Evidence on Resilient Initial Response to COVID-19 Pandemic Among Youth: Findings From the Prospective Study of Mental Health in Two European Countries

Abstract

The current two-wave longitudinal study aimed to investigate changes in stress, anxiety, depression, and positive mental health (PMH) during the first COVID-19 pandemic outbreak in the sample of emerging adults. Data were collected before the COVID-19 and within the first month of the outbreak. The study sample consisted of 775 university students from Lithuania (n = 450, M_age(SD_age) = 19.45 (0.93), 79.3% female) and Germany (n = 325, M_age(SD_age) = 23.08 (2.94), 78.2% female). The results of multivariate Latent Change Analysis revealed that Lithuanian and German emerging adults demonstrated a decrease in stress and anxiety at the COVID-19 outbreak. Lithuanians also showed a decrease in depressive symptoms and an increase in PMH. Three groups with different change patterns were identified: resilient (82%) demonstrating positive changes, high-symptom (8%) with stable high rates of stress and depression and stable low rates of PMH, and vulnerable (10%) with an increase in depressive symptoms as well as a decrease in PMH over time.

Keywords

COVID-19 young adults stress anxiety depression mental health

The outbreak of COVID-19 affected the lives of millions of people worldwide. Many countries introduced measures of everyday life restrictions in the first months of 2020 to mitigate the spread of coronavirus amid the global pandemic outbreak (Flaxman et al., 2020; Hsiang et al., 2020). In the face of the pandemic-related crisis, World Health Organization alerted that the level of experienced stress was rising rapidly and expressed concerns regarding its possible impact on mental health (World Health Organization, 2020). Based on findings from previous epidemics (e.g., Hall et al., 2008; Perrin et al., 2009), as well as initial studies of the COVID-19 pandemic, it has been demonstrated that the COVID-19 may have a significant negative impact on mental health across various countries (e.g., Cullen et al., 2020; Kumar & Nayar, 2020; Rajkumar, 2020; Torales et al., 2020).

Initial findings of cross-sectional population studies emphasized that many people reported moderate to high subjective negative impact of the COVID-19 on their mental health. Moreover, a significant proportion reported high levels of stress, anxiety, and depression symptoms (Wang, Pan, Wan, Tan, Xu, Ho, & Ho, 2020). However, at least one longitudinal population study conducted over the course of the COVID-19 pandemic failed to identify significant changes in stress, anxiety, and depression over time, indicating that positive changes in mental health symptoms are also possible (Wang, Pan, Wan, Tan, Xu, McIntyre, et al., 2020). This may indicate that in face of the COVID-19 pandemic, people may also demonstrate resilience, as conceptualized through the ability to bounce back to adversity and adapt positively in face of challenges and difficulties (Luthar & Cicchetti, 2000). Nevertheless, experiences from previous crises (e.g., Bacigalupe et al., 2016) show that there may be a short-term decline in clinical anxiety, depression, and suicide rates immediately after a disaster, and a significant increase in psychological problems can be expected in the medium and long term. These effects are observed in different segments of the population at varying degrees, as they may depend on the levels of negative socioeconomic consequences. Moreover, an increase in psychological problems may be mediated by the psychological processing of the stress experiences, that is, higher levels of personally experienced distress may result in worse mental health over time (Zivin et al., 2011).

It is known that emerging adults, due to high levels of stress, face an increased risk for developing mental disorders (Arnett et al., 2014). Research evidence suggests that life stressors during student years may diminish mental well-being and may contribute to the enhanced risk of the development of mental disorders, such as depression, anxiety, and adjustment disorder (Schönfeld et al., 2016; Truskauskaite-Kuneviciene, et al., 2020). Moreover, emerging adults may be among those who have been the most affected by the COVOD-19-related restrictions and lockdown measures, as they tend to be more sociable and more outgoing, compared to older adults (Brook & Schmidt, 2020). Nevertheless, it has been found that during the early months of the COVID-19 pandemic, higher resilience was associated with going outside more often, more exercising, and higher perceived social support (Killgore et al., 2020), indicating that being more active may work as an important protective factor for youth to demonstrate resilience in face of the pandemic.

In the previous COVID-19 related studies, concerns were raised that the mental health issues in youth could rise and should be studied and monitored during the COVID-19 pandemic (Cao et al., 2020; Li et al., 2020; Liang et al., 2020). Additionally, a high proportion of emerging adults from various countries reported high levels of stress and anxiety during the outbreak of COVID-19 (Browning et al., 2021, Kaparounaki et al., 2020; Liu et al., 2020; Odriozola-González et al., 2020). A longitudinal study of youth with the pre-test 2 years prior to the outbreak of pandemic reported an increase in perceived stress, but not depressive and anxiety symptoms (Shanahan et al., 2020). When the study with the pre-test 7 months before the pandemic reported a decrease in stress, anxiety, and depressive symptoms among university students during the first months of the COVID-19 outbreak (Elmer et al., 2020).

As the COVID-19 crisis is a new global issue, it is highly challenging to robustly measure its impact on mental health. The currently available findings on the effects of the pandemic on mental health are mostly based on cross-sectional data or data collected during the pandemic outbreak without previous or non-recent baseline measures. To better understand the effects of the pandemic, there is a high need for more high-quality data and international collaboration to have a global perspective on this issue, as well as to address the impact of the pandemic on different populations (Holmes et al., 2020). Longitudinal research is needed to address the question of whether the mental health of youth changes during the outbreak of pandemics (Wade et al., 2020).

To address the lack of longitudinal research utilizing pre-test data collected close to the onset of the COVID-19 and to address the lack of studies on the impact of the COVID-19 on mental health in European countries, the aims of the current study were as follows. First, we sought to investigate the change in mental health indicators, in particular, stress, anxiety, depression as well as positive mental health after the onset of the COVID-19 outbreak in a sample of emerging adults. Second, we aimed to estimate whether the patterns of change in mental health indicators are comparable across two European countries, in particular, Lithuania and Germany. From the cultural perspective, more similarities than differences may be observed between Lithuania and Germany (e.g., Balcytiene, 2012). Similar levels of mental health problems were observed in both countries during the school years (Kovess et al., 2015). Similar behavioral and mental health patterns were reported by university students across two countries (Brailovskaia et al., 2021). Similar measures of the lockdown and moving study process online were taken in the initial months of pandemic. Nevertheless, mental health care is way more developed in Germany (Margraf et al., 2021), compared to Lithuania (Kazlauskas & Grigutyte, 2020), that is, mental health support for university students may be more accessible in Germany than in Lithuania. Also, by the time of the first outbreak of the pandemic, relatively fewer COVID-19 cases were observed in Lithuania, compared to Germany (https://covid 19 .who.int/). Therefore, some differences in mental health response toward the pandemic could be anticipated among the two countries. Finally, we sought to identify emerging adults with possibly different patterns of change in mental health indicators when facing the COVID-19 pandemic, as previous studies indicated that mental health response to the crisis may vary in association to, for example, the levels of personal distress (e.g., Zivin et al., 2011).

Method

Participants

The final study sample consisted of 775 emerging adults studying at universities (78.8% female; M_age (SD_age) = 20.97 (2.71), age range: 18–29) in the two European countries, Germany (n = 325) and Lithuania (n = 450). Participants’ characteristics in each country separately are presented in Table 1. The data from two waves (T1 and T2) were used in the current study, by including only the participants currently studying at university, who are within the age range of emerging adulthood (18–29), and who filled relevant measures both times.

Table 1.

Sample Characteristics (N = 775).

	Lithuania (n = 450)		Germany (n = 325)		χ²(1)
Variables	N	%	N	%	χ²(1)
Gender
Male	93	20.7	71	21.8	0.16
Female	357	79.3	254	78.2
Age range	18–27		18–29
M (SD)	19.45 (0.93)		23.08 (2.94)
Diagnosed with COVID-19
No, no symptoms	443	98.4	314	96.6	2.78
No, similar symptoms	7	1.6	11	3.4
Yes	0	0	0	0
In partnership
No	284	63.1	171	52.6	15.92^***
Yes	140	31.1	154	47.4
N/A	26	5.8	—	—
Employed
No	355	78.9	297	91.4	8.31^**
Yes	66	14.7	28	8.6
N/A	29	6.4	—	—

Note. M = mean; SD = standard deviation.

***p < .001, **p < .01.

In Lithuania and Germany, 27.5% and 41.3% of study participants, respectively, who filled the relevant measures at T1, were reached at T2. Possible attrition bias (Miller & Hollist, 2007) was tested to identify whether the characteristics of retained participants were comparable to drop-out participants who were not reached at T2. MANOVA analysis revealed that in the Lithuanian sample there was a statistically significant but small difference of baseline mental health indicators between retained and drop-out participants (F(4) = 4.51, p = .001, Wilks’ Λ = .989, partial η² = .011). Retained participants had higher levels of stress (η² = .003) and anxiety (η² = .007) as well as lower levels of positive mental health (η² = .004) in comparison to the drop-out group at T1. We found no significant drop-out effects in the German sample.

Procedures

Ethical approvals from the relevant Institutional Review Boards (IRB) in Germany and Lithuania were obtained prior to data collection. Additionally, ethical approval amendments to include the COVID-19 specific items in the study were granted by the IRB’s in both countries. The T1 data were collected in October–December 2019; the T2 data were collected in March–April 2020, within the first month after implementing national social distancing measures due to the COVID-19 in both countries. Some data from the German sample were published previously (Brailovskaia & Margraf, 2020) by including stress, anxiety, depression, and positive mental health at T1 as predictors of burden related to COVID-19.

In both countries, data were collected online on a secure survey platform. Participants were recruited via e-mail or face-to-face invitations with a link to an online survey using the national language by sending invitations to individuals who were studying at a large university in the Ruhr region (Germany) or Vilnius (Lithuania). All participants were properly instructed and provided informed consent online to participate in a longitudinal study. No financial incentives were provided for participation. For the German sample only, participation in the study was compensated by course credits.

Measures

Depression, anxiety, and stress symptoms

The 21-item Depression Anxiety Stress Scales (DASS-21; Lovibond & Lovibond, 1995) was used to measure mental health issues. The DASS-21 consists of three subscales, comprising of seven items each and measuring symptoms of depression (e.g., “I couldn’t seem to experience any positive feeling at all”), anxiety (e.g., “I was worried about situations in which I might panic and make a fool of myself”) and stress (e.g., “I found it difficult to relax”). Participants indicated frequency on each of the symptom items over the past week on a 4-point Likert scale ranging from 0 (= “never”) to 3 (= “always”). The DASS-21 score for each of the three symptoms ranges from 0 to 21, with a higher score indicating a higher level of symptoms. The Cronbach’s alpha coefficients for the Stress, Anxiety, and Depression Scales in a full study sample at T1/T2 were .88/.88, .83/.81, and .90/.89, respectively. In the Lithuanian sample, the Cronbach’s alpha coefficients for the Stress, Anxiety, and Depression Scales at T1/T2 were .87/.89, .82/.83, and .90/.89; in the German sample, .87/.87, .79/.73, and .88/.88, respectively.

Positive mental health

The Positive Mental Health Scale (PMH-Scale; Lukat et al., 2016) is a unidimensional scale that consists of nine items, measuring emotional, psychological, and social aspects of positive mental health (e.g., “I am in good physical and emotional condition”). Each item was rated on a 4-point Likert scale ranging from 0 (= “do not agree”) to 3 (= “agree”). The total PMH-Scale score ranges from 0 to 27, with a higher score indicating a higher level of positive mental health. The Cronbach’s alpha coefficient in a full study sample at T1/T2 indicated a high internal consistency (Cronbach’s α = .91/.92) of the scale. In the Lithuanian sample, the Cronbach’s α coefficients of PMH-Scale at T1/T2 were .90/.92; in the German sample, .92/.92.

Resilience

The Resilience Scale 11 (RS-11; Schumacher et al., 2005) was used to measure psychological resilience. The RS-11 is a unidimensional measure containing 11 items (e.g., “I usually manage one way or another”). Each item was rated on a 7-point Likert scale ranging from 1 (= “do not agree”) to 3 (= “agree”). The total RS-11 score ranges from 11 to 77, with a higher score indicating a higher level of resilience. In the current study, resilience was measured at the COVID-19 outbreak (T2) only. The Cronbach’s α coefficient in a full study sample indicated a high internal consistency (Cronbach’s α = .85) of the scale. In the Lithuanian and German samples, the Cronbach’s alpha coefficients of RS-11 were .84 and .87 respectively.

In the current study sample, the metric longitudinal measurement invariance of both the DASS-21 scale and the PMH-Scale were established, including country invariance. The metric country invariance was also established for the RS-11 scale. The changes in model fit indices are provided in the Supplementary Table S1.

Data Analyses

To examine the changes in mental health indicators at T2, we used the multivariate latent change modeling approach that provides more robust estimates of change over time (Duncan et al., 2013). In latent change models with two measurement points, the intercept represents the mean level of the measure at T1 and the slope represents the change from T1 to T2.

Measurement invariance

As a preliminary step, we tested longitudinal measurement invariance for each measure used in the study. First, we compared the configural model (Confirmatory Factor Analysis (CFA), allowing the correlations between errors of the same items across the two-time points) with the metric model, in which the factor loadings were constrained to be equal across time, and the scalar model, in which item intercepts were also constrained to be equal across time. Model comparisons were conducted by examining the changes in fit indices (e.g., Chen, 2007). In all invariance models, we allowed the residual correlations of the same items across the time-points. We then extracted the factor scores (Yang & Hoyle, 2010) from scalar models of the DASS-21 and the PMH scales that were subsequently used for the multivariate latent change model estimation.

Latent change analysis

In the current study, we conducted the latent change model of four parallel processes, specifically, change in stress, anxiety, depression, and positive mental health (PMH), controlling for possible gender effects on intercepts and slopes. To have the latent change model identified, first, we fixed the residuals to zero; second, we fixed non-significant gender effects to zero one by one until we obtained the final model with the significant links only. In addition, to identify whether the change processes in mental health indicators were linked with each other and whether the initial levels of stress, anxiety, depression, and PMH were associated with the changes, in the final model, we correlated all intercepts and slopes.

Multi-group analysis

After running the multivariate latent change model in the full study sample, we conducted multiple group analysis by including the country of residence as the moderator. To determine possible differences between groups, we assessed differences between the models with fixed versus free parameters of slopes, indicating the same versus different levels of change in mental health indicators across countries. To determine significant differences between the models with fixed versus free parameters, at least two of these three criteria had to be matched: Δχ² significant at p < .05 (Satorra & Bentler, 2001), ΔCFI ≥ .01, and ΔRMSEA ≥ .015 (Chen, 2007). To test for the differences in mental health indicators at T1 as well as to compare the change parameters across countries, we ran the Wald χ² tests.

Latent class change analysis

To identify groups of participants with possibly different patterns of change in mental health indicators, we used the latent class change approach (Jung & Wickrama, 2008). We classified the study participants based on the change in all four mental health indicators and used several criteria to decide on the number of latent classes. First, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) statistics for a solution with k classes should be lower than for a solution with k–1 classes. Second, a statistically significant p-value of the adjusted Lo, Mendell, and Rubin test, which compares improvement in fit between neighboring class solutions after the inclusion of an additional class. Third, Entropy score, with relatively higher values equal to or above .70 indicative of more accurate classification. After the data-driven classification of the study participants regarding their mental health indicators, we have validated the groups by comparing the levels of resilience in the resulting subsamples. To do that, we compared the latent means of resilience among the groups by constraining the mean in the reference group to 0 and freeing the mean in the comparison groups. When comparing the latent means, a significant mean in a comparison group indicates a significant difference among groups.

The model fit in all analyses was evaluated by using the Comparative Fit Index (CFI), the Tucker–Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR), following the goodness of fit recommendation provided by Kline (2006). Namely, CFI/TLI values higher than .90 indicated an acceptable fit and values higher than .95 represented a very good fit; RMSEA and SRMR values below .08 indicated of an acceptable fit and values less than .05 suggested a good fit. The analyses were conducted with Mplus 8.2 (Muthén & Muthén, 1998–2017).

Results

Means and standard deviations of the study variables at T1 and T2 are presented in Table 2. The results of preliminary analyses indicated higher levels of Stress, Anxiety, and Depression as well as lower levels of Positive Mental Health and Resilience in Lithuania, compared to Germany, both at the Baseline and the COVID-19 outbreak. Correlations among study variables are reported in Supplementary Table S2. The results of correlation analysis yielded that in Lithuania and Germany, Stress, Anxiety, and Depression were positively linked with one another as well as negatively linked with Positive Mental Health both at the same time-points and across two measurement points.

Table 2.

Descriptive Statistics of Mental Health Indicators in Lithuania and Germany.

	Baseline (T1)					COVID-19 Outbreak (T2)
	Lithuania (n = 450)		Germany (n = 325)			Lithuania (n = 450)		Germany (n = 325)
	M (SD)	γ_1/γ₂	M (SD)	γ_1/γ₂	t (773)	M (SD)	γ_1/γ₂	M (SD)	γ_1/γ₂	t (773)
Stress	10.2(5.32)	0.21/−0.74	7.12 (4.64)	0.21/−0.74	8.39	8.09 (5.25)	0.54/−0.51	6.09 (4.50)	0.65/ −0.42	5.53
Anxiety	7.07 (4.92)	0.73/−0.06	3.66(3.66)	1.36/1.85	10.55	4.16 (4.21)	1.49/ 2.16	2.33 (2.82)	1.40/ 1.43	6.78
Depression	7.72 (5.66)	0.66/−0.51	5.09 (4.57)	1.08/0.61	6.91	6.54 (5.18)	0.94/ 0.17	4.71 (4.36)	1.43/ 1.95	5.18
Positive Mental Health	15.66 (5.57)	−0.18/−0.41	18.45 (5.68)	−0.38/−0.55	−6.82	16.44 (5.46)	−0.38/ 0.12	18.64 (5.76)	−0.42/ −0.46	−5.41
Resilience	-	-	-	-	-	57.12 (9.36)	−.58/.30	59.39 (9.47)	−.54/.07	−3.32

Note. M = mean, SD = standard deviation, SE = standard error, γ₁ = skewness, γ₂ = kurtosis, all t values are significant at p < .001.

Change in Mental Health Indicators

The multivariate latent change analysis in a full study sample yielded an excellent model fit (χ² (17) = 5.59, p = .348, CFI/TLI = 1.000/.999, RMSEA [90% CI] = .012 [.000, .053], SRMR = .015). Overall, we found significant (p < .001) small decrease in rates of stress (M_slope = −.26; d = −.42 [−.52; −.32]), moderate decrease in anxiety (M_slope = −.36; d = −.65 [−.98; −.33]), small decrease in depression (M_slope = −.12; d = −.20 [−.30; −.10]) as well as small increase in positive mental health (PMH) (M_slope = .07; d = .12 [.02; .22]) at T2. For all mental health indicators, we found significant negative links (p < .001) between intercepts and slopes, indicating that higher baseline rates of stress (r = −.52), anxiety (r = −.67), and depression (r = −.53) are associated with higher/stronger decrease in these negative mental health indicators; lower baseline rates of PMH (r = −.37) are associated with higher increase in this positive indicator of mental health. We also found significant gender effects on intercepts of stress (β_intercept = −.05, p = .001) as well as intercept and slope of anxiety (β_intercept = −.06, p < .001; β_slope = .02, p = .022), indicating higher stress and anxiety female baseline rates, compared to male and higher anxiety decrease in male, compared to female.

The Country as a Moderator of Change in Mental Health

The results of multi-group analysis and the comparison between the latent change models with free versus fixed slope parameters across countries indicated that there were significant differences in change of mental health indicators in Lithuania and Germany (Δχ²(8) = 106.05, p < 0.001, ΔCFI = .013, ΔRMSEA = .072). Therefore, further in the current section, we will present the results of latent change model with all free parameters across countries (χ² (10) = 16.29, p = .09, CFI/TLI = .999/.994, RMSEA [90% CI] = .040 [.000, .074], SRMR = .030). The trajectories of change in mental health indicators in Lithuania and Germany separately with the effect sizes are presented in Figure 1. The results of multiple group analysis indicated that the Lithuanian participants reported significantly higher initial levels of stress (χ²(1) = 33.47, p < .001), anxiety (χ²(1) = 61.68, p < .001), and depression (χ²(1) = 63.48, p < .001), as well as lower levels of positive mental health (PMH) (χ²(1) = 40.66, p < .001) compared to the German participants. However, the Lithuanian sample demonstrated significant (p < .001) moderate decrease in stress and anxiety, small decrease in depression (M_slope (stress/anxiety/depression) = −.34/−.44/−.17) as well as significant (p < .001) small increase in PMH (M_slope = .10). The German sample showed significant (p < .001) small decrease in stress (M_slope = −.15) and moderate decrease in anxiety (M_slope = −.25), while depression and PMH remained stable over time. Moreover, the decrease in stress (χ²(1) = 20.63, p < .001) and anxiety (χ²(1) = 18.86, p < .001) in the Lithuanian sample was significantly higher compared to the German sample. The correlations between intercepts and slopes of the same variables in both subsamples were found to be negative, indicating the higher decrease in negative mental health indicators for higher initial levels of stress, anxiety, and depression as well as a higher increase in PMH for the lower initial level of this indicator (Table 3). In the German sample only, we found a significant gender effect on change in anxiety (β_slope = .04, p = .046), indicating a higher decrease in anxiety for male participants compared to female participants. In the Lithuanian sample, female participants demonstrated higher levels of stress (β_intercept = −.06, p = .004) and anxiety (β_intercept = −.08, p < .001) at T1.

Figure 1.

The trajectories of change in mental health indicators in (A) Lithuania and (B) Germany (N = 775). Note. d = effect size with 95% confidence intervals. Negative score indicates decrease, positive score indicates increase.

Table 3.

Pearson r Correlations of Intercepts and Slopes of Study Variables in Lithuania (n = 450)/Germany (n = 325).

		1	2	3	4	5	6	7	8
1	Stress Intercept	1/1
2	Anxiety Intercept	.96/.93	1/1
3	Depression Intercept	.91/.86	.89/.84	1/1
4	Positive Mental Health Intercept	−.64/−.62	−.63/−.59	−.72/−.70	1/1
5	Stress Slope	−.51/−.48	−.46/−.48	−.44/−.41	.22/.24	1/1
6	Anxiety Slope	−.66/−.64	−.64/−.67	−.59/−.57	.34/.33	.94/.92	1/1
7	Depression Slope	−.52/−.50	−.45/−.45	−.53/−.52	.28/.27	.91/.86	.84/.78	1/1
8	Positive Mental Health Slope	.30/.19	.28/.14	.29/.17	−.40/−.31	−.52/−.43	−.49/−.36	−.58/−48	1/1

Note. All correlation coefficients are significant at p < .01.

Patterns of Change in Mental Health

The latent class change analysis in the full study sample indicated that the three classes solution fitted the data best, based on the non-significant LMR-A p-value of the four classes solution (for details see Supplementary Table S3). Three identified change profiles were found to be distinguishable in terms of differences in changes of factor means of mental health indicators (see Figure 2). The vast majority of study participants (82%) were classified into the group that, compared to other groups, reported the relatively lowest levels of negative mental health and the highest levels of PMH at the baseline. This group demonstrated a significant (p < .001) moderate decrease in stress (M_slope = −.32), and anxiety (M_slope = −.38), small decrease in depression (M_slope = −.19), as well as significant (p < .001) small increase in PMH (M_slope = .11) over time; this class was labeled as resilient. The second class (8%) reported the relatively highest levels of negative mental health and intermediate levels of PMH. Participants who were classified into this group demonstrated a moderate decrease in anxiety over time (M_slope = −.26, p = .002) with no changes in other mental health indicators; this class was labeled as high symptom. The third class (10%) reported relatively intermediate levels of stress, anxiety, and depression with the lowest levels of PMH. In this group, we observed a moderate decrease in anxiety (M_slope = −.34, p < .001), but also a moderate increase in depression (M_slope = .34, p = .002) as well as a small decrease in PMH (M_slope = −.25, p = .007); we labeled this group as vulnerable (see Figure 2 for the trajectories of change in three groups as well as the effect sizes). The resulting groups were also distinguishable in terms of the levels of resilience. Specifically, the resilient group reported higher levels of resilience, in comparison to high symptom (latent mean = −0.70, p < .001) and vulnerable (latent mean = −1.25, p < .001) groups; high symptom group reported higher levels of resilience when compared to vulnerable group (latent mean = −0.55, p < .001).

Figure 2.

The trajectories of change in mental health indicators in (A) Resilient, (B) High-symptom, and (C) Vulnerable latent change classes (N = 775). Note. d = effect size with 95% confidence intervals. Negative score indicates decrease, positive score indicates increase.

Discussion

The aim of the current longitudinal study was to investigate the initial effects of the COVID-19 crisis on mental health among emerging adults in the two European countries, Lithuania and Germany. Overall, we observed positive initial changes in the mental health of emerging adults in the face of the COVID-19 crisis and these results are in line with recent findings of the initial effects of the previous crisis on mental health (Bacigalupe et al., 2016; Forbes & Krueger, 2019). In particular, we found that the levels of stress and anxiety decreased in both countries. Moreover, there was a decrease in depression, as well as an increase in PMH in Lithuania only. However, we also identified three distinct groups with different change patterns of mental health indicators. The vast majority of youth, who reported relatively good mental health before the COVID-19 outbreak, demonstrated a positive change in all mental health indicators. However, we found that there may be a risk group who reported relatively poorer mental health before the COVID-19 and who may be more vulnerable, as indicated by an increase in their depressive symptoms as well as lower PMH over time. Also, the third group was identified to be possibly in need of clinical-level support. This group demonstrated stable high levels of stress and depression and stable low levels of PMH. Interestingly, anxiety decreased in all three groups over a period of 6 months.

Although the positive changes we observed in youth in the face of COVID-19 may seem surprising at the first glance, some recent findings provided evidence that initially, mental health may not change or might even improve (Wang, Pan, Wan, Tan, Xu, McIntyre, et al., 2020) in response to the COVID-19 pandemic. Emerging adults, in general, are not among the high-risk group of the complicated course of the COVID-19 disease, and fatality rates after contracting the virus among children and youth are relatively low (Jordan et al., 2020). Therefore, emerging adults may not experience life threats in the COVID-19 pandemic, resulting in an unlikely negative change in mental health. Moreover, it is possible that at the beginning of the lockdown, emerging adults, who are usually exposed to relatively high levels of life stressors (Schönfeld et al., 2018; Truskauskaite-Kuneviciene et al., 2020), may experience reduced demands of academic and social responsibilities in response to the social distancing measures resulting in lower levels of stress. Moreover, the resilient response to COVID-19 is positively associated with spending time outside, more exercise as well as higher social support (Killgore et al., 2020; Süss & Ehlert, 2020), which is characteristic of youth in different cultural contexts (Lin et al., 2018). These characteristics may contribute to emerging adults’ overall resilience in response to the COVID-19 pandemic. Additionally, the current findings contribute to the theoretical conceptualization provided by Bonanno (2005), suggesting that resilience is a quite prevalent response to adversity. Nevertheless, the observed positive effects on mental health might be temporary, as the data was collected in the first month of lockdown, and further studies are needed to investigate the longer-term effects of the pandemic on mental health in youth (Wade et al., 2020).

Although we found positive changes in mental health indicators both in Germany and Lithuania, more positive effects were observed in the Lithuanian context. This difference could at least partially be attributed to the relatively better mental health in German youth, compared to Lithuanian, as we also found that bigger positive changes were linked to worse initial mental health condition prior to the pandemic. Moreover, as shown by the population-comparative COVID-19 statistics (https://covid19.who.int/), relatively more COVID-19 cases, as well as more deaths, were reported in Germany compared to Lithuania. Factors that are associated with a more resilient response during emerging adulthood in times of crisis, such as higher sociability (Germani et al., 2020) should be addressed in future research.

The current study is one of the first prospective longitudinal studies in Europe on the effects of the COVID-19 on mental health. The present findings should be considered with caution, as they rely on self-reported online-collected data. Also, the attrition rates from T1 to T2 were relatively high. Moreover, the changes in mental health observed in the current study may be attributable to other, for example, developmental factors. However, despite these limitations, the current study provided valuable evidence on the resilience of youth during the COVID-19 crisis in two European contexts. In addition, our findings indicated that among youth there may be vulnerable individuals who might need support and access to mental health services in the outbreak of pandemics.

Supplemental Material

Supplemental Material, sj-pdf-1-eax-10.1177_21676968211031120 - Evidence on Resilient Initial Response to COVID-19 Pandemic Among Youth: Findings From the Prospective Study of Mental Health in Two European Countries

Supplemental Material, sj-pdf-1-eax-10.1177_21676968211031120 for Evidence on Resilient Initial Response to COVID-19 Pandemic Among Youth: Findings From the Prospective Study of Mental Health in Two European Countries by Inga Truskauskaite-Kuneviciene, Julia Brailovskaia, Jürgen Margraf and Evaldas Kazlauskas in Emerging Adulthood

Supplemental Material

Supplemental Material, sj-pdf-2-eax-10.1177_21676968211031120 - Evidence on Resilient Initial Response to COVID-19 Pandemic Among Youth: Findings From the Prospective Study of Mental Health in Two European Countries

Supplemental Material, sj-pdf-2-eax-10.1177_21676968211031120 for Evidence on Resilient Initial Response to COVID-19 Pandemic Among Youth: Findings From the Prospective Study of Mental Health in Two European Countries by Inga Truskauskaite-Kuneviciene, Julia Brailovskaia, Jürgen Margraf and Evaldas Kazlauskas in Emerging Adulthood

Footnotes

Author Note

I.T.K. ORCID ID: 0000-0001-6699-6638

J.B. ORCID ID: 0000-0001-7607-1305

J.M. ORCID ID: 0000-0001-5207-7016

E.K. ORCID ID: 0000-0002-6654-6220

Author Contributions

All authors developed the study concept and contributed to the study design. Testing and data collection were performed by I.T.K. and J.B. I.T.K. performed the data analysis and interpretation under the supervision of E.K. and J.M. I.T.K. drafted the paper, E.K., J.B. and J.M. provided critical revisions. All authors approved the final version of the paper for submission.

Data Availability

The data that support the findings of this study are available from the corresponding author, [I.T.K.], upon reasonable request.

Ethical Statement

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees and with the Helsinki Declaration of 1975, as revised in 2008.

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 Lithuanian Research Council (grant number S-COV-20-12).

Open Practices

The raw data, analysis code, and materials used in this study are not openly available but are available upon request to the corresponding author. No aspects of the study were pre-registered.

ORCID iD

Inga Truskauskaite-Kuneviciene

Evaldas Kazlauskas

Supplemental Material

Supplemental material for this article is available online.

References

Arnett

J. J.

Žukauskiene

Sugimura

(2014). The new life stage of emerging adulthood at ages 18-29 years: Implications for mental health. The Lancet Psychiatry, 1(7), 569–576. https://doi.org/10.1016/S2215-0366(14)00080-7

Bacigalupe

Shahidi

F. V.

Muntaner

Martín

Borrell

(2016). Why is there so much controversy regarding the population health impact of the great recession? Reflections on three case studies. International Journal of Health Services, 46(1), 5–35. https://doi.org/10.1177/0020731415611634

Balcytiene

(2012). Culture as a guide in theoretical explorations of Baltic media. In Hallin

D. C.

Mancini

(Eds.), Comparing media systems beyond the Western world (pp. 51–71). Cambridge University Press.

Bonanno

G. A.

(2005). Clarifying and extending the construct of adult resilience. American Psychologist, 60(3), 265–267. https://doi.org/10.1037/0003-066X.60.3.265b

Brailovskaia

Margraf

(2020). Predicting adaptive and maladaptive responses to the Coronavirus (COVID-19) outbreak: A prospective longitudinal study. International Journal of Clinical and Health Psychology. https://doi.org/10.1016/j.ijchp.2020.06.002

Brailovskaia

Truskauskaite-Kuneviciene

Kazlauskas

Margraf

(2021). The patterns of problematic social media use (SMU) and their relationship with online flow, life satisfaction, depression, anxiety and stress symptoms in Lithuania and in Germany. Current Psychology. https://doi.org/10.1007/s12144-021-01711-w

Brook

C. A.

Schmidt

L. A.

(2020). Lifespan trends in sociability: Measurement invariance and mean-level differences in ages 3 to 86 years. Personality and Individual Differences, 152(August 2019), 109579. https://doi.org/10.1016/j.paid.2019.109579

Browning

M. H. E. M.

Larson

L. R.

Sharaievska

Rigolon

McAnirlin

Mullenbach

Cloutier

T. M.

Thomsen

Reigner

Metcalf

E. C.

D’Antonio

Helbich

Bratman

G. N.

Alvarez

H. O.

(2021). Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States. PLos One, 16(1), e0245327. https://doi.org/10.1371/journal.pone.0245327

Cao

Fang

Hou

Han

Dong

Zheng

(2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research, 287(March), 112934. https://doi.org/10.1016/j.psychres.2020.112934

10.

Chen

F. F.

(2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14(3), 464–504. https://doi.org/10.1080/10705510701301834

11.

Cullen

Gulati

Kelly

B. D.

(2020). Mental health in the COVID-19 pandemic. QJM : Monthly Journal of the Association of Physicians, 113(5), 311–312. https://doi.org/10.1093/qjmed/hcaa110

12.

Duncan

T. E.

Duncan

S. C.

Strycker

L. A.

(2013). An introduction to latent variable growth curve modeling: Concepts, issues, and application. Routledge.

13.

Elmer

Mepham

Stadtfeld

(2020). Students under lockdown: Comparisons of students’ social networks and mental health before and during the COVID-19 crisis in Switzerland. PLoS One, 15(7 July), 1–22. https://doi.org/10.1371/journal.pone.0236337

14.

Flaxman

Mishra

Gandy

Unwin

H. J. T.

Mellan

T. A.

Coupland

Whittaker

Zhu

Berah

Eaton

J. W.

Monod

., Imperial College COVID-19 Response Team Ghani

A. C.

Donnelly

C. A.

Riley

Vollmer

M. A. C.

Ferguson

N. M.

Okell

L. C.

Bhatt

. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. https://doi.org/10.1038/s41586-020-2405-7

15.

Forbes

M. K.

Krueger

R. F.

(2019). The great recession and mental health in the United States. Clinical Psychological Science, 7(5), 900–913. https://doi.org/10.1177/2167702619859337

16.

Germani

Buratta

Delvecchio

Mazzeschi

(2020). Emerging adults and covid-19: The role of individualism-collectivism on perceived risks and psychological maladjustment. International Journal of Environmental Research and Public Health, 17(10). https://doi.org/10.3390/ijerph17103497

17.

Hall

R. C. W.

Hall

R. C. W.

Chapman

M. J.

(2008). The 1995 Kikwit Ebola outbreak: Lessons hospitals and physicians can apply to future viral epidemics. General Hospital Psychiatry, 30(5), 446–452. https://doi.org/10.1016/j.genhosppsych.2008.05.003

18.

Holmes

E. A.

O’Connor

R. C.

Perry

V. H.

Tracey

Wessely

Arseneault

Ballard

Christensen

Silver

R. C.

Everall

Ford

John

Kabir

King

Madan

Michie

Przybylski

A. K.

Shafran

Sweeney

… Bullmore

. (2020). Multidisciplinary research priorities for the COVID-19 pandemic: A call for action for mental health science. The Lancet Psychiatry, 547–560. https://doi.org/10.1016/S2215-0366(20)30168-1

19.

Hsiang

Allen

Annan-Phan

Bell

Bolliger

Chong

Druckenmiller

Huang

L. Y.

Hultgren

Krasovich

Lau

Lee

Rolf

Tseng

(2020). The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature. https://doi.org/10.1038/s41586-020-2404-8

20.

Jordan

R. E.

Adab

Cheng

K. K.

(2020). Covid-19: Risk factors for severe disease and death. The BMJ, 368(March), 1–2. https://doi.org/10.1136/bmj.m1198

21.

Jung

Wickrama

K. A. S.

(2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317. https://doi.org/10.1111/j.1751-9004.2007.00054.x

22.

Kaparounaki

C. K.

Patsali

M. E.

Mousa

D. P. V.

Papadopoulou

E. V. K.

Papadopoulou

K. K. K.

Fountoulakis

K. N.

(2020). University students’ mental health amidst the COVID-19 quarantine in Greece. Psychiatry Research, 290(May), 113111. https://doi.org/10.1016/j.psychres.2020.113111

23.

Kazlauskas

Grigutyte

(2020). Clinical psychology in Lithuania: Current developments in training and legislation. Clinical Psychology in Europe, 2(1 SE-Politics and Education), 1–10. https://doi.org/10.32872/cpe.v2i1.2835

24.

Killgore

W. D. S.

Taylor

E. C.

Cloonan

S. A.

Dailey

N. S.

(2020). Psychological resilience during the COVID-19 lockdown. Psychiatry Research, 291, 113216. https://doi.org/https://doi.org/10.1016/j.psychres.2020.113216

25.

Kline

R B.

(2006). Principles and practices of structural equation modeling, (2nd ed.). Guilford Press.

26.

Kovess

Carta

M. G.

Pez

Bitfoi

Koç

Goelitz

Kuijpers

Lesinskiene

Mihova

Otten

(2015). The school children mental health in Europe (SCMHE) project: Design and first results. Clinical Practice and Epidemiology in Mental Health : CP & EMH, 11(Suppl 1 M7), 113–123. https://doi.org/10.2174/1745017901511010113

27.

Kumar

Nayar

K. R.

(2020). COVID 19 and its mental health consequences. Journal of Mental Health (Abingdon, England), 0(0), 1–2. https://doi.org/10.1080/09638237.2020.1757052

28.

Wang

Jiang

Valdimarsdóttir

U. A.

Fall

Fang

Song

Zhang

(2020). Psychological distress among health professional students during the COVID-19 outbreak. Psychological Medicine, 1–12. https://doi.org/10.1017/S0033291720001555

29.

Liang

Ren

Cao

Qin

Mei

(2020). The effect of COVID-19 on youth mental health. Psychiatric Quarterly, (1163). https://doi.org/10.1007/s11126-020-09744-3

30.

Lin

Hirschfeld

Margraf

(2018). Brief form of the perceived social support questionnaire (F-SozU K-6): Validation, norms, and cross-cultural measurement invariance in the USA, Germany, Russia, and China. Psychological Assessment. https://dx.doi.org/10.1037/pas0000686

31.

Liu

C. H.

Zhang

Wong

G. T. F.

Hyun

Hahm

“Chris.” (2020). Factors associated with depression, anxiety, and PTSD symptomatology during the COVID-19 pandemic: Clinical implications for U.S. young adult mental health. Psychiatry Research, 290(June). https://doi.org/10.1016/j.psychres.2020.113172

32.

Lovibond

S. H.

Lovibond

P. F.

(1995). Manual for the depression anxiety stress scales (2nd ed.). Psychology Foundation.

33.

Lukat

Margraf

Lutz

Der Veld

W. M.

Becker

E. S.

(2016). Psychometric properties of the positive mental health scale (PMH-scale). BMC Psychology, 4(1), 1–14. https://doi.org/10.1186/s40359-016-0111-x

34.

Luthar

S. S.

Cicchetti

(2000). The construct of resilience: Implications for interventions and social policies. Development and Psychopathology, 12(4), 857–885. https://doi.org/10.1017/s0954579400004156

35.

Margraf

Hoyer

Fydrich

In-Albon

Lincoln

Lutz

Schlarb

Schöttke

Willutzki

Velten

(2021). The cooperative revolution reaches clinical psychology and psychotherapy: An example from Germany. Clinical Psychology in Europe, 3(1 SE-Scientific Update and Overview), 1–29. https://doi.org/10.32872/cpe.4459

36.

Miller

R. B.

Hollist

C. S.

(2007). Attrition bias. In Salkind

N. J.

(Ed.) Encyclopedia of measurement and statistics (Vol. 1 pp. 57–60). Sage Publications.

37.

Muthén

L. K

Muthén

B. O

. (1998–2017). Mplus user’s guide (8th ed.). Muthén & Muthén.

38.

Odriozola-González

Planchuelo-Gómez

Á.

Irurtia

M. J.

de Luis-García

(2020). Psychological effects of the COVID-19 outbreak and lockdown among students and workers of a Spanish university. Psychiatry Research, 290(May), 113108. https://doi.org/10.1016/j.psychres.2020.113108

39.

Perrin

P. C.

McCabe

O. L.

Everly

G. S.

Links

J. M.

(2009). Preparing for an influenza pandemic: Mental health considerations. Prehospital and Disaster Medicine, 24(3), 223–230. https://doi.org/10.1017/S1049023X00006853

40.

Rajkumar

R. P.

(2020). COVID-19 and mental health: A review of the existing literature. Asian Journal of Psychiatry, 52(March), 102066. https://doi.org/10.1016/j.ajp.2020.102066

41.

Satorra

Bentler

(2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514. https://link.springer.com/content/pdf/10.1007%2FBF02296192.pdf

42.

Schönfeld

Brailovskaia

Bieda

Zhang

X. C.

Margraf

(2016). The effects of daily stress on positive and negative mental health: Mediation through self-efficacy. International Journal of Clinical and Health Psychology, 16, 1–10. https://doi.org/10.1016/j.ijchp.2015.08.005

43.

Schönfeld

Brailovskaia

Zhang

X. C.

Margraf

(2018). Self-efficacy as a mechanism linking daily stress to mental health in students: A three-wave cross-lagged study. Psychological Reports. https://doi.org/10.1177/0033294118787496

44.

Schumacher

Leppert

Gunzelmann

Strauß

Brähler

(2005). Die Resilienzskala—Ein Fragebogen zur Erfassung der psychischen Widerstandsfähigkeit als Personmerkmal. Zeitschrift für Klinische Psychiatrie und Psychotherapie, 53, 16–39.

45.

Shanahan

Steinhoff

Bechtiger

Murray

Nivette

Hepp

Ribeaud

Eisner

(2020). Emotional distress in young adults during the COVID-19 pandemic: Evidence of risk and resilience from a longitudinal cohort study. Psychological Medicine, 1–32. https://doi.org/10.1017/S003329172000241X

46.

Süss

Ehlert

(2020). Psychological resilience during the perimenopause. Maturitas, 131(June), 48–56. https://doi.org/10.1016/j.maturitas.2019.10.015

47.

Torales

O’Higgins

Castaldelli-Maia

J. M.

Ventriglio

(2020). The outbreak of COVID-19 coronavirus and its impact on global mental health. International Journal of Social Psychiatry, 3–6. https://doi.org/10.1177/0020764020915212

48.

Truskauskaite-Kuneviciene

Kazlauskas

Ostreikaite-Jurevice

Brailovskaia

Margraf

(2020). Positive mental health and adjustment following life-stressors among young adults. Current Psychology. https://doi.org/10.1007/s12144-020-00714-3

49.

Wade

Prime

Browne

D. T.

(2020). Why we need longitudinal mental health research with children and youth during (and after) the COVID-19 pandemic. Psychiatry Research, 290(May), 113143. https://doi.org/10.1016/j.psychres.2020.113143

50.

Wang

Pan

Wan

Tan

C. S.

R. C.

(2020). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. International Journal of Environmental Research and Public Health, 17(5). https://doi.org/10.3390/ijerph17051729

51.

Wang

Pan

Wan

Tan

McIntyre

R. S.

Choo

F. N.

Tran

Sharma

V. K.

(2020). A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain, Behavior, and Immunity, 0–1. https://doi.org/10.1016/j.bbi.2020.04.028

52.

World Health Organization. (2020). Mental health and psychosocial considerations during the COVID-19 outbreak, 18 March 2020 (No. WHO/2019-nCoV/MentalHealth/2020.1). https://apps.who.int/iris/handle/10665/331490

53.

Yang

Hoyle

R. H.

(2010). Using lengthy ordinal scales. Applied Psychological Measurement, 122–142.

54.

Zivin

Paczkowski

Galea

(2011). Economic downturns and population mental health: Research findings, gaps, challenges and priorities. Psychological Medicine, 41(7), 1343–1348. https://doi.org/10.1017/S003329171000173X

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.16 MB

0.10 MB