWB and COVID-19 anxiety using longitudinal data throughout the development of the COVID-19 pandemic.
There are three major contributions of the present study as follows. First, this study might contribute to discriminating the relation of the top-down and bottom-up theories of SWB. As two competing models, the top-down and bottom-up theories have generated contradictory findings. The current study helps determine which one would provide an appropriate explanation for the relationship between SWB and COVID-19 anxiety at different stages of the COVID-19 pandemic. Second, to our knowledge, existing studies that examine or integrate the top-down and bottom-up theories usually adopt natural and longitudinal research using long time intervals, such as years (Brief et al., 1993;Feist et al., 1995;Nakazato et al., 2011). To extend existing research, the present study collected three waves of data within a short time interval of a month, which corresponds to the inflection point of the COVID-19 epidemic, similar to a field experiment. In this way, we might not only capture the subtle fluctuation between SWB and anxiety but also examine the topdown and bottom-up theories from a dynamic perspective. Third, in practice, the current study sheds light on finding feasible ways to alleviate people's COVID-19 anxiety from the perspective of positive psychology.

Overview of the Current Study
To unfold the relationship between people's SWB and their COVID-19 anxiety, we collected three waves data on SWB and COVID-19 anxiety at three key stages of the development of COVID-19 in China: the peak (T1; February 13th, 2020), the decline (T2; March 13th, 2020), and the trough (T3; April 8th, 2020) stages. In fact, the three waves of data belong to a longitudinal project concerning the relationship between SWB and anxiety. Thus, we measured the participants' trait anxiety and SWB on January 3rd of 2020 (T0) when  was not yet declared an emergent public health event. Given that people knew little about the infectivity and harmfulness of COVID-19 and it had not led to widespread panic and global crises on January 3rd , we described T0 as "before the pandemic".
Using data from before the pandemic and three subsequent waves of data from throughout the peak and the trough of the COVID-19 pandemic, the aims of our study were to examine (1) whether people's SWB and anxiety fluctuated dynamically with the development of the COVID-19 pandemic; (2) whether there was a causal relationship between SWB and COVID-19 anxiety; and (3) if so, did people's SWB alleviate their COVID-19 anxiety or did their COVID-19 anxiety damage their SWB?

Participants and Procedure
The current sample size was determined by the availability of potential participants over a four-month period. The participants were 222 Chinese adults (54.50% female, M age = 31.53, SD = 8.17) from 26 provinces via a survey website (www. wjx. cn). We collected our baseline data on January 3rd (T0). Then, we followed-up the participants on February 13th (T1; the peak stage of the COVID-19 pandemic in China; more than 3000 newly confirmed cases in China per day), March 13th (T2; the decline stage of COVID-19 in China; approximately 30 newly confirmed indigenous cases in China per day), and April 8th (T3; the trough stage, a period when the COVID-19 was generally under control in China; lower than 10 newly confirmed cases in China per day). Of all the participants, 164 (73.87%), 123 (54.41%), and 161 (72.52%) participated in the online surveys at T1, T2, and T3, respectively. We obtained participants' informed consent in accordance with the guidelines of the ethics board of the Faculty of Psychology, Beijing Normal University.

COVID-19 Anxiety at T1, T2, and T3
COVID-19 anxiety was assessed using the 10-item Self-check and Self-inspect Scale for COVID-19 Anxiety (Chinese Psychological Society, 2020, February 9, see Table S2). The participants were asked about their anxious behaviours and mood after the outbreak of COVID-19 in the past month (e.g., "I feel more irritable, dissatisfied or angry than usual."). Items were rated on a 5-point scale from 1 = almost never to 5 = almost always. We computed the total scores by adding all the items, with higher scores representing higher levels of COVID-19 anxiety. The Cronbach's alpha values were 0.87, 0.86, and 0.87 at T1, T2, and T3, respectively.

SWB at T0, T1, T2, and T3
SWB was assessed using the 18-item well-validated Chinese version of the General Well-Being Schedule (GWB; Duan, 1996;Fang et al., 2018;Fazio, 1977;Li et al., 2015, see Table S3). The GWB includes six dimensions: anxiety, depression, general health, positive well-being, self-control, and vitality (Brook et al., 1979). The participants reported their levels of SWB in the past month (e.g., "How happy, satisfied, or pleased have you been with your personal life?"). Total scores were computed and used in analyses, with higher scores indicating higher levels of SWB. The Cronbach's alpha values were 0.88, 0.91, and 0.91 at T1, T2, and T3, respectively.

Trait Anxiety at T0
Trait anxiety was assessed using the 20-item Chinese version of the Trait Anxiety Subscale of the State-Trait Anxiety Inventory (STAI; Chen et al., 2014;Li & Qian, 1995;Spielberger et al., 1983, see Table S4). The participants rated their levels of general anxiety (e.g., "I wish I could be as happy as others seem.") on a 4-point scale from 1 = not at all to 4 = very much so. Higher scores indicated higher levels of trait anxiety. In the current study, the Cronbach's alpha was 0.89 at T0.

Planned Analysis
First, to investigate whether SWB and COVID-19 anxiety changed during this period, we conducted mixed-effects modeling using the nlme package in R (Pinheiro et al., 2014). Compared to conventional repeated-measures ANOVA which involves only fixed effects, mixed-effects modeling considers both fixed effects and random effects, which is preferred for analyzing non-random data and data with a large number of missing values. When considering the subject as one random effect, it could prevent the following three problems that might give us problematic results: (1) data with a relatively high rate of nonrandom missing responses, (2) high variability among participants, and (3) the fact that the response of the participants might depend on their past making the data nonindependent (Baayen et al., 2008;Bagiella et al., 2000;Krueger & Tian, 2004). Then, we used the multcomp package in R (Bretz et al., 2016) to perform the Bonferroni-corrected post-hoc test.
Second, to examine whether the base level of SWB would influence the trajectories of SWB and COVID-19 anxiety, we divided the subjects into two groups based on SWB assessed at T0 (high-SWB group and low-SWB group), which could be considered the base level of SWB. We fit two linear mixed-effects models with SWB and COVID-19 anxiety as the outcome variables, with fixed effects of time points (T1, T2, and T3) and SWB-level groups (both of them were considered dummy variables) and their interaction, to investigate whether the base level of SWB could impact the trajectory of SWB and COVID-19 anxiety from T1 to T3. Post-hoc tests were also conducted to better interpret the impact.
Third, to explore the relationships between SWB and COVID-19 anxiety, three-wave cross-lagged panel models (CLPMs) were tested using structural equation modeling (SEM) in the lavaan package in R (Rosseel, 2012). Compared with cross-sectional data collected at a specific point in time that could only help to conjecture causality relying on theoretical inference, panel data with repeated measures of variables at multiple points in time could help to describe the directional influences that one variable has on another across different time points (Falkenström et al., 2020;Shingles, 2007).
The cross-lagged panel model would contain both autoregressive paths and cross-lagged paths. Specifically, the autoregressive paths could suggest the degree of how the current state of a variable would depend on its past, reflecting the persistence in the variable over time. Cross-lagged terms are used to reflect causality, reflecting whether the perturbation of one variable could predict the perturbation of another variable, and estimating the reciprocal relationships between variables over time. To investigate the cross-lagged effects, four competing models were examined: (1) a model with only autoregressive paths that could suggest the stability of the construct, (2) a model with autoregressive paths and crosslagged paths from SWB to subsequent COVID-19 anxiety, (3) a model with autoregressive paths and cross-lagged paths from COVID-19 anxiety to subsequent SWB, and (4) a fully cross-lagged model with both autoregressive paths and cross-lagged paths of SWB and COVID-19 anxiety. Considering the potential correlation with SWB and COVID-19 anxiety, sex, age, income, and trait anxiety were considered covariates in the model (Jebb et al., 2018;Mirowsky & Schieman, 2008;Moksnes & Espnes, 2013;Thomsen et al., 2016).
The models' goodness of fit was evaluated using fit indexes including the χ 2 test (acceptable when χ 2 /df ratio ≤ 3 and good when χ 2 /df ratio ≤ 2), comparative fit index (CFI; acceptable when CFI ≥ 0.95 and good when CFI ≥ 0.97), root-mean-square error of approximation (RMSEA; acceptable when RMSEA ≤ 0.08 and good when RMSEA ≤ 0.05), and standardized root mean squared residual (SRMR; acceptable when SRMR ≤ 0.10 and good when SRMR ≤ 0.05; Schermelleh-Engel et al., 2003). Table 1 showed the descriptive statistics for all the variables in the present study. SWB and COVID-19 anxiety negatively correlated across all time points. In addition, trait anxiety was highly correlated with SWB and COVID-19 anxiety measured at every time point.

The impact of the Base Level of SWB (T0) on the Trajectories of SWB and COVID-19 Anxiety from T1 to T3
For the trajectory of COVID-19 anxiety from T1 to T3, compared to the model with only fixed effects, the mixed effects model could fit the data better (χ 2 (1) = 188.96, p < 0.001) as the variance in intercepts among participants (SD = 5.01, 95% CI [4.41, 5.68]) accounted for 68% of the variance of the model. The results of the mixed effects model could be seen in Table 2. The results of post-hoc test with Bonferroni corrections suggested the different levels and trajectories of COVID-19 anxiety between two groups with different base levels of SWB (see Table 3). There was no difference of COVID-19 anxiety levels at T1 between two groups (M T1(high-low) = − 2.56, p = 0.102), while participants with low base level of SWB would show higher levels of COVID-19 anxiety at T2 and T3 compared with those with high base level of SWB (M T2(high-low) = − 5.20, p < 0.001; M T3(high-low) = − 3.45, p = 0.005). As for the trajectory, for participants with low base level of SWB, their COVID-19 anxiety would keep stable from T1 to T2 (M COV2-1 = 0.24, p = 1.000), and then    Fig. 2). For the trajectory of SWB from T1 to T3, compared to the model with only fixed effects, the mixed effects model shows better goodness of fit (χ 2 (1) = 155.71, p < 0.001) as the variance in intercepts among participants (SD = 9.70,95% CI [8.49,11.08]) accounted for 64% of the variance of the model. The results of the mixed effects model is shown in Table 4. The results of post-hoc test with Bonferroni corrections suggested the different levels and trajectories of SWB from T1 to T3 between two groups with different base levels of SWB assessed at T0 (see Table 5). For participants with low base level of SWB at T0, SWB levels at consequent three time points were significantly lower compared with those with high base level of SWB Fig. 2 The trajectory of COVID-19 anxiety from T1 to T3 in two groups with different SWB baseline level (T0) Table 4 The impacts of baseline SWB on the trajectories of SWB from T1 to T3 The significance level was in bold when p < 0.05, p < 0.01, or p < 0.001 σ 2 = fixed effects variance; τ 00 = random intercept variance; ICC indicates how much variance is explained by a random effect.

Cross-lagged Panel Models
Four models were tested from the model with only autoregressive paths to the full crosslagged panel model as suggested above. Model fit indices of each model and the results of model comparisons were reported in Table 6. Among the four models, autoregressive model (Model 1) did not show an acceptable fit (χ 2 /df = 4.61, CFI = 0.974, RMSEA = 0.127, SRMR = 0.053). Adding the cross-lagged paths from COVID-19 anxiety at preceding time point to subsequent SWB (Model 2) could have better goodness of fit compared to Model 1 (χ diff 2 (2) = 15.07, p < 0.001; χ 2 /df = 3.14, CFI = 0.990, RMSEA = 0.098, SRMR = 0.025). The model with cross-lagged paths from SWB at preceding time point to subsequent COVID-19 anxiety (Model 3) could also fit Table 5 Bonferroni comparison for mixed effects model of SWB between groups with different base levels of SWB (mixed effects model) The significance level was in bold when p < 0.05 or p < 0.001 G1 = group with the low base level of SWB, G2 = group with the high base level of SWB.  (2) = 19.99, p < 0.001) and is of acceptable model fit (χ 2 /df = 1.89, CFI = 0.996, RMSEA = 0.064, SRMR = 0.020). And when adding cross-lagged paths from SWB to subsequent COVID-19 anxiety to Model 2, the model (Model 4) could fit the data better (χ diff 2 (2) = 10.26, p = 0.006; χ 2 /df = 1.15, CFI = 1.000, RMSEA = 0.026, SRMR = 0.016). In general, both Model 3 and 4 have an acceptable fit. While Model 4 can be considered a good fit model regarding all goodness-of-fit indices (χ diff 2 (2) = 5.34, p = 0.069). Therefore, the fully cross-lagged model (Model 4) with all autoregressive and cross-lagged paths was the relatively best fitting model.

Discussion
To understand whether and how people's SWB and COVID-19 anxiety are related to each other, we conducted a three-wave longitudinal study with Chinese participants. There were three main findings in our study. First, consistent with previous research (Kachanoff et al., 2020), COVID-19 pandemic has reduced people's SWB and increased their anxiety. Meanwhile, SWB and anxiety were found to fluctuate dynamically with the development of COVID-19 pandemic. Specifically, the participants' SWB reached its lowest at T1 and recovered gradually at T2 and T3. In contrast, the participants' COVID-19 anxiety reached its peak level at T1 and dropped sharply at T2 and T3. Second, SWB and COVID-19 anxiety were negatively correlated at the three points. Third, the cross-lagged analysis showed Fig. 4 Fully cross-lagged panel model with SWB at T0. The model considered trait anxiety, gender, age, and income as covariates. χ 2 /df = 2.80, CFI = 0.988, RMSEA = 0.090, SRMR = 0.037. *p < .05, **p < .01, ***p < .001 that SWB at T1 was able to predict COVID-19 anxiety at T2. However, SWB at T2 was not associated with COVID-19 anxiety at T3. Meanwhile, COVID-19 anxiety at the preceding time point could not predict subsequent SWB.
The finding that SWB at T1 predicts COVID-19 anxiety at T2 can be explained by the top-down theory of well-being, which considers SWB as a psychological resource for people to respond effectively to problems and challenges (Fredrickson, 2004;Huppert & Baylis, 2004). For the first time, the current study extended the top-down theory of well-being to understand the relationship between SWB and anxiety during the development of global epidemic. Individuals with higher SWB have more resilience when confronting negative life events and are better able to adjust their mindset to lower their anxiety during the peak of the COVID-19 pandemic.
More importantly, the current study showed that the causal relationship between SWB and COVID-19 anxiety might change with the development of the COVID-19 pandemic, which might contribute to understanding the boundary condition of the application of the top-down theory of well-being. Different from the tendency that SWB at T1 significantly predicted the subsequent COVID-19 anxiety at T2, SWB at T2 did not predict subsequent COVID-19 anxiety at T3. The results showed that the causal relationship between SWB and COVID-19 anxiety might disappear with the decline of the COVID-19 pandemic.
Moreover, suggested by Diener and his colleagues' seminal reviews (), the confounding variables such as research conditions inevitably involved could account for the ambiguous results of well-being research. For example, as the situation changes, the direction of the causal relationships might become completely inverse and the effect size unstable. Considering the great discrepancy among the pandemic severity of the three time nodes, it is plausible that the perceived pandemic threat might moderate the influence of SWB on COVID-19 anxiety. Although one's SWB level influences their threat assessment and affective reflection to COVID-19, objective pandemic severity also has a significant effect on people's perception. Therefore, it is advisable to remain cautious about giving a determined affirmation of the causal relationship between SWB and pandemic anxiety, and more research is needed to explore the feasible moderated variables. Furthermore, we suggested taking situations into consideration in examining the relationship between SWB and anxiety. In future studies, a contingent top-down theory of well-being might help understand the dynamic relationship between mental health and mental illness.

Implications for Future Research
The current study found that people's SWB and COVID-19 anxiety fluctuated with the development of the COVID-19 pandemic. Meanwhile, people's SWB at the peak stage of the COVID-19 pandemic predicted their subsequent anxiety. These findings help to clarify the causal relationship from SWB to anxiety and provide a theoretical basis to COVID-19 anxiety intervention.
On the one hand, a high sense of SWB cultivated in individuals' daily lives might serve to buffer the negative effects of unprecedented crises (e.g., COVID-19 pandemic) on mental health. On the other hand, our finding suggests implementing happiness therapy which has already been proved to be efficient in reducing anxiety in patients (Fava et al., 2017), as a practical way to mitigate the mental problems caused by COVID-19.

Limitations of the Current Research
The present study has several limitations. First, the sample is relatively small, which might limit the external validity of our findings. Second, the measurement of COVID-19 anxiety was based on subjective self-reports. More objective indicators, such as actual behavior analysis, are preferred in future studies. Third, although the FIML method we used might minimize the impact of missing data to some extent, it might leave unanswered questions regarding individual differences in COVID-19 anxiety. Fourth, considering that SWB might be correlated with financial situation, future studies are suggested to track individuals' income across different stages of the pandemic and examine its impacts on people's mental health during the epidemic.