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. 2022 Jan 16:1–12. Online ahead of print. doi: 10.1007/s11469-021-00735-4

The Impacts of Psychological Distress on Life Satisfaction and Wellbeing of the Indian General Population During the First and Second Waves of COVID-19: A Comparative Study

Remya Lathabhavan 1,✉, Shyamlin Sudevan 2
PMCID: PMC8761090  PMID: 35069046

Abstract

The current study aims to understand the impact of psychological distress caused by the COVID-19 pandemic on life satisfaction and wellbeing, in the Indian context. The study also analyses the differences in these effects between the first and second waves of the pandemic. For this purpose, a survey was conducted during the two waves of the pandemic. Eight hundred eighty-four and 925 respondents participated in the first and second waves, respectively. The study showed that depression, anxiety, and stress negatively related to life satisfaction and wellbeing during both waves. The study also showed that the effects were stronger during the second wave compared to the first. Life satisfaction and wellbeing of women were found to be affected to a higher level than those of men, due to psychological distress. The study showed that a combined approach involving coping mechanisms and health care can help manage the psychological issues that arise with crisis situations such as the COVID-19 pandemic.

Keywords: COVID-19, Mental health, COVID-19 s wave, Comparative study, India

The COVID-19 pandemic has seriously affected the entire world and has raised health concerns among people (Kontoangelos et al., 2020; Lathabhavan & Padhy, 2022). The health concerns are not only limited to high infection rates and death tolls, but also include mental health issues (Lathabhavan et al., 2021; Tandon, 2020). The mental health problems that have resulted from the pandemic and associated lockdowns have included depression, tension, insomnia, and suicidal tendencies (Hao et al., 2020; Wang et al. 2021a; Xiong et al., 2020). Such mental health concerns have been severe in middle-income Asian countries due to the economic challenges and limited medical resources available for the sustenance of physical and mental well-being during these trying times (Lee et al., 2021; Wang et al. 2021d).

India is among the worst hit countries of COVID-19. The second wave of COVID-19 was severe in India and was associated with a plethora of complications and mental health concerns. As per the latest data, India reported 30,458,251 confirmed cases and 400,312 deaths (WHO, 2021). During the first wave of COVID-19, researchers and practitioners discovered the emergence of mental health concerns associated with the pandemic and resultant lockdown (Lathabhavan & Vispute, 2021; Lathabhavan, 2021b). The second wave, which was characterized by high morbidity rates, shortages of oxygen and beds in hospitals, and complications such as emergence of new variants further exacerbated mental health problems among people (Lathabhavan, 2021c).

Depression has been a common mental health concern during the pandemic (Yom-Tov et al., 2021). COVID-19-related information from media sources, official websites, and discussions was one of the causes of depression among people (Ustun, 2021). Anxiety levels also increased over this period due to loved ones being infected, fear of becoming infected themselves, and often alarming pandemic news (E. P. H. Choi et al., 2020; Moghanibashi-mansourieh, 2020). The pandemic period also resulted in stress due to high infection rates and fear of contracting the disease (Lathabhavan, 2021a; Reznik et al., 2020). Such psychological distress factors have been found to negatively affect life satisfaction and wellbeing during the pandemic (El-Bardan & Lathabhavan, 2021; S. X. Zhang et al., 2020).

Psychological distress adversely affects life satisfaction and wellbeing during normal times as well. Earlier studies have shown that depression, anxiety, and stress negatively affect life satisfaction irrespective of gender and geographical location(Cömert et al., 2016; Tonsing, 2014). Similarly, previous evidences show that depression, anxiety, and stress negatively affect well-being in different groups irrespective of race, place, and gender (Bhullar et al., 2014; Davis et al., 2016; Ryan et al., 2019).

Considering the effects of physiological distress on life satisfaction and wellbeing, reported during both normal and pandemic times, the present study investigated the following relationships:

  1. The relationship of depression with life satisfaction and wellbeing during the two waves of the COVID-19 pandemic

  2. The relationship of anxiety with life satisfaction and wellbeing during the two waves of the COVID-19 pandemic

  3. The relationship of stress with life satisfaction and wellbeing during the two waves of the COVID-19 pandemic

  4. The role of gender in the above-mentioned relationships during the two waves of the COVID-19 pandemic.

The proposed model is shown in Fig. 1.

Fig. 1.

Fig. 1

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Proposed model

Methods

Participants

A survey was conducted during the two waves—June 2020 (during COVID-19 first wave, T1) and May 2021 (during COVID-19 s wave, T2). Eight hundred eighty-four respondents took the survey during T1 (mean age 39.5, SD 10.5) and 925 during T2 (mean age 38.7, SD 9.5). In T1, 540 respondents were men and 344 were women. In T2, the sample comprised 467 men and 458 women. One hundred thirty-eight participants (96 men and 42 women) participated in both surveys. The survey was conducted through online Google forms during both waves and social media platforms were used to reach people. The snowball sampling strategy was used for collecting data.

Measures

The latent variables we considered for the study were depression, anxiety, stress, wellbeing, and life satisfaction. The natures of measures are described below:

Depression Anxiety Stress Scale

Depression, anxiety, and stress were assessed using the Depression Anxiety Stress Scale (DASS-21) (Lovibond & Lovibond, 1996). DASS-21 comprises 21 items and three dimensions (seven items per dimension) (e.g., “I could not seem to experience any positive feeling at all” for depression; “I was worried about situations in which I might panic” for anxiety; and “I found it difficult to relax” for stress). The participants responded on a five-point Likert scale from 1 (did not apply to me at all) to 5 (most of the time). Higher scores on the three dimension reflected higher depression, anxiety, and stress. Given the importance of mental health concerns during the pandemic period, this 21-item scale has been widely validated in various countries including China (Cuiyan et al., 2020), the USA (Wang et al. 2021e), Spain (Wang et al. 2021c), Poland (Wang et al. 2020a, 2020b), Iran (Wang et al. 2021b), Vietnam (Le et al., 2020), Philippines (Tee et al., 2020), and India (Lathabhavan et al., 2021).

Wellbeing

Wellbeing was measured using the WHO-Five Well-being Index (WHO-5). Sample item is “I have felt calm and relaxed”. A five-point Likert-type scale was used with anchors 1 (strongly disagree) to 5 (strongly agree). The Cronbach’s alpha was 0.84 for this study.

Life Satisfaction

Life satisfaction was measured using Satisfaction with Life Scale. Participants completed the SWLS (Diener et al., 1985), which asked direct questions about life satisfaction (e.g., “In most ways my life is close to my ideal” and “I am satisfied with my life”), which were answered on a 5-point Likert scale.

The observed variable considered for the study is gender. We used dummy variables to represent the responses male = 1, female = 2 for SPSS entry. For AMOS, we created different for gender and fetched data from the SPSS data file. Though we collected data for other observed variables such as age and marital status, we did not consider for the present study since we focused for more specific outcome rather a generalized one.

Data Analysis

The Structural Equation Modeling (SEM) method that was implemented using AMOS 24.0 (Arbuckle, 2016) was used to test the research model. Confirmatory Factor Analysis (CFA) was performed with maximum likelihood estimation to examine the goodness of fit of the model. The models’ goodness of fit was evaluated using the criteria: relative χ2: ( χ2/df) < 3, root mean square error of approximation (RMSEA): ⩽0.08, standardized root mean square residual (SRMR): ⩽0.06, Goodness of fit index (GFI) ⩾0.90, comparative fit index (CFI) ⩾0.90, and Tucker-Lewis index (TLI) ⩾0.90(Byrne, 2013; Hu & Bentler, 1998). Cronbach’s α, average variance extracted (AVE), composite reliability (CR), and average loadings (AL) were also used to check the reliability and validity of the instruments.

Results

Descriptive Statistics

The descriptive statistics of the study in two waves T1 and T2 are shown in Table 1. All significant relationships were in the expected direction.

Table 1.

Means, standard deviations, and intercorrelations among study variables (NT1 = 884, NT2 = 925)

Mean SD 1 2 3 4 5 6 7 8 9 10
1. Depression T1 2.21 0.88 1
2. Depression T2 2.37 0.73 0.48 1
3. Anxiety T1 3.01 1.09 0.33 0.38 1
4. Anxiety T2 3.14 0.97 0.45 0.61 0.52 1
5. Stress T1 2.88 0.77 0.22 0.26 0.17 0.37 1
6. Stress T2 2.67 0.84 0.25 0.31 0.19 0.22 0.66 1
7. Life satisfaction T1 3.91 0.92  − 0.21  − 0.32  − 0.13  − 0.22  − 0.33  − 0.12 1
8. Life satisfaction T2 3.94 1.13  − 0.27  − 0.29  − 0.17  − 0.24  − 0.37  − 0.17 0.53 1
9. Wellbeing T1 4.01 0.89  − 0.33  − 0.43  − 0.18  − 0.13  − 0.21  − 0.26 0.16 0.21 1
10. Wellbeing T2 3.57 0.87  − 0.37  − 0.38  − 0.22  − 0.16  − 0.21  − 0.28 0.17 0.22 0.44 1
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Common method bias

Common method bias (CMB) happens when variations in responses are caused by the instrument rather than the actual predispositions of the respondents that the instrument attempts to uncover. On account of a common instrument used for measuring all the variables with different kind of scales, the Common Method Bias (CMB) was assessed. Harman’s single factor test was employed for this purpose. CMB was assessed by factor analysis using SPSS 22.0, in which, all the items were loaded with a threshold to attain one factor. The results showed that a single factor contributed 29.665% of total variance extracted for T1 and 27.24% of total variance extracted for T2, which is below 50%. This confirmed that CMB was not a major concern for this study.

Reliability and Validity

Table 2 shows the details of reliability and validity.

Table 2.

Validity and reliability results

AL CR AVE Cronbach’s α
T1 T2 T1 T2 T1 T2 T1 T2
Depression 0.78 0.82 0.857 0.882 0.765 0.683 0.887 0.892
Anxiety 0.81 0.79 0.793 0.781 0.677 0.712 0.843 0.854
Stress 0.82 0.83 0.824 0.822 0.813 0.834 0.871 0.873
Life satisfaction 0.77 0.78 0.836 0.843 0.775 0.785 0.833 0.843
Wellbeing 0.83 0.84 0.897 0.883 0.734 0.745 0.891 0.901
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The average variance extracted (AVE) values were above 0.50 indicating excellent content and convergent validity of all the measures. The item loadings varied from 0.64 to 0.93, also supporting the validity as all the loadings were above the threshold of 0.60. The composite reliability of all latent variables was above 0.75 and adequate. The Cronbach’s alpha values of variables exceeded the threshold value of 0.70 (Nunnally, 1994). Table 3 shows the discriminant validity details. Discriminant validity was deemed established as the square roots of all AVE scores were higher than their corresponding inter-correlations. Based on the above findings, it can be inferred that the measurement model exhibited an adequate level of reliability and validity.

Table 3.

Structural equation modeling results

Relationships Standardized path coefficient
T1 (N = 884) T2 (N = 925)
Depression --> Life satisfaction  − 0.23**  − 0.32*
Depression--> Wellbeing  − 0.33**  − 0.45***
Anxiety--> Life satisfaction  − 0.18*  − 0.22**
Anxiety --> Wellbeing  − 0.21**  − 0.25**
Stress --> Life satisfaction  − 0.42*  − 0.44**
Stress --> Wellbeing  − 0.28***  − 0.33*
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*p < 0.5, **p < 0.01, ***p < 0.001

Structural Equation Modeling Results

The results of SEM analysis showed that the proposed model (Fig. 1) was a good fit to the data during both waves. During the first wave, the model fit was good with χ2/df (T1) = 1.57, p < 0.01, CFI T1 = 0.94, TLIT1 = 0.92,RMSEAT1 = 0.05, and SRMRT1 = 0.05. Model fit during the second wave study was also good with χ2/df (T2) = 1.69, p < 0.01, CFI T2 = 0.95, TLIT2 = 0.95, RMSEAT2 = 0.04, and SRMRT2 = 0.05.

All relationships were found to be significant and acceptable in both T1 and T2. The results of structural equation modeling are shown in Table 3. During the first wave of COVID-19 in India, depression had shown significant negative relationships with the dependent variables. The relationships of depression was found to be significant with life satisfaction (βT1 =  − 0.23, p < 0.01) and wellbeing (βT1 =  − 0.32, p < 0.05). Anxiety also reported significant relationship with life satisfaction (βT1 =  − 0.18, p < 0.05) and wellbeing (βT1 =  − 0.21, p < 0.01) during the first wave. The relationships of stress with life satisfaction (βT1 =  − 0.42, p < 0.05) and wellbeing (βT1 =  − 0.28, p < 0.001) were also found significant during the first wave study.

During the second wave of COVID-19, significant relationships were seen, as before. The second wave study reported negative significant relationships of depression with life satisfaction (βT2 =  − 0.32, p < 0.05) and wellbeing (βT2 =  − 0.45, p < 0.001). During the second wave, anxiety was found negatively related to life satisfaction (βT2 =  − 0.22, p < 0.01) and wellbeing (βT2 =  − 0.25, p < 0.01). Stress also related negatively during the second wave with life satisfaction (βT2 =  − 0.44, p < 0.01) and wellbeing (βT2 = − 0.33, p < 0.05).

Moderation Analysis-Gender

In order to analyze the moderating role of gender on life satisfaction and wellbeing during the two waves, multi-group analysis through SEM was used. Table 4 shows the moderation analysis of the two-wave study. In both waves, all relationships were found to be stronger in women than in men.

Table 4.

Moderation analysis

Relationships Path coefficient (T1) Path coefficient (T2)
Male Female Difference Male Female Difference
Depression--> Life satisfaction  − 0.19*  − 0.24** 0.05  − 0.28**  − 0.34** 0.06
Depression --> Wellbeing  − 0.23**  − 0.45** 0.22**  − 0.43*  − 0.49* 0.06
Anxiety --> Life satisfaction  − 0.16**  − 0.19* 0.03  − 0.21**  − 0.28** 0.07
Anxiety --> Wellbeing  − 0.22*  − 0.29* 0.05  − 0.26*  − 0.45* 0.19*
Stress--> Life satisfaction  − 0.33**  − 0.52* 0.21**  − 0.43**  − 0.53** 0.10
Stress --> Wellbeing  − 0.26*  − 0.34** 0.08  − 0.29*  − 0.37* 0.08
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*p < 0.5, **p < 0.01, ***p < 0.001

Discussion

The main aim of the present study was to examine and compare the relationships of psychological distress on life satisfaction and wellbeing during the two COVID-19 waves in India. A study was conducted during June 2020 and May 2021, corresponding to the first and second waves. Online surveys were conducted among 884 (T1) and 925 (T2) samples during the two waves.

The present study reported negative relationships of depression with life satisfaction and wellbeing. The study reinforced the relationship in light of previous studies in this area (Padmanabhanunni & Pretorius, 2021; Smith et al., 2020). During the second wave, the effects of depression on life satisfaction and wellbeing were stronger. The effects of depression were higher among women than men. The study revealed the strong negative relationships of anxiety with life satisfaction and wellbeing during both waves. Comparing the time span, in the second wave, the relationships were stronger. In both waves, the effects of anxiety with life satisfaction and wellbeing were slightly higher among women than men. Thus, the study supported the previous finding in this area (Gallagher et al., 2021; Smith et al., 2020). The relationships of stress with life satisfaction and wellbeing were found to be strongly negative during both waves. The severity of stress during the second wave and among women was visible in the study. This is in line with previous observations that stress severely affected life satisfaction and wellbeing of people around the globe during the pandemic (Dymecka et al., 2021; Lathabhavan & Vispute, 2021).

The study showed that psychological distress during the COVID-19 s wave was more severe than during the first wave in India. This could be attributed to unexpected spikes in infection and death toll during this time. Oxygen shortages and unavailability of patient beds during the second wave could also have caused increased psychological distress, that negatively affected life satisfaction and wellbeing. The study also revealed that women face more psychological distress and this affects their wellbeing during times of crisis (Choi et al., 2021). Thus the study supported the previous findings of longitudinal studies conducted in this domain during the pandemic (Wang et al. 2020b).

Since the second wave of COVID-19 affected India severely, the mental health issues were also severe and affected the wellbeing of people. During the pandemic, online psychological interventions are the best option to help the general public in this matter. The most evidence-based treatment has been shown to be cognitive behavior therapy (CBT), especially Internet CBT for the prevention and control of psychological issues during the pandemic (Ho et al. 2020a; Ho et al. 2020b; Soh et al., 2020; Zhang & Ho, 2017). A structured health care system with collaboration between health care workers, mental health specialists, policy makers of all levels has been shown to be beneficial to society (Lathabhavan, 2021d). Along with strong health care measures, people must be trained in coping mechanisms and stress management strategies (Lathabhavan et al., 2017). Family members and friends can help each other by understanding the mental health problems of their loved ones and can seek psychological expert advices for the same.

The study has a few limitations. The COVID-19 pandemic has been shown to cause hemodynamic changes in the brain (Olszewska-Guizzo et al., 2021). This study mainly used self-reported questionnaires to measure psychiatric symptoms and did not make a clinical diagnosis. The gold standard for establishing psychiatric diagnosis involves a structured clinical interview and functional neuroimaging (Ho et al. 2020a; Ho et al. 2020b; Husain et al. 2020a; Husain et al. 2020b). Future researches in this area can focus on the mentioned aspects and different psychological variables, so that an overall approach can be considered. Furthermore, future studies can consider other observed and unobserved factors that may affect life satisfaction and well-being, for a clearer picture of the issue. Geography-specific studies can also help understand differences and similarities across regions.

Conclusion

Both waves of the COVID-19 pandemic have been associated with psychological distress in India. This study showed that depression, anxiety, and stress negatively were related to wellbeing and satisfaction during both waves of the pandemic. These relationships were found to be stronger during the second wave than the first. The mental health of women was found to be affected more than that of men during both waves. Psychological help by self, family, and medical practitioners can help overcome these problems and help in the development of better coping mechanisms during times of crisis.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Remya Lathabhavan, Email: remya.l@vit.ac.in.

Shyamlin Sudevan, Email: sudevanshyamlin@gmail.com.

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